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Operational Analytics? A subset of business analytics that focuses on improving daily operations within an organization!



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The need for business to operate more efficiently is not a new concept. From time immemorial, business has operated under the looking glass with many sets of eyes searching for ways to better produce goods and services while responding to changing customer requirements. For some, the lack of such a focus has led to the demise – for instance the buggy whip manufacturer who thought he was in the horse drawn carriage business and entirely missed out on entering the rapidly evolving transportation business.

In his latest post to the Striim blog, A Comprehensive Guide to Operational Analytics John Kutay draws our attention to that important subset of Business Intelligence that is identified as Operational Analytics. “Operational analytics is a subset of business analytics that focuses on improving daily operations within an organization,” said Kutay. “Operational analytics doesn’t focus on supporting big or strategic decisions that align more with business intelligence (BI). Instead, it influences the small and tactical decisions that are made daily. It focuses on sending data to the tools that business users utilize regularly.”

Almost exclusively, much of HPE’s messaging is focused on the topic of achieving greater business insight. The implication is clear; create the data then move the data and then deliver the data to a platform that can perform analytics. In this way your enterprise can readily claim that they are actively living in the age of insight. When it comes to NonStop then the freshest of data is created in real time as it executes mission critical transaction processing, but even with NonStop, further steps have to be taken before the full value of the data can be realized.

However, and it is also known by NonStop users, gaining meaningful insight is best left to platforms optimized for performing analytics that typically leverage in-memory technologies. Meaningful insights however aren’t limited to the big picture to how the enterprise is performing on the global stage as more often than not, more practical decision making can be best done within well-defined boundaries such as we associate with business operations. Yes, understanding the big picture and where you stand on the global stage is well and good, but where is my car? When is my seat upgrade clearing? Where is my paycheck?

This is where operational analytics intersects with the immediate needs of the business. Of the examples Kutay provides in his post, two should be familiar to many members of the NonStop community as they involve both manufacturing and retail experiences:

Industrial production

Operational analytics can introduce predictive maintenance of machines and machine components. This can help detect issues before it’s too late. Here’s how this works:

  1. You choose a machine that often runs into issues and affects your production output.
  2. You review the machine history and failure modes to know why it breaks down (e.g., when motors overheat).
  3. You build an analytics model that predicts failure probability.
  4. You feed data from sensors that provide your machine’s data points for temperature, vibration, and other metrics to your model.

Over time, your model will use your machine history and the latest data to produce a relatively accurate estimate of when your machine is expected to fail. These models can offer two benefits. First, it helps you plan your maintenance in advance. Second, it can let you know which spare part you are likely to be needed in the near future and keep it in your inventory, speeding up the maintenance process.

Supply chain industry

Operational analytics can help businesses collect insights from the massive amounts of data linked to the procurement, processing, and distribution of goods.

For instance, consider a point of sale (PoS) terminal that uses a demand signal repository — a type of data warehouse or database that manufacturers use to collect PoS and other demand data. Implementing operational analytics can help you set up an ETL with the terminal to send real-time data to your repository and anticipate consumer demand with greater clarity.

By using prescriptive analytics in operational analytics, manufacturers can determine whether their partners are slowing them down. For instance, it can provide the history of a supplier from another country that has been late delivering orders for some time. Other factors can include diminished capacity and an unstable economic climate. The manufacturer might consider having a meeting with the supplier’s management to see if they can fix these issues or if it’s time to partner with another supplier.

The Striim real-time data streaming and integration platform that capitalizes on industry standard Change Data Capture (CDC) methodologies popular with the NonStop community, “can ease your concerns by offering a plethora of capabilities.” Said Kutay. “Striim has real-time integrations for nearly all types of data pipelines. Whether you have to move data from a data warehouse or into one, Striim’s third-party support can ensure that your operational systems receive data more reliably”.

There is a word of caution however that is well worth noting. “Organizational change is one of the challenges organizations face when adopting operational analytics. The decisions made through operational analytics can influence manual decisions, which can be worrisome for the people who originally made those decisions.” As such this warrants taking time to explain the value that comes with operational analytics to all those who may be involved either directly or impacted by the outcomes provided.

To read the post in full and to understand the value that comes with operational analytics simply follow this link:

The NonStop community has always been at the front of end-user engagement; isn’t it the ideal time to consider gaining even greater value from such engagements in a manner that can further fine-tune the experience? After all, speeding up the delivery of a car is sure to please any consumer being tested by operational inadequacies. Should you have any questions about the Striim’s support for operational analytics, don’t hesitate to reach out to the Striim team. We would be only too happy to hear from you, anytime and all the time.

Ferhat Hatay, Ph.D.
Sr. Director of Partnerships and Alliances, Striim, Inc.