Over the centuries, humans have thought a lot about what makes up intelligence. From general theories about reasoning and memory to the development of the Intelligence Quotient (IQ) from 1912 onwards, we have looked at ways to define what makes us smart and – more importantly – how we can be smarter.

Today, organisations are developing their use of analytics to help business people make decisions in smarter ways. These deployments can cover point uses, such as data visualisations and dashboards, to more far-reaching and strategic Business Intelligence (BI) and Analytics implementations. However, as we seek to expand the use of analytics within businesses and public sector bodies, it’s important to recognise that not everyone works in the same way.

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Emotional intelligence, or EI, was developed as a concept to discuss how people think about interpersonal relationships, manage behaviour and achieve their goals. In a work environment, better EI should correlate with strong performance in teams and where roles deal with complex emotional situations. The main thing here is that not everyone within an organisation will process information in an analytic fashion. Getting to a “data-driven” mindset can be more difficult than just providing user-friendly charts.

Instead, successfully managing the decision-making process with the help of data can involve a lot more change management and education. For people used to working intuitively and based on their experience, making a decision based on the output of an analytics query can be a difficult habit to get into and well outside their comfort zone. While finance, marketers and sales leaders have been developing their analytical skills and started the transition to working with data every day, many other departments still have to make this shift. Thinking about EI can help.

Designing data and analytics for everyone

To make analytics work for everybody across the organisation, it’s worth building out a change management process to help educate new users.

As part of this process, it’s important to get feedback on how the team involved currently measures its successes and opportunities. For example, a social care team may currently look at how their services help clients live more independent lives.

This goal may be difficult to translate into traditional reports, so it is worth digging into how each of these successes can be linked to wider data sources such as budget figures or other care costs. By understanding these results in context, it’s then possible to build more appropriate metrics that can be used and modelled.

This level of insight is only possible through discussion with those who are responsible for delivering the service. By understanding how people make improvements, it’s possible to create measures that they can track over time.

These are Key Value Indicators or KVIs. They are different from Key Performance Indicators (KPIs) in that KVIs should link to personal goals, while KPIs are often set at the organisation, group or departmental level. The relationship between KVIs and KPIs is not an exact science, but each individual should be able to feel that ownership of his/her KVIs will lead to direct improvement in overall KPIs, no matter how small.

Alongside this process of setting up KVIs, IT may have insight into how data and decision making can go together. This has to be a collaboration between IT and the team involved to be successful. Without that connection to the emotional side of how people think, it’s difficult to get adoption of analytics to stick.

Delivery of data needs a new approach

From a technology perspective, the shift to more data-driven decision making runs parallel to a trend where companies are generating and keeping more data than ever before. A new approach is to build up data lakes alongside traditional data warehouses. However, combining all this data together and making it accessible and useful is the big challenge.

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Company approaches to BI and analytics have typically focused on small numbers of users – either specialised business analysts with skills in using data and statistics, or new roles such as data scientists working with big data sets. Some departments may have chosen to use data discovery tools to make the information within spreadsheets more usable. However, getting consistent data into the hands of people across an entire organisation requires a different approach.

Rather than relying on central IT teams to share reports, the emphasis should instead be on helping everyone get access to data that they can work with in their own ways. These individuals will know far more about their own needs and the kinds of questions that they want to answer, so IT can help them better by stepping out of the way and providing self-service tools to governed data sets.

This does require more guide rails to be in place around the data that people use – for example, providing access to marketing and sales data in a sandbox environment that makes use of “real world data” but without affecting the production systems that this data comes from. By helping people build queries in a semantic way, based on the questions that they want to answer, analytics can help them get more accurate responses to their requirements.

Key to this is networking data from different sources together in a shared, common analytical fabric, rather than relying on data dumps or spreadsheets that are emailed around. By bringing people into the data to work with it, it’s easier for them to meet their own needs, rather than IT doing what the team thinks is required.

This semantic approach to data also fits better with groups working to shared goals. Rather than looking at data models and building up knowledge of statistics, the emphasis should be on knowing the right questions to ask. Again, guiding the process is more important than defining what reports should be delivered.

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Looking ahead, it’s inevitable that more and more of us will be using data in our decisions every day. However, reaching this goal involves understanding the different needs that everyone has. While we will all need to build up skills in using data, IT should be focusing on how to make analytics useful to everyone.