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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.
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 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”
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).
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.
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:
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.
There are two mains challenges companies face when implementing analytics:
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
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.”
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:
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:
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:
In Customer Success, Operational Analytics is used for the following:
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:
“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”
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 :
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.
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:
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.
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 Operational Analytics, check out the RestApp website or book a demo directly.
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