Four Categories Of Operational Analytics
Reports, dashboards, and financial and operational analyses are common operational analytics that provide insights into regular activities and business decision-making.
There are four categories of analytics, each of which provides distinct value.
Descriptive analytics provide data, facts, and metrics about what has happened in a business operation up to a point in time such as yesterday, last month, or now. Descriptive analytics are part of business designs for operational processes.
Diagnostic analytics use data generated by equipment and IT infrastructure to identify problematic operating conditions, like equipment not operating as needed or anomalous situations like evidence of network infiltrators. Diagnostics are a key to sustaining the health of a company's technical infrastructure.
Predictive analytics use statistical analysis to identify a likely future outcome for an operational situation based on its probability, such as a pending equipment failure. Predictive analytics utilize historical and possibly third-party data to increase accuracy.
Prescriptive analytics incorporate multiple predictive models into another analytic model that determines which course of action, out of the ones possible, to take in a situation. Prescriptive analytics are a time-tested approach to process automation.
Correlating Data For Operational Analytics
Analytics do not simply arise from operational processes and workflows. Work performed with business applications captures data pertinent to actions taken. To be useful, application data needs to be organized and managed for analytics.
Advanced analytic methods are used to improve management and operation of business processes and to automate operational actions to respond as closely as possible in real time. Raw, unprocessed transactional data is needed to make use of advanced analytics.
As regulations become focused on processes and controls in addition to results and metrics, companies need to embed operational analytics in business processes. Data management activities are important for assembling a complete picture of the business through the lens of data. A problem with many legacy applications and systems is that they lose much detailed information, which makes anything other than summary descriptive analytics impossible.
Operational work results and events require classification to make sense of the data. Does data pertain to a time period? Or to a work group or location? Operational analytics use classifications and hierarchies to organize data so it can be related to organization responsibilities and other structures, producing insights and making the results meaningful.
It is critical that classifications and hierarchies be managed, governed, and controlled, just like data, as business assets. They need to be built into business workflows and process designs.
Low-level, granular data is the foundation that supports the four types of analytics. Classifications and hierarchies organize data so it can be used it part of a bigger picture. One additional ingredient is needed: aggregations. Aggregations summarize low-level, granular data so it is usable in classifications and hierarchies without requiring additional data preparation.
It is critical that data be ready for use and available in real time. This requires data aggregations to be done as part of operational business applications so they are immediately helpful.
Incorporating operational analytics into business processes makes them as important as all work actions in each operation. Control over analytics, and their use and efficacy, are central to company success, and need to be managed as part of every operational process.
Operational analytics must be recognized as a company asset and not be handled as separate work units. They need to be woven into all parts of a business design.
Process Automation Requires Incorporating Analytics Into Business Processes
Automation of work is increasing as innovators use digital technologies to revise traditional work processes. Companies are looking to reinvent their operations to respond to, or initiate, new and often disruptive products and services. InfoNovus Instant IT® provides companies with the ability to incorporate analytics into work processes and automate them.
InfoNovus Business Design lets you quickly design the way you want your business to operate and how work will be done, including classifications, hierarchies, aggregates, and analytics. It also provides management, governance, and control required to manage the development, deployment, and evolution of operational analytics.
Integrating analytics with the design of a business process and its work actions is the first step in process automation. Developing a base of success with operational analytics readies the company for automating process actions. Automation is becoming the new objective for improving business operations.
Digital Businesses Embed Operational Analytics Into Their Business Processes
Analytics are typically thought of as separate from operational work and the applications that support it. Operational analytics must be embedded in applications to deliver successful measurements in real time.