Selection criteria for an in-memory analysis solution
Date: Wed, 12 May 2010 04:13:24 -0700 (PDT)
The evolution of reporting and analytics has seen dramatic changes in recent years. Starting with static “green bar” reports in the mid-to- late '70s, information could be abstracted from mainframe systems and, often manually, transferred to spreadsheets where data could be aggregated and analyzed. Data warehousing was the buzz of the '80s, and while this did enable heterogeneous data sources to be centralized, projects were often grossly over budget and far below expectations. As technologies have matured and the advent of servicesbased architectures has become more prominent, data warehousing reinvented itself and emerged as what is now recognized as business intelligence.
However, the recent advent of in-memory analysis means that Business Intelligence expectations have changed forever. Dealing with overly complex software designed for a handful of power users involving long deployment cycles and low project success rates is no longer acceptable. Today, smart companies are striving to spread fact-based decision making throughout the organization, but they know they can’t do it with expensive, hard-to-use tools that require extensive IT hand holding. The pace of business now demands fast access to information and easy analysis; if the tools aren’t fast and easy, business intelligence will continue to have modest impact, primarily with experts who have no alternative but to wait for an answer to a slow query.
The success or failure of in-memory analysis does, however rest to some degree on the technology chosen to be the delivery platform. The fundamental requirement is that this platform is web-centric, beyond that there are some essential technology components that assist to deliver the business benefits sought. These are:
Enterprise scalability and security
All BI solutions must include enterprise administrative features, such as usage monitoring, single sign-on and change management; and this is just as true for in-memory solutions. It is therefore, critical that you choose solutions such as Yellowfin business intelligence, with its integrated in-memory database, that can provide enterprise class infrastructure that enable you to scale your deployment as your users grow.
Integration with your existing data warehouse and OLAP cubes
While some vendors tout in-memory as a way of avoiding building a data warehouse, this option usually applies to smaller organizations that may only have a single source system. For larger companies that have multiple source systems, the data warehouse continues to be the ideal place to transform, model and cleanse the data for analysis.
Look for tools that are designed to integrate with and leverage existing BI environments. An in-memory solution that is tightly integrated into the visualization tool is critical. However, it is equally important that the visualization tool can also access your OLAP cubes and data warehouse tables without the need for an in-memory middle-layer. Without this option a purely stand-alone in-memory solution can lead to yet another version of the truth, adding complexity to your BI environment.
Yellowfin takes a flexible approach whereby the system administrator can configure the server to perform processing either against the inmemory database, or alternatively, push processing down to the underlying data store. The decision on which approach is optimal for a given deployment will depend a lot on the query performance characteristics of the data store. For example, a traditional OLTP data store may benefit significantly from in-memory processing, whereas a query optimized analytic data store may provide performance similar to or better than in-memory processing. Combining this flexible architecture with the cost advantages of not using an OLAP server gives customers choice and a BI platform that can grow as their data and analysis requirements do.
Ensure real time data refresh
Because reporting data is potentially extracted from a source system or a data warehouse and then loaded into memory, data latency can be a concern. Front-line workers in a customer service center, for example, need near-real-time, highly granular (detailed) data. If an in-memory tool contains last week’s product inventory data, it’s probably not of use to customer service reps. Thus, the suitability of an in-memory tool and the success of the deployment may hinge on the degree to which the solution can automate scheduled incremental data loads. One of the criticisms’ of some in-memory analysis tools is their lack of incremental load. This means that whenever a data refresh is required the entire data set need to be refreshed rather than just changed or new transactions. This increases the load times and means that refreshes cannot be frequent enough to enable near-real time reporting. This is nor the case with Yellowfin’s in-memory technology.
Minimize Administration overhead
In-memory analytic tools often introduce some of the same concerns that OLAP stores create: namely, they usually create another data source, with its own calculations and business definitions. This is where tools such as Yellowfin differ from other in-memory approaches: existing queries, reports and dashboards automatically take advantage of an in-memory database, seamless to users. Administrators are not adding calculations and business logic within another layer; they reside within the existing meta-data layer for reporting that is already built.
Web-based development and deployment.
Some in-memory tools are not nearly as Web enabled as their conventional BI counterparts. This seems to reflect both technology immaturity and a tendency to be a niche deployment. However, for successful adoption with minimal administrative overhead web based development and deployment is critical. Both the visualization tool and in-memory database need to be server based deployments to ensure data access security and application upgrades can be easily managed. Solutions such as Yellowfin, provide a single web based platform for delivering your Business Intelligence needs. From connection through to design, modeling and visualization, your users work within a fully integrated browser application that encourages collaboration and an iterative approach to report development - leading to analytical applications that meet the needs of your end users.
Data security must be of paramount concern
In Memory applications have the potential to expose significantly more data to end-users then ever before. This raises security issues regarding how data is accessed, where it is stored and who has access to that data.
In determining the best strategy for your in-memory deployment security needs to be foremost in your selection criteria. There are two aspects of security the location of your data. Where is it stored and is that storage secure? And secondly who has access to that data store. In terms of storage – the most secure location for your data is on a centralized server, whether hosted or internal. Not only is this more secure but it maintains basic controls regarding data governance.
To understand this consider a scenario where users are able to conduct complex queries by downloading up to 100 million rows of data to their desktop from many data sources, or data feeds from the Web. Sure the information can then be sliced and diced into reports or users can create BI applications on their desktops and share them with colleagues. Sounds great in theory but fraught with danger in practice. With this level of data on a laptop it is free to leave your premises and get lost or stolen in the worst case or published without any form of governance at best.
In addition to centralized storage your in-memory analysis need to conform to data security measures as well. These means that data access profiles for your users need to be adhered to through out your reporting process. Organizations spend an enormous amount of effort in securing their transactional applications and so it is critical that when it comes to the data they contain the same level of security is present. This means that users only have access to the data they are authorized to access, and that this access is changes as the employees role changes.
In summary when choosing an in-memory analysis tool set you do need to consider how it will reside within your enterprise architecture. Interaction with your current business intelligence environment, the security framework and the ability to deliver real time reporting are all critical aspects that need to be considered in your selection process.
About Yellowfin Business Intelligence
Yellowfin is passionate about making Business Intelligence easy. Recently recognised among 25 rising companies that CIO’s must know about, Yellowfin is a leading web-based BI solution that can be easily integrated into any third-party application or delivered as a standalone enterprise platform. Yellowfin is an innovative and flexible solution for reporting and analytics, providing a full range of data access, presentation and information delivery capabilities.
www.yellowfinbi.com Received on Wed May 12 2010 - 06:13:24 CDT