Slow & steady wins the race when approaching your data analytics strategy

Friday, February 1, 2019 | By Lyndsay Noble

Slow & steady wins the race when approaching your data analytics strategy

Firms want to win the data game. Fast. All too often we see firms sprint ahead to invest in a data analytics platform without first having a holistic data strategy in place. This can lead to a lot of people running around in different directions, reduced communication across teams, and redundancies in work being done, which may cause some firms to fall short when it comes to their data initiatives.

“Planning is bringing the future into the present so that you can do something about it now.” -Alan Lakein

Before you invest, take time to put together a group of stakeholders and get guidance from an internal or external source who has done this before. A cross-functional plan with a well-defined scope, phases for incremental wins, well-defined roles and responsibilities and risk mitigation plans will aid in the communication needed across teams and keep you focused and on track.

How do you measure success? Every project should have expected outcomes (SMART goals). “We will reduce time on a task by X%” or “we will increase revenue by $Z” and agree to measure the current state and results of the project.

What should a successful data plan include? Answering these questions will help you slow down and think out your approach, before stepping on the gas.

  • Who currently exists in our organization with the skills needed to access, move and process data, and do we need to hire more? Find who will be accountable for the data stewardship, ownership and maintenance.
  • What processing needs to be done to the data prior to using it? The data is likely being stored for a specific purpose and that purpose is rarely analytics or reporting. A common example is “transactions”. These are likely stored for operational purposes and need to be transformed, or aggregated, to get data that is suitable for analytics.
  • Where is the data coming from and where will we store it - now and in future? The operational data store is not likely the ideal place for an analytics professional to be working, and the data needs to be transformed (see above).  The future storage location may be a new warehouse or, at the very least, a data mart for analytics usage
  • Why do we want to implement this plan? What is the short and long term value to the enterprise?

What step do companies miss? Aligning across business areas. Marketing is separate from sales which is separate from operations. Every silo is working to solve the same problem but in different ways.  An example we frequently see is that the goal/idea/project for analytics starts in one department and then finds out how big the project really is, and gets stalled due to lack of budget or resources or time.  However, when an organization gets stakeholders from multiple business areas AND from IT, the cost becomes much more palatable and the initiative is no longer insurmountable.

What does the path to the “promised land” look like? The success will not be linear, it will be hockey stick growth where the ultimate outcome occurs suddenly after a short period of dormancy. The slow, flat “blade” of the curve may cause people to be disenchanted and want faster results. There is a reason we use the term “fail fast” – the first couple of iterations may not provide any financial value.  But ultimately, slow and steady will win the race.

For a deeper dive into how to harness the power of your organizational and customer data, check out my whitepaper on Four Best Practices for Establishing Organizational Data Analytics.

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