In today's competitive environment, businesses are under increasing pressure to reduce costs while improving effectiveness of their operational delivery - usually through optimizing demand and supply across their value chain. Globalised and interconnected value chains create a level of complexity that requires sophisticated real time decisions. This complexity coupled with increasing amount of data that businesses generate through deployment of technologies necessitates a genre of analytics known as operational analytics, or real-time business intelligence. Companies are increasingly turning to operational analytics, not only as a means to bolster productivity levels, but also to support better client-facing activities.
Operational analytics may be defined as the process of developing optimal or realistic recommendations for real-time, operational decisions based on insights derived through the application of statistical models and analysis against existing and/or simulated future data, and applying these recommendations in real-time interactions. Operational analytics enable companies to be more competitive, drive more transactions, eliminate risk and fraud, streamline and optimize operations, and achieve cost efficiencies. The typical other benefits that can be reaped from operational analytics are: reduced downtime, improved productivity, better capacity utilisation, accurate forecasting capability, and higher flexibility in response to external events.
There is a distinct trend of companies moving toward real-time processing involving stream analytics and complex event processing (CEP) for certain types of critical business applications. A study conducted by MIT Sloan Management Review in 2013 had found that at the time around 40% of digital initiatives were focused on customer experience, while 26% were aimed at bolstering the performance of operations, spanning among others production, supply chain and internal processes. However, the recent (2017) research by Capgemini Consulting shows that the momentum is shifting from front-end to back-end. The firm's survey - held among more than 600 executives from the US, Europe and China - finds that today over 70% of organisations put more emphasis on operations than on consumer-focused processes for their analytics initiatives. The same report cites a research which shows that by utilizing data, manufacturing organizations can realize benefits of up to $371 billion globally, with $117 billion of that coming from operations. Analytics tied to customer-facing processes, on the other hand, are now delivering just $38 billion in benefits.
In operational analytics, certain businesses require tracking operations as close to real time as possible. However, the latency introduced by moving data into a data warehouse is usually too high to answer real-time questions or to take real-time actions. This can be obviated by embedding the analytical processing in business process workflows and to analyze operational processing as it occurs. The embedded processing may use results from traditional and predictive analytical processing to aid in determining what recommendations should be made, or actions taken, during operational processing.
There is yet another class of applications where even close to real-time analytics are not sufficient. An application such as algorithmic trading requires split-second actions to be taken based on analyzing hundreds of thousands of trading events per second. This is known as stream analytics because events are analyzed as they stream across networks between devices and across systems. Stream processing requires long-running continuous queries as opposed to one-time queries used by traditional business intelligence applications. Applications that can exploit stream analytics include fraud detection such as insider trading, fleet management, IT alert analysis and RFID tracking analysis for manufacturing, supply chain optimization and pharmaceutical product tracking.
Stream analytics typically requires massive amounts of event processing, heavy use of parallel computing, and new database approaches and languages. The underlying technology that supports stream analytics is known as complex event processing. It primarily involves an event processing concept (looking for pattern in events) that deals with processing multiple events from an event cloud with the goal of identifying meaningful events. The term applies to situations where large number of events from multiple sources can be processed quickly to infer complex events. For example, if suddenly thousands of alarms go off at a data center, the CEP engine would filter the events and infer a new event that an external network link has gone down.
To implement operational analytics in a company, a systematic 5 step approach may help: 1. Set Measurable Objectives: usually two types of business objectives that should be addressed here- monetary gains and process gains, 2. Maintain project governance throughout implementation: strong project management process to make sure all lines of business within your organization are aligned with project objectives, 3. Ensure end user adoption: change management: identify, understand and address the needs of key stakeholders by developing and executing a communications plan early and often, 4. Take a phased implementation approach to demonstrate quick gains and also manage the project well, and last, but not the least, follow best practices.
Operational analytics may be defined as the process of developing optimal or realistic recommendations for real-time, operational decisions based on insights derived through the application of statistical models and analysis against existing and/or simulated future data, and applying these recommendations in real-time interactions. Operational analytics enable companies to be more competitive, drive more transactions, eliminate risk and fraud, streamline and optimize operations, and achieve cost efficiencies. The typical other benefits that can be reaped from operational analytics are: reduced downtime, improved productivity, better capacity utilisation, accurate forecasting capability, and higher flexibility in response to external events.
There is a distinct trend of companies moving toward real-time processing involving stream analytics and complex event processing (CEP) for certain types of critical business applications. A study conducted by MIT Sloan Management Review in 2013 had found that at the time around 40% of digital initiatives were focused on customer experience, while 26% were aimed at bolstering the performance of operations, spanning among others production, supply chain and internal processes. However, the recent (2017) research by Capgemini Consulting shows that the momentum is shifting from front-end to back-end. The firm's survey - held among more than 600 executives from the US, Europe and China - finds that today over 70% of organisations put more emphasis on operations than on consumer-focused processes for their analytics initiatives. The same report cites a research which shows that by utilizing data, manufacturing organizations can realize benefits of up to $371 billion globally, with $117 billion of that coming from operations. Analytics tied to customer-facing processes, on the other hand, are now delivering just $38 billion in benefits.
In operational analytics, certain businesses require tracking operations as close to real time as possible. However, the latency introduced by moving data into a data warehouse is usually too high to answer real-time questions or to take real-time actions. This can be obviated by embedding the analytical processing in business process workflows and to analyze operational processing as it occurs. The embedded processing may use results from traditional and predictive analytical processing to aid in determining what recommendations should be made, or actions taken, during operational processing.
There is yet another class of applications where even close to real-time analytics are not sufficient. An application such as algorithmic trading requires split-second actions to be taken based on analyzing hundreds of thousands of trading events per second. This is known as stream analytics because events are analyzed as they stream across networks between devices and across systems. Stream processing requires long-running continuous queries as opposed to one-time queries used by traditional business intelligence applications. Applications that can exploit stream analytics include fraud detection such as insider trading, fleet management, IT alert analysis and RFID tracking analysis for manufacturing, supply chain optimization and pharmaceutical product tracking.
Stream analytics typically requires massive amounts of event processing, heavy use of parallel computing, and new database approaches and languages. The underlying technology that supports stream analytics is known as complex event processing. It primarily involves an event processing concept (looking for pattern in events) that deals with processing multiple events from an event cloud with the goal of identifying meaningful events. The term applies to situations where large number of events from multiple sources can be processed quickly to infer complex events. For example, if suddenly thousands of alarms go off at a data center, the CEP engine would filter the events and infer a new event that an external network link has gone down.
To implement operational analytics in a company, a systematic 5 step approach may help: 1. Set Measurable Objectives: usually two types of business objectives that should be addressed here- monetary gains and process gains, 2. Maintain project governance throughout implementation: strong project management process to make sure all lines of business within your organization are aligned with project objectives, 3. Ensure end user adoption: change management: identify, understand and address the needs of key stakeholders by developing and executing a communications plan early and often, 4. Take a phased implementation approach to demonstrate quick gains and also manage the project well, and last, but not the least, follow best practices.
