When asked “What’s your data strategy?” do you reply “We’re getting Hadoop…” or “We just hired a data scientist…” or “If we only had a data lake, all our problems would be solved…”? Plotting a good data strategy requires more than buying a tool, hiring a resource, or adding a component to your architecture. You need something to describe:

  • the goals you are trying to achieve,
  • the stakeholders you are trying to serve, and
  • the internal capabilities required to satisfy those stakeholders and achieve those goals

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At least weekly, I am granted the opportunity to meet and work alongside experienced professionals who serve in a corporate business intelligence (BI) leadership function. When they describe their role upon introduction, there is a common thread to the scope of influence and control which usually intersects one or more of these domains: Read more

We’re often asked how “our methodology” helps drive better user adoption. The key to user adoption is satisfying users’ needs, within the context of their environment. This sounds obvious, but it’s surprisingly easy to miss the mark. And all too often, projects are doomed from the beginning…with the requirements. Read more

This is part five in our five part series on the essential capabilities of the competitive data-driven enterprise.

Over the last 20 years of doing business we have seen a number of different analytical data storage and query concepts fall in and out of favor. Throughout this time, a wave of digital transformation in business has dramatically increased the volume of collected data. Machine learning and other probabilistic methods benefit greatly from the law of large numbers so if by now it wasn’t already clear, all that talk about “big data” has really been about the analytics that it enables. As a result, today’s knowledge workers are predisposed to data hoarding, preferring to save everything including the data for which there are no known use cases, since its future value to the organization may still yet be discovered. Read more

This is part four in our five part series on the essential capabilities of the competitive data-driven enterprise.

Businesses have been deploying enterprise data governance (defining what the data should be) and master data management (ensuring the data is as defined) programs for decades. Even if your company doesn’t have a formal master data management program by name, chances are good that they are doing some form of master data management in your data warehouse, CRM or ERP systems. As the trend towards decentralized data analysis continues to progress we see a few forces in play that make the case for incorporating a master data management capability into your organizational roadmap: Read more

This is part three in our five part series on the essential capabilities of the competitive data-driven enterprise.

Most business analysts will reach for their favorite data visualization tool when it comes time to perform driver and correlation analysis when in search of a cause. While this technology is essential for communicating with data, and excellent at identifying new opportunities (i.e. visualizing gaps or data non-relationships), it is limited in its ability to produce reliable, accurate and conclusive results. This is mostly due to our own human limitations when visually processing more than two dimensions of analysis at a time (e.g. revenue over time by product line). Read more

This is part two in our five part series on the essential capabilities of the competitive data-driven enterprise.

For decades, data integration and modeling have been done in either of two likely places: The enterprise ETL or Data Warehouse environment (IT Developers) or Excel (Analysts). Currently the status quo is being challenged in some of the following ways that highlight the importance of empowering domain and subject matter experts to wrangle and model their own data. Read more

This is part one in our five part series on the essential capabilities of the competitive data-driven enterprise.

The most common form of data-enabled business problem solving begins with a hypothesis around business drivers and relationships within the data. Typically, a well tenured business analyst will pull together the data they know about or have access to in their department and proceed to build their analysis. This standard approach assumes that: Read more

If your organization is seeking to better manage its information as a corporate asset that is to be valued and capitalized, you’re likely focused on implementing programs that will catalyze measurable business results from mountains of business information that may be the product of the last decade or more of digital transformation initiatives. Read more

One size does not fit all. Try as they might, there is not a single BI platform that can offer every capability that users require. With organizational complexity increasing, and the growing demand for self-service analytics, it has become commonplace, even recommended, for organizations to maintain multiple BI platforms to meet the needs of people in diverse roles with differing needs across the organization. Read more