Data transformation and standardisation – Once the data is extracted, it needs to be transformed and standardised to be ready for analysis. It is estimated that up to 80 percent of the time spent making data-informed decisions is on tasks related to cleaning, standardising, and organising data. With so much resource going into what are fairly mundane tasks, it’s absolutely vital that organisations have the right skills to be able to clean, transform, profile, tag, catalogue, and standardise data.
Basic math and understanding of data – Not everyone in an organisation requires data science skills to make data-informed decisions, but basic maths skills are essential for everyone involved in the process. This includes a fundamental understanding of data, including types (categorical vs continuous), attributes, and various aggregations and distributions. With this skill, everyone can use descriptive analytics, which is a key step in the data-informed decision-making process. This can range from someone responsible for building and maintaining a measurement framework which includes critical key performance indicators, to decision makers who need to apply meaning to the information they are seeing.
Foundational statistics – Involving an understanding of probability and correlation, simple regression, as well as inferential statistics to ensure things like sample sizes are created properly, foundational statistics skills are vital for an organisation that wants to make data-informed decisions. The individuals making the decision do not necessarily need these skills, but they need someone working with them who can provide foundational statistics to ensure that data is being use accurately.
Data science – While data science is not a single skill in itself, it encompasses everything that an organisation needs to do with machine learning and artificial intelligence, including predictive and prescriptive analytics. With the vast amount of data available to organisations today, machine learning skills are essential to turn the data into insights to make data-informed decisions.
It might seem ironic to talk about the soft, or human, element of data when so much of it is a result of the increasing amounts of technology deployed in everyday life. Yet in many ways, having those less tangible, less measurable attributes are even more important as we become more digital. Being able to empathise, relate to and communicate with others is almost always the difference between a decision being accepted willingly or meeting resistance.
Systems and enterprise thinking – According to Edward Deming, 94% of problems in business are systems driven; only six percent are people driven. Systems thinking helps decision makers understand why people behave as they do. It is a way of looking at an organisation (and the world) as a set of systems that all connect in some way. When viewing the enterprise this way, identifying causes versus symptoms is easier as decision makers can consider how each part interacts with others.
Critical thinking – Part of the data-informed decision making process is the ability to think critically about the data and recognise both the complexity of the decisions and the possibility of multiple valid positions. Decision makers need to understand the possible limitations of both the data presented and their own cognitive bias, and mitigate them. They also need to accept that they will rarely have a full data-set available, so must be prepared to avoid analysis paralysis, deal with the uncertainty, and make the best decision they can with the data that is available to them.
Active listening – People are exposed to information at multiple points of the decision making process, whether requirements, insights from the analysis, or feedback on the decision during the assessment phase. It is human nature to apply meaning to that information based on one’s own cultural and personal perspectives. From that, people may draw conclusions. In reality, those conclusions may be based on what people think someone else said, as opposed to what they really said. This is where active listening, combined with critical thinking, is vital.
Relationship building – One of the key influences on the process is the organisational culture. That culture’s ability to support the process depends on the quality of relationships, which also depends on the quality of conversations. This means that relationship building is a critical skill required, from gathering requirements from the business, to communicating out to all the stakeholders, to gathering feedback on the decision after it is made.
Communicating with data – Stakeholders, whether employees, investors or customers, need to buy into decisions in order for their effect to be realised. This makes communicating those decisions, and the reasons behind it, in a way that motivates and emotionally moves stakeholders, one of the most important skills an organisation can have.
Putting it all together
As mentioned above, organisations need to have this balance of technical skills and soft skills to successfully make the right data-informed decisions. Underpinning all of this, however, is the ability to bring it all together in a harmonious, mutually beneficial manner. It is not a case of having the right individuals with the right skills, but the right teams, with the appropriate abilities, all contributing to creating a data-driven culture. Only then will enterprises be able to put these skills into practice, and reap the rewards of being able to make data-informed decision making.