Demystifying the term ‘Data Driven Decision Making’
In today’s competitive and fragmented industry, Data-Driven decision making is the key to competitive advantages. But for many, it seems like a daunting task.
The stream and sources of data are never-ending, the complexities associated with them are many, and a majority of manufacturing industry professionals still lack the complete understanding of Data-Driven concepts, tools, processes and its overall benefits and impacts.
“Data-Driven decision making is about using the ‘data’ (both historical and real-time) an organization possesses wisely, to make intuitive and accurate decisions. The objective of Data-Driven manufacturing is to allow organizations to orchestrate process for business optimizations (to increase the ‘Bottom-line’), and to create new business offerings or value-add services for their customers (increase the ‘Top-line).”
In the recent survey conducted by Great Software Laboratory in association with Confederation of Indian Industries (Research Link: Data-Driven Manufacturing: Driving Value by Capitalizing on Data) it was discovered that 83% of the Indian manufacturing industry today realizes the importance of Digitalization, but harbours many myths related to Data-Driven decision making and the need to ride the digital wave.
The most commonly observed myths were:
Digital transformation is associated only with Big Data / high volumes of data, and one should get to it only when data volume becomes substantial
Many organizations have a perception that they need to think about digital transformation only when their data volume substantially increases.
- Data-Driven manufacturing is about using every bit of data to derive meaningful insights. Digitalization journey for an organization can commence even if the data is available in excels with right data models in place.
- Our study inferred that even an SME with a moderate amount of data (majority in excel) achieved digital optimization because of right data models and processes in place.
Implementing a solution for Data-Driven insights is a lengthy and complex process.
Of the surveyed participants 83% felt that integrating a solution for Data-Driven insights into their current system would be a lengthy and complex process. The remaining felt that adopting a robust Business Intelligence solution is the need of the hour and achievable with the help of right partners involved.
- Today with the evolution of Industry4.0 majority of the organizations already have modules of ERP, MIS or PLM implemented as part of process digitization. With right data models and solutions engineered on top of these existing systems, an organization can leverage meaningful insights with ease for faster decision making.
Only IT teams can interpret data from complex systems in the value chain.
Of the surveyed participants 96% felt that pulling data from each system was complicated and time-consuming considering the disparateness and complexity of the existing ERP, MIS or PLM systems an organization owns.
The remaining 4% felt the amount of data generated and stored in these systems was quite high and processing it would be very time-consuming which cannot be done without IT team’s intervention (and this was a natural barrier).
- With IT team as a facilitator in acquiring right data from these systems, an individual functional head need to take the next steps in churning the data and creating dashboards highlighting critical parameters for decision making. With available business intelligence tools, the process of identifying critical parameters and creating dashboards is automated with ease.
A Data-Driven solution for decision making can only come at high costs
Of the surveyed participants majority felt that implementing digital optimization would require high investments in IT systems, software, and hardware.
- Generating insights leveraging existing IT software and hardware investment of an organization is easy with right tools and processes in place.
- Data available in numerous excel sheets across systems also contain meaningful information and help in decision making with right data models.