Successful digitalization with a data-driven culture
What does data-driven decision-making look like in practice?
With carefully designed reports, you can get complete insight into your business data with just a few visualizations.
On average, we take around 35.000 decisions every day. Even though most of them are routine, even subconsious decisions, some can cumulatively have a large impact on our life. Managers and leaders make daily decisions that affect not only their lives, but also the trajectory of their organization. Strategic decisions, which are not taken as frequently, have an especially big impact on business performance. Even one such decision can completely change the success of an organization.
No matter whether a decision has a strategic importance or not, leaders want to make the best decision. People want to avoid negative consequences following a bad decision; we don't like to be wrong. Hence, it might take a lot of time for us to make a decision, we might take fewer decisions or not make a decision at all. We are often not fully aware that not making a decision is also a decision, which is rarely optimal.
Decision-making is a process of choosing from a pool of options. The decision can be rational or irrational, but it's always made weighing pros and cons based on information and experiences we've had up to that moment. The result is a choice of one option that we consider best, and is followeded by an action. The bigger the weight of our experiences on this decision, the more subjective it is. If we want to make more objective decisions, decision-making based on data is the way to go.
So, what is data-driven leadership?
Data-driven approach is making strategic decisions by collecting, analyzing and interpreting data.
With data-driven approach, companies can increase the speed of decision-making and ensure that the decisions lead to the best possible outcomes.
The process of data-driven decision-making is described below.
- Based on information from internal and external environment, a decision is made to implement new tool for data analysis
- During implementation phase, necessary data is imported and basic data visualization done
- Then, testing is done to validate requirements (data quality check, KPI validation and report visualization customization)
- These reports can then be used for data collection and analysis
- The next step is for this data to be interpreted by end users
- Modifications and upgrades are continuously made as a result of changes in the environment or requirements. The cycle of testing, data collection, interpretation and changes is then repeated.
Sometimes we have to make decisions that can’t be changed or reversed later. The same process applies – first, we make sure to have the right data, which we then interpret; carefully assessing the situation, including the factors that could indirectly affect results.
Data-driven approach in practice
The first step to ensure data quality and the right interpretation of this data is having a good analytical tool. One of the leading analytical tools is Microsoft Power BI, which is very user friendly and provides a wide range of functionalities. Pictured below is an example of Sales report prepared in Power BI. The fastest way to identify if our data is incorrect or incomplete is through the process of data visualization. Data quality is key for data-driven decision-making. Decisions based on incorrect or poor-quality data will very likely be bad decisions.
We can filter an entire report by clicking on a specific graph. The report below includes product hierarchy and sales representative and is filtered by market, month, and partner.
We can see which product’s sales are the highest and compare sales performance with the same period in the previous year. Based on this information, we can, for example, decide which products to market more and which to stop selling.
The next phase is testing for which having well-defined key performance indicators (KPI) is key. KPI is a measurable value that show how successful a company is in accomplishing their goals. Organizations set KPIs for all levels of decision-making and for all departments. When new data is imported to the report, comparison to previous year is usually shown. Once the KPIs are well established, comparison of current performance vs plans is often also added.
The next level of data-driven approach is complete automation of decision-making with alghoritms, which depends even more on data quality as well as established KPIs.
To sum up, data-driven organization is making decisions based on interpretation of data from internal and external environment. When it comes to digitalization initiatives, it is a good practice to combine data-driven approach with top-down approach.
Read the next article to read more about top-down approach.
