Why is Data Literacy important?
During a consulting session at a F500 company, participants – senior business and technology managers – were posed this simple question: What is our biggest source of long-term competitive advantage? The responses coursed through the usual suspects – brand, loyalty, track record, and so on. The clear winner, however, not surprisingly, was Data. This led to the obvious follow-up question: How confident are our people, particularly businesspeople, in working with data and making data-driven decisions? It emerged, surprisingly, that the ability to work with data was confined to small pockets while the larger workforce lacked the confidence to derive meaningful value from data.
This is a familiar narrative. Despite investments in planning and acquiring specialized skills and tools for data assimilation and analysis, many companies find themselves overtaken soon, wondering what zipped past.
The message is clear. To stay ahead in the business, entrusting only a select group of experts to weave magic with data is not enough. Data must be democratized. That is, for data to become a true asset for the organization, data literacy, or the ability to confidently read, interpret, query, and analyze data for decision-making, must be intrinsic to everyone – from entry-level employees to C-level executives. While data expertise may be the domain of a select few, data literacy must be woven into the organization’s culture.
The payback of enterprise-wide data literacy more than justifies the effort and investment in its acquisition:
- Quicker insights by those who need it, when they need it
- More informed business decision making
- An empowered workforce
- Deeper employee engagement
- Sharper competitive edge
Here are the 5 most important considerations on the path to becoming a data literate enterprise:
1. Assuming that your organization is already data literate is a big mistake
Some organizations take for granted that their people are already literate enough to read (prefabricated) tables and charts, and hence consider the exercise of building data literacy unnecessary. This is a misleading notion. Building and sustaining data literacy requires focused effort, factoring in new realities. For example,
- As the sources of enterprise data multiply, organizations must continually upgrade the skills, empowerment, and technology for developing and sustaining enterprise-wide capacity to work with data.
- Data literacy goes beyond a purely linear and mathematical reading of data. Enterprises must cultivate the ability to intuitively draw contextual inferences. This comes through good practice of data-driven problem solving, supplemented by focused skill-building workshops.
- At some point, the explosion of data is sure to exceed the average employee’s skill and confidence level to make sense of it, leaving them overwhelmed, even defeated. It could be too late to do much at that stage. The time to set the data literacy drive in motion is Now.
Credible surveys by reputed agencies have corroborated that data literacy remains low in most organizations. Boosting data literacy is, however, on the critical path for many of these organizations.
2. Acquiring breadth of literacy is more challenging than attaining depth
A society in which only a handful of ‘language experts’ (etymologists) could read and write but others couldn’t, would be quite passive and embryonic. Similarly, a business where data proficiency is the domain of just a few experts (data scientists) would be unequal to the opportunities and challenges of the digital world, where the language of business is data.
Data literacy is about democratizing data. That is, allowing everyone to gather and analyze data without expert help. Core technical skills like SQL, big data analysis, or R can be acquired relatively quickly by at least a section of the workforce. But this is not the goal of data literacy, which is concerned with cultivating an organization-wide data-driven mindset.
Essentially, data literacy is a 3-legged stool. If any of these legs is missing or short, it will flop:
- Access to enterprise data
- Skills and Tools to derive insights from data
- A data-driven enterprise Culture
Here is a quick dipstick to check data literacy: How many of your employees can answer all these questions with a clear Yes:
Do you have the means to access the data related to your current job function?
- Do you understand which data is relevant to the problem at ha
nd and how to test its validity? - Can you confidently use the necessary tools/coding for deriving decisive insights from data?
- Are you able to critically analyze the data for trends, predictions, or diagnosis?
- Can you create simple visualizations of relevant data for your stakeholders to convey your viewpoint?
- Are you able to ask the right questions (or challenge others’ viewpoint) backed by data?
- Have you been trained to work with data in your current role?
If over 80% of the people check all these boxes, you are indeed a data-literate organization, geared to deliver greater value to the business. It would be wise to sustain the advantage.
3. A strong foundation is critical to the data literacy drive
Chances are, you are significantly short of 80% in your assessment of data literacy, even on the above simple check. In other words, you need to take urgent steps to boost data literacy as an organization.
Data literacy is a critical need for every role in the organization. Attaining it must, therefore, be accompanied by careful planning and preparation. In general, attaining data literacy is a four-stage process, of which the first three are about building a strong foundation:
Review: While data skills are essential for every enterprise, there are variations across industries, organizations, roles, and individual proclivities. A review of the business/market environment, company strategy and roadmap, functional interdependencies, and personal effectiveness standards must precede the data skills development.
Assess: This is, perhaps, the most crucial stage of the development cycle. It involves assessment of the current state of each function, group, team, and individual in the enterprise on parameters such as problem-solving skills, analytical ability, data-interpretation, and critical thinking.
Promote: Data literacy cannot happen by imposition. Employees must be motivated to adopt the new skills and behaviors that data literacy entails. Ease of working, customized coaching and prepping (one size does not fit all), leadership examples, trusted champions, success stories, incentives and recognition, are all known to increase motivation levels and inspire adoption. Do not underestimate the value of this stage.
The foundation-building requires detailed appraisal of external and internal factors, which may not be expedient for an enterprise to perform on its own. It is a good practice to work with trusted external partners who are thought-leaders in the data domain, understand your business, and bring in the all-important neutrality to the process.
4. Continuous learning and reinforcement are key to data literacy
The first step in achieving data literacy is assuring organization wide proficiency in basic data tools through skill-building programs. Make a short list of tools that you would like everyone in the organization to be confident about. A spreadsheet like Excel is a good starting point. You may think this too trivial, but it isn’t. Look around you, how many of your HR or Marketing people know Excel really well? Go a bit beyond Excel, and the scene gets murkier. Can most people use some coding language or an analytic tool like Tableau to delve into data?
The second step is to create the equivalent of a centre of excellence (CoE) as a go-to place for data skills. Here, employees can acquire advanced role-based skills in data analytics, learn the use of sophisticated tools, do practice drills, or request handholding and refreshers. The CoE fills the gaps left behind by a one-time orientation program.
Skill-building, while essential, is not a guarantee for whole-hearted adoption. The third step of the strategy is the ongoing process of upholding a data-driven culture. Continuous emphasis on data literacy through leadership communication, workshops, demonstrations, rewards, recruitment, and performance appraisals go a long way in culture enhancement. In cases where gaps in data-driven culture are unclear, consultant-led Culture Mapping sessions may prove useful.
5. Data literacy can thrive only in an environment of trust
Upgrading skills, acquiring tools, and enhancing culture mean little if employees are not empowered to access relevant information when they need it, and where they need it. If a sales executive at a customer site is questioned on the status of a pending complaint, s/he should be able to access the relevant information to provide the update. Data literacy requires that employees of the organization be not only equipped with the right skills and attitude, but have the autonomy to access all relevant information, so that they can challenge hypotheses, take decisions, and ultimately drive business growth
With data literacy, data becomes the language of business to all its stakeholders. It is critical that employees relying on this data fully trust the data, underlining a stronger need for maintaining data integrity. On the other hand, enterprises must repose trust in employees with wider access to its data. This two-way trust is at the core of data literacy, enabling enterprises to stay competitive while operating efficiently in a world where data continues to grow in all its varied forms.
