Self Service Analytics

Knowledge, data blending and storylines are the key to turn collected data into valuable information.

Roy van den Wollenberg

Senior Business Development Manager

Self-Service Analytics: knowledge, data blending and storylines

The business is playing an increasingly important role in making the right decisions in analytics-based business processes. In the past, the emphasis was often on collecting data, but nowadays putting the data together has become far more important. Knowledge, data blending and storylines are the key to turn collected data into valuable information.

The business as a driving force

Because the business itself knows exactly what they want to control and understands the processes within the organisation, it can see where any deviations arise and where the opportunities for improvement are greatest. The business can use this knowledge to set up powerful storylines. These storylines (dashboards with the most common scenarios) are the basis for your organisation’s management, planning and strategy for the future. The business therefore plays an increasingly important role in the use of analytics.

However, this does not happen by itself. If data comes from different systems, it is important to compile the data properly in a controlled way. The data quality must be right, complex adjustments and calculations must be carried out automatically and security must be very strong. Once this step has been completed and there is a solid foundation, it is a pleasure to get started.

Business users can then use drag and drop to put together data sets and add data themselves (data blending). This enables end users to model things in a much more flexible way and make adjustments in the storylines, giving your organisation the opportunity to carry out in-depth, ad-hoc analyses to gain completely new insights.

New Skool Media
New Skool Media works with CTAC to lay the foundations for predictive analytics in PowerApps
Ctac helps New Skool Media to make their brands even more successful with the help of data and predictive analytics.


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