How-to-make-better-business-decisions-with-Data

How to make better business decisions with Data

It’s shockingly easy to lose sight – and control.

Following the much-hyped digital transformation that started a couple of decades ago, every business organisation is now data-driven. From the local grocer to commercial aircraft companies, digitisation has arrived – every asset involved in any stage of the value chain is digitised, connected, and raking in a deluge of data.

Let’s consider a few scenarios. An aircraft alone – given the thousands of in-flight sensors – generates multiple terabytes of data in a single day. This data can be gathered in an endless array of permutations and combinations to help reach destinations safer, faster and more efficiently. The gross merchandise value of an e-commerce business, a major KPI in the retail industry, can alone be sliced by revenue, product category, geography, customer segment, or any other random parameter for further analysis with the single goal of winning against competition.

Similarly, any business, at any point of time, could be tracking anything from 20 to 30 major KPIs (revenue, overhead expenses, net promoter scores, quality of service etc.) or more on average. Each of these can be further sliced, cross-sectioned and drilled down for varied perspectives/ isolated analysis.

It’s a crying shame to waste data.

The truth is, a majority of business owners went digital with the right intentions – in operations it was in search of efficiencies, in customer interface channels, it was to help improve customer satisfaction and net promoter scores, and in products, the aim was to identify new sources of revenue.

Going digital had an interesting side effect – it enabled easy collection of massive amounts data with the promise of better decision-making. However, despite the deluge of data with deep business insights hidden within, decision makers struggled to make sense out of it. They literally ended up sitting on goldmines of hidden business insights that never saw the light of day. Paradoxically, it was the vastness of the amount of data generated that eventually became decision-makers’ biggest nemesis – they were unable to hunt down the relevant business insights.  In other words, all the data they’d so studiously work towards gathering was literally snowballing its way down the drain.

Going ostrich never helped anyone. The only way out is via automation.

The data’s already there, and it’s multiplying by the millisecond – there’s nothing you can do to stop it, and you definitely can’t ignore it. To make things worse, the deal about data is that by itself, it’s of no use – it’s the business insights derived from it that are indispensable. Enter automated data analytics. Using deep learning techniques, it’s now possible to learn data’s normal behavior, identify outliers, correlate them with other outliers and unearth business insights, giving businesses the opportunities to make sure that they stay on track.

What’s more, it frees them up to focus on their core operations, rather than on deciphering the data generated by their operations. This is particularly useful given today’s hyper-competitive and increasingly tough economy, when businesses are often faced with conflicting goals such as spiralling demand versus shrinking resources, and the need to reduce lead times versus the need to enhance product/ service quality.

The best part? Since the entire process is automated, it happens in a fraction of the time taken by manual techniques. In fact, it generates insights in real time – and can also predict potential opportunities and outcomes.

The question remains – what are we looking for? The key is to work backwards.

One of the biggest advantages of automated data analytics is that you don’t need to know what you’re looking for – all you need to know is your business goals in the form of key KPIs. Armed with just these goals, your algorithm does the rest of the work. It identifies, predicts and ranks all potential known as well as unknown anomalies, not only giving you enough time to prevent them, but also indicating how you should prioritise them to achieve the best outcomes (such as maximised revenue, optimised overall efficiency and minimised operational expenses).

There’s also an inbuilt safety mechanism – incidents are only classified as anomalies once they’ve been correlated with relevant indicators. This reduces the appearance of false positives, simultaneously increasing the accuracy of anomaly detection and reducing the waste of precious time and resources.

You’ll never again fly blind.

The whole point of the digital transformation is the ability to gain an unprecedented visibility into business operations. We’re no longer living in an age where we can afford ignorance. As the saying goes, “an investment in knowledge always pays the best interest”. Thanks to digitisation, we’ll never run out of it.

In your opinion, how well is your business doing in terms of owning the race that is data analytics? For a quick discussion to take stock of your status quo, drop us a mail to info@crunchmetrics.ai, we’ll get back to you in no time to schedule a call at your convenience.

Experience the power of AI on your data with CrunchMetrics now!

Request a Demo

This article is originally published at ReadItQuick

Share :

Leave a Reply

Your email address will not be published. Required fields are marked *