Merging BPM and Big Data

Big data. Image source

Process and data might vary by a wide berth in terms of management philosophy and mindshare. However, it is a proven fact that process and data are interrelated and management should encompass management of both.

As the organization leverages Big Data or analytics on a large scale, the processes should also be given due importance simultaneously.

Tim Huenemann, who is senior principal for process management and business architecture at Trexin Consulting, enumerates certain areas where BPM intersects with Big Data. He states that in these areas, process should be given adequate importance hand-in-hand with the large-scale analytics being implemented.

Deeper insights

Analysis often results in better understanding and improved actionable insights. These insights can then be used for the betterment of the business processes which may lead to even overall business process redesign.

However, this does not mean a change in the approach of BPM initiative or business goals. With the analysis of more data and the garnering of better insights, the desire for better processes naturally comes into existence.

Improvement in processes

There can be explicit benefits to processes via Big Data analysis. What used to be previously a crude decision or a gut-feel guess work can be evolved into decisions that are supported by large-scale data analysis.

Managers will have a huge role in leveraging Big Data to influence the operations that exist today to mould them into better processes.

Implementation of new processes

Big Data initiative can result in the creation of a whole new set of processes that can be used to analyze, summarize and segment data and gain meaningful insights and conclusions from the data. This will require the design and implementation of new processes that can be used in the management approach to meet the business goals.

Identifying statistics

Perhaps, one of the biggest obstacles in Big Data analytics is the sheer volume of data that is available for analysis. Analyzing this huge volume of data without any planning can not only prove to be fruitless but time consuming as well.

Identifying persistent and predictive statistics in the data and then analyzing the data based on these statistics can help get better results in lesser time.

Data becomes meaningful when analysis gets a direction. The processes in the organization would already have determined some key metrics that can be used as inputs to Big Data implementation.

Every process in an organization is interdependent and it is crucial that all aspects are linked together for effective and smooth running of the organization. The same is true for Big Data and processes as well, which should have close interdependence to ensure a smooth road to meeting the business goals.


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