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What is Operational Analytics and its business use cases?

Operational analytics focuses on monitoring the current and real-time operations.
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The analysis of data sources within the operations department of an organization can result in increased productivity and efficiency, and hence profitability. Operational analytics is a type of business analytics that focuses on monitoring the current and real-time operations. It employs real-time data analysis and business intelligence to boost productivity and streamline daily operations. This article will be focused on understanding operational analytics and the impact of its usage on business. Following are the topics to be covered.

Table of contents

  1. About Operational Analytics
  2. Difference between Traditional and Operational Analytics
  3. Working operational Analytics work
  4. Why organization should invest in Operational Analytics?
  5. Discussing some use cases

Every operational transaction has an associate decision; every action is preceded by a decision. Let’s start with a high-level understanding of Operational Analytics.

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About Operational Analytics

In operational analytics, recommendations are developed based on insights derived by applying statistical models and analysis to existing and simulated future data and then implemented in real-time interactions. Operational analytics uses data mining, artificial intelligence, and machine learning to provide organisations with more transparency and help them make better decisions.

If the construction of information and automation systems is done correctly and technology is updated regularly, operational data analytics may provide a company with a competitive advantage. When it comes to operational data analytics, there are hundreds of variables to consider. Available technical platforms, ability, and expenses associated with certain additions all factor into whether or not to include operational data analytics. While operationalizing data analytics might be a costly process, there are several advantages.

Operational Analytics allows you to integrate data from your data warehouse straight into the frontline applications your staff uses every day (like Salesforce, Hubspot, and Marketo) to drive action, not simply insights. It implies more efficient workflows, improved automation, and better communication amongst cross-functional teams.

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Difference between Traditional and Operational Analytics

Traditional AnalyticsOperational Analytics
Structured DataAny type of data structured, semi-structured or unstructured.
The object of analysis is a sample from the know populationThe entire population is the object of analysis
Answers to already defined questionsNew and unexpected findings and facts
Knowledge of analytical techniques and tools and basic knowledge of reporting are required.Advanced analytical, mathematical, statistical and computer knowledge are required.

Working of Operational Analytics

Operational Analytics works in a closed-loop process in which data is copied from the operational environment into and analysed in Bussiness Intelligence (BI). The choices are derived that influence the operational environment, and the output can be compared to what was predicted. This closed-loop process is divided into five major phases which are Gathering information, in-depth analysis,  model application,  real-time analysis, and feedback to in-depth analysis.

In practice, the first three phases of strategic and tactical BI go slowly, and the last loop-closing step is tough to complete owing to the broader breadth of the decisions being made. 

Gathering information

Data integration has long been at the heart of BI. Traditionally, the focus has been on reconciling and consolidating data from disparate operational systems classic ETL. The ETL stands for extract, transform and load. 

The “Extract” step entails gathering data from its data sources. The rows and columns of your analytic database will be created from this data. Extraction used to mean collecting data from Excel files and Relational Management Database Systems, which were the key sources of data for corporations (e.g. purchase orders written in Excel). 

The “Transform” step modifies (transforms) the data acquired during the extractor stage before saving it to the analytic database. There are a variety of transformations available.

  • Data cleaning entails finding and correcting or eliminating questionable data. Operations like removing missing data, outliers should be removed, encoding etc. are performed
  • Data enrichment is the process of adding extra information to previously obtained raw data. Operations like combining information from many sources, Deduplication, etc are performed

The “load” stage includes transferring data from the transform stage to a target data store (relational database, NoSQL data store, data warehouse, or data lake), where it may be analysed.

While this is still important, operational analytics requires two additional features.

  • Speed and timeliness of data collection 
  • Sources outside of typical operational systems, such as “big data” and sources within the warehouse itself.

In-depth analysis 

The focus of operational analytics at this stage is on in-depth statistical analysis and conventional querying of a wide variety of relevant data. The idea is to find unanticipated connections between hundreds, if not thousands, of aspects of behaviours, features, and activities. This phase should not be expected to be real-time because of the large number of characteristics and typically millions of data.

However, following the first cleaning, preparation, and investigation phase, timeliness is still required. Data mining operations that last overnight (or longer) and frequent exports/imports between platforms are no longer appropriate for continuing research. Depending on the features of the data utilised and the urgency of the analytic demand, scale and timeliness can be achieved in a variety of ways. The utilisation of a Hadoop-based platform for initial preparation and research is frequently beneficial.

Model application

The procedure of transforming interactions between an organization and its consumers on the fly to improve business performance is an operational activity. It could be stated it happens outside the BI system and move on swiftly in a typical BI manner. The terminology operational analytics, on the other hand, suggests that we must deal with both operational and analytical issues. Sophistication is divided into four stages.

  1. Having an impact on a manual procedure. For example, A representative’s engagement with a client at a call centre is guided by a model developed from a prior study of the customer’s total lifetime value, comparable metrics, and demographically-based predictions of what response or offer should be given. With no real-time data, the call centre application is only weakly coupled to the analytics system via pre-loaded model data. The agent retains considerable control over the engagement.
  2. Managing a manual process. Similar to the last example, but with a closer relationship between the call centre and the analytic programmes. Phase 4 of operational analytics (real-time analysis) must be activated for the agent to have both real-time data and suggestions based on it; the agent’s interaction flexibility is constrained.
  3. The procedure of making an offer has been automated. Customers’ perceptions of the retail website offer, for example, were produced automatically using models based on previous interactions and the present clickstream. The analytic environment is closely linked to the operational system, and real-time analysis is carried out in a closed loop. 
  4. Model and offer procedures that are integrated. Multiple models can run simultaneously for various clients and self-tune based on the findings in an integrated system, allowing for real-time analysis and operational actions. Real-time operations and analysis are nearly indistinguishable. 

Real-time analysis

Pushing or pulling data into the system is how real-time data analytics works. Streaming must be in place for massive data to be sent into a system. Streaming, could use a lot of resources and may be prohibitive for some applications. Instead, schedule data to be fetched at different times, ranging from seconds to hours. The following components are included in real-time data analytics.

  • The aggregator collects and analyses real-time data from a variety of sources.
  • While doing analysis, the analytics engine compares the values of data and streams it together.
  • Create data availability by acting as a broker.
  • The stream processor receives and sends data to execute logic and do analytics in real-time.

Feedback to in-depth analysis

Technically, the fifth step of operational analytics is rather simple: just updating the data used in in-depth analysis with fresh data and findings acquired in real-time. This stage, however, should not be overlooked; it ultimately closes the loop between operational and informative efforts. Because of incompatibilities in design and ownership between the two domains, it has long been a stumbling block in BI systems. 

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Why organizations should invest in Operational Analytics?

The advantages of analytics are undeniable. Organizations may update and restructure their operations to compete in digital economies by adopting analytics. Here are some of the reasons listed.

Facilitating Decision Making

Organizations that analyze and react to client data in real-time can make better choices faster. Businesses would be aware of any obvious faults in their operations only based on quarterly or yearly statistics in the old style of working, and by the time they make adjustments reactively to their operations, there is always the risk that they will not be able to address these issues on time.

Businesses that embrace operational analytics, on the other hand, can make the required modifications to processes and workflows in real-time or near-real-time, allowing them to boost profitability and minimise waste. This would also aid them in promptly detecting and responding to faults and inefficiencies.

High-efficiency

Businesses may use operational analytics to simplify their operations by identifying inefficiencies in their processes and making the required changes. For example, based on operational analytics data, a company discovered there is a problem with the process of their billing system due to which the average waiting time of customers has increased. This information may lead the company to rethink the process by reducing the number of approvals and streamlining the process, resulting in a shorter turnaround time.

Customer loyalty

Operational analytics can improve customer experience by allowing businesses to react in real-time to business situations. For example, the temporary churning of a food ordering portal that uses operational analytics has increased in recent months despite being offered a discount. 

With the help of the operational analytics data, the company that owns the portal discovers a bug in the software that affects users of a certain operating system. Food items do not get added to the cart when users add them to the cart, and the application crashes as a result. The bug is resolved quickly, and the Portal does not lose customers. This kind of user experience would result in customer loyalty.

Discussing some use cases

Service Providers

Operational Analytics is used by Mobility Service Providers like Uber to offer flawless trip experiences for its consumers, from selecting the most convenient passenger pickup spots to projecting the shortest routes.

Online merchants

Operational Analytics is used by online merchants to evaluate which goods are the most popular in their stores and modify inventories appropriately. They also get access to real-time data on customer searches and hot trends.

Medical

Operational Analytics is used by hospitals to forecast the number of emergency room patients they will see each day. Nurses can utilise this information to prepare prescriptions ahead of time.

Finance

Operational Analytics is used by banks and financial institutions to detect fraud and liquidity risk. They are given the task of analysing client spending patterns and categorising them based on credit risk and other factors. This information is used to match clients with the appropriate items for their needs.

Manufacturing

In the manufacturing industry, operational analytics is utilised for preventive maintenance. Manufacturing businesses employ operational analytics to initiate preventative maintenance of machines, machine components, and other assets to detect possible issues before they arise. The manufacturer can be notified when servicing is necessary using this information.

Supply Chain Management

If the Supplier is unable to deliver the items agreed upon on a specific day for businesses that are not digitally linked, it will necessitate administrative efforts from all parties concerned, including the Supplier, Planner, personnel in charge of goods receipt, Enterprise Resource Planning system, and so on. The absence of a thorough analysis of the consumption, stock, and supply conditions is the cause of this additional manual labour. The use of operational analytics in the Supply Chain gives employees well-structured dashboards containing vital data, which they can analyse and promptly agree on a supplemental delivery with the Supplier.      

Marketing

A marketing manager or another expert in data systems may use operational analytics to run numerous experiments at once, collect findings in the form of data, terminate unproductive trials, and nurture the ones that succeed, all while employing data-based software systems. The more trials they can perform and the faster they can get results, the more effective they will be at selling their product.

Optimizing products

A product manager looks at product-usage logs provided by operational analytics to determine which features of the product are liked by its users, which features slow them down, and which features are disliked by its users. The product manager can then find the necessary answers by querying data that records usage patterns from the product’s user base and feeding this information back to make the product better.

Conclusion

Operational Analytics tackles the problem by synchronising real-time data from your warehouse with BI tools. This guarantees that your operational routines and systems are used efficiently. Using Operational Analytics, the organization puts the power of Real-time Business Intelligence in the hands of front-line employees, allowing them to give the most value to the company. With this article, we have understood Operational Analytics and its impact of it on businesses.

References

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Sourabh Mehta
Sourabh has worked as a full-time data scientist for an ISP organisation, experienced in analysing patterns and their implementation in product development. He has a keen interest in developing solutions for real-time problems with the help of data both in this universe and metaverse.

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