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Operational BI - Blurring the Lines

Data Integration Adviser

Before operational business intelligence (BI) came on the scene, classifying BI applications was simple: enterprise applications and BI/DW activities were clearly separate domains with their own IT staffs, budgets and expertise. There was no overlap between the tools, the data, the expertise or the people working in the different camps. The closest these groups came to working together was the scheduled feeds from the enterprise resource planning (ERP) system to the data warehouse (DW), but even these took place before any BI work was done and were generally handoffs to the DW team.

It was also easier to categorize software vendors by ERP and DW/BI domains. Nowadays, however, ERP systems can be deployed on multiple relational databases, their schemas and application programming interfaces (APIs) are published and DW/BI vendors partner with ERP vendors. You can use an independent BI vendor's products to query, report and analyze data from ERP applications. In addition, ERP vendors are selling their own DW/BI solutions as extensions to their product lines.

The Benefits and Risks of Operational BI

The upside of operational BI is that it is simpler for both business users and IT to use one suite of BI tools to access, report and analyze business data, regardless of where it is stored. Business users need only learn one suite of tools. IT only has to learn, design, deploy and support one set of tools. The enterprise cost is lower with reduced licensing, maintenance and infrastructure costs.

The downside to operational BI is that vendors, IT and business users oversell the concept and confuse the BI tool and the data it accesses. Because operational BI systems let you use the BI tool to access an ERP system directly, users start thinking that they should always go to that data directly and in real time. Just because you can do something does not mean you should or that it makes sense in a business context. Somehow people start thinking they do not need those data warehouses and data marts anymore. After all, they think, if you can get to the data directly, then why spend all that time building and maintaining a data warehouse?

Don't give up on the data warehouse. Operational BI shines when it is part of an overall portfolio of business information solutions. Data integration that involves data cleansing, data integrity and data transformation is vital to creating useful business information out of raw data. Many companies have multiple customer, product, part, services and employee lists. Health care has multiple doctors, patients, providers, insurers, procedures and prescription lists. They all need a data warehouse to handle the creation of consistent reference lists (or conforming dimensions in data modeling lingo). You are not going to do this on the fly when accessing ERP data from multiple systems across the world in real time.

The danger is considering operational BI as a panacea for your business information needs. Too often, the latest technology is seen as a solution that avoids all that tough, time-consuming, data integration stuff we have been doing for years. It is not a shortcut. IT and business still need to communicate and agree on the data definitions and data transformations for business information.

A Framework for Operational BI Success

With the qualification that operational BI is part of the overall solution and not the solution, here are my recommendations for putting it to good use:

  • Create a data and information portfolio for your business users that includes the enterprise applications, data warehouses, data marts, cubes and even spreadsheets that are needed to monitor, measure and manage corporate performance. Create a data architecture that enables data to be transformed and moved appropriately for its business purpose. There are many times that data should be queried from the enterprise application directly rather than insisting that the data be moved to a DW before the business users can access it. Enterprise information integration (EII), for example, enables querying across disparate data sources, i.e., enterprise applications, where in the past the data had to be physically moved into a common database.
  • Create a reporting and analysis (BI) portfolio for your business users that enables the reporting and information analysis to support performance management. Business users do not need to understand the difference between operational BI and management reporting. They simply need to perform their analysis regardless of how the IT industry classifies what they are doing.
  • Leverage common BI tools across your data portfolio. Business users should be able to use the same BI tools for their reporting and analysis, regardless of where the data comes from. They should not need to change their behavior based on where in the data portfolio their analysis needs are met. Simplify their lives by letting them go to one place and use one set of tools.

With a little forethought and planning, operational BI will only blur distinctions between categories, not the quality of your BI initiatives. 


Rick Sherman has more than 20 years of business intelligence and data warehousing experience, having worked on more than 50 implementations as a director/practice leader at PricewaterhouseCoopers and while managing his own firm. He is the founder of Athena IT Solutions, a Boston-based consulting firm that provides data warehouse and business intelligence consulting, training and vendor services. Sherman is a published author of over 50 articles, an industry speaker, a DM Review World Class Solution Awards judge, a data management expert at searchdatamanagement.com and has been quoted in CFO and Business Week. Sherman can be found blogging on performance management, data warehouse and business intelligence topics at The Data Doghouse.You can reach him at rsherman@athena-solutions.com or (617) 835-0546.

In addition to teaching at industry conferences, Sherman offers on-site data warehouse/business intelligence training, which can be customized and teaches public courses in the Boston area. He also teaches data warehousing at Northeastern University 's graduate school of engineering.

 

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