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Data-informed decision making — 10 skills you can’t do without

Tue 21 Jan 2020 | Kevin Hanegan

The skills that improve data decision making 

“Making good business decisions is a critical part of every executive’s job and is vital to every company’s well-being.” So says McKinsey and Co, in an article on the importance of decision making. It’s undeniable that being able to take action rapidly is the difference between success and failure in today’s markets – consider how many once-leading businesses have atrophied as their ability to make the right decisions has diminished.

Yet knowing what to do is hard, doubly so when you consider the amount of information we are all assailed with on a day-to-day basis. How can anyone make definitive decisions when faced with so many choices, so much data?

It’s this question that lies at the heart of data-informed decision making: the ability to use information effectively and appropriately to make choices which deliver the right results. Data-informed decision making is a team sport. To get it right requires an array of skills which will need to be spread evenly across the organisation.

Broadly speaking, they can be categorised into two camps – technical and soft skills. While nominally very different, each type is vital, and having them even more so. So often, organisations prioritise one form over another – often the technical, being easier to measure than the less tangible soft skills.

So, what are these skills, and how do they support businesses to make better data-informed decisions?

Technical skills

It is likely that an organisation will have significant technical skills in small pockets, with a data science team perhaps, or analysts. These abilities vary in complexity and specificity – some will remain the preserve of specialist teams, while others should be more broadly understood by other functions. They include:

Data extraction – When an organisation has an analytical question they need to answer, the key is understanding what data has information that will aid in answering that question. Once the data and information are identified, it needs to be extracted. With some analytical questions, that may just involve taking data as-is from an excel file. In more complex questions and situations, it involves extracting information from big data systems and technologies.

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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.

Soft skills

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.

Experts featured:

Kevin Hanegan

Chief Learning Officer
Qlik

Tags:

data science skills strategy
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