BI & Warehousing

Kscope18: It's a Wrap!

Rittman Mead Consulting - Thu, 2018-06-21 08:23
 It's a Wrap!

As announced few weeks back I represented Rittman Mead at ODTUG's Kscope18 hosted in the magnificent Walt Disney World Dolphin Resort. It's always hard to be credible when telling people you are going to Disneyworld for work but Kscope is a must-go event if you are in the Oracle landscape.

 It's a Wrap!

In the Sunday symposium Oracle PMs share hints about the products latest capabilities and roadmaps, then three full days of presentations spanning from the traditional Database, EPM and BI tracks to the new entries like Blockchain. On top of this the opportunity to be introduced to a network of Oracle experts including Oracle ACEs and Directors, PMs and people willing to share their experience with Oracle (and other) tools.

Sunday Symposium and Presentations

I attended the Oracle Analytics (BI and Essbase) Sunday Symposium run by Gabby Rubin and Matt Milella from Oracle. It was interesting to see the OAC product enhancements and roadmap as well as the feature catch-up in the latest release of OBIEE on-premises (version 12.2.1.4.0).

As expected, most of the push is towards OAC (Oracle Analytics Cloud): all new features will be developed there and eventually (but assurance on this) ported in the on-premises version. This makes a lot of sense from Oracle's point of view since it gives them the ability to produce new features quickly since they need to be tested only against a single set of HW/SW rather than the multitude they are supporting on-premises.

Most of the enhancements are expected in the Mode 2/Self Service BI area covered by Oracle Analytics Cloud Standard since a) this is the overall trend of the BI industry b) the features requested by traditional dashboard style reporting are well covered by OBIEE.
The following are just few of the items you could expect in future versions:

  • Recommendations during the data preparation phase like GeoLocation and Date enrichments
  • Data Flow enhancements like incremental updates or parametrized data-flows
  • New Visualizations and in general more control over the settings of the single charts.

In general Oracle's idea is to provide a single tool that meets both the needs of Mode 1 and Mode 2 Analytics (Self Service vs Centralized) rather than focusing on solving one need at a time like other vendors do.

Special mention to the Oracle Autonomous Analytics Cloud, released few weeks ago, that differs from traditional OAC for the fact that backups, patching and service monitoring are now managed automatically by Oracle thus releasing the customer from those tasks.

During the main conference days (mon-wed) I assisted a lot of very insightful presentations and the Oracle ACE Briefing which gave me ideas for future blog posts, so stay tuned! As written previously I had two sessions accepted for Kscope18: "Visualizing Streams" and "DevOps and OBIEE: Do it Before it's too late", in the following paragraph I'll share details (and link to the slides) of both.

Visualizing Streams

One of the latest trends in the data and analytics space is the transition from the old style batch based reporting systems which by design were adding a delay between the event creation and the appearance in the reporting to the concept of streaming: ingesting and delivering event information and analytics as soon as the event is created.

 It's a Wrap!

The session explains how the analytics space changed in recent times providing details on how to setup a modern analytical platform which includes streaming technologies like Apache Kafka, SQL based enrichment tools like Confluent's KSQL and connections to Self Service BI tools like Oracle's Data Visualization via sql-on-Hadoop technologies like Apache Drill. The slides of the session are available here.

DevOps and OBIEE: Do it Before it's Too Late

In the second session, slides here, I've been initially going through the motivations of applying DevOps principles to OBIEE: the self service BI wave started as a response to the long time to delivery associated with the old school centralized reporting projects. Huge monolithic sets of requirements to be delivered, no easy way to provide development isolation, manual testing and code promotion were only few of the stoppers for a fast delivery.

 It's a Wrap!

After an initial analysis of the default OBIEE developments methods, the presentation explains how to apply DevOps principles to an OBIEE (or OAC) environment and precisely:

  • Code versioning techniques
  • Feature-driven environment creation
  • Automated promotion
  • Automated regression testing

Providing details on how the Rittman Mead BI Developer Toolkit, partially described here, can act as an accelerator for the adoption of these practices in any custom OBIEE implementation and delivery process.

As mentioned before, the overall Kscope experience is great: plenty of technical presentation, roadmap information, networking opportunities and also much fun! Looking forward to Kscope19 in Seattle!

Categories: BI & Warehousing

ChitChat for OBIEE - Now Available as Open Source!

Rittman Mead Consulting - Fri, 2018-06-15 03:20

ChitChat is the Rittman Mead commentary tool for OBIEE. ChitChat enhances the BI experience by bridging conversational capabilities into the BI dashboard, increasing ease-of-use and seamlessly joining current workflows. From tracking the history behind analytical results to commenting on specific reports, ChitChat provides a multi-tiered platform built into the BI dashboard that creates a more collaborative and dynamic environment for discussion.

Today we're pleased to announce the release into open-source of ChitChat! You can find the github repository here: https://github.com/RittmanMead/ChitChat

Highlights of the features that ChitChat provides includes:

  • Annotate - ChitChat's multi-tiered annotation capabilities allow BI users to leave comments where they belong, at the source of the conversation inside the BI ecosystem.

  • Document - ChitChat introduces the ability to include documentation inside your BI environment for when you need more that a comment. Keeping key materials contained inside the dashboard gives the right people access to key information without searching.

  • Share - ChitChat allows to bring attention to important information on the dashboard using the channel or workflow manager you prefer.

  • Verified Compatibility - ChitChat has been tested against popular browsers, operating systems, and database platforms for maximum compatibility.

Getting Started

In order to use ChitChat you will need OBIEE 11.1.1.7.x, 11.1.1.9.x or 12.2.1.x.

First, download the application and unzip it to a convenient access location in the OBIEE server, such as a home directory or the desktop.

See the Installation Guide for full detail on how to install ChitChat.

Database Setup

Build the required database tables using the installer:

cd /home/federico/ChitChatInstaller  
java -jar SocializeInstaller.jar -Method:BuildDatabase -DatabasePath:/app/oracle/oradata/ORCLDB/ORCLPDB1/ -JDBC:"jdbc:oracle:thin:@192.168.0.2:1521/ORCLPDB1" -DatabaseUser:"sys as sysdba" -DatabasePassword:password -NewDBUserPassword:password1  

The installer will create a new user (RMREP), and tables required for the application to operate correctly. -DatabasePath flag tells the installer where to place the datafiles for ChitChat in your database server. -JDBC indicates what JDBC driver to use, followed by a colon and the JDBC string to connect to your database. -DatabaseUser specifies the user to access the database with. -DatabasePassword specifies the password for the user previously given. -NewDBUserPassword indicates the password for the new user (RMREP) being created.

WebLogic Data Source Setup

Add a Data Source object to WebLogic using WLST:

cd /home/federico/ChitChatInstaller/jndiInstaller  
$ORACLE_HOME/oracle_common/common/bin/wlst.sh ./create-ds.py

To use this script, modify the ds.properties file using the method of your choice. The following parameters must be updated to reflect your installation: domain.name, admin.url, admin.userName, admin.password, datasource.target, datasource.url and datasource.password.

Deploying the Application on WebLogic

Deploy the application to WebLogic using WLST:

cd /home/federico/ChitChatInstaller  
$ORACLE_HOME/oracle_common/common/bin/wlst.sh ./deploySocialize.py

To use this script, modify the deploySocialize.py file using the method of your choice. The first line must be updated with username, password and url to connect to your Weblogic Server instance. The second parameter in deploy command must be updated to reflect your ChitChat access location.

Configuring the Application

ChitChat requires several several configuration parameters to allow the application to operate successfully. To change the configuration, you must log in to the database schema as the RMREP user, and update the values manually into the APPLICATION_CONSTANT table.

See the Installation Guide for full detail on the available configuration and integration options.

Enabling the Application

To use ChitChat, you must add a small block of code on any given dashboard (in a new column on the right-side of the dashboard) where you want to have the application enabled:

<rm id="socializePageParams"  
user="@{biServer.variables['NQ_SESSION.USER']}"  
tab="@{dashboard.currentPage.name}"  
page="@{dashboard.name}">  
</rm>  
<script src="/Socialize/js/dashboard.js"></script>  

Congratulations! You have successfully installed the Rittman Mead commentary tool. To use the application to its fullest capabilities, please refer to the User Guide.

Problems?

Please raise any issues on the github issue tracker. This is open source, so bear in mind that it's no-one's "job" to maintain the code - it's open to the community to use, benefit from, and maintain.

If you'd like specific help with an implementation, Rittman Mead would be delighted to assist - please do get in touch with Jon Mead or DM us on Twitter @rittmanmead to get access to our Slack channel for support about ChitChat.

Please contact us on the same channels to request a demo.

Categories: BI & Warehousing

Real-time Sailing Yacht Performance - Kafka (Part 2)

Rittman Mead Consulting - Thu, 2018-06-14 08:18

In the last two blogs Getting Started (Part 1) and Stepping back a bit (Part 1.1) I looked at what data I could source from the boat's instrumentation and introduced some new hardware to the boat to support the analysis.

Just to recap I am looking to create the yachts Polars with a view to improving our knowledge of her abilities (whether we can use this to improve our race performance is another matter).

Polars give us a plot of the boat's speed given a true wind speed and angle. This, in turn, informs us of the optimal speed the boat could achieve at any particular angle to wind and wind speed.

Image Description

In the first blog I wrote a reader in Python that takes messages from a TCP/IP feed and writes the data to a file. The reader is able, using a hash key to validate each message (See Getting Started (Part 1)). I'm also converting valid messages into a JSON format so that I can push meaningful structured data downstream. In this blog, I'll cover the architecture and considerations around the setup of Kafka for this use case. I will not cover the installation of each component, there has been a lot written in this area. (We have some internal IP to help with configuration). I discuss the process I went through to get the data in real time displayed in a Grafana dashboard.

Introducing Kafka

I have introduced Kafka into the architecture as a next step.

Why Kafka?

I would like to be able to stream this data real time and don't want to build my own batch mechanism or create a publish/ subscribe model. With Kafka I don't need to check that messages have been successfully received and if there is a failure while consuming messages the consumers will keep track of what has been consumed. If a consumer fails it can be restarted and it will pick up where it left off (consumer offset stored in Kafka as a topic). In the future, I could scale out the platform and introduce some resilience through clustering and replication (this shouldn't be required for a while). Kafka therefore is saving me a lot of manual engineering and will support future growth (should I come into money and am able to afford more sensors for the boat).

High level architecture

Let's look at the high-level components and how they fit together. Firstly I have the instruments transmitting on wireless TCP/IP and these messages are read using my Python I wrote earlier in the year.

I have enhanced the Python I wrote to read and translate the messages and instead of writing to a file I stream the JSON messages to a topic in Kafka.

Once the messages are in Kafka I use Kafka Connect to stream the data into InfluxDB. The messages are written to topic-specific measurements (tables in InfluxdDB).

Grafana is used to display incoming messages in real time.

Kafka components

I am running the application on a MacBook Pro. Basically a single node instance with zookeeper, Kafka broker and a Kafka connect worker. This is the minimum setup with very little resilience.

Image Description

In summary

ZooKeeper is an open-source server that enables distributed coordination of configuration information. In the Kafka architecture ZooKeeper stores metadata about brokers, topics, partitions and their locations. ZooKeeper is configured in zookeeper.properties.

Kafka broker is a single Kafka server.

"The broker receives messages from producers, assigns offsets to them, and commits the messages to storage on disk. It also services consumers, responding to fetch requests for partitions and responding with the messages that have been committed to disk." 1

The broker is configured in server.properties. In this setup I have set auto.create.topics.enabled=false. Setting this to false gives me control over the environment as the name suggests it disables the auto-creation of a topic which in turn could lead to confusion.

Kafka connect worker allows us to take advantage of predefined connectors that enable the writing of messages to known external datastores from Kafka. The worker is a wrapper around a Kafka consumer. A consumer is able to read messages from a topic partition using offsets. Offsets keep track of what has been read by a particular consumer or consumer group. (Kafka connect workers can also write to Kafka from datastores but I am not using this functionality in this instance). The connect worker is configured in connect-distributed-properties. I have defined the location of the plugins in this configuration file. Connector definitions are used to determine how to write to an external data source.

Producer to InfluxDB

I use kafka-python to stream the messages into kafka. Within kafka-python there is a KafkaProducer that is intended to work in a similar way to the official java client.

I have created a producer for each message type (parameterised code). Although each producer reads the entire stream from the TCP/IP port it only processes it's assigned message type (wind or speed) this increasing parallelism and therefore throughput.

  producer = KafkaProducer(bootstrap_servers='localhost:9092' , value_serializer=lambda v: json.dumps(v).encode('utf-8'))
  producer.send(topic, json_str) 

I have created a topic per message type with a single partition. Using a single partition per topic guarantees I will consume messages in the order they arrive. There are other ways to increase the number of partitions and still maintain the read order but for this use case a topic per message type seemed to make sense. I basically have optimised throughput (well enough for the number of messages I am trying to process).

kafka-topics --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic wind-json

kafka-topics --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic speed-json

kafka-topics --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic gps-json 

When defining a topic you specify the replaication-factor and the number of partitions.

The topic-level configuration is replication.factor. At the broker level, you control the default.replication.factor for automatically created topics. 1 (I have turned off the automatic creation of topics).

The messages are consumed using Stream reactor which has an InfluxDB sink mechanism and writes directly to the measurements within a performance database I have created. The following parameters showing the topics and inset mechanism are configured in performance.influxdb-sink.properties.

topics=wind-json,speed-json,gps-json

connect.influx.kcql=INSERT INTO wind SELECT * FROM wind-json WITHTIMESTAMP sys_time();INSERT INTO speed SELECT * FROM speed-json WITHTIMESTAMP sys_time();INSERT INTO gps SELECT * FROM gps-json WITHTIMESTAMP sys_time()

The following diagram shows the detail from producer to InfluxDB.

If we now run the producers we get data streaming through the platform.

Producer Python log showing JSON formatted messages:

Status of consumers show minor lag reading from two topics, the describe also shows the current offsets for each consumer task and partitions being consumed (if we had a cluster it would show multiple hosts):

Inspecting the InfluxDB measurements:

When inserting into a measurement in InfluxDB if the measurement does not exist it gets created automatically. The datatypes of the fields are determined from the JSON object being inserted. I needed to adjust the creation of the JSON message to cast the values to floats otherwise I ended up with the wrong types. This caused reporting issues in Grafana. This would be a good case for using Avro and Schema Registry to handle these definitions.

The following gif shows Grafana displaying some of the wind and speed measurements using a D3 Gauge plugin with the producers running to the right of the dials.

Next Steps

I'm now ready to do some real-life testing on our next sailing passage.

In the next blog, I will look at making the setup more resilient to failure and how to monitor and automatically recover from some of these failures. I will also introduce the WorldMap pannel to Grafana so I can plot the location the readings were taken and overlay tidal data.

References
Categories: BI & Warehousing

OAC - Thoughts on Moving to the Cloud

Rittman Mead Consulting - Tue, 2018-06-12 12:43

Last week, I spent a couple of days with Oracle at Thames Valley Park and this presented me with a perfect opportunity to sit down and get to grips with the full extent of the Oracle Analytics Cloud (OAC) suite...without having to worry about client requirements or project deadlines!

As a company, Rittman Mead already has solid experience of OAC, but my personal exposure has been limited to presentations, product demonstrations, reading the various postings in the blog community and my existing experiences of Data Visualisation and BI cloud services (DVCS and BICS respectively). You’ll find Francesco’s post a good starting place if you need an overview of OAC and how it differs (or aligns) to Data Visualisation and BI Cloud Services.

So, having spent some time looking at the overall suite and, more importantly, trying to interpret what it could mean for organisations thinking about making a move to the cloud, here are my top three takeaways:

Clouds Come In Different Shapes and Flavours

Two of the main benefits that a move to the cloud offers are simplification in platform provisioning and an increase in flexibility, being able to ramp up or scale down resources at will. These both comes with a potential cost benefit, depending on your given scenario and requirement. The first step is understanding the different options in the OAC licensing and feature matrix.

First, we need to draw a distinction between Analytics Cloud and the Autonomous Analytics Cloud (interestingly, both options point to the same page on cloud.oracle.com, which makes things immediately confusing!). In a nutshell though, the distinction comes down to who takes responsibility for the service management: Autonomous Analytics Cloud is managed by Oracle, whilst Analytics Cloud is managed by yourself. It’s interesting to note that the Autonomous offering is marginally cheaper.

Next, Oracle have chosen to extend their BYOL (Bring Your Own License) option from their IaaS services to now incorporate PaaS services. This means that if you have existing licenses for the on-premise software, then you are able to take advantage of what appears to be a significantly discounted cost. Clearly, this is targeted to incentivise existing Oracle customers to make the leap into the Cloud, and should be considered against your ongoing annual support fees.

Since the start of the year, Analytics Cloud now comes in three different versions, with the Standard and Enterprise editions now being separated by the new Data Lake edition. The important things to note are that (possibly confusingly) Essbase is now incorporated into the Data Lake edition of the Autonomous Analytics Cloud and that for the full enterprise capability you have with OBIEE, you will need the Enterprise edition. Each version inherits the functionality of its preceding version: Enterprise edition gives you everything in the Data Lake edition; Data Lake edition incorporates everything in the Standard edition.

alt

Finally, it’s worth noting that OAC aligns to the Universal Credit consumption model, whereby the cost is determined based on the size and shape of the cloud that you need. Services can be purchased as Pay as You Go or Monthly Flex options (with differential costing to match). The PAYG model is based on hourly consumption and is paid for in arrears, making it the obvious choice for short term prototyping or POC activities. Conversely, the Monthly Flex model is paid in advance and requires a minimum 12 month investment and therefore makes sense for full scale implementations. Then, the final piece of the jigsaw comes with the shape of the service you consume. This is measured in OCPU’s (Oracle Compute Units) and the larger your memory requirements, the more OCPU’s you consume.

Where You Put Your Data Will Always Matter

Moving your analytics platform into the cloud may make a lot of sense and could therefore be a relatively simple decision to make. However, the question of where your data resides is a more challenging subject, given the sensitivities and increasing legislative constraints that exist around where your data can or should be stored. The answer to that question will influence the performance and data latency you can expect from your analytics platform.

OAC is architected to be flexible when it comes to its data sources and consequently the options available for data access are pretty broad. At a high level, your choices are similar to those you would have when implementing on-premise, namely:

  • perform ELT processing to transform and move the data (into the cloud);
  • replicate data from source to target (in the cloud) or;
  • query data sources via direct access.

These are supplemented by a fourth option to use the inbuilt Data Connectors available in OAC to connect to cloud or on-premise databases, other proprietary platforms or any other source accessible via JDBC. This is probably a decent path for exploratory data usage within DV, but I’m not sure it would always make the best long term option.

alt

Unsurprisingly, with the breadth of options comes a spectrum of tooling that can be used for shifting your data around and it is important to note that depending on your approach, additional cloud services may or may not be required.

For accessing data directly at its source, the preferred route seems to be to use RDC (Remote Data Connector), although it is worth noting that support is limited to Oracle (including OLAP), SQL Server, Teredata or DB2 databases. Also, be aware that RDC operates within WebLogic Server and so this will be needed within the on-premise network.

Data replication is typically achieved using Data Sync (the reincarnation of the DAC, which OBIA implementers will already be familiar with), although it is worth mentioning that there are other routes that could be taken, such as APEX or SQL Developer, depending on the data volumes and latency you have to play with.

Classic ELT processing can be achieved via Oracle Data Integrator (either the Cloud Service, a traditional on-premise implementation or a hybrid-model).

Ultimately, due care and attention needs to be taken when deciding on your data architecture as this will have a fundamental effect on the simplicity with which data can be accessed and interpreted, the query performance achieved and the data latency built into your analytics.

Data Flows Make For Modern Analytics Simplification

A while back, I wrote a post titled Enabling a Modern Analytics Platform in which I attempted to describe ways that Mode 1 (departmental) and Mode 2 (enterprise) analytics could be built out to support each other, as opposed to undermining one another. One of the key messages I made was the importance of having an effective mechanism for transitioning your Mode 1 outputs back into Mode 2 as seamlessly as possible. (The same is true in reverse for making enterprise data available as an Mode 1 input.)

One of the great things about OAC is how it serves to simplify this transition. Users are able to create analytic content based on data sourced from a broad range of locations: at the simplest level, Data Sets can be built from flat files or via one of the available Data Connectors to relational, NoSQL, proprietary database or Essbase sources. Moreover, enterprise curated metadata (via RPD lift-and-shift from an on-premise implementation) or analyst developed Subject Areas can be exposed. These sources can be ‘mashed’ together directly in a DV project or, for more complex or repeatable actions, Data Flows can be created to build Data Sets. Data Flows are pretty powerful, not only allowing users to join disparate data but also perform some useful data preparation activities, ranging from basic filtering, aggregation and data manipulation actions to more complex sentiment analysis, forecasting and even some machine learning modelling features. Importantly, Data Flows can be set to output their results to disk, either written to a Data Set or even to a database table and they can be scheduled for repetitive refresh.

For me, one of the most important things about the Data Flows feature is that it provides a clear and understandable interface which shows the sequencing of each of the data preparation stages, providing valuable information for any subsequent reverse engineering of the processing back into the enterprise data architecture.

alt

In summary, there are plenty of exciting and innovative things happening with Oracle Analytics in the cloud and as time marches on, the case for moving to the cloud in one shape or form will probably get more and more compelling. However, beyond a strategic decision to ‘Go Cloud’, there are many options and complexities that need to be addressed in order to make a successful start to your journey - some technical, some procedural and some organisational. Whilst a level of planning and research will undoubtedly smooth the path, the great thing about the cloud services is that they are comparatively cheap and easy to initiate, so getting on and building a prototype is always going to be a good, exploratory starting point.

Categories: BI & Warehousing

Why DevOps Matters for Enterprise BI

Rittman Mead Consulting - Tue, 2018-06-12 09:44
Why DevOps Matters for Enterprise BI

Why are people frustrated with their existing enterprise BI tools such as OBIEE? My view is because it costs too much to produce relevant content. I think some of this is down to the tools themselves, and some of it is down to process.

Starting with the tools, they are not “bad” tools; the traditional licensing model can be expensive in today’s market, and traditional development methods are time-consuming and hence expensive. The vendor’s response is to move to the cloud and to highlight cost savings that can be made by having a managed platform. Oracle Analytics Cloud (OAC) is essentially OBIEE installed on Oracle’s servers in Oracle’s data centres with Oracle providing your system administration, coupled with the ability to flex your licensing on a monthly or annual basis.

Cloud does give organisations the potential for more agility. Provisioning servers can no longer hold up the start of a project, and if a system needs to increase capacity, then more CPUs or nodes can be added. This latter case is a bit murky due to the cost implications and the option to try and resolve performance issues through query efficiency on the database.

I don’t think this solves the problem. Tools that provide reports and dashboards are becoming more commoditised, up and coming vendors and platform providers are offering the service for a fraction of the cost of the traditional vendors. They may lack some of the enterprise features like open security models; however, these are an area that platform providers are continually improving. Over the last 10 years, Oracle's focus for OBIEE has been on more on integration than innovation. Oracle DV was a significant change; however, there is a danger that Oracle lost the first-mover advantage to tools such as Tableau and QlikView. Additionally, some critical features like lineage, software lifecycle development, versioning and process automation are not built in to OBIEE and worse still, the legacy design and architecture of the product often hinders these.

So this brings me back round to process. Defining “good” processes and having tools to support them is one of the best ways you can keep your BI tools relevant to the business by reducing the friction in generating content.

What is a “good” process? Put simply, a process that reduces the time between the identification of a business need and the realising it with zero impact on existing components of the system. Also, a “good” process should provide visibility of any design, development and testing, plus documentation of changes, typically including lineage in a modern BI system. Continuous integration is the Holy Grail.

This why DevOps matters. Using automated migration across environments, regression tests, automatically generated documentation in the form of lineage, native support for version control systems, supported merge processes and ideally a scripting interface or API to automate the generation of repetitive tasks such as changing the data type of a group of fields system-wide, can dramatically reduce the gap from idea to realisation.

So, I would recommend that when looking at your enterprise BI system, you not only consider the vendor, location and features but also focus on the potential for process optimisation and automation. Automation could be something that the vendor builds into the tool, or you may need to use accelerators or software provided by a third party. Over the next few weeks, we will be publishing some examples and case studies of how our BI and DI Developer Toolkits have helped clients and enabled them to automate some or all of the BI software development cycle, reducing the time to release new features and increasing the confidence and robustness of the system.

Categories: BI & Warehousing

Real-time Sailing Yacht Performance - stepping back a bit (Part 1.1)

Rittman Mead Consulting - Mon, 2018-06-11 07:20

Slight change to the planned article. At the end of my analysis in Part 1 I discovered I was missing a number of key messages. It turns out that not all the SeaTalk messages from the integrated instruments were being translated to an NMEA format and therefore not being sent wirelessly from the AIS hub. I didn't really want to introduce another source of data directly from the instruments as it would involve hard wiring the instruments to the laptop and then translating a different format of a message (SeaTalk). I decided to spend on some hardware (any excuse for new toys). I purchased a SeaTalk to NMEA converter from DigitalYachts (discounted at the London boat show I'm glad to say).

This article is about the installation of that hardware and the result (hence Part 1.1), not our usual type of blog. You never know it may be of interest to somebody out there and this is a real-life data issue! Don't worry it will be short and more of an insight into Yacht wiring than anything.

The next blog will be very much back on track. Looking at Kafka in the architecture.

The existing wiring

The following image shows the existing setup, what's behind the panels and how it links to the instrument architecture documented in Part 1. No laughing at the wiring spaghetti - I stripped out half a tonne of cable last year so this is an improvement. Most of the technology lives near the chart table and we have access to the navigation lights, cabin lighting, battery sensors and DSC VHF. The top left image also shows a spare GPS (Garmin) and far left an EPIRB.

Approach

I wanted to make sure I wasn't breaking anything by adding the new hardware so followed the approach we use as software engineers. Check before, during and after any changes enabling us to narrow down the point errors are introduced. To help with this I create a little bit of Python that reads the messages and lets me know the unique message types, the total number of messages and the number of messages in error.

 
import json
import sys

#DEF Function to test message
def is_message_valid (orig_line):

........ [Function same code described in Part 1]

#main body
f = open("/Development/step_1.log", "r")

valid_messages = 0
invalid_messages = 0
total_messages = 0
my_list = [""]
#process file main body
for line in f:

  orig_line = line

  if is_message_valid(orig_line):
    valid_messages = valid_messages + 1
    #look for wind message
    #print "message valid"

    if orig_line[0:1] == "$":
      if len(my_list) == 0:
        #print "ny list is empty"
        my_list.insert(0,orig_line[0:6]) 
      else:
        #print orig_line[0:5]
        my_list.append(orig_line[0:6])

      #print orig_line[20:26]

  else:
    invalid_messages = invalid_messages + 1

  total_messages = total_messages + 1

new_list = list(set(my_list))

i = 0

while i < len(new_list):
    print(new_list[i])
    i += 1

#Hight tech report
print "Summary"
print "#######"
print "valid messages -> ", valid_messages
print "invalid messages -> ", invalid_messages
print "total mesages -> ", total_messages

f.close()

For each of the steps, I used nc to write the output to a log file and then use the Python to analyse the log. I log about ten minutes of messages each step although I have to confess to shortening the last test as I was getting very cold.

nc -l 192.168.1.1 2000 > step_x.log

While spooling the message I artificially generate some speed data by spinning the wheel of the speedo. The image below shows the speed sensor and where it normally lives (far right image). The water comes in when you take out the sensor as it temporarily leaves a rather large hole in the bottom of the boat, don't be alarmed by the little puddle you can see.

Step 1;

I spool and analyse about ten minutes of data without making any changes to the existing setup.

The existing setup takes data directly from the back of a Raymarine instrument seen below and gets linked into the AIS hub.

Results;
 
$AITXT -> AIS (from AIS hub)

$GPRMC -> GPS (form AIS hub)
$GPGGA
$GPGLL
$GPGBS

$IIDBT -> Depth sensor
$IIMTW -> Sea temperature sensor
$IIMWV -> Wind speed 

Summary
#######
valid messages ->  2129
invalid messages ->  298
total mesages ->  2427
12% error

Step 2;

I disconnect the NMEA interface between the AIS hub and the integrated instruments. So in the diagram above I disconnect all four NMEA wires from the back of the instrument.

I observe the Navigation display of the integrated instruments no longer displays any GPS information (this is expected as the only GPS messages I have are coming from the AIS hub).

Results;

$AITXT -> AIS (from AIS hub)

$GPRMC -> GPS (form AIS hub)
$GPGGA
$GPGLL
$GPGBS

No $II messages as expected 

Summary
#######
valid messages ->  3639
invalid messages ->  232
total mesages ->  3871
6% error
Step 3;

I wire in the new hardware both NMEA in and out then directly into the course computer.

Results;

$AITXT -> AIS (from AIS hub)

$GPGBS -> GPS messages
$GPGGA
$GPGLL
$GPRMC

$IIMTW -> Sea temperature sensor
$IIMWV -> Wind speed 
$IIVHW -> Heading & Speed
$IIRSA -> Rudder Angle
$IIHDG -> Heading
$IIVLW -> Distance travelled

Summary
#######
valid messages ->  1661
invalid messages ->  121
total mesages ->  1782
6.7% error
Conclusion;

I get all the messages I am after (for now) the hardware seems to be working.

Now to put all the panels back in place!

In the next article, I will get back to technology and the use of Kafka in the architecture.

Categories: BI & Warehousing

Rittman Mead at Kscope 2018

Rittman Mead Consulting - Thu, 2018-05-31 02:20
Rittman Mead at Kscope 2018

Kscope 2018 is just a week away! Magnificent location (Walt Disney World Swan and Dolphin Resort) for one of the best tech conferences of the year! The agenda is impressive (look here) spanning over ten different tracks from the traditional EPM, BI Analytics and Data Visualization, to the newly added Blockchain! Plenty of great content and networking opportunities!

I'll be representing Rittman Mead with two talks: one about Visualizing Streams (Wednesday at 10:15 Northern Hemisphere A2, Fifth Level) on how to build a modern analytical platform including Apache Kafka, Confluent's KSQL, Apache Drill and Oracle's Data Visualization (Cloud or Desktop).

Rittman Mead at Kscope 2018

During the second talk, titled DevOps and OBIEE:
Do it Before it's Too Late!
(Monday at 10:45 Northern Hemisphere A1, Fifth Level), I'll be sharing details, based on our experience, on how OBIEE can be fully included in a DevOps framework, what's the cost of "avoiding" DevOps and automation in general and how Rittman Mead's toolkits, partially described here, can be used to accelerate the adoption of DevOps practices in any situation.

Rittman Mead at Kscope 2018

If you’re at the event and you see me in sessions, around the conference or during my talks, I’d be pleased to speak with you about your projects and answer any questions you might have.

Categories: BI & Warehousing

When Screen Scraping became API calling – Gathering Oracle OpenWorld Session Catalog with ...

Tim Dexter - Sun, 2018-05-20 03:16
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A dataset with all sessions of the upcoming Oracle OpenWorld 2017 conference is nice to have – for experiments and demonstrations with many technologies. The session catalog is exposed at a website here.

With searching, filtering and scrolling, all available sessions can be inspected. If data is available in a browser, it can be retrieved programmatically and persisted locally in for example a JSON document. A typical approach for this is web scraping: having a server side program act like a browser, retrieve the HTML from the web site and query the data from the response. This process is described for example in this article – https://codeburst.io/an-introduction-to-web-scraping-with-node-js-1045b55c63f7 – for Node and the Cheerio library.

However, server side screen scraping of HTML will only be successful when the HTML is static. Dynamic HTML is constructed in the browser by executing JavaScript code that manipulates the browser DOM. If that is the mechanism behind a web site, server side scraping is at the very least considerably more complex (as it requires the server to emulate a modern web browser to a large degree). Selenium has been used in such cases – to provide a server side, programmatically accessible browser engine. Alternatively, screen scraping can also be performed inside the browser itself – as is supported for example by the Getsy library.

As you will find in this article – when server side scraping fails, client side scraping may be a much to complex solution. It is very well possible that the rich client web application is using a REST API that provides the data as a JSON document. An API that our server side program can also easily leverage. That turned out the case for the OOW 2017 website – so instead of complex HTML parsing and server side or even client side scraping, the challenge at hand resolves to nothing more than a little bit of REST calling. Read the complete article here.

PaaS Partner Community

For regular information on business process management and integration become a member in the SOA & BPM Partner Community for registration please visit www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center.

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Categories: BI & Warehousing

Solve digital transformation challenges using Oracle Cloud

Tim Dexter - Sun, 2018-05-20 03:15

 

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Digital transformation is an omnipresent topic today, providing a lot of challenges as well as chances. Due to that, customers are asking about how to deal with those challenges and how to leverage from the provided chances. Frequently asked questions in this area are:

  • How can we modernize existing applications?
  • What are the key elements for a future-proven strategy IT system architecture?
  • How can the flexibility as well as the agility of the IT system landscape be ensured?

But from our experience there’s no common answer for these questions, since every customer has individual requirements and businesses, but it is necessary to find pragmatic solutions, which leverage from existing best Practices – it is not necessary to completely re-invent the wheel.

With our new poster „Four Pillars of Digitalization based on Oracle Cloud“ (Download it here) , we try to deliver a set of harmonized reference models which we evolved based on our practical experience, while conceiving modern, future-oriented solutions in the area of modern application designs, integrative architectures, modern infrastructure solutions and analytical architectures. The guiding principle, which is the basis for our architectural thoughts is: Design for Change. If you want to learn more, you can refer to our corresponding Ebook (find the Ebook here, only available in German at the moment).

Usually the technological base for modern application architectures today is based on Cloud services, where the offerings of different vendors are constantly growing. Here it is important to know which Cloud services are the right ones to implement a specific use case. Our poster „Four Pillars of Digitalization based on Oracle Cloud“ shows the respective Cloud services of our strategic partner Oracle, which can be used to address specific challenges in the area of digitalization. Get the poster here.

 

Developer Partner Community

For regular information become a member in the Developer Partner Community please visit: http://www.oracle.com/partners/goto/wls-emea ( OPN account required). If you need support with your account please contact the Oracle Partner Business Center.

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Categories: BI & Warehousing

Oracle API Platform Cloud Service Overview by Rolando Carrasco

Tim Dexter - Sat, 2018-05-19 03:25

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  Oracle API Platform Cloud Services - API Design This is the first video of a series to showcase the usage of Oracle API Platform Cloud Services. API Management Part 1 of 2. Oracle API Cloud Services This is the second video of a series to show case the usage of the brand new Oracle API Platform CS. This is part one of API Management Oracle API Platform Cloud Services - API Management part 2 This is the 3rd video of the series. In specific here we will see the second part of the API Management functionality focused on Documentation. Oracle API Platform CS - How to create an app This is the 4th video of this series. In this video you will learn how to create an application. Oracle API Plaform Cloud Services - API Usage This is the fifth video of this series. In this video I will showcase how you will interact with the APIs that are deployed in APIPCS.

 

PaaS Partner Community

For regular information on business process management and integration become a member in the SOA & BPM Partner Community for registration please visit www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center.

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Categories: BI & Warehousing

Why are Universal Cloud Credit and Bring Your Own License a great opportunity for Oracle Partners?

Tim Dexter - Sat, 2018-05-19 03:24
image

Oracle simplified buying and consuming for PaaS and IaaS Cloud. Customer can purchase now Universal Cloud Credits. This universal cloud credits can be spend for any IaaS or PaaS service. Partners can start a PoC or project e.g. with Application Container Cloud Service and can add additional service when required e.g. Chabot Cloud Service. The customer can use the universal cloud credits for any available or even upcoming IaaS and PaaS services.

Thousands of customers use Oracle Fusion Middleware and Databases today. With Bring Your Own License they can move easy workload to the cloud. As they already own the license the customer needs to pay only a small uplift for the service portion of PaaS. This is a major opportunity for Oracle partners to offer services to this customers.

To learn more about Universal Cloud Credits and Bring Your Own License Attend the free on-demand training here

 

Developer Partner Community

For regular information become a member in the Developer Partner Community please visit: http://www.oracle.com/partners/goto/wls-emea ( OPN account required). If you need support with your account please contact the Oracle Partner Business Center.

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Technorati Tags: PaaS,Cloud,Middleware Update,WebLogic, WebLogic Community,Oracle,OPN,Jürgen Kress

Categories: BI & Warehousing

Event Hub Cloud Service. Hello world

Tim Dexter - Sat, 2018-05-19 00:46

In early days, I've wrote a blog about Oracle Reference Architecture and concept of Schema on Read and Schema on Write. Schema on Read is well suitable for Data Lake, which may ingest any data as it is, without any transformation and preserve it for a long period of time. 

At the same time you have two types of data - Streaming Data and Batch. Batch could be log files, RDBMS archives. Streaming data could be IoT, Sensors, Golden Gate replication logs.

Apache Kafka is very popular engine for acquiring streaming data. It has multiple advantages, like scalability, fault tolerance and high throughput. Unfortunately, Kafka is hard to manage. Fortunately, Cloud simplifies many routine operations. Oracle Has three options for deploy Kafka in the Cloud:

1) Use Big Data Cloud Service, where you get full Cloudera cluster and there you could deploy Apache Kafka as part of CDH.

2) Event Hub Cloud Service Dedicated. Here you have to specify server shapes and some other parameters, but rest done by Cloud automagically. 

3) Event Hub Cloud Service. This service is fully managed by Oracle, you even don't need to specify any compute shapes or so. Only one thing to do is tell for how long you need to store data in this topic and tell how many partitions do you need (partitions = performance).

Today, I'm going to tell you about last option, which is fully managed cloud service.

It's really easy to provision it, just need to login into your Cloud account and choose "Event Hub" Cloud service.

after this go and choose open service console:

Next, click on "Create service":

Put some parameters - two key is Retention period and Number of partitions. First defines for how long will you store messages, second defines performance for read and write operations.

Click next after:

Confirm and wait a while (usually not more than few minutes):

after a short while, you will be able to see provisioned service:

 

 

Hello world flow.

Today I want to show "Hello world" flow. How to produce (write) and consume (read) message from Event Hub Cloud Service.

The flow is (step by step):

1) Obtain OAuth token

2) Produce message to a topic

3) Create consumer group

4) Subscribe to topic

5) Consume message

Now I'm going to show it in some details.

OAuth and Authentication token (Step 1)

For dealing with Event Hub Cloud Service you have to be familiar with concept of OAuth and OpenID. If you are not familiar, you could watch the short video or go through this step by step tutorial

In couple words OAuth token authorization (tells what I could access) method to restrict access to some resources.

One of the main idea is decouple Uses (real human - Resource Owner) and Application (Client). Real man knows login and password, but Client (Application) will not use it every time when need to reach Resource Server (which has some info or content). Instead of this, Application will get once a Authorization token and will use it for working with Resource Server. This is brief, here you may find more detailed explanation what is OAuth.

Obtain Token for Event Hub Cloud Service client.

As you could understand for get acsess to Resource Server (read as Event Hub messages) you need to obtain authorization token from Authorization Server (read as IDCS). Here, I'd like to show step by step flow how to obtain this token. I will start from the end and will show the command (REST call), which you have to run to get token:

#!/bin/bash curl -k -X POST -u "$CLIENT_ID:$CLIENT_SECRET" \ -d "grant_type=password&username=$THEUSERNAME&password=$THEPASSWORD&scope=$THESCOPE" \ "$IDCS_URL/oauth2/v1/token" \ -o access_token.json

as you can see there are many parameters required for obtain OAuth token.

Let's take a looks there you may get it. Go to the service and click on topic which you want to work with, there you will find IDCS Application, click on it:

After clicking on it, you will go be redirected to IDCS Application page. Most of the credentials you could find here. Click on Configuration:

On this page right away you will find ClientID and Client Secret (think of it like login and password):

 

look down and find point, called Resources:

Click on it

and you will find another two variables, which you need for OAuth token - Scope and Primary Audience.

One more required parameter - IDCS_URL, you may find in your browser:

you have almost everything you need, except login and password. Here implies oracle cloud login and password (it what you are using when login into http://myservices.us.oraclecloud.com):

Now you have all required credential and you are ready to write some script, which will automate all this stuff:

#!/bin/bash export CLIENT_ID=7EA06D3A99D944A5ADCE6C64CCF5C2AC_APPID export CLIENT_SECRET=0380f967-98d4-45e9-8f9a-45100f4638b2 export THEUSERNAME=john.dunbar export THEPASSWORD=MyPassword export SCOPE=/idcs-1d6cc7dae45b40a1b9ef42c7608b9afe-oehtest export PRIMARY_AUDIENCE=https://7EA06D3A99D944A5ADCE6C64CCF5C2AC.uscom-central-1.oraclecloud.com:443 export THESCOPE=$PRIMARY_AUDIENCE$SCOPE export IDCS_URL=https://idcs-1d6cc7dae45b40a1b9ef42c7608b9afe.identity.oraclecloud.com curl -k -X POST -u "$CLIENT_ID:$CLIENT_SECRET" \ -d "grant_type=password&username=$THEUSERNAME&password=$THEPASSWORD&scope=$THESCOPE" \ "$IDCS_URL/oauth2/v1/token" \ -o access_token.json

after running this script, you will have new file called access_token.json. Field access_token it's what you need:

$ cat access_token.json {"access_token":"eyJ4NXQjUzI1NiI6InVUMy1YczRNZVZUZFhGbXFQX19GMFJsYmtoQjdCbXJBc3FtV2V4U2NQM3MiLCJ4NXQiOiJhQ25HQUpFSFdZdU9tQWhUMWR1dmFBVmpmd0UiLCJraWQiOiJTSUdOSU5HX0tFWSIsImFsZyI6IlJTMjU2In0.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.aNDRIM5Gv_fx8EZ54u4AXVNG9B_F8MuyXjQR-vdyHDyRFxTefwlR3gRsnpf0GwHPSJfZb56wEwOVLraRXz1vPHc7Gzk97tdYZ-Mrv7NjoLoxqQj-uGxwAvU3m8_T3ilHthvQ4t9tXPB5o7xPII-BoWa-CF4QC8480ThrBwbl1emTDtEpR9-4z4mm1Ps-rJ9L3BItGXWzNZ6PiNdVbuxCQaboWMQXJM9bSgTmWbAYURwqoyeD9gMw2JkwgNMSmljRnJ_yGRv5KAsaRguqyV-x-lyE9PyW9SiG4rM47t-lY-okMxzchDm8nco84J5XlpKp98kMcg65Ql5Y3TVYGNhTEg","token_type":"Bearer","expires_in":604800}

Create Linux variable for it:

#!/bin/bash export TOKEN=`cat access_token.json |jq .access_token|sed 's/\"//g'`

Well, now we have Authorization token and may work with our Resource Server (Event Hub Cloud Service). 

Note: you also may check documentation about how to obtain OAuth token.

Produce Messages (Write data) to Kafka (Step 2)

The first thing that we may want to do is produce messages (write data to a Kafka cluster). To make scripting easier, it's also better to use some environment variables for common resources. For this example, I'd recommend to parametrize topic's end point, topic name, type of content to be accepted and content type. Content type is completely up to developer, but you have to consume (read) the same format as you produce(write). The key parameter to define is REST endpoint. Go to PSM, click on topic name and copy everything till "restproxy":

Also, you will need topic name, which you could take from the same window:

now we could write a simple script for produce one message to Kafka:

#!/bin/bash export OEHCS_ENDPOINT=https://oehtest-gse00014957.uscom-central-1.oraclecloud.com:443/restproxy export TOPIC_NAME=idcs-1d6cc7dae45b40a1b9ef42c7608b9afe-oehtest export CONTENT_TYPE=application/vnd.kafka.json.v2+json curl -X POST \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: $CONTENT_TYPE" \ --data '{"records":[{"value":{"foo":"bar"}}]}' \ $OEHCS_ENDPOINT/topics/$TOPIC_NAME

if everything will be fine, Linux console will return something like:

{"offsets":[{"partition":1,"offset":8,"error_code":null,"error":null}],"key_schema_id":null,"value_schema_id":null}

Create Consumer Group (Step 3)

The first step to read data from OEHCS is create consumer group. We will reuse environment variables from previous step, but just in case I'll include it in this script:

#!/bin/bash export OEHCS_ENDPOINT=https://oehtest-gse00014957.uscom-central-1.oraclecloud.com:443/restproxy export CONTENT_TYPE=application/vnd.kafka.json.v2+json export TOPIC_NAME=idcs-1d6cc7dae45b40a1b9ef42c7608b9afe-oehtest curl -X POST \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: $CONTENT_TYPE" \ --data '{"format": "json", "auto.offset.reset": "earliest"}' \ $OEHCS_ENDPOINT/consumers/oehcs-consumer-group \ -o consumer_group.json

this script will generate output file, which will contain variables, that we will need to consume messages.

Subscribe to a topic (Step 4)

Now you are ready to subscribe for this topic (export environment variable if you didn't do this before):

#!/bin/bash export BASE_URI=`cat consumer_group.json |jq .base_uri|sed 's/\"//g'` export TOPIC_NAME=idcs-1d6cc7dae45b40a1b9ef42c7608b9afe-oehtest curl -X POST \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: $CONTENT_TYPE" \ -d "{\"topics\": [\"$TOPIC_NAME\"]}" \ $BASE_URI/subscription

If everything fine, this request will not return something. 

Consume (Read) messages (Step 5)

Finally, we approach last step - consuming messages.

and again, it's quite simple curl request:

#!/bin/bash export BASE_URI=`cat consumer_group.json |jq .base_uri|sed 's/\"//g'` export H_ACCEPT=application/vnd.kafka.json.v2+json curl -X GET \ -H "Authorization: Bearer $TOKEN" \ -H "Accept: $H_ACCEPT" \ $BASE_URI/records

if everything works, like it supposed to work, you will have output like:

[{"topic":"idcs-1d6cc7dae45b40a1b9ef42c7608b9afe-oehtest","key":null,"value":{"foo":"bar"},"partition":1,"offset":17}]

Conclusion

Today we saw how easy to create fully managed Kafka Topic in Event Hub Cloud Service and also we made a first steps into it - write and read message. Kafka is really popular message bus engine, but it's hard to manage. Cloud simplifies this and allow customers concentrate on the development of their applications.

here I also want to give some useful links:

1) If you are not familiar with REST API, I'd recommend you to go through this blog

2) There is online tool, which helps to validate your curl requests

3) Here you could find some useful examples of producing and consuming messages

4) If you are not familiar with OAuth, here is nice tutorial, which show end to end example

Categories: BI & Warehousing

Why Now Is the Time for ERP in the Cloud

Tim Dexter - Fri, 2018-05-18 20:20

“The movement to cloud is an inevitable destination; this is how computing will evolve over the next several years.” So said Oracle CEO Mark Hurd at Oracle OpenWorld 2017. Based on the results of new research, that inevitability is here, now.

In our first ERP Trends Report, we surveyed more than 400 finance and IT leaders. We found that 76% of respondents said they either have plans for ERP in the cloud or have made the move already. They are recognizing that waiting puts them at a disadvantage; the time to make the move is now.

The majority of respondents cited economic factors as the reason they made the leap, and it’s easy to see why: Nucleus Research recently published a report that cloud delivers 3.2x the return on investment (ROI) of on-premises systems, while the total cost of ownership (TCO) is 52% lower.  

But even more surprising were the benefits realized once our survey respondents got to the cloud. An astonishing 81% cited “Staying current on technology” as the main benefit of moving to cloud ERP. With a regular cadence of innovation delivered by the cloud, it is easier for companies to quickly incorporate game-changing technologies into everyday business processes—technologies like artificial intelligence, machine learning, the Internet of Things (IoT), blockchain and more. In the cloud, the risk of running their businesses on obsolete technology drops to zero. It’s the last upgrade they will ever need.

“One of the key value propositions in engaging with Oracle and implementing the cloud solutions has been the value of keeping current with technology and technological developments,” said Mick Murray, CFO of Blue Shield of California. “In addition to robotics, we’re looking at machine learning and artificial intelligence, and how do we apply that across the enterprise.”

As new capabilities are rolled out, cloud subscribers like Blue Shield can take advantage of them immediately. This gives them the agility to be both responsive and predictive. Uncertainty is the new normal in business and managing amid uncertainty is a must. It’s no longer enough to be quick-to-change; competitive companies must also have reliable insight into how potential future scenarios could impact performance.

So, what does that mean in terms of daily operations? Basically, it means people using knowledge to make good decisions in a fast, productive, and highly automated manner at all levels of the business. Cloud systems provide the data integration and ongoing technology refresh to incorporate best practices and technology advances.

The cloud also makes it easier to integrate external sources of valuable, contextual knowledge that helps improve the accuracy of data models. This is important considering the scope of threats to sustainable operations for businesses with large, global footprints. Political, environmental, and economic factors across multiple regions could impact business, such as limited travel capabilities slowing down delivery of key supplies.

Business uncertainty is everywhere, and organizations must be able to say, “What is our plan if X happens? What is our plan if X, Y, and Z happen, but W doesn’t?” And this insight must come quickly. Business moves too fast for reports to take days to compile.

ERP Replacement Effort Is Not What It Used to Be

One final stone on the scale in favor of ERP cloud is that migrating does not have to be painful. Don’t let memories of past onsite replacements haunt you. With the right products and the right expertise behind them, cloud migrations happen quickly, cause minimal business disruption, and don’t require intense user training.

For example, Blue Shield of California had set aside $600,000 on change management for the adoption of cloud; in the end, they barely spent anything. Change adoption, they reported, happened quickly and seamlessly.

Considering the benefits for cost savings, elimination of technology obsolescence, and ease of adopting emerging technologies, it is becoming harder to justify a wait on migration to cloud ERP. Disruption is not an issue, and long-term cost saving are substantial. Most importantly, modernizing ERP is an opportunity to modernize the business and embed an ever-refreshing technology infrastructure that enables higher performance on multiple levels.

 

Categories: BI & Warehousing

7 Machine Learning Best Practices

Tim Dexter - Fri, 2018-05-18 20:11

Netflix’s famous algorithm challenge awarded a million dollars to the best algorithm for predicting user ratings for films. But did you know that the winning algorithm was never implemented into a functional model?

Netflix reported that the results of the algorithm just didn’t seem to justify the engineering effort needed to bring them to a production environment. That’s one of the big problems with machine learning.

At your company, you can create the most elegant machine learning model anyone has ever seen. It just won’t matter if you never deploy and operationalize it. That's no easy feat, which is why we're presenting you with seven machine learning best practices.

Download your free ebook, "Demystifying Machine Learning"

At the most recent Data and Analytics Summit, we caught up with Charlie Berger, Senior Director of Product Management for Data Mining and Advanced Analytics to find out more. This is article is based on what he had to say. 

Putting your model into practice might longer than you think. A TDWI report found that 28% of respondents took three to five months to put their model into operational use. And almost 15% needed longer than nine months.

Graph on Machine Learning Operational Use

So what can you do to start deploying your machine learning faster?

We’ve laid out our tips here:

1. Don’t Forget to Actually Get Started

In the following points, we’re going to give you a list of different ways to ensure your machine learning models are used in the best way. But we’re starting out with the most important point of all.

The truth is that at this point in machine learning, many people never get started at all. This happens for many reasons. The technology is complicated, the buy-in perhaps isn’t there, or people are just trying too hard to get everything e-x-a-c-t-l-y right. So here’s Charlie’s recommendation:

Get started, even if you know that you’ll have to rebuild the model once a month. The learning you gain from this will be invaluable.

2. Start with a Business Problem Statement and Establish the Right Success Metrics

Starting with a business problem is a common machine learning best practice. But it’s common precisely because it’s so essential and yet many people de-prioritize it.

Think about this quote, “If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.”

Now be sure that you’re applying it to your machine learning scenarios. Below, we have a list of poorly defined problem statements and examples of ways to define them in a more specific way.

Machine Learning Problem Statements

Think about what your definition of profitability is. For example, we recently talked to a nation-wide chain of fast-casual restaurants that wanted to look at increasing their soft drinks sales. In that case, we had to consider carefully the implications of defining the basket. Is the transaction a single meal, or six meals for a family? This matters because it affects how you will display the results. You’ll have to think about how to approach the problem and ultimately operationalize it.

Beyond establishing success metrics, you need to establish the right ones. Metrics will help you establish progress, but does improving the metric actually improve the end user experience? For example, your traditional accuracy measures might encompass precision and square error. But if you’re trying to create a model that measures price optimization for airlines, that doesn’t matter if your cost per purchase and overall purchases isn’t going up.

3. Don’t Move Your Data – Move the Algorithms

The Achilles heel in predictive modeling is that it’s a 2-step process. First you build the model, generally on sample data that can run in numbers ranging from the hundreds to the millions. And then, once the predictive model is built, data scientists have to apply it. However, much of that data resides in a database somewhere.

Let’s say you want data on all of the people in the US. There are 360 million people in the US—where does that data reside? Probably in a database somewhere.

Where does your predictive model reside?

What usually happens is that people will take all of their data out of database so they can run their equations with their model. Then they’ll have to import the results back into the database to make those predictions. And that process takes hours and hours and days and days, thus reducing the efficacy of the models you’ve built.

However, growing your equations from inside the database has significant advantages. Running the equations through the kernel of the database takes a few seconds, versus the hours it would take to export your data. Then, the database can do all of your math too and build it inside the database. This means one world for the data scientist and the database administrator.

By keeping your data within your database and Hadoop or object storage, you can build models and score within the database, and use R packages with data-parallel invocations. This allows you to eliminate data duplications and separate analytical servers (by not moving data) and allows you to to score models, embed data prep, build models, and prepare data in just hours.

4. Assemble the Right Data

As James Taylor with Neil Raden wrote in Smart Enough Systems, cataloging everything you have and deciding what data is important is the wrong way to go about things. The right way is to work backward from the solution, define the problem explicitly, and map out the data needed to populate the investigation and models.

And then, it’s time for some collaboration with other teams.

Machine Learning Collaboration Teams

Here’s where you can potentially start to get bogged down. So we will refer to point number 1, which says, “Don’t forget to actually get started.” At the same time, assembling the right data is very important to your success.

For you to figure out the right data to use to populate your investigation and models, you will want to talk to people in the three major areas of business domain, information technology, and data analysts.

Business domain—these are the people who know the business.

  • Marketing and sales
  • Customer service
  • Operations

Information technology—the people who have access to data.

  • Database administrators

Data Analysts—people who know the business.

  • Statisticians
  • Data miners
  • Data scientists

You need the active participation. Without it, you’ll get comments like:

  • These leads are no good
  • That data is old
  • This model isn’t accurate enough
  • Why didn’t you use this data?

You’ve heard it all before.

5. Create New Derived Variables

You may think, I have all this data already at my fingertips. What more do I need?

But creating new derived variables can help you gain much more insightful information. For example, you might be trying to predict the amount of newspapers and magazines sold the next day. Here’s the information you already have:

  • Brick-and-mortar store or kiosk
  • Sell lottery tickets?
  • Amount of the current lottery prize

Sure, you can make a guess based off that information. But if you’re able to first compare the amount of the current lottery prize versus the typical prize amounts, and then compare that derived variable against the variables you already have, you’ll have a much more accurate answer.

6. Consider the Issues and Test Before Launch

Ideally, you should be able to A/B test with two or more models when you start out. Not only will you know how you’re doing it right, but you’ll also be able to feel more confident knowing that you’re doing it right.

But going further than thorough testing, you should also have a plan in place for when things go wrong. For example, your metrics start dropping. There are several things that will go into this. You’ll need an alert of some sort to ensure that this can be looked into ASAP. And when a VP comes into your office wanting to know what happened, you’re going to have to explain what happened to someone who likely doesn’t have an engineering background.

Then of course, there are the issues you need to plan for before launch. Complying with regulations is one of them. For example, let’s say you’re applying for an auto loan and are denied credit. Under the new regulations of GDPR, you have the right to know why. Of course, one of the problems with machine learning is that it can seem like a black box and even the engineers/data scientists can’t say why certain decisions have been made. However, certain companies will help you by ensuring your algorithms will give a prediction detail.

7. Deploy and Automate Enterprise-Wide

Once you deploy, it’s best to go beyond the data analyst or data scientist.

What we mean by that is, always, always think about how you can distribute predictions and actionable insights throughout the enterprise. It’s where the data is and when it’s available that makes it valuable; not the fact that it exists. You don’t want to be the one sitting in the ivory tower, occasionally sprinkling insights. You want to be everywhere, with everyone asking for more insights—in short, you want to make sure you’re indispensable and extremely valuable.

Given that we all only have so much time, it’s easiest if you can automate this. Create dashboards. Incorporate these insights into enterprise applications. See if you can become a part of customer touch points, like an ATM recognizing that a customer regularly withdraws $100 every Friday night and likes $500 after every payday.

Conclusion

Here are the core ingredients of good machine learning. You need good data, or you’re nowhere. You need to put it somewhere like a database or object storage. You need deep knowledge of the data and what to do with it, whether it’s creating new derived variables or the right algorithms to make use of them. Then you need to actually put them to work and get great insights and spread them across the information.

The hardest part of this is launching your machine learning project. We hope that by creating this article, we’ve helped you out with the steps to success. If you have any other questions or you’d like to see our machine learning software, feel free to contact us.

You can also refer back to some of the articles we’ve created on machine learning best practices and challenges concerning that. Or, download your free ebook, "Demystifying Machine Learning."

 

Categories: BI & Warehousing

Announcing PeopleSoft Cloud Manager Support for Oracle Cloud Infrastructure

Tim Dexter - Fri, 2018-05-18 19:45

Oracle released PeopleSoft Cloud Manager in 2017 featuring in-depth automation to help accelerate adoption of Oracle Cloud (Classic) as an efficient deployment platform for PeopleSoft customers. With the excitement generated around Oracle Cloud Infrastructure (OCI)--a cloud designed for the enterprise customer--several customers and partners have been looking forward to taking advantage of the enhanced OCI with PeopleSoft Cloud Manager.  Oracle is pleased to announce Cloud Manager’s support for OCI beginning with today’s release of PeopleSoft Cloud Manager Version 6.

So, what is new and exciting in PeopleSoft Cloud Manager Version 6?  For the first time, there are two images provided: one for OCI, and the other for OCI Classic.  The Cloud Manager Image 6 for OCI supports a number of OCI features, including Regions, Virtual Cloud Networks, Subnets, Compute and DB System platforms.  With this image, instances will be provisioned on VM shapes.  Customers can lift and shift PeopleSoft environments from on-premises to OCI using the same approach they used OCI Classic.

For PeopleSoft Cloud Manager on OCI Classic, we have enabled support for the lift and shift of on-premises databases encrypted with Oracle Transparent Data Encryption (TDE).  TDE offers another level of data security that customers are looking for as data is migrated to the cloud.  A ‘Clone to template’ option is also available for encrypted databases. 

The lift utility requires a few parameters for TDE so that the encrypted database may be packaged and lifted to the cloud.

During shift process, the same parameters are required to deploy the lifted database.

Customers have also requested an enhancement to support non-Unicode databases for PeopleSoft environments.  PeopleSoft Cloud Manager Version 6 supports lift and shift of environments that use non-Unicode Databases.  Unlike image 5, a conversion of the on-premises database to Unicode is no longer required prior to using Cloud Manager’s Lift and Shift automation.

To get your hands on the new Cloud Manager images, go to the Oracle Marketplace and look for either the OCI-Classic image or the OCI image…or try both!   Be sure to review the documentation and additional important information mentioned in the Marketplace listings.

We are excited to combine the automation of provisioning and maintenance that PeopleSoft Cloud Manager provides with the robust benefits of Oracle Cloud Infrastructure.  Combining support for OCI with the additional features of non-Unicode databases and TDE encrypted databases, we expect all customers to benefit from the latest Cloud Manager image, using whichever Oracle Cloud is right for you. 

Stay tuned for additional information and more Cloud Manager features.  Now, off to the next image!

 

Categories: BI & Warehousing

Emerging Tech Helps Progressive Companies Deliver Exceptional CX

Tim Dexter - Fri, 2018-05-18 19:18

It’s no secret that the art of delivering exceptional service to customers—whether they’re consumers or business buyers—is undergoing dramatic change. Customers routinely expect highly personalized experiences across all touchpoints, from marketing and sales to service and support. I call each of these engagements a moment of truth—because leaving customers feeling satisfied and valued at each touchpoint will have a direct bearing on their loyalty and future spending decisions.

This is why customer experience (CX) has become a strategic business imperative for modern companies. Organizations that provide effective, well-integrated CX across the entire customer journey achieved compound annual growth rates of 17%, versus the 3% growth rates logged by their peers who provided less-effective customer experiences, according to Forrester’s 2017 “Customer Experience Index.”

Fortunately, it’s becoming easier to enter the CX winner’s circle. AI, machine learning, IoT, behavioral analytics, and other innovations are helping progressive companies capitalize on internal and third-party data to deliver highly personalized communications, promotional offers, and service engagements.

How can companies fully leverage today’s tools to support exceptional CX? If they haven’t already done so, companies should start evolving away from cloud 1.0 infrastructures, where an amalgam of best-of-breed services runs various business units. These standalone cloud platforms might have initially provided quick on-ramps to modern capabilities, but now, many companies are paying a price for that expediency. Siloed data and workflows hinder the smooth sharing of customer information among departments. This hurts CX when a consumer who just purchased a high-end digital camera at a retail outlet, for example, webchats with that same company’s service department about a problem, and the service team has no idea this is a premium customer.

In contrast, cloud 2.0 is focused on achieving a holistic view of customers—thanks to simplified, well-integrated services that support each phase of the customer journey. Eliminating information silos benefits companies by giving employees all the information they need to provide a tailored experience for every customer.

Achieving modern CX requires the right vendor partnerships. That starts with evaluating cloud services according to how complete, integrated, and extensible the CX platform is for supporting the entire customer journey. One option is the Oracle Customer Experience Cloud (Oracle CX Cloud) suite, an integrated set of applications for the entire customer lifecycle. It’s complemented by native AI capabilities and Oracle Data Cloud, the world’s largest third-party data marketplace of consumer and business information, which manages anonymized information from more than a billion business and 5 billion consumer identifiers. This means that business leaders, besides understanding customers based on their direct interactions, can use Oracle Data Cloud for insights into social, web surfing, and buying habits at third-party sites and retailers and then apply AI to find profitable synergies.

As new disruptive technologies come to the market—whether that’s the mainstreaming of IoT or drones for business—companies will be under constant pressure to integrate these new capabilities to improve their CX strategies. Modern, integrated cloud services designed for CX don’t support just today’s innovations. With the right cloud choices, companies can continually evolve to meet tomorrow’s CX challenges.

(Photo of Des Cahill by Bob Adler, The Verbatim Agency)

Categories: BI & Warehousing

5 Subjects Every Computer Science Student Should Learn

Tim Dexter - Fri, 2018-05-18 18:55

I was fortunate this year to attend the Association for Computer Machinery’s SIGCSE (Special Interest Group on Computer Science Education) conference, where there was a good deal of conversation about what a modern computer science curriculum should include.

Technology changes quickly and it can be difficult for academic programs to keep pace. Still, if computer science students are to contribute meaningfully to the field in either industry or research jobs, it’s critical that they learn modern computing skills. Here are five subjects I think every higher education institution should teach their undergraduate computer science majors:

1. Parallel Programming

The single, standalone server with one CPU has gone the way of the dodo bird, displaced by the cloud, server farms and multithreaded parallel processors. Yet colleges and universities are still mainly teaching their undergraduates sequential programming—programs that execute instructions one after the other—as they have for decades.

Modern computing environments and massive data sets demand not just that we process multiple instructions simultaneously across multiple servers (distributed computing), but also that programs be written to process multiple instructions simultaneously on multicore chips within multiple servers and devices.

Too often, parallel programming is relegated to a single chapter in a textbook, easily skipped when time in the semester runs short. To prepare students for high-performance computing, big data, machine learning, blockchain and more, we must teach them to both think and program in parallel.

2. Green Programming

With the ubiquity of battery-driven computers, energy efficiency is more important than ever. The more we ask our smart devices to do, the more energy they need to do it and the more quickly they exhaust their batteries. The same is true for massive server clusters, where fires related to energy-consumption are not uncommon as we demand faster and faster processing of more and more data.

How you architect a software program directly affects how much energy is needed to execute the program, yet few undergraduate programs teach students about this relationship. In a fast-warming world, one in which we dream big dreams about all the ways artificial intelligence and high-performance computing will make our lives better, it is imperative that we write energy-optimized software. Students will not be able to do that if we don’t teach them how.

3. Collaborative Development

Academia persists in trying to measure what individual students know. In most programming classes, students start from a blank screen and write clean code independently or, less often, with a partner.

But this isn’t how software is engineered in the real world. Professional software engineers almost always start with someone else’s code and work collaboratively in large groups to modify, improve and correct that code, which is then integrated with code written by other engineers in other groups.

It’s common for software development groups to include people from different countries, in different time zones. Working effectively requires team members to communicate well in different languages and across different cultures. It also means that someone else needs to be able to look at your code and know what it does, so following formatting standards and providing clear commenting are critical.

However, in our desire to ensure that each student understands every programming concept and rule of syntax, we overlook opportunities to teach collaborative software development and help students develop critical professional skills.

4. Hardware Architecture

In the minds of most college students, IBM, Intel, and AMD—the inventors and developers of the multicore processor—are old news…old companies founded by old guys. Mobile applications are where the action is.

But mobile apps are driven by data, usually by a lot of data, and they won’t be of much use without the processors, databases and networks that power them.

Computing works and advances based on the entire system, from the power source to the user interface, and students will be more successful if they know how to open the box and “kick the tires.” They can then optimize for energy efficiency and write parallel code that makes use of new hardware architectures. They can manage caching, memory architecture and resource allocation issues. They can explain and explore quantum computing.

Computer science doesn’t stop at software or coding. Students need foundations in hardware architecture, too, including electrical engineering and physics. We need computer scientists who can test and push the boundaries of hardware just as much as they push what can be achieved with software.

5. Computer History and Ethics

Something I heard at the Turing 50th Anniversary celebration last summer has stuck with me: Computing is not neutral. It can be used for good or evil. It can be used to help people and it can be used to manipulate and harm them.

For several decades now, we have been making computing advances for the sake of computing, because what we can make computers do is cool, because the challenge of the next thing is too alluring to pass up, because there is money to be made if we can do “X.”

Just because we can do something with computing, however, doesn’t mean we should. Computing power is so great that we need policies to regulate and manage it, in order to protect and benefit people.

It’s important for students of computing to understand its history and to take courses grounded in ethics so they can make responsible decisions and guide others. They should know computing’s historical villains and heroes, its inventors and detractors, and how it has been used to benefit and hurt people. The old saw applies here: If we do not learn our history, we are doomed to repeat it.

Even in a crowded curriculum, we must ensure students are gaining the skills and knowledge they need to become technology innovators, business leaders and positive contributors to society in the coming decades. This list is only a starting point.

Alison Derbenwick Miller is vice president of Oracle Academy.

Categories: BI & Warehousing

How Blockchain Will Disrupt the Insurance Industry

Tim Dexter - Fri, 2018-05-18 18:49

The insurance industry relies heavily on the notion of trust among transacting parties. For example, when you go to buy car insurance you get asked for things like your zip code, name, age, daily mileage, and make & model of your car. Other than, maybe, the make & model of your car you can pretty much falsify other information about yourself for a better insurance quote. Underwriters trust that you are providing the correct information, which is one of the many risks in the underwriting business.

Enterprise blockchain platforms such as one from Oracle essentially enables trust-as-a-service in such interactions. Participants (insurer and insured) need to come together to do business, but they do not necessarily trust each other. Blockchain provides a scalable mechanism to securely and easily enable trust in such scenarios. There are 4 key properties of Blockchain that enable trust-as-a-service:

  1. Transparency of digital events and transactions it manages,
  2. Immutability of records stored on the blockchain. through append-only time-stamped and hashed records,
  3. Security and assurance that records stored on blockchain aren't compromised through built-in consensus and encryption mechanisms,
  4. Privacy through cryptography

Blockchain can be a good solution for a number of insurance use cases such as:

  • Reducing frauds in underwriting and claims by validating data from customers and suppliers in the value chain
  • Reducing claims by offering tokenized incentives to promote safer driving behavior by capturing data from insured entities like motor vehicles
  • Enabling pay-per-mile billing for insurance by keeping verifiable records of miles traveled
  • And, in the not so distant future, using blockchain to determine liability in case of an accident between two autonomous vehicles by using blockchain to manage timestamped immutable records of decisions made by deep-learning models from both autonomous vehicles right before the accident.

Besides these use cases, blockchain has potential to eliminate intermediaries, improve transparency of records, eliminate manual paperwork, and error-prone processes, which together can deliver orders of magnitude improvement in operational efficiency for businesses. Of course, there are other types of insurance such as healthcare, reinsurance, catastrophic events insurance, property and casualty insurance, which would have some unique flavor of use cases but they would similarly benefit from blockchain to reduce risk and improve business efficiency.

There is no question that blockchain can, potentially, be a disruptive force in the insurance industry. It would have to overcome legal and regulatory barriers before we see mass adoption of blockchain among the industry participants. 

If you are working on an interesting project related to the use of Blockchain for insurance industry feel free to get in touch by leaving a comment or contact us through social media or Oracle sales rep. We’d be glad to help you connect with our subject matter experts and with your industry peers who may be working on similar use cases with Oracle. For more information on Oracle Blockchain, please visit Oracle Blockchain home pages here, and here

 

 

Categories: BI & Warehousing

Oracle's No-Cost Platinum-Level Support Is the New Baseline in the Cloud Market

Tim Dexter - Fri, 2018-05-18 18:36

Companies may give up their servers, storage, and entire data centers when they move to the cloud, but their need for support services doesn’t go away, it changes. Recognizing a growing need for enterprise-class support in the cloud, Oracle is making its Platinum-level support services available at no additional cost to all customers of Oracle Fusion software-as-a-service applications.

“Our objective is to put out a service capability that is simply the best—bar none,” said Oracle CEO Mark Hurd, in announcing that a range of support services would be available for Oracle Fusion enterprise resource planning, enterprise performance management, human capital management, supply chain, manufacturing, and sales and service cloud applications.

The SaaS support services include 24/7 rapid-response technical support, proactive technical monitoring, success planning, end-user adoption guidance, and education resources.

“Most of our customers are going to cloud,” Hurd said in a briefing with journalists at Oracle’s headquarters in Redwood Shores, California. As that happens, he said, “it’s important for someone in the industry, particularly an industry leader in these mission-critical applications, to take a position” on what level of service that transition demands.

“SaaS application support offerings need to become more agile and responsive,” Hurd added. “We need to provide our SaaS customers with everything they need for rapid, low-cost implementations and a successful rollout to their users.”

Catherine Blackmore, Oracle group vice president of North America Customer Success, said Oracle will also offer new advanced services, including dedicated support and certified expertise, for customers that need a higher level of support. “We have a shared interest in our customers’ success, so we’re going above and beyond to ensure our customers have everything they need to succeed,” she said.

Cloud Levels the Playing Field

Oracle also announced the names of first-time cloud customers and others that are expanding their use of Oracle Cloud services. They include Alsea, Broadcom, Exelon, Gonzaga University, Heineken Urban Polo, Providence St. Joseph Health, Sinclair Broadcast Group, and T-Mobile US.

In a Q&A with the journalists, Hurd was asked about his outlook for SaaS adoption outside of the United States and, in particular, in the Latin America region. He said modern cloud applications can be “game changing” for businesses in places where outdated software applications are still the norm.

“You don’t need armies of experts and system integrators,” Hurd said.

Oracle develops thousands of features that are made available regularly to its SaaS application customers. “That’s a feature stream you don’t have to manage from a data center that you don’t have to operate,” Hurd said.

Self-Driving Technology

Hurd pointed to Oracle’s development of autonomous technologies, including the recently introduced Oracle Autonomous Data Warehouse Cloud Service, as another big area of focus at the company. “It gets upgraded, optimized, secured, patched, and tuned, all automatically without any human intervention,” he said.

As the next step in the delivery of autonomous cloud services, Oracle announced the availability of three new Oracle Cloud Platform services with built-in artificial intelligence and machine learning algorithms: Oracle Autonomous Analytics CloudOracle Autonomous Integration Cloud, and Oracle Autonomous Visual Builder Cloud.

Categories: BI & Warehousing

Modern Customer Experience 2018 was Legendary

Tim Dexter - Fri, 2018-05-18 17:53

During his keynote at Modern Customer Experience 2018, Des Cahill, Head CX Evangelist, stated that CX should stand for Continuous Experimentation. He encouraged 4,500 enthusiastic marketers, customer service, sales, and commerce professional us to try new strategies, to take risks, strive to be remarkable, and triumph through sheer determination.

Casey Neistat echoed Des, challenging us to “do what you can’t,” while best-selling author Cheryl Strayed inspired us to look past our fears and be brave. “Courage isn’t success,” she reminded us, “it’s doing what’s hard regardless of the outcome.”

CX professionals today face numerous challenges: the relentless rise of customer expectations, the accelerating pace of innovation, evolving regulations like GDPR, increase ROI, plus the constant pressure to raise the bar. Modern Customer Experience not only inspired attendees to become the heroes of their organization, but it armed each with the tools to do so.

If you missed Carolyne-Matseshe Crawford, VP of Fan Experience at Fanatics talk about how her company’s culture pervades the entire customer experience, or how Magen Hanrahan VP of Product Marketing at Kraft Heinz is obsessed with data driven marketing tactics, give them a watch. And don’t miss Comcast’s Executive VP, Chief Customer Experience Officer, Charlie Herrin, who wants to build proactive customer experience and dialogue into Comcast’s products themselves with artificial intelligence.

The Modern Customer Experience X Room showcased CX innovation, like augmented and virtual reality, artificial intelligence, and the Internet of Things. But it wasn’t all just mock-ups and demos, a Mack Truck, a Yamaha motorcycle, and an Elgin Street Sweeper were on display, showcasing how Oracle customers put innovation to use to create legendary customer experiences.

Attendees were able to let off some steam during morning yoga and group runs. They relived the 90s with Weezer during CX Fest, and our Canine Heroes from xxxxx were a highlight of everyone’s day.

But don’t just take it from us. Here’s what a few of our attendees had to say about the event.

“Modern Customer Experience gives me the ability to learn about new products on the horizon, discuss challenges, connect with other MCX participants, learn best practices and understand we’re not alone in our journey.” – Matt Adams, Sales Cloud Manager, ArcBest

 “Modern Customer Experience really allows me to do my job more effectively. Without it, I don’t know where I would be! It’s the best conference of the year.” – Joshua Parker, Digital Marketing and Automation Manager, Rosetta Stone

We’re still soaking it all in. You can watch all the highlights from Modern Customer Experience keynotes on YouTube, and peruse the event’s photo slideshow. Don’t forget to share your images on social media, with #ModernCX and sign up for alerts when registration for Modern Customer Experience 2019 opens!

Categories: BI & Warehousing

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