What is Operational Analytics?
(and why is it essential to your business?)

By Brian Laleye · March 10, 2022 · 16 min read

You will learn in this guide how Operational Analytics activates your data by turning insights into actions.

We live in a world where data is accumulating at an ever-increasing rate. Businesses are acquiring more and more data from their environment (customers, product, marketing, etc).

The new generation of managers and business owners wants to make decisions based on the data they collect. But how can you make sure you are using all your data effectively? How do you ensure that every person within your organization is using the same data? Why is Operational Analytics becoming more and more important in the era of big data and the Internet of Things (IoT)?

In this guide you will learn what Operational Analytics is, why it is so important for your business, how to empower your teams with Operational Analytical skills and what are the use cases for each team.

Let’s dive right in.

Summary

The definition of Operational Analytics

Operational Analytics is the use of data, predictive analytics and business intelligence tools to gain insight into business operations and to generate real-time actions thanks to activated data.

It provides a view into what’s going on across your operation. Operational analytics can be used for a number of different areas, such as marketing campaigns, product improvement, demand planning, supply chain management and asset utilization.

Across all these scenarios, the common factor is that data is not used for insights after the fact. Rather, new data is immediately processed and consumed by live software applications to take action automatically. The goal of this type of analysis is to provide more value from the data being collected by other systems, such as CMS, ERP or CRM.

Data is collected and analyzed to address specific business questions, but the answers to these questions must be interpreted properly in order to have value.

Trying to use operational analytics without a well-defined business problem will likely result in an expensive system that doesn’t return much value. Operational analytics solutions usually require software, a skilled analyst and regular upkeep in order to remain useful. You’ll also need access to large amounts of data across all aspects of your business in order to truly gain insights from your operational analytics system.

Fortunately, RestApp empowers your teams with Operational Analytics in a couple of clicks by integrating with the most popular tools and applications.

Traditional Analytics vs. Operational Analytics

Traditional analytics focuses on analyzing historical data.

It helps understand what has happened and why. It is used to support business decisions by identifying patterns and trends through data comparison, benchmarking and other statistical methods.

It makes sense for example to have a report that has the number of orders placed, average order value and total orders placed in a given time period.

Operational analytics is a forward-looking concept. It focuses on predicting what will happen next, so that it can help make the most of the opportunities that arise in the future.

To do this, it extracts insights from huge volumes of unstructured data, using machine learning technologies and artificial intelligence algorithms. – it makes sense to know the number of orders placed in a time period and if it is greater than the previous time period, by how much and by which product.

The same logic applies to other business metrics like costs, inventory levels etc.

“Fancily prepared pretty-looking, but essentially static and stale reports sent out once a week (or even once a day) are just not enough. Compounding the problem is a lethargy resulting from data consumers who are typically ill equipped to consume insightful reports. In an ideal world, there should be no delay or extra layers of humans between the consumer of data and where the data is residing”

Sudheesh Nair, ThoughtSpot’s CEO
Put me in, Coach!

The emergence of Operational Analytics

The term operational analytics — or operational intelligence (OI) — is relatively new. The earliest recorded mention of it comes from 2007, when author Lance Eliot wrote an article for CMSWire called “Operational Intelligence: A New Buzzword.” The first conference devoted to the topic, Operational Analytics Europe, was held in London in 2009, and by 2011 Gartner had added it to its Hype Cycle for Business Intelligence and Information Management.

Operational Analytics is an emerging discipline at the intersection of business intelligence, big data, and machine learning.

Tech companies like Uber have already explained how they use analytics to create optimals trip experiences, such as determining the most convenient pick-up points in order to predict the fastest routes for riders.

Today, Operational Analytics is not limited to tech-savvy companies like Amazon, Uber, Apple. Any company using data can make more data driven decisions thanks to real time data. For instance, let’s take any reseller who wants to improve its margin. The data gathered at one of his points of sale can be used to manage inventory and ship more units to local stores that are running promotions.

According to the IDC (International Data Corporation), 30% of all data created will be real-time by 2025 (compared to less than 20% in 2021).

Operational Analytics as a database workload

Traditionally, analytics is a batch-oriented workload. The data is ingested from multiple sources and then stored in a data warehouse like Google Big Query, AWS… Batch jobs are launched periodically to process the data and generate insights. Later, the results of these jobs are made available to business users to make better business decisions.

Operational Analytics is different because of its focus on analyzing data in real time, or near real time. Operational Analytics makes use of multiple databases to perform operations like integration, aggregation and filtering to derive valuable information from enterprise data sources. This process typically involves multiple source systems that run high-volume workloads across a diverse set of workload patterns – online transaction processing (OLTP), reporting and analytics (OLAP), machine learning and artificial intelligence (ML/AI) training – which poses challenges for ensuring availability, efficiency and agility in deployment options.

To sum up, the data is ingested from multiple sources and processed as it arrives. The results of the analysis are then used to trigger actions immediately.

For example, consider a large retail chain that sells its products online. The company wants to analyze customer behavior in real time and respond quickly to any unusual activity or potential fraudulent transactions before customers make the payments using their credit cards. If a credit card transaction looks fishy, the company can decline the payment immediately instead of waiting until the next day or next week to find out if it was indeed fraudulent. This would prevent a large financial loss for the company.

Data silos issue solved thanks to Reverse ETL

In technical terms, a data silo is a repository of data that is isolated from other repositories. An organization with multiple data silos needs to use different tools or platforms to retrieve data from each of them, which adds complexity and inefficiency. Data silos can also lead to redundant copies of the same data, which means that when changes need to be made, those changes need to be made in multiple places. And with multiple copies of the same data, there’s a higher chance of inconsistencies arising between them.

The Graph highlights the important number of data silos in companies from CompTIA

In other words, the idea of data silos is that your company has all kinds of different data sources. Some are in an accounting system, others in a CRM tool like Salesforce or Hubspot. Others are scattered around the company in various files and spreadsheets. A common problem is that companies use different tools for different parts of their business, whether it’s online marketing or logistics. This means that your data is all over the place, not centralized, and getting any kind of holistic view of your business can be difficult.

When you have all your data in one place, you can ask questions across all of it at once instead of looking at one piece at a time, which should lead to better insights and better decision-making.

For example, if your sales figures are stored in a CRM tool, your accounting info in an accounting package, customer service data in customer service software and so on, you might not be able to answer questions like:

How many customers did we add last month?
What’s our churn rate right now?
How many customers did we have last year vs this year?
What’s our average revenue per customer?

The solution to this issue has been developed in the form of a new modern data stack and pipeline for data integration: Reverse ETL.

Reverse ETL syncs data from a central data warehouse to operational systems of actions such as business applications (CRM, Marketing & Customer Success tools…), Slack or even GoogleSheets.

RestApp uses Reverse ETL technology to connect, model and sync any data with your business tools in minutes with No/Low Code interface. Operational Analytics, enabled by Reverse ETL, helps you to answer the questions above.

Why do companies fail to implement analytics?

There are two mains challenges companies face when implementing analytics:

Lack of expertise

Analytics requires an understanding of the business problem at hand. IT resources alone are not enough; you need business intelligence (BI) tools, too. And these tools should be accessible by people who actually use them — not just analysts, developers or engineers.

With RestApp, it is from now on, a no-brainer for anyone to transform and clean data without coding or requesting the help of engineers

Slow decision-making processes

When executives don’t have good visibility into their business processes, they often postpone decisions that could save money or help them gain a competitive edge. This complicates relationship management and makes difficult any implementation of a new system like Operational Analytics.

“ It is incredibly tempting to make small incremental changes to modernize the current data infrastructure, but the reality is that the most successful new projects embrace a whole new cloud-native stack that allows them to move fast and show real value quickly.”

Shruti Bhat, SVP Product Management & Marketing at Rockset
From Good To Great: How Operational Analytics Can Give Businesses A Real-Time Edge

Why You Should Use Operational Analytics?

Operational Analytics is the process of using data and analytics to improve business performance. It’s not just a nice-to-have, it’s a must-have.

In our view, here are the main goals when companies implement analytics:

Make data actionable
Make data easy to access by all company employees
Make data trustworthy
Make data easy to share across different teams

Once those goals are achieved, using Operational Analytics will enable to increase profits, cut costs, mitigate risks and stay ahead of the competition. You need Operational Analytics to compete effectively in today’s business world. Here is why:

Gains in operational efficiency. Companies can use operational analytics to gain better operational efficiency by optimizing their processes and improving quality control.

Better customer experience. Businesses can use operational data to gain a better understanding of their customers, anticipate demand for services and products and provide better customer service.

Improved business intelligence and decision-making. Operational data provides an objective view of the status of your business, which helps you make better decisions about how to run your organization successfully.

Minimized cost and risk factors. Using Operational Analytics for risk management allows you to identify potential risks before they become problems that cost your company money and damage your reputation with customers and other stakeholders.

Competitive advantage over competitors who don’t use it.

How is Operational Analytics used in the Business and Revenue analyst teams?

The most common use of Operational Analytics in business is to improve revenue. Whether it’s through an increase in sales or cost reduction, Operational Analytics can help make better business decisions by providing real-time data on the operating condition of a company and where improvements can be made.

Operational Analytics has also been used in many other ways to improve business operations. From pricing models that predict the future value of a product, to operational insight about how customers interact with products, operational analytics can provide valuable insight into the most important aspects of your business.

Some common examples include:

Operational Analytics gives business analysts teams access to the data they need to not just track key metrics, but identify trends and get alignment in business objectives with other business teams.

Reconciling inventory records between multiple systems so that they are always in sync, avoiding hard-to-sell products being offered at full price when they are actually out of stock.

Providing accurate sales forecasts to understand what products are trending and what products need to be discontinued.

How is Operational Analytics used in Customer Success?

In Customer Success, Operational Analytics is used for the following:

Compare Key Performance Indicators (KPIs) across customers to determine best practices for different types of customer profiles.

Track the customer’s journey from initial contract to renewal.

Understand customers’ health via customer scoring mechanisms, allowing CSMs to prioritize customers by those most at risk of churning and proactively engage with the customer before churn occurs.

Monitor and alert on critical events such as contract renewals and upsell opportunities that need to be acted upon immediately by CSMs to ensure they do not lapse or miss out on new revenue opportunities with existing customers.

Set up hierarchical models to prioritize tickets based on account types.

Bring response times for common support issues down from days to minutes.

How is Operational Analytics used in Sales?

The role of the Sales team is to drive revenue by increasing their customer base. In order to do this, they must be able to locate and prioritize those prospects with the highest likelihood of becoming customers. They then need to optimize the process of converting those prospects into customers.

This requires access to data that can help them identify high-value targets, qualify them as leads, and close the sale.

Operational Analytics can help sales teams achieve these objectives by providing access to key performance metrics in real time, along with tools for analysis and visualization.

These include:

Sales forecasts: Historical sales data is used to predict future outcomes based on current sales activity.

Sales funnels: Sales managers can use a funnel report to analyze conversion rates between stages in the sales pipeline.

Customer tracking: Sales teams use customer tracking reports to monitor their performance and assess the effectiveness of their marketing campaigns.

Optimization of Freemium model: Better target customers that are more likely to subscribe to the premium plan in order to personalize the sales experience.

“When you are making a purchase on a website, do you want recommendations when you are going through the sale or a day later? You can make the perfect recommendation, but if it is too late then it will not lead to a great customer experience. Prediction and placement wait for no one”

Patrick McFadin, VP of developer relations at Datastax

How is Operational Analytics used in Marketing?

Marketing is one of the areas where Operational Analytics can be applied. Operational Analytics has become popular in marketing as it can help marketers determine how to improve their campaigns.

Marketing teams can use Operational Analytics for different uses cases :

Optimize their campaign in real time: the campaign manager should have a workflow that enables them to see how the campaign is performing on an hourly or daily basis. This will allow them to make adjustments in order to optimize their campaign. If a digital ad is performing well, it could be promoted more heavily or repurposed for use in other channels. If an ad isn’t performing as expected, it could be eliminated entirely before too many resources are wasted on it.

Study their existing campaigns and assess where they are most successful: calculate their return on investment (ROI) for specific marketing campaigns, and then use that information to make changes to improve the overall ROI for future campaigns.

Segment their audiences based on attributes, behavioral signals and actions performed during their customer journey.

The best part is that marketing teams don’t have to wait for engineering to release the data they need to launch their campaigns. Instead, they may explore and iterate quickly in a Test & Learn approach.

How is Operational Analytics used in the Data team?

Operational Analytics is about assisting data teams in becoming better strategic partners to ops teams and gaining the seat at the table they deserve in the company, not merely doing less of the work they despise (e.g. create and maintain custom integrations work) but by empowering their business teammates.

Here are the benefits of Operational Analytics for data teams, it:

Enables data teams to spend less time on data integration and more time on more value-added tasks like co-developing and activating data models.

Helps data teams better promote their team’s skills and define themselves as a key internal partner.

Empowers data teams to serve data consumers by letting them access data they need in their respective scope.

Assists data teams give reliable and accurate data to everyone without any friction.

Ensures data teams with a full stack monitoring over data models from start to end.

As such, data teams skills are more and more solicited within any organization. Operational Analytics ensures that your company’s data teams are fully able to provide high value-added work that any business team can benefit.

Conclusion

Operational Analytics improves decision making by analyzing large amounts of raw data into real-time actionable insights. Operational Analytics evaluates the entire business from multiple angles, including the customer experience, human resources, finance, supply chain and technology.

It uses both historical data and real-time data to measure gaps in current operations so that they can be improved upon or fixed. In short, Operational Analytics helps businesses make better decisions by giving them access to more information.

If you’re interested in starting with connecting all your favorite tools, check out the RestApp website or try it for free with a sample dataset.

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