Barriers to Data Literacy and Measuring Analytics Maturity

Data Literacy
Data Literacy

In 2002, Major League Baseball (MLB) team Oakland Athletics struggled to attract top-tier talent with their US$41-million team salary. In contrast, the New York Yankees — one of the top teams at the time — had over US$125 million in its pockets. This gap translated directly into team performance: Those with more money could afford to sign superstars, while the rest hired the remaining available talent.

That is, until the Athletics’ general manager Billy Beane used data to their advantage.

During that year, he adopted the “moneyball theory,” which evaluates a player based on two criteria: their slugging percentage or their ability to hit the ball, and their on-base percentage or their ability to get on base. In contrast, scouts of the era focused on other statistics such as a player’s batting average, stolen bases, and hits.

Beane and company then analyzed the data of hundreds of baseball players to find those who fit the criteria. Looking at areas traditionally overlooked by scouts eventually gave them a talent pool of undervalued players who could deliver wins for the team without overshooting their financial resources.

Their efforts paid off as the 2000s became one of their most successful decades. Since then, other teams with modest resources embraced the moneyball theory and allowed them to compete against teams with deeper pockets.

While the Oakland Athletics’ success helped prove the moneyball theory, it also presented Beane as a data-literate general manager as he was able to gather player data, interpret it, and apply it according to their goals.


The growing need for data literacy in companies

Today’s organizations collect more and more data. For example, popular messaging app WhatsApp transmits up to 65 billion messages daily while Facebook users post 510,000 comments per minute. However, only a few have been able to fully unlock the potential that lies within their data.

This highlights the increasing need for executives and employees to learn how to interpret data. The majority of business decisions today are still made based on gut feel and experience, while only 48% are based on data and analytics, according to Forrester’s survey. One can therefore surmise how much more accurate or reliable the organization’s decisions would be if more people in their workforce were data-literate. This would allow them to interpret their data better, unlock more opportunities, and innovate when facing challenges or disruptions.


Why companies still struggle to foster data literacy

Despite the popularity of buzzwords such as big data and AI, many organizations still struggle to develop effective data and analytics teams. Gartner’s 2020 survey of chief data officers revealed several roadblocks in developing these teams, namely:

  • Changing the company’s culture. Promoting data literacy in an organization requires fundamentally changing how every employee operates. Because of this, some may resist this transformation or require more time to adjust.

  • Limited resources and funding. This may happen in organizations where leaders are unable to appreciate how data and analytics align with the company’s business objectives. Therefore, these leaders might be outright unwilling or hesitant to fund and support data literacy programs.

  • Poor data literacy. Having few or no employees who can read, write, and communicate data in context can also hinder companies from creating a data-driven culture. Furthermore, it will prevent them from knowing what data they have, how to sift through them, and how to use them.

  • Lack of focus on data literacy initiatives. An organization may already have data literacy initiatives in place, but a lack of focus could pose challenges in areas such as improving their analytics maturity and funding the right programs.


Overcoming roadblocks in moving data literacy forward

Any organization can develop a data-driven culture despite initial challenges and pushback from employees. However, it is not as simple as equipping them with dashboards and expecting them to use these tools effectively. The following steps will allow organizations to start with a blank slate and motivate their workforce to handle data confidently:

Set achievable goals and measure regularly. It takes time to create a data-literate company. To accelerate this process and ensure the organization is on the right track, they need to set achievable goals and constantly measure their results.

Having manageable goals is crucial especially at the beginning, as it will allow everyone to immediately understand and experience the benefits of becoming data-literate. Easier, achievable goals will also encourage those resistant to culture change to have a more favorable perception of it. It’s also equally important to constantly measure the company’s performance. This will allow them to uncover areas for improvement and adjust their approach based on user feedback.

Invest in new technologies and the right tools. In a survey on big data, 97.2% of executives revealed that their firms are investing in building or launching initiatives on big data and AI. While it’s important to train personnel to interpret and analyze data, they’ll be able to work faster and more effectively with the right tools, such as Power BI and Tableau.

Implement with everyone’s participation. Many perceive data and analytics as only being within the realm of IT people and data analysts or engineers. However, the reality is that any group that needs to make business decisions will benefit from being data-literate, including those looking to improve employee productivity or accurately forecast demand.

Supply chain is another area where data literacy can give companies an advantage. Gartner’s 2021 survey found that organizations with a data literacy program were more likely to reach their objectives across various supply chain metrics. For example, 78% of respondents with data literacy training met their KPIs for manufacturing costs compared with 50% of those who didn’t undergo a similar training.

Measure analytics maturity and digital capability. Companies can ensure their progress toward data literacy by measuring their analytics maturity and digital capability. Organizations can use an analytics maturity model that comprises several stages, which include:

  1. No analytics. Companies without analytical processes fall under this level.

  2. Descriptive. Here, employees can explain what happened through reactive reporting (by gathering and analyzing data).

  3. Diagnostic. At this stage, employees can explain why something happened by identifying patterns, analyzing historical data, taking other dependencies into account.

  4. Predictive. This is where forecasting, statistical analysis, scenario planning, and generating predictive models come in. Employees use machine learning techniques to handle large amounts of data in order to predict possible outcomes.

  5. Prescriptive. Finally, employees can propose how to achieve a certain result with a mix of optimization options, decision support, and actionable insights.

Meanwhile, organizations can measure their digital capability by assessing the returns on their digital investment. For example, when investing in chatbots, the company can measure the number of prospects who used them and how many of them became paying customers. The return on internal tools, on the other hand, can be measured by how these support the company’s efforts to convert more customers.

Another example is gauging the tangible benefits in the investment, delivery, and adoption of technologies and custom tools. Organizations can also look into balancing the cost, speed, transparency, and accountability in owning and using cloud resources.

Start small — today. As with any other initiative, there’s always the temptation to go big from the onset. However, this may not be helpful when initially establishing data literacy in a company. Rather, it’s ideal to start small with a minimum viable product (MVP) or a proof of concept (PoC) when introducing new technologies. These will help gauge employee reception and the resources required to fully implement the data literacy program. Starting with fewer moving parts will also enable the organization to adjust their approach more easily when necessary.


Data literacy goes beyond technology

In Lingaro, we work with Fortune 500 companies and global brands not only in developing business intelligence solutions, but also in nurturing a culture where people proactively use data to move their organization forward.

For instance, our journey with one of our partners — a large manufacturing company — started in late 2019 when they were starting in their analytics and digital transformation. They were migrating their assets to the cloud and setting up automation in many of their business processes. The success of an initial project we worked on reinforced the stakeholders’ view on the potential of their data, and how it can be used to optimize workflows as well as serve different business functions and end users. Over time, we’ve seen the company’s data literacy improve beyond the adoption of the analytics solutions that Lingaro’s experts helped build.

Technology alone didn’t enable them to become more data-driven. Their openness and determination to digitally transform as well as their keenness to involve everyone in the journey helped enact transformative changes within the company. As more of these business intelligence solutions were expanded and enriched, their users continued to develop and even upskill their data literacy, where data became the core of their daily business decisions.

Indeed, creating a data-driven organization requires a fundamental shift in the people who work there, how they work, and the tools they use to do their work. This is a massive undertaking that will naturally generate resistance among employees. However, starting small, starting now, measuring results constantly, and involving everyone in this transformation will yield dividends not only for the organization but for everyone who works there. They will be able to see challenges in a new light and thus develop more creative solutions, find opportunities in places they’ve never looked into before, and be ready to face today’s data-driven world with the skills and confidence to succeed.

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