Many organisations claim to be data-driven. But are they, really?
To build a culture where data is understood and leveraged to its true potential, a business needs to address and solve these five key challenges.
1. Data glossary
As the saying goes, “it’s better to understand little, than to misunderstand a lot”.
Having your team on the same page, using the same vocabulary to discuss various data projects and metrics is incredibly important. It’s one of the aspects of a successful data culture that organisations tend to overlook. And therefore, one of the underlying reasons why arguments and misunderstandings between teams occur. A business should be proactive in drawing up an internal data dictionary that defines how an organisation understands various metrics. Without a clean and agreed-upon internal glossary, you’re almost always bound to have people interpreting data in their own subjective meaning-making process. And that’s where problems arise.
Image Credit: Konstantin
In most cases, the core metrics are understood equally well on all teams, and common definitions prevail. It’s the more complicated, deeper metrics that leave too much room for an interpretive spin that causes trouble. A successful construction of an internal data dictionary, and one that an entire organisation happily subscribes to, requires buy-in from all teams and management levels. In other words, it can’t be cranked out behind a closed door and handed down to teams to follow. The only way to invent a ‘data language’ within a business is to engage everyone. This might simply be the case of discussing which similar metrics could be collapsed into one common metric, fleshing out situations where deeper metrics would be more appropriate or talking through different business scenarios where data is used for decision-making.
In the simplest sense, making sure all employees are talking the same data language is one of the key building blocks of a strong data culture. You know what I mean?
2. Centralised, holistic source of data
Once you’re confident your organisation is fluent in data talk, the next major step is to establish a single source of data that feeds the whole business. This master data should be controlled and updated regularly to ensure the business is drawing from a high-quality, up-to-date information well. And most importantly, that all teams are working with the same data.
Image Credit: Diboune JubaHaving a centralized data source allows organisations to be more nimble and actually leverage the data they have in the day-to-day decision-making processes. If the data is hard to access or people don’t know it’s there, organisations run a risk of losing money or opportunities due to poorly informed decisions. Even something as simple as having a centralized data directory that points people to the best sources for certain data can make a big difference.
3. Data democratisation
What’s the use of data if people can’t access it?
Data-driven companies are well-known for their inclusive approach to data sharing within the business. Of course, it doesn’t mean that everyone should have access to everything, but data democratisation holds immense potential for employee empowerment.
The most successful organisations tend to take great interest in assessing the data needs of all teams, not just the analysts and key decision makers. Even the customer-facing employees and back-of-house teams can benefit from increased access to valuable insights.
Essentially, it isn’t just about hooking your employees up with analytics dashboards. Instead, data democratisation helps organisations foster a culture where people feel comfortable handling and applying data in their day-to-day decision making. This can greatly boost any company’s performance and help cultivate new ideas.
4. Data literacy
Data literacy is the ability to derive meaning from data.
As soon as an organisation decides to follow the data democratisation path, it must critically assess its employees’ ability to examine and use data effectively. If staff can’t fully understand and apply the insights from dashboards, reports and analyses they encounter on a daily basis, the efforts to become a data-driven organisation are in vain.
Even the simple solutions available to all businesses, like training employees on data visualisation or basic statistics skills, could provide organisations with massive gains. Often, such seemingly unimportant decisions as choosing the right chart or the right colour palette can do a lot of damage if best practices aren’t followed. Inappropriate chart types and colours make data visualisations difficult to interpret, detracting from the value of data or worse, making the insights inaccessible.
Investing in continuous staff training to enhance data literacy throughout the organisation is a decision that will pay dividends.
5. Putting data to work
Building a data-driven culture is all fine and dandy, until those pretty-looking data visualisations and reports land on a desk of someone who’s ‘trusting their gut’.
Image Credit: Eran Mendel
Just to give you an idea of how big (and common) of a problem this is, Google’s Avinash Kaushik has coined the term “highest paid person’s opinion” (or HiPPO). These are the people with ‘years of experience’, who’ve seen and know it all, and they don’t care about your data insights if they clash with their very strong gut feeling. The Financial Times explains HiPPO this way:
“HiPPOs can be deadly for businesses because they base their decisions on ill-understood metrics at best, or on pure guesswork. With no intelligent tools to derive meaning from the full spectrum of customer interactions and evaluate the how, when, where and why behind actions, the HiPPO approach can be crippling for businesses.”
A data-driven culture needs to start at the top. Organisations that tend to value intuition over data insights are almost always led by people who don’t understand how data works or don’t know how to use it. But even that can be overcome with solid training and a data literate team.
Lastly, organisations don’t become data-driven overnight. Building a company culture where data is valued, understood and utilised is a multifaceted process, but once in place, it’s one that’s really hard to top.
