In 2019, it’s no longer a question of whether to become an analytics-driven organization, but how. To be successful, organizations need to build information architectures that match analytics workloads to answer the questions that will create the most value for the business. The one-size-fits-all database is long dead, and so is the one-size-fits-all data/analytics platform.
On a personal level, I’ve faced these realities while building out an analytical informatics infrastructure as SVP and CIO at Infinity Pharmaceuticals and later as Global Head of Software and Data Engineering for Novartis Institutes for Biomedical Research (NIBR). These challenges were also starkly clear when bringing new database and analytics products to market as a founder, advisor, and investor in startups like Vertica, VoltDB, Cloudant & Recorded Future.
I have learned that competing on analytics is not about large, multi-year projects: it’s about having an impact for the people who are making decisions every day, week, and month. Analytics and data are about people and, most importantly, empowering them. It starts with questions–from real people at all levels of an organization. What are the 100 (or 200 or 500) questions that would create significant value if they could be answered with the support of all the data available in your organization at any given time?
The questions that need to be answered aren’t just the broad strategic questions that C-level executives talk about, but also the very tactical questions. In the past, most enterprises have focused on the former, because it cost too much and was a huge pain to extend analytics to anyone but business analysts or senior management. Democratizing analytics is a key trend that Tableau founder Christian Chabot described to me in 2005, and it’s been amazing to watch his vision become a reality at Tableau.
Great analytics provides the context for all business people to create value throughout their day so they can make more strategic decisions on tactical matters. Think about product support. Pretty prosaic stuff, right? Not really. When someone calls in for product support, what’s the value of knowing that caller represents a top 5% customer— or whether she’s even a customer? How do the support people know how to prioritize requests without analytic context?
The role of the enterprise technology professional over the next 20 years is to enable analytical context for every employee in the company—so that he or she can make the best decisions about how to allocate his or her time and the company’s resources.
I recently wrote a white paper about the 12 steps to build a successful, analytics-driven organization. I have outlined some of the initial steps here, but to view the full list be sure to download the white paper.
1. Start with the Questions
As I discussed above, delivering impact from analytics starts by gathering questions from people at all levels of the organization. What are the questions that they find most interesting, and want/need to answer? Learn to embrace agile projects that focus on collecting and answering very specific questions with high quality data, using repeatable and shareable queries of data that interconnect sources across the company, and leverage both external (publicly available) and internal data.
2. Remember the 3 Key Types of Analytics
Analytics can be classified into three types: descriptive, predictive, and prescriptive.
Descriptive: Reporting on historical data and trends
Predictive: Descriptive data and models that enable the statistical anticipation of what might happen in the future (which can range from very short-term and tactical to very forward-looking and exploratory)
Prescriptive: Recommendations of actions based on descriptive and predictive analytics. Eventually if these get good enough, you can even automate the execution of action with human oversight.
3. Statistics Matter for all Analytics
For predictive and prescriptive analytics, you can’t operate without significant statistical expertise and infrastructure. R and SAS are no longer good enough. You need next generation tools and infrastructure—one of my favorites is DataRobot. So, start with descriptive and work your way up. For an interesting reference framework for an infrastructure spanning (or ready for) all three kinds of analytics, check out Mu Sigma.
4. Define Clear Endpoints Between Systems and Data Sources
Most vendors don’t make it easy to integrate between products. I strongly advocate for working with vendors who have clear/well defined and documented APIs/endpoints to get stuff in/out of their system. I believe strongly in building best-of-breed infrastructure versus relying on a single vendor. DevOps is a great reference here—the DevOps ecosystem in any modern software engineering organization relies on a number of third party tools, both FOSS and proprietary that all interact via well defined endpoints/APIs. The next generation of data/analytics infrastructure will likely be similar—best of breed connected via well defined endpoints/APIs.
Competing on analytics requires a combination of great systems and empowered, motivated people who believe in their right to information and analytics for optimal, value-creating decisions. It’s the seamless integration of systems and people that creates non-incremental value.
We need to empower business people at the point of decision-making with analytics that will help them create significant value for their companies—every single day.