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Thought Leader Interview
Wayne Eckerson: Finding the
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Preface
Dan Power, Editor of DSSResources.com, conducted an email interview with Wayne Eckerson in late June 2005.
Q1: How did you get involved with data warehousing and decision support?
Eckerson's Response: I was an industry analyst at the Patricia Seybold Group, a small research consultancy in Boston, and began to see a lot of interest among our clients in data warehousing. I attended the first TDWI conference in 1995 and was hooked. As a former educator, I loved this topic. In essence, data warehousing and business intelligence are about how to create a learning organization. This field is not about technologies and tools--although there are plenty of them--it's about how to help business professionals exploit information to improve what they do and make better decisions and plans. If you believe information is power, then DW and BI are designed to empower employees to enrich their work experience and the coffers of their parent organization.
Q2: In your role at The Data Warehousing Institute (TDWI) as Director of Research, you conduct a number of surveys on current practice. Please summarize what you have found are "best practices" in Business Performance Management, Business Intelligence and Data-Driven Decision Support.
Eckerson's Response: That's a big question!!
BPM is all about the execution of business strategy. So first, you need a good strategy. Then you need a plan to implement the strategy. And then you need to allocate business resources (people, funds, equipment) to execute the plan. Then, you need to monitor your progress--which is where BI comes in-- and make course corrections along the way. This is just plain common sense, but it's amazing how hard it is for most organizations to do. Most executives come up with a great strategic plan and then throw it over the wall and don't--or can't--track how well their subordinates are executing the strategy. So, there's been a big disconnect between strategy and execution. BI, and especially the deployment of dashboards and scorecards, help close this gap, and give executives, managers, and staff greater visibility into the operations of the organization and whether they are on track to make plan. The key, of course, is to monitor activity so you can make course corrections before it's too late. That involves delivering "actionable information" to the people who manage the processes and activities that contribute to bottom- and top-line results. By actionable, I mean getting the right information to the right people at the right time. Sometimes, this even involves delivering information before events actually happen. For example, if you want to increase share of wallet of large customers and you think the best way to do that is have your sales people spend more time with those accounts, then you need to have sales people forecast every week how much time they have scheduled to meet with those accounts the following 2 weeks. That gives you enough time to change behavior and activities to have the optimal impact.
Business Intelligence (BI) is a broad term that encompasses the tools, technologies, and processes needed to turn data into information and information into knowledge and plans that drive beneficial results. (Most people also use BI to refer to query, reporting, and analysis tools.) The key to BI is getting the data right. Most executives make the mistake in thinking that they can deliver BI by buying a BI tool and that's that. That's actually the last thing in a whole series of activities that need to happen to succesfully exploit information for business gain. Sixty to eighty percent of all the work involved in delivering a BI solution involves pulling data together in a way that makes sense to business users and accurately reflects reality. Data warehouses are really just reflections of the business. They are mirrors of organizational processes. Data warehouses can be hard to build because most companies are dis-integrated organizationally, and their systems reflect this dis-integration. The more disorganized the company, the harder it is to build a DW. Building a DW is like trying to put Humpty Dumpty back together again after a big fall. So, if a data warehouse is hard to build, don't fire the IT team, fire the CEO. In many cases, a failed DW is symptom of a larger problem.
Q3: What vendors do you think are the major innovators in BI/BPM/DSS?
Eckerson's Response: There's a lot of innovation in BI. Even though this is a mature industry, we still see many start-ups and new vendors exhibiting at our conference. The small guys have interesting new technologies to solve thorny problems like metadata and scalable BI tools and change management and data quality. The big guys often do a good job of listening to customers and incorporating their feedback into new releases.
Q4: You have been advocating a data warehousing maturity model. Please summarize the six stages and explain how business value increases with each successive stage.
Eckerson's Response: The maturity model consists of six stages: Prenatal, Infant, Child, Teenager, Adult, and Sage. Business value increases as the data warehouse moves through each successive stage. Organizations evolve at different rates through these six stages, and each may exhibit characteristics of multiple stages at a given time. There are two pivotal points in the evolution of any DW/BI initiative, represented in the model as the “gulf” and the “chasm.” Many DW/BI initiatives stall at these points.
Most established organizations have management reporting systems that generate a standard set of static reports, which are printed and distributed to large numbers of employees on a regular basis, usually weekly, monthly, or quarterly. Since the reports are hand-coded against legacy systems (or an operational data store), the IT department can’t respond rapidly to requests for custom reports. Spreadmarts are spreadsheets or desktop databases that function as surrogate data marts. Each contains a unique set of data, metrics, and rules that do not align with other “spreadmarts,” management reports, or analytical systems. Spreadmarts prevent the CEO from getting a clear, consistent picture of the enterprise. But eliminating spreadmarts is difficult because they offer high local control at extremely low costs, making it difficult for organizations to cross the “gulf” between stage one and two.
Most organizations are currently in the Child and Teenager stages. In the Child stage organizations create data marts. A data mart is a shared, analytic structure that generally supports a single application area, business process, or department. The departmental team gathers information requirements and tailors each data mart to meet the needs of the members in its group. They then provide knowledge workers with an interactive reporting tool (e.g. OLAP or ad hoc query tool or parameterized reports.) The tool lets knowledge workers drill down or across a dimensional structure to follow trends and gain a deeper insight into events driving the process or tasks they manage. But data marts often fall prey to the same problems that afflict spreadmarts. Each data mart supports unique definitions and rules and extracts data directly from source systems. These so-called “independent” data marts do a great job of supporting local needs, but their data can’t be aggregated to support cross-departmental analysis. What’s needed is a mechanism to integrate data marts without jeopardizing local autonomy. That’s the hallmark of the Teenager stage. The most common strategy for integrating data marts is to create a central data warehouse with logical “dependent” data marts running in the same database as the data warehouse. This type of data warehouse is commonly referred to as a hub-and-spoke data warehouse. Although a data warehouse delivers many new benefits, it doesn’t solve the problem of analytic silos. In the Adult stage, organizations make a firm commitment to achieve a single version of the truth. Executives view data as a corporate asset that is as valuable as people, equipment, and cash. They anoint one data warehouse as the system of record or build a new enterprise data warehouse (EDW) from scratch.
Once the data warehouse becomes a strategic enterprise resource and your organization has entered the Adult stage, you may think your job is done. However, there are additional opportunities to increase the strategic value of your EDW by driving the resource outward and downward. Many companies today are already opening up their data warehouses to customers and suppliers and creating Interactive Extranets. At the same time, EDW development teams are turning analytical data and BI functionality into Web services that developers -- both internal and external to the organization -- can leverage with proper authorization. The advent of BI services turns the EDW and its applications into a market-wide utility that can be readily embedded into any application. These BI services will also make it possible for companies to fully capitalize on their investments in statistical analysis and modeling. They will turn models into “decision engines” embedded in internal and external applications. Workers without any statistical background will feed information into these engines and receive recommendations instantaneously. Today, decision engines already form the basis of several types of powerful applications, including fraud detection, Web personalization, and automated loan approval applications. Once your DW enters the Sage stage, its value increases exponentially as its visibility declines.
Bottom line: Strong ROI doesn't happen until the final stages because only until you get all the data back together in an integrated fashion can you begin to move fast enough to deliver information that answers new questions business users have. Also, since DW is a process not a technology, there are many things you need to learn along the way to deliver an adaptable system, which is very different in concept, architecture, and management than anything IT teams have ever built before.
Q5: In 1998 you coined the term "The Decision Support Sweet Spot". What was your initial idea? Please explain what you currently see as tools in the "sweet spot".
Eckerson's Response: The sweet spot is the intersection of reporting and analysis. At the time, I said most users (not business analysts or power users) want a "drillable report" - that is, they want to monitor the same data and if something goes awry, drill down to get more information. In 2005, a drillable report is a performance dashboard (hence why I wrote a book on this same topic) which does the same thing but much better. It puts a graphical monitoring layer on top of the analysis (OLAP) layer and a reporting layer. Basically, it lets casual users access the data at a higher level of abstraction and then drill down in a structured way to quickly find the data they need without getting lost. The problem with BI tools in the past is that we forced users to come in at the reporting layer or analysis layer which is too complex for most users and too difficult and time consuming to find the right info.
Q6: What do you see as major trends in computerized decision support? Where are we headed?
Eckerson's Response: Decision support is headed toward BI Services. BI becomes a utility with SOA. BI becomes automated using decision engines. The data warehouse goes away because we reorganize our companies and standardize terms and definitions on the operational side. At this point, DWs become a big archive. ETL becomes just EL because there is no longer any need to radically transform information to get it into the right format and standard nomenclature. Our economy has commoditized innumerable services in the past – such as electricity, sewage, water, transportation, and so on. Insights delivered via BI services is simply the next in line.
References
Eckerson, W.W., “The decision support sweet spot”, Journal of Data Warehousing, 3:2, Summer, 1998, 2-7.About Wayne Eckerson
Wayne is the Director of Research and Services for The Data Warehousing Institute (TDWI), a worldwide association of business intelligence and data warehousing professionals that provides education, training, certification, and research. Eckerson has 17 years of industry experience and has covered data warehousing and business intelligence since 1995. Eckerson is the author of many in-depths reports, a columnist for several business and technology magazines, and a noted speaker and consultant. He has recently written a book titled Performance Dashboards: Measuring, Monitoring, and Managing Your Business which will be published by John Wiley & Sons in October 2005. Get more information or order it now! Wayne can be reached at weckerson@tdwi.org.
Citation
Power, D., "Wayne Eckerson Interview: Finding the Decision Support Sweetspot", DSSResources.COM, 08/05/2005.