Are you wondering why your data projects fail to deliver on their (too high?) initial expectations? Are you part of those leaders convinced of the value of data but still falling short of reaping its full potential? If yes, rest assured, you are not alone. Back in 2017, Gartner stated that 85% of big data projects failed. Although focusing on big data projects only and difficult to verify, this statistic was widely shared and agreed upon in the data community. Why? Because for those of us active in the data industry, experienced with the intricacies of data and the subtle complexity of leveraging its value, we know that a certain level of data maturity and proficiency is required to ensure the success of data projects. We know that, for organizations to be successful in this area, they need to know what data is, when to use it and for which purpose. Only organizations with a data culture enabling these capabilities have been, are and will be able to fully leverage the added value of their data assets.

Let’s put things into perspective for a moment. In Ancient Egypt, for more than three thousand years, hieroglyphs were used as writing system. Their usage extended until the 4th century AD but knowledge of hieroglyphic writing was lost with the closing of pagan temples. Although hieroglyphs are composed of signs which we think we understand such as an eye or a bird, it took centuries to decipher hieroglyphic writing. It is only in the 19th century that we were finally able to understand the meaning of those combinations of signs. Before that, hieroglyphs were nothing more than signs with no meaning. For some organizations, data are exactly that: signs with no meaning. Their staff think they understand what they are, but they cannot make sense out of it. For those organizations, data project failure is almost inevitable. They need to learn how to read these signs, how to interpret them, how to use them and, most importantly, how to get value out of them.



This is where Data Literacy kicks in. Over the last few years, the concept of Data Literacy has gained significant traction in the data community as one of the key reasons behind data projects failure. Organizations willing to become truly data driven and leverage the value of their data assets have come to realize that the understanding of the data value chain and its associated data culture are key. A data culture is not only the result of a data strategy, the deployment of a data governance program nor a series of AI pilots. A data culture should be embedded in the company’s DNA. Ensuring that all parts of the organization are knowledgeable when it comes to data is essential. To that end, enabling data literacy focuses on bringing the right content to the right people with the right skills, ensuring a proper handling of data related matters. Data literacy encompasses the analytical, technical, and critical thinking skills and knowledge needed by employees at every level of your company.

The successful deployment of Data Literacy requires a cultural change within an organization. This change aims at enabling an environment where data is understood by the organization and used when appropriate and adequate. Doing so requires developing your workforce and helping them reach a level of data literacy in adequation with the needs of your organization.


For this cultural change to happen and have lasting effects, setting up a change program is essential. To that end, standard change management methodologies provide an interesting framework to support your change efforts once adapted to the specific data needs of your organization. Overall, what truly matters is to develop the “data acumen” of individuals. How can you achieve that? Like any change program, activities will focus on mindset, culture, communication and training. Audiences need to be assessed and addressed in specific ways. Even if some messages or trainings are generic and can address all audiences, specificity and personalization will be key. Triggering the interest in data of non-data professionals and encouraging/ convincing them in becoming data literate requires a different approach than the one used to further develop data scientists, data analysts or data governance practitioners. Furthermore, once initiated, this cultural change will need to be maintained. Data literacy cannot be considered as a one-time thing. It is a learning process and as such requires practice and continuous improvement.


In our digital age where information is always one click away, learning methods in companies need to depart from traditional academic approaches. Yes, traditional learning methods such as classroom trainings and coaching/ mentoring will always be required. But they need to be used diligently, adapted to specific audience needs and proficiency levels. Digital learning should be the norm rather than the exception when it comes to Data literacy. It allows companies to provide a flexible learning environment to their employees while being both adapted to their specific needs and available on demand. Obviously, learning paths have to be designed and prepared beforehand to address the specific learning needs of audiences. In fact, the learning process should be personalized to match each persona targeted by the data literacy program. One aspect that should not be under-estimated is that learning, even when taking place in a professional context, should be fun and engaging. Gamification plays an important part in the motivation of individuals to engage in a learning path.


For companies to become truly data driven, data literacy is key.  However, there is no one size fits all approach for deploying data literacy in an organization. Here are some few steps to follow to initiate your own program:

  • Assess the data literacy level of your organization. Either via a formal survey or a series of interviews with key stakeholders, what matters is to obtain an unbiased view on the organization regarding the full data value chain.
  • Set realistic targets for data literacy improvement. These targets need to be embedded in the corporate data strategy. Should the latter be missing, this is a good time to define one.
  • Set the objectives of the data literacy program. To do so, you need to define target audiences and personas and their respective learning objectives.
  • Design learning paths per audience and personas. Learning paths need to be customized, make use of the proper learning method and be engaging.
  • Design a communication plan. Explain the rationale for the data literacy program and manage the change. No one should be left behind.

Leveraging the value of data assets depends entirely on the ability of an organization to manage, consume and exploit data assets. Data literacy allows organizations to develop and maintain this ability. So, is your organization data literate?

Contact: Laurent Fayet

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