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Are your Analytics Investments not bearing the fruits you expected?


A quick scan on social media and the opinions of leading analytics training providers and consultants reveal a focus on the technical data skills on the most popular platforms, programming languages and buzzwords of the day. Referencing insights from the top training providers in the Data Science space (Datacamp, Coursera and Udemy) suggest that Python, R, SQL, NoSQL, Machine Learning, Statistics are sure bets for data success in the near future.


Upon closer inspection, one would notice that most of these are the "harder" technical data skills; often related to popular programming languages, platforms or buzzwords of the day. While the platforms and programming languages have evolved, the demand for "harder" technical data skills has been persistent over the last decade.


During this time, organizations have excelled at collecting and organizing larger and more complex data. Aside from a few companyes, the ROI on data investments and realization of business value has been more modest; or at least has not kept up with the expected gains promised from data. More organizations are starting to realize that the Hard Data skills alone are not sufficient to build the necessary competitive data advantage to create sustainable business value. Something is missing.

Understanding How Data Creates Value in Organizations

Many leaders and organizations misunderstand how data creates value in oranizations. Many believe that winning the data race was about collecting more data; more data would give organizations the right answers to win. Most organizations were able to eke out some data wins, but it is often disproporationately smaller than the amount of data collected or the resources expended to collect and manage the data.


Data creates value by facilitating informed decision making; by providing objective insights to specific questions, upon which leaders can weigh the risks and trade offs before committing resources to a decision. When conduct on a smaller scale, it may be known as Data Science or Data Analytics; where informed business decisions are made. When done on a larger scale, it may be called Machine Learning or Artificial Intelligence; where the organizations makes the informed decision to rely on the algorithm to make decisions for th organization at scale without human supervision. In fact some organizations have even rebranded their data analytics functions as Data Management.


The Data Analytics Value Chain

Contrary to popular beliefe, data analytics doesn't start with Data.

  • It actually starts with asking the right questions. Asking questions sound trivial, defining problems for analytics is more technical than most people realize.

  • The next steps is to consider a comperehensive set of possible hypotheses that could be causing the problem and how to convert it into a data.

  • Data is then collected and analyzed to prove or disprove each hypotheses. This process will help the analyt separate the facts from fiction and determine the most likely causes.

  • Knowing the likely causes of an organization issue doesn't necessarily lead to a solution or action plan. The insights need to be interpreted by the right people to build recommended action plans around the insights.

  • Finally the entire process needs to be communicated to decision maker in an effective manner to facilitate informed decision making.

Informed decision is not the same thing as making the right decision. Data Analytics provides a consistent and objective approach to consider the stated problem with data, where decision makers are made aware of the evidence, insights available options, associated tradeoffs and recommendations before committing to a decision. Any decision made would be based on the best information available at the time. The process doesn't end there, the processes is depicted as a cycle, because can then be used to assess if the decision achieved the intended outcome. One of the often downplayed benefits of data analytics is how it facilitates learning; where organizations can take a structured consistent approach to nudge their way to a optimal solution.


Crucial skills for sustainable data capabilities



Based on the Data Analytics value chain, sustainable data skills requires hard and soft skills. FYT would argue that the softer data skills are actually more important. Hard data skills are deployed in 2 out of 6 segments of the data value chain - Data Collection & Processing and Data Analysis. Softer data skills are essential in the steps before and after data is involved.

  • Business Acumen and Critical Thinking are required to define a problem clearly for Analytics; failing which data will give very good answers to all the wrong questions

  • Business Acumen and Problem Solving Skills are required to interpret & contextualize the insights from the analysis into actionable options for consideration.

  • Finally, Data Storytelling, Communication and Influencing Skills are crucial to helping leaders and the rest of the organization to not only understand the insights from the data, but to care enough to take concerted action based on the data insights

As with the usual stereotypes, those who are good at the hard data skills are usually weaker in the softer data skills; and vice versa. And talent who are well versed in both, are high sought after in the job market and thus very expensive to hire and even harder to keep. The most practical and sustainable approach is to assemble teams where members have the requisite hard and soft data skills and a manager who can create an environment where they can play to their strengths as needed along the value chain.


Building Soft Data Skills

The importance of soft skills in the data age is not a new idea. In fact, with reference to the top 10 skills of 2025 by the World Economic Forum, you will notice that both hard and soft data skills are featured quite evenly. The soft skills in question are also not new, these are the same set of soft skills that have helped many a professional become successful over the last two decades. The key is to reinterpret these soft skills for the data age.


The good news is that professionals who currently lack or are less proficient with the new technology or hard data skills can find ways to remain relevant. Rather than having to to do a total skills overhaul, they can continue to create value with a tune up of their existing skills base for the data age.



An understanding of Mathematics and Statistics is a prerequisite. Business Acumen and Problem Solving skills comes with work experience. Anyone trained in maths, science or engineering have been trained for critical thinking; though not as many get to put these skills to use at work. That would depend on the job family they are in. The key is learn to repurpose these skills and experience in defining problems and hypothesis for data analytics; or to interpret the data insights into action plans. Those with experience in sales or marketing typically have practical storytelling and influencing skills in order to perform well. They challenge is to tell concise, coherent and compelling stories with data to influence decision makers towards the right decisions.


Platforms and trends come and go, the mathematics and statistics have not changed; neither have the challenges and opportunities that organizations face day in and day out. I would argue that the soft data skills combined with an understanding of mathematics and statstics form the foundation for sustainable analytics. The peripheral data skills make the analytics process much faster and more efficient; but it is only useful if it is deployed to answer the right questions, if the data insights can be converted into actionable options and if decision makers understand and care about their options that data has revealed to them.


Building the right Data Culture

Having the right technology, large data sets and the right hard and soft data skills does not guarantee a return on the data investments. Organization culture plays a much larger role than many organizations admit or realize.; more specifically, the decision making culture. This is not something most organizations think of nor objectively assess. For example:


  • Decisions made in some organizations could be based on the highest paid or highest ranking person in the room

  • While others make decisions based on who shouts the loudest

  • Others make decisions based on their gut or opinion regardless of what data says. If the data aligns with their predetermined outcomes, then data is considered; but if data doesn't align with the predetermined outcomes, they find ways to discredit the data

  • And others do not make decisions without considering data and analysis


Clearly no organization entirely ignores all data all the time, and no organization is paralyzed in decision making without data; it is a spectrum. Most organizations are usually somewhere in between; and where your organization is along this distribution can determine success or failure to an organization's data analytics agenda.


In the data age, it is almost a given that many organizations are busy investing in technology and talent to move the needle on the technical data capabilities; few organizations would be on the bottom left quadrant. What sets them apare is their cultural readiness for data.

  • In the upper left quadrant, organizations are Able but Unwilling. In this scenario, the organizations have made all the right investments in data capabilities and have the ability to great work; but no one is listening because leadership and decision making is still opinion driven even in the face of data insights. As a result many of the talent will eventually leave out of frustration, and the technology become white elephants

  • In the bottom right quadrant, organizations have Limited Capabilities but Willing to rely on data. This is a good place to start, since leadership is ready to bet on data; all that is left is to find the resources to make the right data investments. In today's tech and labour market, both are widely available at affordable prices.

  • In the top right quadrant, organizations are both Able and Willing to rely on data. This is the scenario when organizations have achieved the optimal analytics maturity. Leaders understand enough about data and the methods to ask the right questions and understand the outputs and the organization have the capabilities to find and present their analyses for informed decision making.

Many organizations are in the top left quadrant and are finding it hard to make the transition. Organization cultures are hard to change. But there are an increasing number of organizations who are recovering from the top left quadrant and had to restart the process in the bottom right quadrant. It will take time and hard work, but being aware of the cultural readiness for analytics is a great start.


What's next?

If you have any questions about any of the above content, frameworks or views, or would like to find out more about how FYT Consulting can help to build the foundational data skills, please do not hesitate to contact us here.


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