Few tips to implement a real-time data analytics strategy

real-time data analytics

Real-time data analytics allow data teams to perform modelling, simulations and optimisations based on a complete set of transaction data.

Real-time data analytics allows faster, more informed decisions, an improvement from conventional decision-making, usually done with stale data or in the absence of it. Real-time analytics requires a structured decision process with predefined logic, and the data must be immediately available. Acquiring the data is often the limiting factor in speedy decision-making.

With real-time insight into your market, your target audience and your competitor’s activities, you can form up-to-date strategies that reflect the changing trends of the market. For example, if a competitor lowers their prices, real-time analytics lets business analysts see the change immediately and make specific recommendations to management that realign priorities.

Read our tips below on how to implement a real-time data analytics strategy in your organisation.

Tip #1 – Understand what your company actually needs

Your team needs to answer questions like “What are the company’s goals with real-time reporting? What types of data sources matter, and what do you want to measure? What existing tools does the company already have in place? And who will utilise real-time analytical insights?”

Once you are able to gauge what your company actually requires from your real-time data analytics tool, you will be able to curate a tailor-made experience to boost your company’s optimal efficiency.

Tip #2 Triangulate sources of data and store it in a cloud

Before you can build the right data infrastructure to process and analyse all this data in real-time, you need to pinpoint precisely where you want to collect that data. Common types of data you may want to analyse in real-time include customer relationship management (CRM) data and enterprise resource management (ERP) data.

You also need to take into account data sources and the best way to manage them. Make sure to take a broad look at your company’s needs and map out all the sources of data you wish to centralise as part of your strategy for real-time analytics. Now that you know what types of data you want to analyse, you’ll need to build a data pipeline to collect and store that data in a cloud warehouse or data lake, for analysis.

Tip #3 Pick the right real-time data analytics tools

While there are a variety of analytics tools that can analyse real-time data, you want to be sure you invest in the right one. The wrong choice could leave you with more than a negative effect on your balance sheet—think low employee adoption, headaches for the engineering team and extended system downtime, if you have to replace it in the future.

It’s essential to get a good idea of what you need from your real-time analytics tool. Every company is unique, and needs will vary. We recommend evaluating internal requirements and aligning purchasing decisions with those goals.

Tip #4 Consult experts in real-time data analytics strategy

When implementing a real-time data analytics strategy, you may need to seek external assistance to curate your strategy. There are companies that would take over this tedious, complex job and will help you expertly streamline your real-time data analytics strategy.

To filter through the noise to optimise your operations and minimise downtime or hassle, a consultant could step in. More often than not, these services are provided with support teams on call 24/7 for any woes or troubles-faced.

Time to adopt real-time data analytics

Big data and real-time analytics was once a pipe dream for many enterprises. But now, businesses need real-time tools to analyse their data– and both things are more attainable than you think. Do not be afraid to venture down this road!

The modern-day business model requires quality delivered in real-time and be ever-present for changes on the horizon. To achieve this real-time performance, enterprises must now harness the power of real-time data analytics.

Gartner conducted studies on real-time analytics. Between 2017 and 2019, spending on real-time analytics grew three times faster than any other type of analytics and will be worth $22.8 billion by 2020.

Visit our website for more information on how you can leverage the power of real-time data analytics in your industry.

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