Learn more about operational analytics for your database including what it is, why it benefits your organization and use cases.
Table of Contents
Operational Analytics for Your Database
What Is Operational Analytics and Why Do You Need It?
Operational Analytics vs. Traditional Analytics
How Will Operational Analytics Benefit You?
Use Cases of Operational Analytics
What Are the Database Requirements for Operational Analytics?
Support for Complex Queries
Low Data Latency
High Query Volume
Live Sync with Various External Sources
Mixed Types
Conclusion
Operational Analytics for Your Database
Operational analytics, or operational analytical processing, is a form of data analytics that is focused on improving business operations. It is easily distinguishable from other forms of analytics, as it’s carried out on the fly. This means that data generated from different parts of a business or system is processed in real time and instantly fed back into the decision-making arm of the business for strategic planning. Operational analytics has also been described in some circles as continuous analytics, a name that emphasizes the continuous nature of the analytics loop.
This form of business analytics has gained prominence in recent years due to the increasing rate of adoption of digital technologies and digitalization across every industry. Whether you’re a software developer, a data engineer, or a decision maker in your organization, you have a need to leverage real-time information from your IT infrastructure to make decisions that will benefit your bottom line. If set up correctly, operational data analytics will assist your organization in speeding up this decision-making process, giving you a competitive advantage in the market.
In this article, you will learn about operational analytics and its benefits for your organization. You'll also look at the typical requirements needed for any database to be considered an engine for operational analytics.
What Is Operational Analytics and Why Do You Need It?
An operational analytics system is one that allows you to make quick decisions from streams of real-time data. It lets you receive data from multiple sources and sync that data directly to the interactive user-facing business intelligence tools, such as Braze, Salesforce and Marketo, that your team relies on for insights and decision-making.
Operational analytics shifts your focus from conventional analytics, which involves using software systems to understand data, to actually turning insights from your data into action to improve your bottom line. Usually, operational analytics makes use of the combination of data mining, machine learning and AI to help your organization make better decisions.
Operational Analytics vs. Traditional Analytics
Traditionally, business analytics is focused on providing decision makers a high-level overview of organizational key performance indicators (KPIs) on everyday operations for strategic purposes. The general idea has always been to aggregate data from different sources and then visualize this data to paint a picture of the current status of the business.
Operational analytics does not deviate from these basic principles of business analytics. In fact, it was developed as an improvement upon traditional analytics as businesses grew to require faster decision-making. The big differentiator is that operational analytics ensures complete integration between all your systems.
This makes your warehouse data a single source of truth and accessible across all tools used by the business, on both the technical and non-technical sides. Operational analytics is effectively the analysis of your organization’s day-to-day operational data.
As an example, operational analytics is at play when you automatically load your company’s product usage data into a customer relationship platform to provide actionable and real-time insights to your marketing team. The system empowers you to quickly react to any anomaly in the day-to-day business operations, such as when there’s a sudden drop in user engagement, and allows you to immediately implement initiatives that address the anomaly.
To apply traditional analytics to the previous example, you would require historical data, which would need to be pulled, processed and visualized, after which you would need to conduct meetings with several stakeholders before any serious action can be taken. This is a time-consuming process. Operational analytics significantly reduces the time needed for data processing and deliberation by ensuring your team reacts to customer behavior as it changes in real time.
How Will Operational Analytics Benefit You?
According to a poll conducted by Capgemini Consulting, over eighty percent of participants agreed that operational analytics contributes to driving profits and creating a competitive advantage. By investing in operational analytics, your company can benefit in several target areas, including:
- Near-real-time decision-making
- Trustworthy data from one central hub
- Improved customer loyalty by reaching every customer at the right time
- Having a consistent picture of the business in every tool
- Improved efficiency for data teams since they can spend less time doing integration and more time on models and analyses
Use Cases of Operational Analytics
Almost every industry across the globe has adopted operational analytics for one purpose or another. It’s impossible to capture every industry scenario where operational analytics has found a practical application, but some popular applications include:
- In financial institutions: Operational analytics is used by financial institutions for fraud detection and liquidity risk analysis. It takes on the task of analyzing consumer spending patterns, categorizing customers based on their credit risk, analyzing product usage patterns and much more, and uses that data to segment customers in fraud and risk classifications.
- In oil and gas industries: One of several ways operational analytics is used in the oil and gas industries is to facilitate the preventive maintenance strategy of mechanical assets. Since real-time operational data of these assets can be streamed to maintenance management systems, it’s easy for maintenance teams to detect potential mechanical faults and take preventative actions before they occur.
- In medicine: Nowadays, hospitals and emergency services employ operational analytics to predict the number of patients to be received daily and even prepare beds and prescriptions before patients arrive.
What Are the Database Requirements for Operational Analytics?
A well-deployed operational analytics system will have a pipeline for gathering data into your database, transformation steps to make sense of the data and a final key-value pair storage for quick retrieval by your frontline applications.
A database for operational analytics will have the following qualities.
Support for Complex Queries
Data-driven businesses need the ability to perform complex queries to offer solutions to business problems they face every day. For instance, the operational analytics engine of an online payment provider must execute complex queries in real time to monitor its global transactions for fraud detection. A typical operational database allows your application code to express complex queries in a declarative manner.
This allows your team to focus on what data to retrieve for your application logic without needing to worry about how the query is executed. This means that when real-time analysis is needed in an operational analytics database, your developers do not have to embed complex data logic like join optimizations, aggregations, sorting or relevance in the original application code. The database should support these operations to ensure fast and efficient processing of information from multiple sources.
A SQL database is an example of a database that allows declarative queries for complex operations on data.
Low Data Latency
A low-latency database is a database management system (DBMS) designed specifically for high performance and near-zero lag time for end users. Latency itself measures the interval it takes for a database to receive and execute a query.
The databases that support operational analytics are designed to store streams of data that come at varying rates. They are optimized for high-throughput operations, and an update to any record in the database is usually visible within seconds. This ensures high database availability with no service interruptions.
High Query Volume
As stated earlier, operational analytics engines are built for a high-throughput operation. Depending on their use case, it's common for some businesses relying on operational analytics to execute thousands of concurrent queries every second.
A financial institution, for example, needs to simultaneously process enormous numbers of transactions for multiple users in real time. This means that hundreds or thousands of database queries must be executed in parallel for your user-facing fraud detection application to flag fraudulent transactions.
For your database to be effective for operational analytics, it must be capable of processing a high number of queries simultaneously without compromising on performance.
Live Sync with Various External Sources
Your organization probably has different sources of data that need to talk to each other to maintain a single source of truth. An operational analytics database must have inherent mechanisms that will allow it to connect and continuously sync with these multiple data sources. Your team should be able to easily incorporate multiple applications and services from different arms of the business without losing the state of the database. This removes any data silos and helps your team attain data consistency across all systems.
Mixed Types
An operational analytics database must be capable of storing data of mixed types in the same database field. With the low-latency requirements for operational databases, your operational analytics database should be able to store new data without having to transform them to a single data type at write time. In databases without this capability, the additional layer of data cleaning can slow down your data ingestion.
Conclusion
In this article, you learned that operational analytics is a form of business analytics that helps you draw actionable insights from your real-time operational data. You also saw how operational analytics is different from the conventional business analytics you might be used to, and looked at some of its use cases across several industries. Additionally, you learned what to look for in an operational analytics engine when your organization is investing in one.
SingleStore is a real-time, distributed SQL database that meets all the requirements for operational analytics mentioned in this article, and many more. It provides fast, scalable analytics across all of your operational data. We provide global corporations with intelligent databases that can simultaneously run transactions and analytics, allowing clients to focus on running their businesses. You can try out Singlestore Helios for free.
