A Foundation for Business Decision Support

According to Gartner, Forrester, IBM and The Data Warehouse Institute, 50-70 percent of every major information systems initiative involves data handling. Thomas Quintal, EVP of LoganBritton, explains why confidence in that data is essential.

In days past, storing all this data was viewed as an unavoidable expense. Now, of course, we know that operational and historical data are valuable company assets, typically leveraged using business intelligence and data warehousing. However, while we acknowledge the value, we also know that there often are missing pieces. We’re getting some value from the data, but the potential is much greater. The breadth and depth, the flexibility, the speed, and the reliability required as we try to shape business decisions and respond quickly to business events are not always there. We’re not getting optimal value.

Achieving optimal value is possible today – but it requires a next generation data warehouse that incorporates a data confidence foundation. This is an architected solution to data optimization. It involves five key components: meta data integration that is comprehensive and accurate, as well as easily accessed by end-users; exception management that anticipates potential failure points; data quality standards that are implemented during extract, transform and load (ETL) processing and post-load testing; testing processes that are rigorously defined and ruthlessly applied; and a commitment to ongoing development, maintenance and continuous improvement of the data warehouse environment. This approach leads to confidence and reliance in their results; if people trust the data, they use it.

First of all, such a system must provide the needed information. This is actually more difficult than it sounds, because you are often dealing with highly complex, enterprise-wide data to support the strategic information needs of managers and executives. Next generation data warehousing takes advantage of the many technology advances of the past 5-10 years in the areas of storage and processing speed, but also in advanced data integration tools, powerful new query and reporting systems, and analytical capabilities. We also include advanced development approaches and methodologies that address both initial cost and total cost of ownership.

These include competency centers (standard processes that can be applied across the enterprise), agile and iterative development, and evolved solutions to classic problem sets such as master data management and customer data integration. It is critical to look at requirements from multiple perspectives – at the employee, department and company level; the competition, market and industry; customer/prospect; supply chain, and so on. It is also very important to understand current business needs to be able to anticipate the future. In other words, the discussion begins with current needs and then quickly moves to blue-sky futures – with hyper-attention to the user. As we’ve said over and over again, it must be business-driven; not technology-driven. It’s really what business intelligence is all about.

There are examples of how optimized data can benefit users across all verticals – healthcare, manufacturing, financial services, entertainment – but the classic data warehouse example is retail banking, where different copies of the same information (for instance, customer data) is stored in discrete silos. Without integrated data, banks miss opportunities to up-sell and cross-sell existing customers, or identify new customers. Or they may miss market shifts when creating new products or deciding where to locate new branches. Next generation warehousing resolves these issues by allowing all the data, tremendously rich data, to be quickly stored, accessed and analyzed. The next generation warehouse provides all of the right data, at the right time, and in the right context for optimized decision-making.

So what are the critical criteria for companies looking to build or upgrade their data warehouse today? As with any major business expense, you need to look at performance, security, risk mitigation, flexibility, depth and breadth of requirements, compliance issues and data confidence, in addition to total cost of ownership. The strategic architecture sets a long-term vision for the data warehouse. It provides top-down technical vision that avoids data redundancy, islands of information, disparate systems and interfaces, and arguments over who has the ‘right data’. It allows system designers to integrate current and future developments and provides a scalable framework that facilitates adding source systems and enhanced functionality.

This approach, with data confidence at its core, enables users to trust their data and discern the value of the data warehouse for strategic business insight. If users don’t trust the data they are accessing, or it takes too long, or is too cumbersome to retrieve and review, they won’t use it.

About the company
LoganBritton helps companies build and deploy data-intensive solutions such as data warehouses and business intelligence that deliver trusted, reliable results – data confidence.