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The Chess Game of Data & Analytics

Jordan Morrow, Global Head of Data Literacy, Qlik


In my last blog post, I spoke about a series of articles I will be doing with regards to the world of data and analytics, specifically looking at the 4-levels of analytics, and data and analytical strategy. In this post, we are going to dip our toes into the world of data and analytical strategy, specifically looking at its importance and tie to the world of data literacy.

To start, let’s take a look back at our childhood and playing board games. How many of us liked to play the game of chess? Well, me being an absolute nerd, I still love to play chess. When we were younger and playing board games, we probably started off just playing the games, moving pieces, and knowing how to win, but not really having any strategy. Over time, as we developed our skills in board games, and maybe more importantly, as we started to develop more wisdom and understanding overall, we started to figure out different ways we can play our favorite games. Eventually, we started to develop strategies to winning. When we started to develop these strategies, sometimes they worked, but other times, the other player would have strategies, and we had to revise ours. We became stronger players because we had developed strategies to winning, and most importantly, those strategies tied directly back to our goals and objectives. The game of chess is an absolutely great example of this. When we first start playing, we are scattered, excited to know how pieces move, take other pieces, and how to put people in check. But, when we come up against those who really know how to play, we noticed we were getting crushed. Then, we study, learn, and grow, and find out there is a whole world of strategy to that of chess. Of course, the overall objective and goal is to win, but at times, maybe we bring in different goals and objectives on that path to winning, and a strategy is crucial to success.

This analogy draws perfectly into the world of data and analytical strategy trends around the world. As I have been traveling and working around the world, it is a rarity that I find an organization that has a strong data and analytical strategy that ties back to the organization’s goals and objectives. I am finding organizations of all shapes and sizes, industries, etc. know it is important to had data and analytical work, but when I ask those organizations about their strategy, goals, and objectives, I am met with blank stares. A group here or there might have a strategy, but can they tie it to the organization’s overall strategy? A different group may have its own strategy, but are they talking together? Then, an even different group wants to invest in a new, shiny software they think is going to drop knowledge bombs on the world, but they aren’t talking to anyone nor the sourcing team. Finally, underpinning all of this, is the organization’s lack of data literacy skills that will enable the goals and strategies of the data world to exist. I find this trend both promising and troubling: promising because organizations are truly wanting to succeed with data, but troubling because they will not find success with the current trends.

The first step for an organization to build that successful data and analytical strategy is to understand the purpose of data and analytics, and how they tie back to the organization’s goals and objectives, not to understand technology and software. For far too long, organizations have been hearing software is the magic potion, only to be disappointed again and again as the ROI is not strong. This type of thinking has to shift to outcome-based approaches, where we are determining just what we want as an organization. When we do this, software and solutions can fall into place. This allows us to not force fit a strategy to a recent purchase of software.

To help enhance this, organizations need to know what is the purpose of data and analytical strategy? For organizations to understand this, one should understand that data has many purposes and through those purposes, organizations can truly succeed in the 4th industrial revolution. In Bernard Marr’s great book, Data Strategy, he pins down three distinct things data can be used for in a data strategy:

  • Operational Improvement
  • Decision Making
  • Monetization

These outcomes should help to underpin the data and analytical strategy an organization implements. Along with these three, organizations should also focus on:

  • Data Literacy
  • Empowerment of everyone through democratization of data and self-service analytics.

As organizations look to capitalize on the amazing and valuable asset that is data, they should have a strong data and analytical strategy that ties back to its goals and objectives. When organizations do this, they will find the ROI they are looking for with their investments in data and analytics.