Building an Analytics Center of Excellence – A Practical Guide
For companies newly endeavoring in establishing capabilities in Data Science, it is important to keep a few crucial points in mind. Clean data, applicable models, and business intuition are all key to success. Do not remove any of them from the equation. Data Science is essentially about identifying and/or creating the cleanest possible data set, then searching mathematically for patterns within it. The goal should be to help business users make important data-driven decisions, prove or disprove their intuitions, predict the future, or optimize outcomes and processes. The following is an introduction on what it takes to establish an Analytics Center of Excellence in a systematic way.
There is a clear difference in the approach followed by Data Scientists and business users, so it is vital that everyone is on the same page before embarking on a first project. Start by convening a stakeholder discussion with as many departments as possible, and brainstorm on problems that can be solved. For instance, if you are working with customer data, think about things such as analyzing patterns of good and bad customers to predict churn or identify opportunities for cross-sell and upsell. If you are working with supply chain data, think about things such as demand forecasting or inventory optimization.
Once opportunities have been identified, it is important to prioritize them by complexity, time to solve, and ROI. Though a complex problem may seemingly have more value, it is often best to start with low hanging fruit. It is easy to lose business support when it takes several months to deliver a solution, so it is important to split projects into phases, each with demonstrable value. This is especially important for companies starting out in analytics, as they may not have the technical capability or internal support to invest significant money and time.
Once there is a firm understanding of the business problem and desired outcome of the project, it is important to agree on an initial scope. An understanding of how the business will consume the output, including a precise business definition of the desired results and how they should be reported, must be determined. Also consider how the project is going to be implemented into your existing workflow. Is the output going to be a dashboard, simple visualizations, or a score? Is it going to be a standalone application or is it going to be integrated into an existing infrastructure? How are business users going to consume the output?
There are many canned products for analytics, however there has been an ongoing shift towards open source solutions. Relevant policies and governance are now well-established, so this should no longer be a business concern. The benefits of this approach include no licensing fees, access to more algorithms, and access to the latest and greatest packages being developed by researchers and professors. Actively encourage your teams to explore open source options such as R and Python. These provide the benefits mentioned above and can provide all the functionality, if not more, than out-of-the-box tools.
When assembling a team in house, or while working with a partner, figure out what skill-sets everyone brings to the table.An ideal Data Scientist has a combination of programming skills, math and stats knowledge, and some domain expertise, but finding someone who fits all three categories is almost impossible. Most Data Scientists have a background in Computer Science, Engineering, Math, or Statistics – which means they likely are not adept at communication. Thus, it is typically advisable to also include a business user that can logically explain the results. A solid analytics team should include at least three members: a programmer, a math guy, and a domain expert (that can creatively tell stories). It is also good to have a senior Data Scientist for particularly tricky problems or during the architecting phase.
