Many companies have their sights on using sophisticated analytics tools and artificial intelligence to strengthen their competitive capacity or improve their operations. But often they should think about improving numeracy and basic data skills of their people first. 

The vast majority of us are making huge assumptions about our ability to use data, which is inhibiting our growth. Whether we are tech-savvy millennials or hugely experienced executives, we take our expertise for granted and this miscalculation is holding us back in the world of digital and digitization. My experience is that when you dig deeply into most people’s capabilities, you find very few who have been educated in data, let alone have the expertise to use it constructively in anything but the most basic of business problems. My assertion is that if companies are serious about leveraging their data for competitive advantage, that all their people from top to bottom should be at a minimum base level of data savviness.

In 2015, the World Economic Forum published an article “Are digital natives really good at using technology?” Their conclusion was that so-called digital natives may be adept at communicating via social media, texting, and using apps, but   “……they were basically clueless about the logic underlying how the search engine organizes and displays results”. If you have kids, does this ring true? My 10-year-old daughter is quite incredible at understanding how apps work on the iPhone, the PC, and even alternative operating systems. But does this mean she understands data, its veracity, and how to use it?  I think not.

Look to our 2020 COVID pandemic experiences which have thrown up the good, the bad, and the ugly in how data is used. In his excellent Financial Time Article, “Statistics, lies and the virus”,  the economist Tim Harford provides us with an overview of the importance of data and how it is easily misused or misinterpreted including by experts. He drew 5 lessons that every business leader should take note of:

  1. The numbers matter: Without statistical information, we haven’t a hope of grasping what it means to face new, mysterious, invisible, and fast-changing situations.
  2. Don’t take the numbers for granted: While numbers help us calculate risks and make decisions, too often we fail to answer the question “Where do these numbers come from?”.
  3. Even experts see what they expect to see: Remember that while solid data gives us insights, the numbers never speak for themselves. They too are shaped by our emotions, our politics, and above all our preconceptions.
  4. The best insights come from combining statistics with personal experience: Too often we are so close to the data that we struggle to pull our head out and look around at what is actually happening. This is especially important as most decisions we make are in situations where the data story is incomplete.
  5. Everything can be polarised: Increasingly in the world, people see the argument as black or white. In reality, it is usually grey, especially where data is concerned.

And it’s not just politicians, leaders (and millennials) that need to confront these challenges. Over the past 18 months, I have been running a series of workshops for over 150 mid-level business professionals on how to turn a ‘business problem into a data solution’.  It does not matter whether people came from banking, financial or industrial sectors, perhaps 70% of participants had only a very minimal understanding of data-driven problem-solving: We observed that they lacked a structured or systematic approach to analyzing data and were very limited in their understanding of data visualization, basic statistical techniques as well as communicating their findings. What we often saw instead was a tendency to either jump to conclusions based on gut feel and not data or a lack of critical deliberation about the data itself: its source, its reliability, or its quality.

It is important to appreciate that these were well-educated professionals, but very few appear to have been taught skills around working with data or understand how to integrate data into their work processes and decision making.

The other 30% of participants had at least some grasp of data and statistical analysis, but often lacked the confidence to use it to drive action. Furthermore, even when they had proficiency with  techniques, with the exception of a few, they did not have the skills and vocabulary to engage with a Data Scientist in developing sophisticated analytical solutions and problem-solving tools.

Indeed, as I reflected on each of these workshops, I grew to realize that as the apparent “expert” on the topic, I too, in the real business world, had struggled with using data. Even having used pretty advanced statistical testing techniques as an engineer, then later in life as a business manager and a management consultant, my understanding of data was mainly what I picked up along the way.  I now see that I am not alone. An example from the Service Management community that highlights how many of us are very well-meaning in our use of data but more naïve and simplistic than we are perhaps willing to admit is the number of articles written on Service Metrics and KPI’s. Usually headlined as the ‘Must Have’ or ‘top 10’, they represent as Harford puts it, a ‘Polarised’ interpretation, when in fact managers should display far more nuanced thinking. See here also our article from a couple of years ago Driving success in Service Operations through leading indicators – 7 key messages/.

The point I am trying to make is that if leaders really believe that ‘data is the new oil’, then they need people who appreciate and understand how to use data to drive insights and then action.

What should organizations do? At the very least, they need to work hard to improve their data savviness starting with their people. For example, I have noticed that all job postings by Amazon, refer to a working knowledge of Tableau, Qlik, or Power BI, all leading visualization software. The reality is of course that not all employees are data experts even at Amazon. But the “digital” companies are pushing their expectations of the basic digital skills required by their employees -something many industrial companies are reluctant to do.

From my own experience, if you want to understand how data-savvy your organization is, you might want to reflect on these five key elements:

  1. Mindset: The data-driven mindset is one that goes from the very top of an organization to the very bottom and is embedded in its culture. Probably the most visible measure is that when developing data solutions, do people in your organization articulate the business problem in terms of the business metrics they are trying to influence? Do they understand the parameters that might drive the metrics and the expected business outcomes?. If not then you should be looking to educate your people on how to think about and use data.
  2. Basic Understanding of statistics: How good an understanding does your organization have of basic statistics? Do you sense that often people do not really understand basic trends and ratios? We all assume that experienced people do have this understanding, however, when running simple problem-solving activities, I have seen many who are in fact not comfortable with figures and their analysis.  The problem is that they are often too embarrassed to admit it and so it remains hidden while doing damage.
  3. Data Integrated into Process: Is data embedded in your processes? You can check your organization from two perspectives: i) Do your people have a formal data analytics process that takes them from problem identification to sharing and acting on information? This methodology is as basic as a problem-solving process is to service engineers or DMAIC is to continuous improvement; ii) Do you incorporate data collection into your business processes and formally define the key performance metrics that are used across your organization?
  4. Communication and Storytelling: Often the most data proficient professionals diminish the power of their analysis through failing to clearly communicate, explain, or “tell a story”.  A good way to check this is to observe the visualizations that are used within the organization to summarise and present data. If only basic tables and the odd line and bar chart are used, you will know that people are not really thinking about how to turn data into business stories and action.
  5. Tools & Organisation: Most people use spreadsheets in some sort of capacity for basic analysis and visualization. The majority lack knowledge of other types of tools that can create great visualizations, save enormous amounts of time during the data preparation process and are the basic building blocks of analytics. For example, have your people ever used (or even heard) of any of these tools: Tableau, Power BI, Qlik, Alteryx, Python, SQL. If not, then it is likely that your organization is not very data-savvy. In addition, look at whether your organization has formal data analytics capacity: Do you have a business intelligence team or data scientists and project managers that deliver data solutions? Again, if the answer is no, then it is very likely your company needs to upgrade its competencies.

 If you want to do a quick self-check try our 3-minute assessment with just 7 questions by following this link

The good news is that most of us have picked up at least some of the necessary data-savvy skills in the course of our education and career. With some reflection, training, and practice, it is possible to significantly improve our ability to use data to solve business problems. These are necessary first steps an organization must take if it wants to leverage the benefits offered by more complex analytics methodologies often grouped under the banner of machine learning and artificial intelligence.