SAP Data Analytics and Insights Trends

Businesses constantly use data to gain efficiencies and deliver new customer and trading partner insights. Our sister company, Clarkston Consulting, published an Analytics Trends Report to help you identify opportunities for employing analytics in your business. Below is a quick summary of the themes explored in the report. Visit our Book A Project page to get more information for your SAP data analytics and insights project needs.

Key data analytics themes are emerging in 2020:

• Demand for advanced analytics drives growth in the influence and scale of analytics teams and COEs.
• Automated workflows are enhanced by rapid adoption of new analytics tools.
• Organizations are compelled to drive explainability and auditability of analytics models.
• Areas of untapped potential gain greater focus: machine learning (ML), time series forecasting, organizational analytics, and RPA (robotic process automation).

Explainability and Auditability of Data Analytics Models:

Thorough documentation needs to accompany greater adoption of automated decisions. As such, analytics teams should be prepared to explain how decisions were made. This comes down to two factors: explainability and documentation.

Explainability refers to the ability to trace through an algorithm to determine exactly where a prediction came from.  For example, certain algorithms act as “black boxes” and it can be nearly impossible to interpret how a decision was made. As for documentation, those in regulated industries will need a higher level of documented validation. However, it is best practice in any industry to have processes in place to conduct a technical review on models before they go live and to document workflows and predictions for reference.

Data Analytics COE Influence: 

Establishing a SAP data analytics and insights COE is critical. Once a COE is established and generating value, the next step is getting the right tools in the hands of the right people. This sometimes means setting up the processes or front-end user interfaces so that certain roles don’t need to know the underlying tool.

Educational sessions will also be critical and most effective when delivering contextual use cases. Analytics roll-out will thrive if everyone can see the benefit to their own day-to-day. Focus on delivering value and the rest will come.

Data Analytics Workflow Automation:

Automation, once adopted, quickly becomes indispensable. Data engineering teams will be responsible for automating data pipelines to get data from the source, integrated across systems, and ready for processing by analysts.

Tools and components found in commonly used platforms can help:

• automatically provide histograms of your data distributions,
• let you know the number of missing values, and
• provide other helpful flags that let you initially analyze your data.

Similarly, on the modeling side, you can select a handful of algorithms to test on the data to quickly prototype. These automation tools save time and allow your data scientists to focus on value-add activities.

Bull City Talent connects you with analytics experts to assess needs, implement solutions, or provide some of the heavy lifting in realizing your analytics goals. Consultants in our Talent Communities offer insights and solutions from relevant, referenceable experience in technology and business projects. Contact us for more information and to explore how our resources can become an extension of your IT and analytics departments.