Data elevates everything.
In 2021, it is plain as day that data-driven decision-making, championed by data analytics, is more accurate and fruitful.
The result is higher productivity, cost-efficiency, better benchmarking, gap analysis, and customer satisfaction.
All the significant benefits of data analytics can be narrowed down to two things: a rise in sales and ROI. In fact, a study by McKinsey and Company estimated the increase to be nearly twofold!
And the incredible success is not exclusive to the Big Players. Given how cheap data and data-crunching technologies are, most businesses can make the most of data analytics.
But they do not. Since those businesses almost always lack these three key things.
Data literacy is perhaps the biggest challenge businesses face when adopting data analytics solutions and driving growth.
How are businesses supposed to leverage data and data-driven decision-making if they do not understand what data is and how they should use it?
Even today, data illiteracy is shockingly widespread, not just in developing countries but also in developed ones. That is because either —
- Management is hesitant to change.
- Or because businesses lack the proper infrastructure to support data analytics.
The first reason should not come as a surprise.
Data-driven processes like data management and engineering mean new work habits. And every new habit comes with a learning curve.
Of course, if the changes are big and sudden, they seem overwhelming. Naturally, workforces think that the time lost to climbing the curve is not worth the errors, and hence the losses, they make during that climb. Most simply, give up.
To minimize the distress, changes ought to be small and gradual. Otherwise, in the long run, the losses incurred by management inertia — the reluctance to embrace change — can be colossal. Remember, twofold rise in sales and ROI.
Lack of data infrastructure
Businesses could be data literate, or at least have the ambition to be literate, and still fail to leverage the power of data because they lack the proper infrastructure to store, manage, and analyze data.
But there’s more to data infrastructure than just establishing systems that enable businesses to collect, store, sort, and analyze data. Businesses must also reckon with how the data must be collected, stored, sorted, and analyzed.
Data infrastructure is, therefore, half-execution, what we call data management. And half-planning, what we call data governance.
And without a plan or strategy, processes can be unnecessarily enmeshed, making access to data by the right person at the right time highly inefficient.
That’s lost time — and money. Think about it: imagine customer service with delayed or no access to customer details. Or a sales team that lacks access to marketing data. And vice versa.
A well-planned business is a well-run business.
Well-planned businesses are well-run. Unless they are run on poor fuel. In other words, poor quality data.
The businesses could be data literate and establish a data infrastructure of the highest standard, but still be unable to make the most of data analytics because they collect and process low-grade data.
Yes, data analytics provides predictions and actionable insights that make sales forecasting more accurate or make customer behavior more transparent through customer analytics. But the predictions and insights are only as good as the data collected and analyzed.
High-quality data — which is data collected from a wide range of sources — is unbiased, granular, and high in quantity as well as quality. As a result, the analysis produces results that are unbiased, thorough, and high in quality. In other words, more accurate predictions, which lead to more accurate strategic decisions.