Don’t pigeonhole your data analytics team!
Data Analytics are a great tool to understand trends, correlations between events and can help enormously make the right business decisions. But like every tool, it can only be as efficient as we trust it to be.
If we are afraid of flying, trains can be great alternatives for short distances, but New York to San Francisco will take you 3 days instead of 6 hours. Similarly, if we are afraid of data analytics, we might be missing out on understanding our business and our customers.
This means that, we should not leave it to data scientists and specialists alone to trust data analytics, but it is the whole company, at every level, that needs to trust data and the tools to leverage it. After all, we already know that pilots and crews are not afraid of flying, it is the customers that we have to convince, if needed, that planes are safe.
And the customers of data analytics are the decision-makers of the company and each and every one of its employees. We cannot leave data analytics to data scientists and AI specialists alone, as much as it makes no sense to fly an empty plane with only pilots and crew on board.
This is why it is extremely important not only to hire the best technical data scientists, but also to hire those who are good at building a data driven culture inside the company. Building a data-driven culture involves fostering a mindset where data is seen as a strategic asset and decision-making is grounded in evidence-based insights.
If businesses fail to cultivate this culture, data may be undervalued or overlooked, and the potential for leveraging data effectively will be diminished, which is why data scientists need to be involved in decision-making and be part of the business decision process.
In a Harvard Business Review (HBR) article (https://hbr.org/2020/02/10-steps-to-creating-a-data-driven-culture) they state: “Companies with strong data-driven cultures tend have [sic] top managers who set an expectation that decisions must be anchored in data — that this is normal, not novel or exceptional”.
Their advice? Don’t pigeonhole your data scientists!
One way to do this, as HBR article suggests, is “in addition to dragging data science closer to the business, […] pull the business toward data science, chiefly by insisting that employees are code-literate and conceptually fluent in quantitative topics. Senior leaders don’t need to be reborn as machine-learning engineers. But leaders of data-centric organizations cannot remain ignorant of the language of data.”
This does not mean, of course, that everyone needs to become a data scientist, but it is as important for management to understand some technicalities as it is for data analytics teams to make their content accessible to everyone, avoiding being too theoretical. Access to and understanding of data analytics business studies should be a two-way street.
In addition, while many businesses now offer some form of free training, it does not often bring results because it is not aimed and anchored at a specific project. HBR states: “Employees […] rapidly forget what they’ve learned if they haven’t put it to use right away. […] It is more effective to train staff in specialized analytical concepts and tooling just before these are needed — say, for a proof of concept”. Training is useful; the timing of when it is done matters even more.
Exactly on point, they end their article with these inspiring words: “Companies — and the divisions and individuals that comprise them — often fall back on habit, because alternatives look too risky. Data can provide a form of evidence to back up hypotheses, giving managers the confidence to jump into new areas and processes without taking a leap in the dark. But simply aspiring to be data-driven is not enough. To be driven by data, companies need to develop cultures in which this mindset can flourish. Leaders can promote this shift through example, by practicing new habits and creating expectations for what it really means to root decisions in data”.
In fact, another article by HBR (https://hbr.org/2023/05/what-does-it-actually-take-to-build-a-data-driven-culture) provides some practical advice and summarises “the first two years of a new data program at Kuwait’s Gulf Bank in which we worked to build a culture that embraced data”.
The article goes through all the steps they took to achieve a culture at the company that embraced data, and, in summary, these are:
Start by focusing on improving data quality, rather than quick wins like building a data lake, as good quality data is foundational.
Create a "data ambassadors" program - a network of employees to lead data quality efforts in their teams. Ambassadors should be trained, branded, and publicized to make the role rewarding.
Create a "Data 101" training program teaching all employees about their roles as data creators and customers and the impact of quality. This can make frontline staff excited about their data responsibilities.
Allow employees to innovate on their own, using their new data skills for projects. You can even let them compete in an innovation tournament.
Connect data to existing cultural values, involve everyone, start with quality, take a long-term view, and persist through challenges.
The result? After 2 years, while more work remains, data is being used differently by hundreds of employees. The bank now has a foundation to build towards AI and other data goals.
Creating a data driven culture is not a single achievement, and it may be hard to measure the impact which is why so many businesses remain resistant, but it is a transformational effort which will bring incredible rewards.