How to Formulate a Strategy to Achieve Real-Time Data Analysis

Real-Time Data Analysis

Consumers increasingly expect up-to-the-minute information and this need is putting stress on existing data infrastructures. Businesses struggle to know how to extract useful business insights from the increasing amounts of data they collect.

Mobile devices and the Internet of Things (IoT) are fuelling the need for real-time analytics where businesses can react to data soon after it enters a system. Here is how to develop a strategy if you want to analyze data in real-time.

What is real-time analytics?

Real-time analytics enable users to draw conclusions or get insights straight after (or very quickly after) data enters a system. This means businesses can react to it quickly, seizing opportunities or preventing problems as they arise.

Businesses can offer value-added services and products exactly when customers want them. They can also predict a problem, such as when a device is likely to fail or when credit card fraud occurs so they can take action before it happens. Dynamic pricing and trade risk analysis are other instances in which real-time reporting is critical.

Unlike real-time analytics, batch-style analytics can take hours or even days to provide results. Batch processing is useful for tasks such as generating monthly sales reports.

Traditional solutions are inadequate

With the Internet of Things (IoT) and edge computing, businesses increasingly need to process data while it is still in motion. Traditional data solutions that focus on data warehouses are not suited for real-time processing.

By using an Operational Data Store (ODS), you can integrate the data coming from multiple sources. Previously isolated systems feed into a consolidated repository and you can report across multiple systems of records with a comprehensive view of the data. A traditional ODS supports operational reporting but is unsuitable to meet the requirements of new digital applications that require real-time data.

A new generation operational data store is designed to meet the need for real-time data. If you have a traditional ODS, you can augment it to make it suitable or you can deploy a solution that includes all the components your business needs.

Understand what your business needs

Solutions that work for other businesses may not work for you and this is why you need to answer some questions such as the following ones before making any decisions.

  • What types of data sources matter?
  • What existing tools do you have in place to collect and analyze data?
  • What real-time analytical insights do you need to support your business goals?
  • How will you use the real-time analytical insights from the data?

Map out all of your data sources

You will need to map out all the sources of data you would like to centralize and gain insights from as part of your real-time analytics strategy.

Many businesses use a number of different SaaS products that generate data critical data for different departments. You won’t know what type of infrastructure you need to build if you don’t know what data you need to process and analyze.

One of your sources of data may be hybrid mobile apps that run across multiple platforms and across different operating systems. Namaste UI discusses the future of hybrid mobile apps in 2021 and says that increased demand and customized user options available today have led to exponential growth in the mobile app market. If you want to use digital applications, they require speed, agility, and the ability to scale.

Build your data infrastructure

Modern cloud data warehouses make it possible to store huge amounts of data and cloud vendors offer many different Software-as-a-Service (SaaS) or Platform-as-a-service (PaaS) solutions to support real-time analytics.

Requiring rapid access to and analysis of your data means it is important to optimize every level of your infrastructure – from your CPU and memory to your storage subsystems.

Solutions, like in-memory technology, help to power real-time analytics and swift decision-making. They can provide real-time performance, scalability, and they can integrate with popular data platforms.

By collocating applications with data, the data does not need to travel over the network, which means performance improves. A distributed in-memory core means many users can use apps without having an impact on their performance. The ability to scale means that planned or unplanned peaks don’t matter and there is no need for over-provisioning.

A next-generation ODS means you can run analytics on real-time data and also make use of historical data to enrich it so your predictive modeling is as accurate as possible.

Choose real-time analytics tools

Not all Business Intelligence (BI) tools are created equal. You want to choose one that makes it easy for your team to adapt and allows team members to collaborate and share insights. Make sure that the option you choose can support your data types and doesn’t limit access to data for those who need it.

If the tool isn’t easy to use, employees will resist using it. It should be simple for coworkers to share reports and build upon them within the tool. It must be up to the task of searching through data and returning actionable insights quickly.

Your tool mustn’t just support your current needs but it must be able to scale as your analytics requirements change over time. It must keep your users, data and reports organized to meet future demand as your need for analytics grows. You also need to keep security and compliance in mind when selecting it.

Most analytics tools today have some type of cloud offering but a fully-managed cloud solution has many benefits. Some of these are flexibility, usage-based pricing, sharing, and access to real-time data.

A final word

Real-time analytics can help you to get quick value from your data. This can improve the way you handle inventory, deal with problems before they escalate and offer value-added services to customers exactly when they need them. Real-time analytics make your decision-making better and your predictions are more accurate. Being able to generate real-time analytical insights can give your business an edge that will improve your bottom line.

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