AI for Business

Data-driven companies: when being good enough is no longer sufficient.

Nowadays, it’s no longer sufficient to be good enough. It’s a given fact that data-driven companies tend to outperform their competitors. The Amazons, Facebooks and Googles of this world made Big Data and Artificial Intelligence an inherent part of their existence. Causing them to dominate the consumer base and claim ever-increasing chunks of market share.

So, what is stopping mainstream companies from using data science to their advantage and becoming data-driven? And, consequently, regain their fair share of the market?

Why is it hard to become a data-driven company?

While steadily increasing their investments in data and AI each year, companies still fail big time to fully reap the benefits of Big Data. They are really struggling to become data-driven.

Why is that? 9 out of 10 mainstream firms point a finger at the cultural challenges. It might come as a surprise, but it’s not the technological challenges creating bottlenecks within organizations. It’s the impact of challenges such as organizational alignment, business processes, change management and (internal) communication that stretches much further than most corporations expect. Also, people’s skill sets and resistance or lack of understanding to enable change are significant components that slow down the process.

Keep in mind to never underestimate the power of your corporate culture. As management guru Peter Drucker once said: “Culture eats strategy for breakfast”. 😉

Why is being data-driven so important?

Nowadays, being data-driven is vital because data science helps solve business problems. Like we already mentioned, data-driven companies outperform their competitors. Many organisations realise the value of data, but only a handful manage to capture, process and utilise it to its full potential. And those firms nibble away market share every single day.

When you are data-driven, you take a few things entirely out of the equation. Where gut feeling and historical best practices could potentially cloud your judgement, data sheds light on legitimate sales forecasting, trends, optimisations and improvements. Leading to better decision-making and – ultimately – better business results. 

Three practical recommendations

If you want to become data-driven and put your Big Data and Artificial Intelligence to good use, know that it takes time, focus, commitment, and persistence. More often than not, companies minimise the effort or underestimate the time it takes to go through a business transformation.

In the article “Why is it so hard to become a data-driven company”, Randy Bean states 3 practical recommendations for businesses looking into data science.

Pick the low-hanging fruit first
Go full force on your quick wins. Focus your data on clearly identified high-impact problems or goals and start where there is a critical business need. This builds value, credibility and momentum to convince management, departments or teams who didn’t jump on the data science bandwagon just yet. 

At Arinti, we use a few techniques that help you determine what and where your low-hanging fruit is. By conducting a Data Audit or by filling out an AI Project Canvas with our clients, we uncover valuable and attainable AI cases with an interesting ROI.

Let it flow
Re-examine the ways you think about data as a business asset for your organization. Data should freely flow like a river through the whole company. It shouldn’t be kept a secret or tucked away in a dark corner.

Practice patience
Becoming a data-driven organization doesn’t happen overnight. It’s a long-term process that requires persistence and determination. Don’t lose patience or abandon your efforts when the results are not immediately impactful. Or not as impactful as you might like.

The data mindset

Over the past decade, technology has evolved exponentially. And many companies understood that there are solutions for their specific business obstacles. But they realised that they need a partner who can guide the way and lead them to success.

At Arinti, we notice first-hand that taking on a data science project requires a whole cultural shift for some businesses. The experimental nature of data science calls for a certain level of agility and flexibility within an organisation. We also see that there are rarely any technological challenges preventing projects from being successful. Mostly cultural challenges.

Our point of view is: mindset comes first. Some might think that start-ups and smaller companies are inherently data-driven. However, that’s not always true. Yes, it might be easier to implement a data culture and evangelise data when your organisation is smaller or less hierarchical. But that’s not set in stone, especially when the right mindset is not there. 

It doesn’t matter how big a company is, how many people it employs or how long it has been around already. Mindset always comes first. And this mindset is a determining factor in whether a data science project will succeed or not.

Are you looking into a data science project? We have some tips for you!

  1. A data science project is not straightforward. We don’t go from point A to point B. We might start at C, then go to P and start back at A, in just a few days’ time. 
  2. Know that not every experiment will succeed. There are always several ways of approaching your unique business problem. We want to find the best solution for you, and we are not afraid to fail in the process.
  3. Teamwork makes the data dream work. So expect regular check-ins, ad hoc updates and intense brainstorming sessions.
  4. And last but certainly not least: be open-minded, think critically and keep a flexible attitude.

Need some help kickstarting your data science initiative?

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