- Adaptive Enterprise
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In a constantly changing world, with challenges for you as a business analyst ranging from big data, business intelligence/ analytics, BYOD and Lean/Six Sigma it is safe to say that business analysis is increasingly important for enterprise effectiveness. A few decades ago the main task for a business analyst was cerebral in nature: attempting to understand and get to grips with the complexity of organizations in light of change. Over the last few decades, we have seen that the role of the business analyst slowly started to grow, and also include more design-related activities (giving rise to fields such as “enterprise engineering” and “enterprise architecture”).Parallel to this development, we have also seen a shift in focus for the business analyst. Traditional business analysis techniques had a strong focus on business processes, roles & responsibilities, environment analysis and maturity. When IT became more important (from business improvement, strategic enabler, to strategic driver) we slowly saw topics such as business-IT alignment make its way into the realm of business analysis. These days the focus has shifted away from IT per se. It is often claimed that “data is the new oil” in the sense that many organizations now realize that in data should be managed as a corporate asset, independent of their supporting systems. If you have missed out on this trend, we highly recommend The case for the chief data officer and The data asset.
What is Data?
Data is an asset
Organizations increasingly use data to guide decision making. Not just to “look over the shoulder” and analyse how well they performed yesterday/ the last quarter/ the last year, but also to look ahead: what trends do we see in our data, and how does that help us in figuring out the best course of action for our business? Answering these questions and guiding decision makers through this strategic process is the biggest challenge for business analysts these days.
The following diagram illustrates this trend further. We researched several management disciplines in which business analysts are typically involved in terms of popularity with readers (measured by the search volume) and availability of information (measured by page volume). Not online did we find data and data management to be in the top-right corner of the resulting matrix, we also observed a steady growth over the last few years: something business analysts should be aware of indeed!
Understanding the enterprise – of course – remains a core activity for a business analyst. More than ever we now see analysts use a holistic, model-based approach to capture and document their knowledge of the enterprise.
In our view, there are three core aspects that a business analyst (should) focus on:
Even more, it is not “just” about these aspects, but also about capturing the interplay between them. While many modelling languages and techniques have been proposed and used at the design level (i.e. BPMN for process, TDM for logic and ERD for Information) we believe that a business analyst can be empowered further by also capturing the architecture level using ArchiMate.
Let’s look at the renewed interest in data from a business analysis perspective in more detail. History in a nutshell: first we focused on data, than on siloed systems, and the need for integrated data gave rise to fields such as master data management (MDM) and business intelligence (BI). An MDM-hub typically presents us with the truth (golden record) for key business entities, whereas an enterprise data warehouse (EDW) typically shows integrated, historical data.
In the context of performance management, until recently there was a lot of focus was on ‘looking back’. A business analyst worked with management to translate strategic goals to key performance indicators (KPI’s) related to organizational units or end-to-end processes (first time right percentage, straight through degree, etc.). After that the puzzle to be solved was: how do we get our hands on the data that shows how well we really did. The EDW functions as a corporate memory and was typically seen as the place to answer these questions.
Only a fraction of what we want to know about the organization comes from structured data captured in corporate systems. A lot can be gained by looking at unstructured data as well. For example:
New technologies (e.g. Hadoop) has opened the door to actually do something with big data. With a growing reputation for data also came new requests and responsibilities. The name of the game for business analysts has shifted from looking back to looking ahead and support management in figuring out the best course of business action based on analysis of the enterprise, supported by all sorts of data. Business analysis on steroids for a business analyst indeed:
We believe that business analysis on steroids for a business analyst implies the following:
Business analysts will continue to increase their data sources: relying on structured data in our systems is no longer enough, we use whatever (quality) data we can get our hands on!
Is your business analyst ready for the future?