Operational Analytics: Real-Time Insights That Matter

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Operational Analytics IOblend

Operational Analytics: Real-Time Insights That Matter

At IOblend we mainly specialise in operational analytics (OA) use cases, where decisions are made automatically, right there and then. A sensor detects a change, that change is then processed and acted upon by a system. A customer places an order on your website, the systems automatically process it, update the inventory, and dispatch the goods. The magic happens behind the scenes, hidden well away from the users. Both, the customer and you expect this process to work seamlessly time after time.

The world of OA is nowhere near as glamorous as the BI and data science (in relative terms, before you all get too excited). There are no flashy front ends or dashboards with colourful charts. No data experimentation takes place or deep insights are derived. Most of the data wizardry happens automatically. The end users don’t see any of the complexity that happens in the background. And they don’t care. As long as the systems keep on working as intended, orders get shipped, the right goods arrive, sensors send readings, etc., they are happy.

I find the world of operational analytics very fascinating. It’s all about automation, efficiency and speed. It enables us to do more things with fewer resources. Do them quicker and more reliably. Deliver new products and services like never before. I just love it.

What is operational analytics?

Operational analytics is the analysis of data generated by the day-to-day activities of a business. It involves processing and analysing operational data in “real-time” to gain insights that inform immediate and actionable decisions.

For example, if you run a grocery shop, operational analytics helps you keep an eye on which items fly off the shelves and which ones don’t. This means you can order just the right amount of stock, so you’re not left with things nobody wants to buy. Or, if you’re delivering parcels, OA helps you figure out the quickest routes so your drivers can drop off more parcels in less time. It saves your business fuel and makes customers happy because they get their stuff quicker.

It’s not just about spotting problems, though. OA also lets businesses predict what’s going to happen next. So, if you know you’re going to sell loads of sun cream in hot weather, operational analytics helps you stock up before the heatwave hits. This way, you’re always one step ahead. We helped a manufacturing company save big $$ by allowing them to predict machine stoppages before they occurred.

Plus, operational analytics is great for making sure customers are happy. By looking at what people buy or how they use your website, you can make their shopping experience better and more personal. If you know someone likes reading about airplanes, you can show them new airplane magazines first when they log in.

Operational analytics is all about making the most of the nitty-gritty details of how your business runs day-to-day. It’s about being smart with the data you’ve got. So you can make quick decisions, solve problems before they get big, and keep your customers coming back for more.

Operational analytics vs data science

How is that different from BI and data science?

Operational analytics is about the here and now—making sure everything’s running smoothly today. OA’s purpose is to improve business efficiency through the streamlining of operations. It enables fast decisioning (real time allows proactive approach to developing issues), lowers costs (heavy automation), increases revenue (dynamic packaging/pricing, up-sales), and promotes better collaboration through live data sharing.

Data science takes a longer view. It’s more about understanding the big picture and making changes that’ll help the business in the future.

I’m a car guy, so I’ll use a vehicular example to illustrate. Imagine you’ve got two toolkits for fixing up cars: one is for quick fixes to keep the car running smoothly day-to-day. The other is for big overhauls and figuring out how to tune the car to perform better. Operational analytics is like the first toolkit—it’s all about making quick decisions and fixes based on what’s happening right now. It helps businesses keep things ticking over smoothly in BAU.

Data science, on the other hand, is like the second toolkit. It’s used for digging deep into all the data a business has collected to find patterns, solve complex problems, and come up with new ways of doing things. Data scientists take a load of data, analyse it, and come up with insights that lead to a whole new way of doing things.

OA is quick and practical, keeping the engine running right now. Data science is more about taking the engine apart. Seeing how it all works. Finding ways to make it run better than ever. But both disciplines go hand-in-hand and are equally important, as one informs the direction of the other.

How is operational analytics used by businesses?

Operational analytics executed properly has a lot of benefits for business. There are upsides for revenue generation and cost efficiencies in equal manner.

Real-time decision making: With operational analytics, businesses can analyse and act on data as events are happening. This immediacy enables companies to be more agile, responding to issues or capitalising on opportunities as they arise.

Enhancing efficiency and productivity: By identifying inefficiencies in operations, companies can streamline processes, reduce waste, and enhance productivity. This might mean automating repetitive tasks or redesigning workflows for better efficiency.

Predictive insights for proactive management: Predictive analytics allows businesses to forecast future trends based on historical data. This allows to avoiding problems before they occur or preparing for expected surges in demand.

Personalised customer experiences: Analysing customer interaction data helps businesses adapt their offerings, providing personalised experiences. This tends to lead to enhanced customer satisfaction and loyalty. If only more of the companies used this OA…

Risk management: Operational analytics can identify patterns that signal potential fraud or security breaches, allowing businesses to pre-emptively address risks before they escalate. In aviation, OA helps with safety management.

Where does OA apply?

Companies use OA all the time. It covers all sorts of business activities across an array of industries. Whenever decisions need to be made on-the-fly, mainly automatically, you will deploy OA.

Manufacturing firms use operational analytics to monitor production lines in real time, identifying and rectifying bottlenecks. In logistics, it optimises routes and delivery schedules, reducing costs and improving customer satisfaction. Aviation uses real-time OA to predict customer demand or manage delays.

Healthcare companies leverage it to improve patient care through efficient management of medical supplies and dynamic scheduling of staff. Websites and online services utilise OA to enhance user experiences by analysing clickstream data to understand user behaviour and preferences.

The applications are broad. As companies realise the positive impact of data on their operations, operational analytics use cases will only grow further. Especially so with the recent advent of GenAI.

Data must be “fit” in operational analytics

Operational analytics requires the data to be in a “usable” state to make informed decisions. As the decisions are made automatically, poor quality and out-of-date data can have a material impact on business operations. In BI and data science, you can have time to validate, cleanse and question the data after you extract it. In live systems you do not. The data must be fit-for-purpose all the time.

Data quality and consistency are serious concerns for all companies. You don’t want to be questioning your data when it comes to automated decisioning. The consequences can be truly dire in mission-critical settings like flight safety, for example. You want to make sure your data is trustworthy before you make decisions with it and press the proverbial button.

This is where you need to spend time thinking through the business process end-to-end. How reliable is the data source? How’s is my data collected and processed? What are the data SLAs? Backups? Recovery? Plans A, B and C when things turn sour? And so on, so forth. The more effort you put upfront, the more resilient your OA will be.

Then automate as much as you can. Automation is at the heart of operational analytics. The more automation you build into your data processing and management, the more robust your process will be. Minimise manual interventions as much as possible in production settings. As smart as us, humans, are the machines are much better at making fast, repeatable, mechanical decisions. And they only get better with time.

Embrace operational analytics

Operational analytics is about making the most of the data your business generates every day. OA allows you to improve decision-making, enhance efficiency, and deliver better value to your customers. It’s not just about collecting data and dumping it into a lake somewhere. It’s about translating that data on-the-fly into timely, actionable, automated insights that drive your business.

More and more companies are embracing the use of operational data in their day-to-day decisions. It’s not often an easy journey, however. There are cultural, organisational, procedural challenges that need to be considered before you ever get to the technical complexities. We’ve seen just as many examples of OA implemented amazingly well and spectacularly wrong. Sometimes even in the same organisation.

We are all about operational analytics at IOblend. We have designed our solution with automated data decisioning in mind. We have built a no-code platform that enables fast and efficient movement of data from system to system. Our goal is to help you put your operational data to work for you fast and keep it working reliably. Get in touch with us to see how we can help you make operational analytics work for you.

 

IOblend is a proud ISV partner of Microsoft, Snowflake, AWS, GCP, Salesforce, SAP and Yocova

IOblend presents a ground-breaking approach to IoT and data integration, revolutionizing the way businesses handle their data. It’s an all-in-one data integration accelerator, boasting real-time, production-grade, managed Apache Spark™ data pipelines that can be set up in mere minutes. This facilitates a massive acceleration in data migration projects, whether from on-prem to cloud or between clouds, thanks to its low code/no code development and automated data management and governance.

IOblend also simplifies the integration of streaming and batch data through Kappa architecture, significantly boosting the efficiency of operational analytics and MLOps. Its system enables the robust and cost-effective delivery of both centralized and federated data architectures, with low latency and massively parallelized data processing, capable of handling over 10 million transactions per second. Additionally, IOblend integrates seamlessly with leading cloud services like Snowflake and Microsoft Azure, underscoring its versatility and broad applicability in various data environments.

At its core, IOblend is an end-to-end enterprise data integration solution built with DataOps capability. It stands out as a versatile ETL product for building and managing data estates with high-grade data flows. The platform powers operational analytics and AI initiatives, drastically reducing the costs and development efforts associated with data projects and data science ventures. It’s engineered to connect to any source, perform in-memory transformations of streaming and batch data, and direct the results to any destination with minimal effort.

IOblend’s use cases are diverse and impactful. It streams live data from factories to automated forecasting models and channels data from IoT sensors to real-time monitoring applications, enabling automated decision-making based on live inputs and historical statistics. Additionally, it handles the movement of production-grade streaming and batch data to and from cloud data warehouses and lakes, powers data exchanges, and feeds applications with data that adheres to complex business rules and governance policies.

The platform comprises two core components: the IOblend Designer and the IOblend Engine. The IOblend Designer is a desktop GUI used for designing, building, and testing data pipeline DAGs, producing metadata that describes the data pipelines. The IOblend Engine, the heart of the system, converts this metadata into Spark streaming jobs executed on any Spark cluster. Available in Developer and Enterprise suites, IOblend supports both local and remote engine operations, catering to a wide range of development and operational needs. It also facilitates collaborative development and pipeline versioning, making it a robust tool for modern data management and analytics

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