Three Tips to Jump Start Your Data Science Plan

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Are you looking to form a Data Science capability at your company?

If you answered, yes, then you probably already understand the Machine Learning concept (The 4 Machine Learning Problems).  Maybe you are coming from a Statistics or Computer Science background.  Either way, you see the potential of Data Science and Predictive Analytics and you’re ready to demonstrate some tangible benefit to management.

How are you getting started?  I’m hearing about two core hurdles:

  1. We’re looking for a great business problem to solve, one which could reasonably be solved with data the business already collects.
  2. Our internal resources have very little practical experience working on a formal data science team, and don’t understand how it aligns to more traditional project teams.

Time to Value is critical, but you need to do it in a way that has a formal process for managing risk, one which can be communicated inside and outside the team. Here are the things you want to have in place in order to launch your first project.

Establish Your Data Science Methodology

Every project has a plan, and data science projects are no different.  What should the Data Science project plan look like? Several teams of very smart people have already asked this question and independently arrived at the same conclusion. My favorite is the “Cross Industry Standard Process for Data Mining”  (CRISP-DM) because it calls out the need for basic business understanding of the problem first.  Basically there are six phases of the process:

  1. Set the Business Objectives
  2. Find the Data
  3. Prep and Cleanse the Data
  4. Do the Machine Learning Work
  5. Evaluate the Model You Created – does it meet the Business Objectives?
  6. Deploy the Model

Assess Your Data Capabilities

Data Science needs Data. Teams that try to predict outcomes without relevant data are set up for failure. For example, let’s say that you would like to forecast demand for your products, in order to reduce your inventory. You might start with basic sales data and find that you are not getting the level of prediction accuracy you expected. What other factors might be driving demand? Customer satisfaction might be one you decide to include. But what if your company is not measuring customer satisfaction in any quantifiable way? Data Science leaders need to understand the capabilities of their company (in effect, the Data Science customer) with respect to data assets, in order to effectively determine which business problems are ripe for prediction.

Outsource the Team

Data Science requires a very specialized set of skills.  You probably have some of those skills yourself: Computer Science, Statistics and an understanding of the principles behind Machine Learning. These three are important, but equally important is Business and Domain Knowledge.  Do you have a team of resources that possess all four? If you are working with a technology provider who already understands your business and who also has demonstrated capability in  delivering data science value – then outsourcing the work to that team becomes very attractive.  If you don’t have such a resource, consider a business and technology consulting partner such as blumshapiro.  Provided you already understand the CRISP-DM process, you’ll be able to effectively manage a seasoned team of business and data science pros.

Can Data Science increase your bottom line?  Improve customer loyalty?  Drive down costs?  Yes it can, provided you have a methodology to manage the work as a project, data to support it and a capable team.  If you’re convinced the opportunity is there, follow these tips and Data Science will have a strategic role within your company after your first big win!

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