Updated on 1 Sept 2022

How to Build a Data-driven Business in 2022?

Ned Krastev Published on 1 Sept 2022 3 min read

We keep hearing phrases such as “big data knows everything” or “data is the new oil”. But, in reality, a considerable number of businesses fail to take advantage of data-driven decision-making.

Besides tech giants who have a 360-degree view of user behavior and effectively use data science for marketing, smaller companies have been adopting practices that allow them to benefit from the growth opportunities data offers at a much slower pace.

If you work in a firm that is lagging behind, then you will find this article very helpful.

In what follows, we will describe the 6 steps that would enable your business to reap the rewards of data-driven decision-making. 

We also have a great video on the topic you can watch below or scroll down to keep reading. 

 

How to Build a Data-Driven Business in 2022 - Table of Contents: 

  1. Start from the Top
  2. Create a Strategy
  3. Collect as Much Data as You Can
  4. Begin Extracting Insights
  5. Build Data Infrastructure
  6. Assemble Your Data Team  

Step 1: Start from the Top

First of all, you may ask: ‘’But is it possible to apply a data-first mentality for a non-tech company that doesn’t generate a ton of data?” The short answer is “yes”! There are many ways to collect information and make decisions based on numerical evidence rather than personal taste or intuition.  

That said, building a data-driven strategy requires a significant amount of energy, dedication, and focus across the entire organization. In the beginning, your company might not be able to invest huge resources in this endeavor, but it will most definitely need a change of culture. For this reason, we advise that the first step in the process is to commit to data-driven decision-making. It’s best if this pledge comes from the top – a CEO or leadership realizing how important data is for business transformation will inspire the rest of the team members as well. As a result, they will be empowered by a framework that promotes transparency and allows for quick and confident decision-making based on objective factors.

Step 2: Create a Strategy

The second step in the process is to formulate a data strategy that is in sync with your corporate and business strategy. In this way, you’ll determine what type of questions data can answer for the company’s specific needs, thus allowing your team to build an infrastructure for collecting the most relevant information only. It is very important to be as efficient with your resources as possible, especially early on. We don’t simply mean saving storage space, but also allocating how developers or data engineers will spend their limited time. So, thread wisely!

Step 3: Collect as Much Data as You Can

Once you have outlined your pressing business questions and what type of information you need, it’s time to initiate step 3 – collecting the data. Make sure you have the proper procedures in place to perform quality assurance and verify that there are no problems with the data you will later use for analysis. Of course, you can always add additional layers to your existing database structure, but it will be great to have an idea of what you’ll need in the near future and work with that in mind. If your company is very small and cannot invest a fortune, you can start with some modest data engineering efforts, generating only the most essential data at first.

Step 4: Begin Extracting Insights

In step 4, you can proceed to analyze what you have collected. Consider creating dashboards for decision-makers to use for continuous performance monitoring. With these first, your team will identify and undertake different improvement projects. At this stage, your efforts will likely yield remarkable results because you have not done many optimizations before. And what’s even better is that sometimes the data itself will offer clues on what you should prioritize.

For example, if you decide to collect UTM information, you can find out where the users who visited your website and purchased your product came from. For an e-commerce business, this is an invaluable insight as it shows the effectiveness of different marketing campaigns that the company runs. Identifying the weak points in the workflow and emphasizing the strong suits can make a sizeable difference for every business.

Step 5: Build Data Infrastructure

Hopefully, the quick wins from step 4 will win support across your organization and result in additional investments. Once that happens, you can commence with step 5. This includes building an infrastructure to leverage the power of A/B testing and rigorous statistical measurements. All of these actions will directly boost performance.

Step 6: Assemble Your Data Team

And finally, step 6 requires you to form a team of data scientists and machine learning engineers who can make the most of advanced analytics and ML techniques. With this, you’ll uncover complex second-order relationships that represent opportunities you couldn’t see when relying solely on traditional analysis. Moreover, you can use ML algorithms to solve business problems such as:

  • sales forecasting,
  • anomaly detection,
  • data storage optimization,
  • targeting,
  • recommender modelling,
  • and so on.

Please remember that, before reaching an advanced stage of data maturity in your organization, it is much better to focus on traditional forms of data analysis. Becoming a truly data-driven business is a long and winding road, yet the journey is very rewarding.

How to Build a Data-Driven Business: Next Steps

One of the best ways to prepare your team is by relying on the 365 Data Science program. We offer courses in Data Literacy, Data Strategy, Python, SQL, and Excel Fundamentals, as well as Business Analytics, Tableau, Power BI, Machine Learning, and many other topics. What is more, we’re constantly upgrading the program with new content, such as our new Data-Driven Business Growth Course with industry professionals Tina Huang and Davis Balaba who will teach you precisely how to transform your business into a data-driven enterprise. Find out more about the program, resources, and other services from 365 Data Science by heading over to our dedicated business section.

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Ned Krastev

Co-founder of 365 Data Science

Ned is a Master of Finance at Bocconi University with years of advisory experience in some of the world’s top international enterprises. A visionary and co-founder of 365 Data Science, he has helped thousands of students gain competitive advantage through his courses Introduction to Excel, Advanced Microsoft Excel, Data Analysis with Excel Pivot Tables, Python for Finance, and Introduction to Tableau.

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