American engineer, statistician and consultant William Edwards Deming once famously said “Without data, you're just another person with an opinion.”

Nowhere is this adage more true than in today's data-driven business world. As human beings we are all notoriously flawed thinkers, prone to our own cognitive biases. While intuition and gut feeling might once have been enough, to succeed in the modern business world, your business needs to become a data-driven organisation.

But what exactly does that mean? What processes need to be implemented to embrace the world of data science and be truly data driven? In this article, we look at some of the key challenges that you will face when you make data the foundation of your business decision making.

Keep It Clean: The Art of Data Collection

Clearly to be data driven you need to ensure you are collecting useful data. But simply amassing large quantities of data will not be enough. If you are going to rely on it to support your strategy then that data needs to be timely, accurate and trustworthy.

That means cleaning, transforming and massaging your data into a form you can analyse.

And it is important not to underestimate the scale of this task—it's often said that a typical data scientist will spend 80 pecent of their time “simply finding, cleansing, and organising data, leaving only 20 percent to actually perform analysis.”

Make Data Accessible Again

Even if you have solved the problem of clean, reliable data, if you don‘t have easy access to the source database, or an environment where you can easily extract the necessary data, then your data-driven decision making will struggle to get off the ground.

There are two key considerations when choosing the right tool for data analytics: you need a tool that enables widespread access to the data across your organisation, and you need a tool that will scale as the volume of data grows. If the solution isn't accessible, then your employees simply won't use the data, and if the solution can't scale, then you risk wasting even more analyst time massaging data and working with the tools, rather than carrying out actual analysis, creating visualisations, and building hypotheses.

Get the Right Team in Place

Of course, data-driven decision making is not just a technology problem—it‘s also a people problem. You need to make sure you have the right people in place to analyse and report on that data.

While modern business intelligence software providers are increasingly developing sophisticated Artificial Intelligence (AI) capabilities that attempt to derive context for your data insights, machine learning is still no substitute for a team of analysts with sufficient domain knowledge to understand and explain why the numbers show what they do.

For example, suppose your sales figures show an 8% month on month increase last month. Does that tell the whole story? Well, maybe the increase represents strong sales of seasonal products (and would be better analysed against the same month last year). Or maybe you just ran a marketing campaign or had some temporary special media exposure. And if the change is driven by a specific marketing campaign, does the change represent a good return on investment? Should the numbers in fact have grown by a much larger amount to reflect the cost incurred?

In truth, the raw numbers will only get you so far. A key determination of whether you are truly data driven is whether you have the analyst capability to fully contextualise that data.

Data Driven Starts At The Top

Even with the best analysts, the best tools, and accessible data, it may still not be enough.

The final key is that the data philosophy needs to be embraced by senior management. If there are still people in the organisation who rely on intuition and rules of thumb, then your organisation cannot be a true data-driven one. It is essential that decision makers recognise the importance of data analytics as a key component of corporate planning. (And if they don‘t, then it‘s everybody‘s job to convince them of the strategic importance of using data wisely).

Is your company data-driven? What challenges did you encounter introducing data analytics? Join the conversation in the comments below.

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