Being a ‘real-time’ enterprise today is typically evaluated under two criteria: the ability to capture, collect and store data as it comes in; and the ability to respond to it at the point of consumption. Analytics solutions that allow for this are highly sought after, as it’s considered a huge competitive differentiator and critical capability in our fast-paced digital world.
However, while there’s much buzzword bingo about real-time data, decision-making and insight, the readiness of the enterprise to become real-time is varied due to a lack of understanding in how it practically aligns with their goals, resulting in lost opportunities and wasted resources.
The real (time) questions to ask
In my experience, I don’t necessarily believe some enterprises are able to shift toward real-time data and analysis in the way most believe they can, nor is there as much of a need for these technologies as it may seem right now. This is due to misunderstandings around what real-time really offers and how it works, in the context of modern analytics and business intelligence (BI).
The reality is today’s business systems are mostly already capable of providing the first half of what ‘real-time’ technology promises: collecting data in-real time. Whether it’s coming from marketing, finance, or logistics solutions, your business may already be capable in this area.
The second half of the criteria, which is being able to analyse this data and gain valuable insight in real-time, is a whole other challenge. It also often gets confused with the former, taking away from what should be the main considerations when planning to adopt a real-time analytics tool:
- What are the decisions your business needs to make at the moment you receive data?
- What is slowing down your business from making those decisions?
- How will your users gain value from having this capability for their decision-making?
Enterprises must first be able to answer these considerations and articulate them to the rest of the business before any effective adoption of real-time analytics can be guaranteed. Otherwise, you may be chasing something expensive that you can’t guarantee you will gain value from.
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The problem with real-time FOMO
We find the sudden hurried shift among enterprises to grasp real-time analytics typically starts when organisations examine their data and see they are not making decisions fast enough to affect business outcomes (e.g. not being able to discover important trends, respond to failures or repeat successful campaigns).
Many organisations potentially misconstrue the cause of these common analytics problems as a lack of real-time analytics capability (both collection and processing), when there are likely several other factors at play preventing them from making decisions efficiently and effectively, such as a long and arduous analysis process, analysis fatigue and human bias resulting in accidental discovery, and a lack of guidance in understanding what the insights mean.
When real-time makes sense: timely use cases for enterprise
In certain sectors, having real-time analytical capabilities makes more sense as a priority. Call centres, finance, supply chain, logistics, and manufacturing all need data to make decisions in the moment to operate successfully and gain competitive advantage. These industries deal with day-to-day, hour-to-hour, minute-to-minute changes that require instant response.
Similarly, there are many marketing use cases, such as those using near-field communications to offer deals to customers while they are in-store, that warrants a shift toward real-time insights.
For every other enterprise, whether real-time analytics is a priority depends on the maturity of their decision horizon and understanding of whether or not having real-time information will help. The typical business does not need to make decisions every minute, so you must be able to identify exactly what use case your users need such real-time data for and why they need in-the-moment analysis to determine whether they will gain value or actually use that capability.
For example, while having real-time data capability can solve the problem of data latency (information not arriving to users fast enough for your liking), what happens when users see real-time data delivered to their dashboards? Or to their mobile apps? Have you provided them the means to analyse and make decisions in real-time from here? If you don’t have a ready answer, real-time may not be the answer.
Similarly, it’s highly unlikely you will want your data analysts in a corner crunching through data coming in by the minute. Even if the data wasn’t moving as quick as real-time solutions allow for, analysts still need time. typically days, to figure out what happened and why — and for regular users, it wouldn’t be a realistic expectation.
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The real solution
One capability, automated business monitoring (ABM), enables enterprises to automatically monitor and analyse data in real-time. This can be used to help users discover the root cause of what happened and why it happened, and then alert them to statistically relevant insights instantly. In our experience, we’ve found ABM is often closer to what enterprises chasing after real-time are actually looking for as a solution.
An important distinction to make between ABM products and the mainstream concept of real-time analytics is that ABM can not only reduce the amount of steps required to get from data to insight, but also equips users with the speed and guidance needed to act on what is truly important at the point of consumption, which is the missing piece of the real-time puzzle.
Time will tell
To avoid adoption pitfalls with real-time analytics, it’s important to take the time to define what type of capability you’re really asking for (real-time data? real-time analysis? ABM?), and discuss whether having real-time everything does help drive your objectives, or if it’s simply a case of falling prey to FOMO (fear of missing out).
Ultimately, if data and decision-making is truly needed at a real-time level, ensure your team assesses requirements in-depth to avoid chasing the wrong solution. There’s no use spending a lot of money, hardware, manpower and systems to get your data platform in a state where you can say you’re real-time capable, if your people may not actually be able to take advantage of that in real-time from an analytics and decision-making perspective.