Few tips to implement a real-time data analytics strategy

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.

How to measure and maximise your analytics investment

Investing in data analytics is the key to improving business prospects. With the right analytics platform, organisations can transform the way they work, making smarter decisions, improving insights and eliminating inefficient processes. However, to make the most out of their data analytics platform, organisations need to know how to maximise their investment and set the right measurements in place. In this blog, I explain the best way organisations can measure and maximise their analytics investment.

Measuring the value of your analytics investment

There is no value in an analytics investment if the correct measurements are not in place. While the specific measurements (or KPIs) vary depending on industry and business objectives, they should cover the following areas: Quality, speed and robustness.

Quality

High-quality data should be relevant to an organisation. To measure an organisation’s data, you need to have the right KPIs in place, which varies based on industry. For example, if you are in retail, your KPIs would be sales and inventory and, for finance, a key KPI would be expected ROI. Finally, hotels would consider revenue and room occupancy levels as relevant KPIs. With the right KPIs in place, you can optimise the analytics platform to focus on the right variables, making it easier to gauge and measure the correct business outcomes.

Robustness

Often an overlooked factor in measuring an analytics investment, robustness is the ability to yield good results despite unexpected changes. For example, a change in the market is going to affect an organisation’s bottom line or an unexpected outbreak forces an NGO to reallocate its resources. Organisations need robust data because circumstances change and data should be able to account for these changes. Data should be detailed and comprehensive enough to anticipate several scenarios and project predictions based on simulated situations.

To accomplish this, you need to use machine learning and Decision Optimisation. Machine learning forecasts different situations. These situations are added to the Decision Optimisation model, which in turn, predicts the effect of alternative decisions.

Speed

Your data analytics model should collect and analyse data in quick time. The faster the analytics model works, the bigger your competitive advantage. An analytics platform that comes with sophisticated technology, like Decision Optimisation, allows data analysts to work faster. However, there is no definite timeframe for speed as this varies depending on the skill of the data analyst and how familiar they are with the data analytics platform. This is where a well-established company like SAS comes into play as most SAS products are designed for efficiency and speed, optimised by a team of SAS experts who know the platform inside and out.

Maximising the value of analytics investment

Measuring the right KPIs is not just about variables, it is about knowing how to maximise value.

Make sure data is interconnected

For most companies, their data is within a silo. However, having information in a silo only undermines the gains made from data and reduces the effectiveness of your platform. To get the most out of your data, you need to break the silos and interconnect the different variables. Accomplishing this is incredibly challenging, but in the long run, you can maximise your analytics investment.

Selecting the right analytics platform

Many organisations select the wrong analytics platform for their need and goals. The mistake occurs because decision-makers focus on a platform’s capabilities rather than the organisation’s needs. To maximise your analytics investment, it is important to choose a platform based on its ability to address your business’ problems, instead of just raw capability. Hence, the reason why SAS builds different analytics products to address specific problems like banking fraud.

Focus on useful, not interesting KPIs

Choosing the right KPIs is crucial for attaining success in your analytics investment. Sadly, many organisations make an error in this area by focusing on what is interesting and not what is useful. For example, when reporting digital marketing returns, the CEO does not care about the specific keywords that got traffic. However, they are going to care about the type of media that attracted the most visitors and how that is from last quarter’s strategy. Focusing on the right KPIs not only focuses on the right business problems but makes reporting much easier.

What does it all boil down to?

To maximise your analytics investment, organisations need to set the right KPIs and maximise the value of the current analytics platform. Maximising utility means giving due diligence to the right platform, the most relevant KPIs and making sure data is connected. To make sure the analytics platform is providing genuine value to your organisation, you have to select the right KPIs. The specific KPIs will change depending on the organisation, industry and objectives. However, in our experience, we find that the most suitable areas to focus on are quality, robustness and speed.

If you are eager to learn more about data analytics, then visit our blog for more information.