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4 Don’ts to Become a Better Data-Driven Company

This is what you need to avoid as you transition your business operations towards data science.

Paul Azorin

By Paul Azorin

Chief Marketing Officer Paul Azorin defines BairesDev's vision while leading company modernization and fostering a customer-centric culture.

10 min read

Data-driven company

Most companies these days strive to become “data-driven”. However, implementing data science into business operations has never been an easy feat, especially when you’re doing it on your own (which is why so many business leaders prefer to accelerate their digital transformation via software outsourcing services).

In essence, the 4 mistakes below tend to stem from other fundamental issues that already had an effect on how everything is run. What makes them so common is that most companies only find out about them just when their data-driven strategies are starting to kick off. Other times, they are just the result of a poor understanding of how data science works for businesses

Let’s take a closer look.


#1 Don’t Rely On Undefined Metrics

Most companies focus on collecting as much high-quality data as they can. Some even put a lot of extra effort into keeping it organized in their database. However, that is only the first step of the process—and it’s not something that you can directly get value from. 

Your goal is to know what actions that data informs and how you can interpret the patterns in it. Setting realistic and clearly defined metrics is perhaps the best way to insert data in that context. Otherwise, it’s just impossible to make data-driven decisions (and understand the true value of your data) since no one can really tell which inputs generate which outputs. 

Keep in mind that having more metrics doesn’t necessarily mean better-defined metrics overall. I’ve seen many cases in which a simple analytics question can lead to following overly complex paths with the sole purpose of covering as many metrics as possible. That isn’t sustainable behavior. 

It’s best to contextualize your data and your metrics and use both as efficiently as possible. That way, your metrics can always act as the basis for any data-driven decision while maintaining transparency and useful reporting. 


#2 Don’t Be A Buzzword Company

No one can dispute the fact that the business world is filled with buzzwords like never before. If you’re a business leader, you’ve probably felt the temptation of adding some of them over everything you do. While this practice is certainly arguably good for SEO and getting some headlines here and there, it shouldn’t distract you from laying out the much more important groundwork of getting things done. 

To avoid being a buzzword company, you need to get your priorities straight, especially if you’re at the start of your digital acceleration journey. Implementing data science into complex and ambitious projects can take a lot of time and effort, so it’s often recommended to go for the quick wins at first. Take a close look at current business decisions and determine how data analysis can make an impact in the short term. 

Leaving data science buzzwords behind will help you deliver real value and avoid likely disillusionment in your data-driven initiatives. The more real connections you can form between data science and your business operations, the better outputs you will achieve. 


#3 Don’t Underestimate Quality Issues

We’ve all heard the phrase “Garbage in, garbage out”, which remains true in data science. However, most business leaders don’t really have the time to stay on top of the many details that can impact data quality as it moves through the pipeline. 

Believe it or not, data quality issues can fly under the radar in many different and subtle ways. A simple malformed data entry case can snowball into serious (and totally avoidable) consequences. Similarly, your database might unintentionally contain multiple near-duplicate data that distorts your perspective on the matter. 

Innocent mistakes or issues like this introduce unnecessary complexity that hinders your company’s capability to perform a thorough and accurate analysis of the situation, and can often invalidate the work done previously. This is one of the reasons why companies prefer to outsource data science services to an expert team that can guarantee proper Quality Assurance and database maintenance while maximizing automation and reporting opportunities.   


#4 Don’t Forget to Hire Top Data Scientists

Just like you need to hire the best software developers to develop high-quality software, you will need to hire top data scientists to become a truly data-driven company. Depending on the size of your operations and their complexity, that could imply hiring different positions that manage the entire data infrastructure of your organization. You’ll probably want to learn the difference between data scientists and data engineers for that. 

If you already have a team dedicated to these tasks and you still want to scale your operations, then it is probably best to look into custom delivery teams or staff augmentation, both of which are ideal to immediately add top data scientists to your team. 


Always Keep Exploring

Data science is, by nature, an extraordinarily complex and constantly-evolving field. The characteristics of your progress, expectations, and results will certainly vary from day to day, and you must be prepared to ride the wave. Keeping these four tips in mind will take you a long way and accelerate your path to improving your data-driven operations.

Paul Azorin

By Paul Azorin

Chief Marketing Officer Paul Azorin outlines BairesDev's vision and mission while leading company-wide modernization initiatives and fostering a customer-centric culture globally. Paul spearheads core business strategies and the overall brand experience.

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