The value of a data-driven organization

What is data analytics? 

Data is a collection of information that is recorded in a digital information system. Data analytics is the process by which this data is converted into insights and can be seen as a production process. Compare it to a conveyor belt in a factory. In the data analytics process, raw data are the ingredients that are processed through a series of steps and come out at the end of the line in the form of information in reports, models and visualizations. The end products provide insights for decision making and to take action on. This process takes place at different levels of technique. It varies from structuring disorganized data in Excel to turn it into a graph, to combining very large datasets in a data warehouse, on which real-time dashboards run.  

What characterizes data-driven organizations and why is data important for organizations?

Proper analysis of the available data improves the quality of decisions, reduces costs and increases turnover. Within data-driven organizations, all decisions, plans and activities are backed with data. In all parts of the organization, people are aware of the possibilities and value of data, and the use is ingrained in the processes. All available data is unlocked and combined to generate insights. In addition, a good and reliable IT landscape ensures high-quality data that is easily accessible for analysis. Data that you have as an organization provides insight into the progress and results on targets and goals, or shows where and how to optimize and innovate. Especially by combining data from different corners of the organization, new insights can be obtained. There are endless possibilities for the application of data analytics.

Data that you have as an organization provides insight into the progress and results on targets and goals, or shows where and how to optimize and innovate. Especially by combining data from different corners of the organization, new insights can be obtained. There are endless possibilities for the application of data analytics. Here are three examples where data analytics provided new insights and thus saved costs or increased revenue. 

Grid operators frequently inspect their assets – such as power lines – to detect problems early and thereby prevent downtime on the grid. This is heavily labor-intensive and the many inspections often do not prevent the problems. To increase the effectiveness of its asset management, a network operator has set up a program to improve asset data and combine different source systems for better insights. Such as data about inspections (condition of the asset), geographical data, planning data, etc. This provides insight into which assets need maintenance and allows inspection and maintenance to be carried out more efficiently. This reduces maintenance costs and the risk of failures of the electricity grid.  

An e-commerce platform regularly has to deal with fraudulent selling parties. These parties sell products to customers on the platform without delivering them. The organization has been able to map indicators of fraudulent parties by combining different data sources. Now parties with a high risk profile that is validated by screening of the fraud team are not paid out. This saves the platform unnecessary costs every month. 

An energy supplier saw a decline in its customer base and, as a result, a decrease in turnover. That's why the entire customer experience was mapped out and the pain points were addressed. Data was used to provide more insight into different contact moments of the customers with the energy supplier, such as lead times, difference in experience of different types of customers, etc. This allowed the energy supplier to implement improvements in the customer experience, differentiate marketing between different types of customers and increase sales. 

Where are the opportunities within your organization to use data to optimize processes or innovate? And where do you start? 

Starting the journey towards a data-driven organization

The road to a data-driven organization starts with determining the starting point. Is the organization just getting started or already more skilled in working with data? Examples of questions that organizations can ask themselves are: Is data analytics central in our vision and strategy? Is this vision also supported and do people really see data as a valuable asset? Do our processes support the use of insights? Do our employees have technical and analytical skills? Are employees developing themselves in reading and using data? What data do we collect and how reliable is the data we use? Do we use internal and external data sources that are accessible and structured? What is the level of our hardware and software – such as advanced data storage or visualization tooling – to support the development of analytics? 

Asking these questions is the first step towards a data-driven organization. You can start today. From there you can determine what the next steps are, such as quickly setting up a pilot to be able to show the first value, and thereby accelerating the embracement of data in your organization.