For Leaders: Your Step-by-Step 2017 Analytics Plan

For Leaders: Your Step-by-Step 2017 Analytics Plan

Meg Hari Prasad
11 January 2017

Acing analytics is not optional anymore. Managing by the numbers, using data to learn about your customers and how they are using your products is the only way to survive whether you are a series A start-up or a Fortune 100 blue chip company.

Yet, the landscape of analytics is getting murkier by the day. You are wooed daily by companies touting artificially intelligent bots that can shift through petabytes of data and tell you where the problems are in your business. Every day you see news of competitors successfully increasing their revenue by x% using some fancy machine-learning algorithm.

And here you are. You don’t have a clear view of your top 25 metrics and your internal team is still squabbling about the definition of gross revenue and retention. Managing by those metrics seems out of reach.

But still, you dream.

You want to be one of those CEOs presenting at a conference about how you have just used IBM Watson to crack down a nebulous customer service issue that was eating a couple of percentage points of your profits every year.

More importantly, you want to present the board with a complete picture of what the future holds using data and insights.

Your reality, however, is a presentation cobbled together at the 11th hour using patched up numbers you neither fully understand nor believe.

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So what to do?

Mobilize your team with my 2017 Analytics Plan, based on these key insights:

  1. Analytics training is not optional, neither for your data team, nor your business team.
  2. A clear analytics agenda is not optional either.  If your priorities are in flux, fix those first. Your long and short-term priorities directly drive your analytics agenda.
  3. You are the lynchpin of this entire journey towards a data-driven culture. Begin by holding yourself and your team accountable for what they plan to deliver. Accountability requires development of a well formulated data-driven plan and the discipline to “look back and learn” when things don’t go as per plan. In other words, a data-driven decision-making process.
  4. Bad data produces bad insights. So invest in infrastructure that enables access to accurate data.
  5. Lastly, remember this is a journey. You won’t be the world’s most efficient, leanest, most loved, most innovative company overnight or in a quarter or two. But if you have a clear vision and you execute correctly, you will have a series of wins, many of which you would have predicted. So remember to have fun and enjoy the journey.

If these key insights resonate with you, then the next step is to put a 2017 Analytics Plan in motion.

  1. January: Nail down priorities and the analytics agenda. Depending on your company’s size, either sit down with your key executive(s) and prioritize the agenda, or have your chief analytics officer draft it based on your priorities. If you need help with the agenda, check out my book “Behind Every Good Decision.”
  2. February: Identify a SWAT team of early adopters from products, marketing, and analytics who can deliver on your top 3 projects. Aim for a team size of about 5 people. Depending on the size of your company, you may have as many as 3 teams. If you are a much smaller company, that SWAT team is you. Have the team take analytics training together and start working on your project with an analytics mentor. We offer that service both on-site and online training followed by mentoring on a real project. Your first set of project results should start trickling in by April.
  3. March: Enable accurate collection and access of data. This is no trivial task given the insanely crazy number of vendors who are selling you pipe dreams. Put an internal or external consultant with prior experience leading a large-scale data re-architecture and Enterprise Data Warehouse development project, on this task. Hold them accountable for decisions they make. Start the hiring process in January so you can have the first set of data and reports by March.
  4. May: Widely share the results and work of the SWAT team and get your organization excited about analytics. Now, identify a customized training plan for your entire organization. Every decision-maker needs to get trained in analytics. Most will need business analytics training. Some in marketing and other consumer roles may also need training on A/B testing. The data scientist should receive additional training in predictive analytics and all executives need an introduction to analytics along with training on how to develop an analytics agenda.
  5. June: As the individual teams begin training, start instilling data-driven decision-making processes in your teams. Bake it into your quarterly business review. Change your current process to provide for objective data-supported views of decisions for the upcoming quarter and establish clear guidelines on how the success of the projects will be evaluated in next business review cycle.
  6. August: Make the SWAT team members mentors for individual teams who have finished their training and have them start working on key initiatives for their teams.
  7. September: First QBR to test out and refine your look back process. Evaluate if the data infrastructure is delivering what it is supposed to. Fine-tune both as needed.
  8. October: Your current team is all trained in analytics. Set up a comprehensive training including analytics, data cube review, and decision-making process, to onboard for future hires.
  9. December: Continue fine-tuning. Celebrate how far you and your organization have come.

Sketching out a measurement plan for PR and marketing activities will help you invest in the programs that perform and experiment with new initiatives. To get your data-centric marketing strategy in place, download our free ebook.

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This article was written by Piyanka Jain from Forbes and was legally licensed through the NewsCred publisher network.

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