This is the second part of the Data Literacy for Professionals article

Examples of Data Literacy in Practice
Data literacy adds value to an organization in various ways, one of which is to support decision-making. Say you need to create a new marketing campaign to increase customer retention, for example. Where would you start? Would you just come up with a few random ideas, select the most promising ones, and run with it? A data literate professional would take a step back, examine the data at hand, see if there was a way to collect additional data, if necessary, and then work with some analyst to make sense of it all. Then, upon contemplating the insights gathered from this analysis, he would find some practical alternatives and discuss them with the marketing team (which might be even his immediate subordinates). Afterward, he might want to dig deeper into a specific option or two and after examining the corresponding data and insights further, come up with a data-backed decision to present to the marketing executives.

Another example of how data literacy would add value to decision-making could be UI enhancement, a vital part of the UX. For example, say that the site doesn't get many people to click on the "buy" button. Someone identifies this problem by observing how the sales funnel is and brings it to your attention. You then explore various alternatives for making the "buy" button easier to stand out, perhaps experimenting with its color, size, etc. Which option would you go with, however? If you are data literate, you get the help of a data scientist to design an experiment, run it, and come up with some insights as to which option yields a better click-through rate. This analysis can involve some statistical tests too. You'd then think about all this, evaluate its reliability, and decide on the most promising option for the button based on the results of this analysis. This mini-project can help improve the UI and possibly the whole UX, driving more sales for the company.

New products and services can also come about through data literacy. For instance, you could have a new analytics service for the users made available either on the website (if each user has an account there) or via an API. Perhaps even something geared towards a more personalized interface, based on the user's behavior on the website, an interface that can even change dynamically. All these may seem like magic to someone unaware of the potential of data but for a data-literate professional, especially someone with a bit of imagination, they are as commonplace as a coffee shop.

In the competitive world we live in, getting an edge is paramount and something that data literacy can enable. Data literacy can help solve problems faster and more reliably, come up with new viable products/services based on data, and even bring about a more efficient and transparent culture in the organization, one based on a data-driven mindset. Additionally, data literacy can help make the most of the specialists involved in data work, improve employee retention (for these and other information workers), and transform the organization into a place that keeps up with the latest tech developments. An organization like this can foster more contentment and efficiency in its employees and enable them to commit long-term. A place like this can help individual professionals in many ways while at the same time benefiting other people involved, such as users and customers. In times like these, when the economy and the market are unstable, such improvements can bring about stability and even growth on both an individual and a collective level.

Final thoughts
Data literacy isn't a nice-to-have, not anymore anyway, but a necessity for any organization that wants to keep its place in today's increasingly digital world. A company comprising data-literate people, especially managers and other knowledge workers, can leverage resources it didn't know it had because they are in the form of data. Also, it can make it more agile as it can explore acquiring additional data resources and using them to improve existing data-related processes and services further. This company's competitors are probably doing this already, so it's no longer a luxury to invest in data literacy and use it to cultivate a data culture and make data-driven decisions.

Data literacy also helps individual professionals through a series of specific improvements. For example, a mid-level manager can become more data literate and have a more modern approach to tackling the problems her team faces. Additionally, she can hire new members or train existing ones so that they can become more competent in handling and analyzing data to tackle the tasks in their workflows. Maybe even discover new ways to deal with bottlenecks and other issues that hinder efficiency. All that progress might eventually manifest as a promotion for that person and other people on her team. And if that person decides to leave that organization, she can have more options in future work placements and even new roles, thanks to her data literacy ability.

Overall, data literacy is a powerful tool, or mindset and skill-set to be more accurate, for leveraging data and making better decisions accordingly. It is very hands-on and benefits people and organizations in various ways. It involves understanding data and how we govern it, analyzing it effectively, presenting and communicating the insights discovered, and protecting data/information, among other things. It entails understanding where privacy fits and taking actions to keep data related to people's identities under wraps. Data literacy is closely linked to data strategy and having a data culture in the organization. Being literate about data can also give professionals a competitive advantage over their data-illiterate colleagues and make a company more agile and relevant in the market. Especially when it comes to complex problems and difficult decisions, it can help bring about more reliability and objectivity to the whole matter. This leveling up can enhance the organization overall, along with everyone associated with it, be it partners, vendors, and, most importantly, its customers and users.

To learn more about data literacy, feel free to explore the corresponding page of this site. Cheers!

Introduction
Different people mean different things when they talk about data literacy. For this article, so that we are on the same page, let's use the definition of "the ability to create, manage, read, work with, and analyze data to ensure & maximize the data's accuracy, trust and value to the organization" (D. Marco, Ph.D.). Note that this definition highlights a crucial characteristic of data literacy which entails coherency and collaboration within the organization, something that often reflects a particular kind of culture. I'm referring to the data culture, which is an integral part of data strategy, merging the business objectives and plans with the data world where data becomes a kind of asset.

All that's great, but it may seem a bit abstract. However, data literacy is very hands-on, even if it's not as low-level work as analytics. It is also utterly significant for all sorts of professionals, particularly decision-makers. Many people talk about making data-driven decisions and having a data-driven approach to problem-solving. How many of them do it, though, and to what extent? Well, data literacy enables professionals to do just that and make data something they value and leverage for the benefit of the whole.

Data literacy is beneficial for other people too. For instance, when someone works in a data-literate organization, there tends to be more transparency about how decisions are made and what different pieces of data mean. So, if you have a role that involves data in some shape or form, you can be sure that it's not a black box and that you can learn from it. Naturally, this implies that you are data literate to some extent too!

Data literacy is a state of mind, a way of thinking and acting about data. As such, it has many benefits that depend on the organization and the data available to it. The fact that many companies base their entire business model on data attests to the fact that data is crucial as an asset. To unlock its value, however, you need data literacy.

Key Ideas and Concepts of Data Literacy
Let's delve deeper into this by looking at the components of data literacy. For starters, data literacy involves understanding data and how it is governed. This part of data literacy is vital since many organizations have lots of data that is essentially useless because it's in silos and inaccessible to those who need it. This problem is essentially a data governance one. Also, as much of it involves personally identifiable information (PII), it has to abide by specific regulations such as GDPR. Otherwise, this data may be a huge liability.

Data literacy also involves analytics, as it is when data is turned into information that it truly becomes useful. The latter we can understand better and reason with, especially in decision-making. Data in its original form is usually understandable only by computers. Analytics makes this transformation and enables others to benefit from the data. Usually, analytics work is handled by specialists, such as data scientists.

Data literacy also involves presenting and communicating data. This part of data literacy often entails reasoning about insights and exploring how they can apply to an organization's challenges. Otherwise, data has just ornamental value, which may not be enough to justify people working with it. Perhaps that's why today every data professional is assessed based on communication skills too, not just technical ones.

Finally, data literacy involves protecting the data and whatever information it spawned. It's usually specialized cybersecurity processes that ensure the protection aspect of data literacy, which also includes preserving the privacy of the people behind that data. In larger organizations, there may even be specialized professionals involved in this kind of work.

What does a data-literate professional look like, though? For starters, it's not like he stands out from the crowd. But when that person engages in a conversation on a business topic, it becomes clear that they know how data can be used as an evergreen asset. Such a person may also undertake responsibilities related to the use of data in decision-making, be it through a data-driven marketing initiative, a cohort analysis of the customers or users, etc. A data-literate professional can undertake numerous roles, not just those related to hands-on data work. He can be a competent team leader, a business liaison, a consultant, and even an educator, promoting a data culture in the org. As long as that person has a solid understanding of data and how the organization can put the data available into good use, that person is a data literate professional and can add value through that.

Generally, data-literate professionals are very competent in leveraging data for making decisions and driving value in the org. This aspect of data literacy often involves having a sense of data and its potential. For someone else, data may be just something abstract and interesting to data professionals only. For the data-literate professional, however, it's something as powerful as a product sometimes. At the same time, it's a pleasant challenge because just like products need work before you can trade them for money, data also requires special treatment. A data-literate professional accepts this challenge and works towards making it a reality. This special treatment may involve getting the right people in a team or leveraging the existing ones, doing some mentoring even, and turning this understanding of data into a set of processes that transform data into something of value.

When it comes to data literacy, there are several challenges most professionals change. I say most because people with a data background tend to find this whole matter intuitive and relatively easy. However, people who come from different backgrounds tend to struggle with data literacy in various ways. After all, traditional education stems from a time when data wasn't something educators knew or cared about. Their data literacy skills were rudimentary at best, while they focused on educating people about those business models and concepts that were more relevant back then. That's not to say that business acumen isn't that important. It's more important, however, when it's integrated with data acumen (as Bernard Marr eloquently illustrates in his book and courses on data strategy).

Data literacy is a journey for most professionals, and there are different levels to it. Maintaining a sense of humility about this matter and understanding there is always more to learn can go a long way. This isn't an easy task, especially for accomplished professionals who got far in their careers using traditional ways of thinking about assets and business processes. Perhaps through proper coaching, mentoring, and other educational tools, they can overcome the challenges that plague this journey toward complete data literacy.

Data literacy is crucial in today's digital economy. As data is what some refer to as the new oil, the prima materia of many products, data literacy is the equivalent of an oil-based engine. The main difference is that it doesn't pollute and there are no practical limitations on the fuel! Nevertheless, it's not trivial as some data people make it out to be. Of course, you can plug this data into some off-the-shelf model and get it to spit out some results that you can put into some slick presentation and share with the stakeholders. However, this is often not enough or relevant. Data literacy helps people see how the data relates to the business objectives, tackling specific problems and answering particular questions. Having some fancy data model may be something interesting to boast about, but if it doesn't help the organization with its pain points, it seems like an ornament rather than something of value. Going back to our metaphor, it's more like a gadget than an engine that can help us traverse the distance between where we are as an organization and where we need to be.

To be continued...
In the meantime, feel free to learn more about data literacy in the corresponding page of this site.