The ability to collect and process data has grown exponentially over the past 20 years and the cloud is at the heart of this sea change. Businesses can now easily store and access vast amounts of information, and while we have the technological tools to exploit this data, what often lags behind is our understanding of it. To get the most out of it, everyone in the organization needs to understand how to access it, secure it, and think critically about its potential use cases and applications.
What is Data Literacy?
While it’s true that data scientists spend their days analyzing and analyzing data, that doesn’t let the rest of the organization off the hook. In fact, data literacy is becoming an important universal skill set. Even the ability to take data from a spreadsheet and create a table is a necessary requirement in many jobs. But the ability to shape that data, present it accurately, and critically assess the value of the information is less common.
To increase data literacy in your organization, consider each person’s role and how much “data literacy” they might need. Consider these three essential components of data literacy: understanding, access, and analysis.
- Agreement. In today’s digital environment, everyone should achieve this level of data literacy. It’s about understanding the whole concept of data: where it comes from, how to secure it, and why it’s valuable. It is also important for users to become familiar with tools for working with and presenting data, such as spreadsheets, tables, and visualizations. Without a fundamental understanding, employees cannot adequately do their part in compliance and data integrity.
- To access. Having data is great, but users need to be able to easily access that data and put it to meaningful and acceptable use. They need to understand what data is important to their work, how it is organized and structured, and how to use the tools to access it safely and securely.
- Analysis. Ideally, all employees should be able to critically reflect on data and learn from it. In some roles, the use of statistical and analytical methodologies may be required. However, the processes involved in the analysis should be transparent and explainable to people who are not data scientists. Everyone should be able to determine if the analysis is valid. Where does the data come from? Are the conclusions based on an inadequate or flawed data set, creating an unintended bias? How, where, what and from whom data is collected, sampled and interpreted has a profound impact on the end results.
The role of data governance
Suppose your employees are data savvy i.e. they are able to understand, access and analyze data. Does that mean anyone has permission to access all data? In a word, no. With the immense amount of information available, pulled from so many sources, confidentiality and security must be scrupulously maintained. It’s actually a matter of ethics. Thus, structuring and controlling data at all levels becomes essential.
Data governance is how organizations manage, use, and protect data. It encompasses data quality, maintenance, access and security. At every stage of its lifecycle, data must be governed. This lifecycle begins with data acquisition and continues through storage, synthesis, use, publication (through analytics and products), archiving, and purging. For example, acquisition is not just about the original provenance of data, but the frequency and reliability of updates. At some point, the data must also be deleted. The organization may lose the rights to it or no longer need it. This kind of data hygiene is an integral part of good data governance.
With the scope of data governance, to help guide organizations, industry associations like the EDM Consulting were created to elevate the practice of data management as a business and operational priority. When it comes to data governance, there are some key fundamentals:
Set up an internal structure to oversee the data. Strong data governance establishes roles and responsibilities at all levels of the organization, from enterprise to business units. Typically, data governance policies, standards, processes, and success metrics are centralized. The business units are responsible for setting up data governance.
Determine where and in what state the data should reside. Many companies store data in a “data lake”, which is made up of different areas depending on the type of data stored. To keep data secure, organizations create rules about how it is processed, whether it should be encrypted, whether it can be transferred, and more. There may be different rules for a particular state or country. This may affect the ability to transfer data or create a need to protect data in specific ways. For example, the European Union
has its own set of data privacy rules, just like states like California.
Define and enforce who can access data. This means designating what data can be accessed and by whom. A common practice is to provide access based on zone, data type and user profile, or permissions based on use case. These permissions are built into the code base. It is also essential to regularly check the history and usage. If someone wants to access sensitive data, for example, multiple levels of approval should be required to review the purpose and ensure the data will remain secure.
Assess the goal. Part of the authorization process is asking how the data will be used. For example, is it for research or analysis, or the development of new products? Some organizations have instituted a data ethics committees
to assess data access requests. The board reviews the idea and its potential uses. It can also provide meaningful guidance and feedback to ensure data is used fairly and in accordance with legal requirements and the company’s own standards.
Make every employee a data steward. Establishing policies and governance structures is essential. But for the process to resonate with employees, all employees need to feel responsible and held accountable for how they create, modify, and use data.
The Value of Data Literacy
There’s no point in having data if your employees don’t understand it or can’t access it for meaningful applications. For too long, too much organizational data has been locked in data silos. By being able to share data across the enterprise, new insights can emerge. Marketing needs to know what’s going on in the supply chain before it can strategically launch new offerings. HR needs to see all hiring activity across the company — even contractors hired directly by a business unit — to budget and forecast properly. Creating a culture of data literacy has the power to inform better business decisions and drive better results.