Data-driven management and data-driven business development are widely discussed topics. However, they are often surrounded by misunderstandings, overly high expectations and the inevitable disappointments that follow. In this text, I will take a closer look at a few key aspects.

Ensuring data quality and availability

It is clear that you cannot lead with data, nor develop your business in a data-driven way, if your data is scattered across different databases. Therefore, a great deal of energy must first be devoted to compiling, aggregating and enriching data. For us, this first step is known as data ecosystem development. It forms the essential foundation for designing effective AI-driven analytics views.

The next phase is to concentrate on designing the actual data views. It is typical that data scientists are tasked with designing various business-related reports, without any clearer guidance on what metrics need to be included. And a skilled analyst can produce reports based on generalised models or their own guesses.

The challenge is that when these reports are admired by the management team, no one dares to point out that they don't really understand how to interpret the figures, or that the report doesn't reveal anything new. Therefore, before modelling and visualising the data, preferably already when planning data collection, it is worth stopping for a minute to design the exact data views that serve the needs of each business manager.

Designing data views based on user needs

User-centric design of analytics views is the bridge between raw data and real business decisions. A single mega report may be the easiest and cheapest option to produce, but it is by no means the most useful. Humans can only process a limited amount of data at a time, and if they are faced with an infinite number of different data tables and graphs, they get exhausted. Therefore, role-specific data reports should only present the key performance indicators (KPIs) that are critical for that role.

Sales managers need some of the same data as CEOs and CFOs, but also much more specific data, for example on the performance of their team. A marketing director can use some of the same data as a sales director, but needs much more specific information about campaigns, channels, target groups and so on. The HR manager's data needs are quite disconnected from all of this.

In particular, sophisticated analytics views that leverage AI and focus on predictive analytics should serve as a problem-solving tool. Only relevant data will help analyse the current situation and predict the future. A report that is too general or opaque will lead to disappointment, delayed decision making and "guesswork" management.

Making data visible is only the first step

The ultimate purpose of AI-based predictive analytics views is to create insights that can drive action. A traditional data report is a compilation of historical data. The fact that the data is compiled together helps identify changing trends, such as whether sales are up or down. However, this information in itself does not change anything. Alongside the data, you need an analysis of what the change means, what causes it and what it indicates. This is either a human, or increasingly an AI, task.

Predictive AI analytics data is supposed to provide the impetus for change. Although this may seem self-evident, the interpretation of data is still frequently neglected in many organisations. In some cases, this is because existing data reports fail to address the questions that are essential for developing the unit in question.And sometimes it is unclear whose role it is to interpret the data and make observations or suggest changes based on them.

Thus, as role-specific and AI-enabled data views are completed, operational reforms must also be made: define and agree on data governance roles and responsibilities jointly, embedding them into the organisation’s existing decision-making processes.

A single data analyst is unlikely to be able to serve the data management needs of all departments in a large organisation. It is practically impossible for him or her to have sufficient insight into the sales team's  SLA targets, conversion targets for marketing campaigns or changes in customer behavior to be able to derive relevant insights and recommendations for change from the data. And it is ultimately the responsibility of managers and business leaders to lead.

Turning insight into impact requires action

For data to genuinely support the organisation and its leaders, data-driven management practices must be designed in line with each unit’s objectives, decision-making structures and the data literacy level of the people involved.

We can only talk about data-driven management when reading a data report is directly connected to making a business decision. If the report is merely skimmed without leading to any concrete decisions, it amounts to nothing more than archiving and reiterating information. Data-driven management as an activity requires that data analysis is embedded in the organisation's ongoing decision-making cycle.

Data becomes valuable when it is transformed into information, developed into insight and translated into concrete actions that impact the business.This is what data-driven management is all about. If this chain is broken before it generates action, the investment in data is wasted.

A data report is just a bunch of numbers. Corrective action based on the data is management.

If you need help developing your data ecosystem, setting business metrics, designing AI-enabled data views or designing a data-driven management process, contact us!