Building a Data-Driven Culture: Strategies for Successful Analytics Adoption

Read this article
⸻ Beyond Data

Building a Data-Driven Culture: Strategies for Successful Analytics Adoption


Our first guest from Lucas Maretti covers 3 tips for better analytics adoption

Beyond Data’s First Guest Post

This week’s post is written by Lucas Maretti, and it’s great! Lucas is Regional Analytics Lead for Mondelez International and has some great content online already. Take a look at his portfolio here, and be sure to follow him on LinkedIn and Medium to catch more of his content.

Lucas covers some strategies for making analytics adoption less painful and touches on many of my favourite themes (you’ll see why I liked the post so much!).

Take a read and be sure to reply to this email or comment on the webpage with your feedback, and if you think you’d like to write a post, please reach out.

The Skills Gap

The skills required to work with data have been defined in several ways: data literacy, and digital capability, among others. In practice, it is the knowledge one needs to identify and delimit a problem, access and manipulate a database, generate insights and communicate them. Knowing how to execute these steps well is an excellent indicator of a data-driven person or team.

What exactly is the set of skills a good data literacy program needs to cover is a highly debated topic. In my experience, there are different data personas within organisations, and the most effective tend to be the ones customised for each.

For example, a business user working in marketing that only needs to track sales of different product types and regions in a dashboard does not need in-depth knowledge of machine learning tools. Instead, the focus should be on descriptive statistics, such as knowing the difference between a categorical and numerical value and data visualisation and communication – how data is presented and processed to get them the number they need. Similarly, a process engineer responsible for using a predictive maintenance solution based on machine learning must learn different concepts.

I bring these examples to illustrate why I believe data literacy strongly correlates to building a successful data culture and data product adoption in companies. It is a less discussed topic than “the democratisation of data” and “data quality”, but it is the element that helps tie it all together. Without knowing the basics of descriptive statistics, how can we expect a business user to read a dashboard or build an effective visualisation? Or to have a critical perspective on the data available and its quality?

The same applies to machine learning in our predictive maintenance example. It is fundamental that the process engineer understands the basics of the development of an ML solution, such as metrics, error, and data drift, to properly make decisions based on the solution’s output, as well as to assess its performance and relevance.

Based on my experience working on implementing data solutions across multiple organisations, I believe there are three important steps that need to be taken when fostering a data-driven culture and ensuring product adoption:

1. Communicating strategy to end-users

Business leaders ‘ effective communication of the data strategy is paramount for ensuring the proper adoption of analytics products and technologies within an organisation. By articulating the data strategy, leaders provide a clear direction and purpose for the utilisation of analytics, setting the stage for successful implementation. Communicating the data strategy helps stakeholders understand analytics’ value and benefits, creating employee buy-in and enthusiasm. In larger companies, some acknowledged strategies for this are via town halls with business users’ participation and via workshops that illustrate successful applications of analytics solutions.

A good communication strategy also enables alignment between business objectives and data initiatives, ensuring that analytics efforts are focused on addressing key challenges and driving meaningful outcomes. Furthermore, clear communication of the data strategy fosters a data-driven culture, where employees understand the importance of data and are empowered to leverage it for decision-making.

2. Listening to end-user needs before acquiring solutions

I have repeatedly dealt with the fallout from ignoring this second point when working with consultancy for manufacturing industries. The status quo in most industries was siloed data with difficult access. And what people needed was easy access to data for quick decision-making.

Instead of addressing these issues, leadership was buying ML solutions that were applied to low-quality data, which led to poor adoption, loss of money and frustration with analytics solutions. Monica Rogati brilliantly wrote about the same situation in her 2017 piece ‘The AI Hierarchy of Needs’.

End users, who interact with the day-to-day operations and processes, possess invaluable insights into the pain points, challenges, and requirements specific to their roles. By actively engaging with end users and soliciting their input, organisations can comprehensively understand their needs and expectations. This collaborative approach ensures that the analytics solution addresses the actual challenges faced by end users and fosters a sense of ownership and engagement among them.

By incorporating end-user feedback, organisations can tailor the analytics solution to their specific requirements, resulting in enhanced usability, adoption, and overall effectiveness.

3. Upskilling the workforce on concepts related to the product

As discussed earlier, skills are essential to ensure proper usage and adoption. For a better illustration of this point, in a May of 2023 article for Harvard Business Review, author Joel Shapiro writes:

However, when it comes to engaging in predictive modeling and advanced data analysis that could fundamentally change a company’s operations, it’s crucial to consider the skill level of the “citizen.” A sophisticated tool (…) in the hands of someone who is merely “playing around in data” can lead to errors, incorrect assumptions, questionable results, and misinterpretation of outcomes and conclusions

When considering the adoption of a machine learning solution, upskilling the workforce becomes crucial for organisations. Machine learning technologies require specific expertise to implement, interpret, and leverage effectively. This empowers the workforce to understand the potential applications of machine learning, identify opportunities for its utilisation within the organisation, and make informed decisions regarding its implementation. Additionally, upskilling cultivates a continuous learning and innovation culture, fostering adaptability and agility.

In conclusion, fostering a data-driven culture and successfully adopting analytics products and technologies in organisations requires a multi-faceted approach.

Business leaders ‘ effective communication of the data strategy sets the direction and purpose, aligning business objectives with data initiatives. Listening to end users’ needs and incorporating their feedback ensures that the analytics solutions address real-world challenges and foster ownership and engagement among users. Finally, upskilling the workforce on relevant concepts empowers them to leverage data effectively and make informed decisions.

By implementing these three steps, organisations can build a strong data culture, enhance product adoption, and unlock the full potential of data-driven decision-making.

Lucas Maretti

Final thoughts (Adam)

Thanks again to Lucas for reaching out and taking the time to write this article; it’s well worth checking out his other content, and if we’re lucky, he might share more of his insights again in the future.

I’m keen to use this platform to showcase more insights from the community. If you want to get involved, please get in touch.

All the best,

If you enjoyed this, consider sharing this newsletter with others that would find value from it or follow what we’re doing at Hypercube


When you’re ready, there are a few ways I can help you or your organisation:

What do you think of this issue?

I’d love your feedback to ensure I’m writing about the topics you want to read, let me know if this one hits the mark

Want to write for Beyond Data?

Have something you want to share? Interested in writing an issue or working with our team?

If you enjoyed this, consider sharing this newsletter with others that would find value from it or follow what we’re doing at Hypercube.