Data-driven management is a strategic concept that can apply to any business. In practice, it means making decisions based on analysis and interpretation of data. Every business has to deal with data. A data-driven company uses data systematically and methodically to make business decisions. This practice contrasts with decision-making that can be driven by emotions, external pressure or instinct.
Driving by data offers several advantages:
- Data is a company’s most important asset. Properly using this data in the decision-making process is one of the biggest advantages of data-driven management.
- Data-driven steering replaces steering based on emotion and feeling with steering based on facts, eliminating the possibility of bias or misconception creeping into decision-making
- Data analysis provides actionable insights in different areas, both internal and external
- Data-driven intelligence offers a competitive advantage
Becoming a data-driven company doesn’t happen overnight. It’s a complex process with stumbling blocks occurring regularly. While many companies recognise the usefulness and necessity of data-driven management, only a handful actually manage to become fully data-driven. So why is this?
The concept of data-driven rests on a number of pillars:
- Culture
- Data management strategy
- Data architecture and infrastructure
- Data literacy in the organisation
Culture
Moving to data-driven management can be challenging due to an organisation’s culture. This culture is supported by four key components within a company:
- Traditions and customs.
- The organisation’s skill set,
- Its beliefs
- Its personality or the way things are done.
All these components must be strong for an organisation to succeed in becoming data-driven. The initiative will struggle if the skill set is not strong enough to handle data-driven processes. If the organisation doesn’t fully believe in the power of data, the initiative will struggle. If the organisation’s personality relies more on gut feelings than data, the initiative will struggle.
To ensure success, it’s crucial to fully recognise and work with the organisational culture to embrace a data-driven approach. Ignoring or underestimating the impact of organisational culture is a primary reason why data-driven projects fail to meet their objectives.
Data governance strategy
Having a data governance strategy is essential for success. If the organisation does not know where it is going with data and how data will be used to help the organisation succeed, overall work with data and analytics can be hampered.
So, what does a data governance strategy do to help shape an organisation to be data-driven? Here are some examples:
- A data engineer or data architect in charge of setting up the organisation’s data modelling knows where the company is going with data, allowing this data engineer to build the right modelling for the strategy at hand.
- A data analyst who knows the organisation’s data strategy knows what directions, questions, and insights can help the organisation fully implement the strategy.
- A data scientist knows the modelling, coding, and other aspects needed to arrive at sound predictive analytics that can help create forecasts for various business units trying to move the organisation towards strategic success.
- An organisation’s data strategy is a starting point for its success as a data-driven organisation. Figure out exactly what you want to do with data in your organisation, then develop a strategy to achieve that outcome, and your organisation can be on its way to becoming data-driven.
Leadership must take the time and effort to have the right leaders to drive the strategy or hire them. With the right strategy, organisations can succeed with data. If you don’t have a holistic, enterprise-wide data strategy, establish one as soon as possible.
Data Architecture and Infrastructure
Data must be accessible to those who need it to enable digital transformations. However, outdated infrastructure is often inadequate for this purpose. Prior to transitioning to a data-driven organisation, a comprehensive analysis of the existing data, technology, and skills within the organisation is required. Evaluate this and then understand what needs improvement and what is too heavy to carry into the future.
When upgrading infrastructure, it’s important to ensure that the necessary resources and skills are in place to effectively utilise the technology. Otherwise, an organisation may be left with a great solution that can only be used to a limited extent due to a lack of resources or understanding.
Data literacy in the organisation
People are at the heart of any company’s success, and digital transformation is no different. Advanced technology and business analytics are worth nothing if staff cannot use them to achieve better business results.
Data literacy is an essential skill that data and analytics leaders must nurture. Employees need to be able to use new tools, tell stories with data, and draw the right conclusions from data-driven insights. Therefore, it is important that everyone in the organisation is aware of the value of data and the implications if errors creep into the data or the data becomes public knowledge.
Besides having experts who know the processes and how the data is fed through these processes, it is also essential that every employee is aware of their part of the data and where it can be found in systems and dashboards. Here, too, technology can be supportive, using data dictionaries, among other things. A company where employees can find their own information or know who to go to for information works much more completely than competitors who cannot.
Finally, a data-literate organisation can collaborate around data at all levels. As the organisation moves from making decisions based on gut feeling or individual expertise to data-driven decision-making, it is essential to have a community of subject matter experts who can come together to build and transfer knowledge. A data intelligence platform or data catalogue helps facilitate that collaboration. It paves the way for others in the company to find and use information and ultimately extract value from the data.
