Talk Data to Me: Why Employee Data Literacy Matters
Organizations are flooded with data, so they’re scrambling to find ways to derive meaningful insights from it – and then act on them to improve the bottom line.
In today’s data-driven business, enabling employees to access and understand the data that’s relevant to their roles allows them to use data and put those insights into action. To do this, employees need to “talk data,” aka data literacy.
However, Gartner predicts that this year 50 percent of organizations will lack sufficient AI and data literacy skills to achieve business value. This requires organizations to invest in ensuring their employees are data literate.
Data Literacy & the Rise of the Citizen Analyst
According to Gartner, “data literacy is the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied — and the ability to describe the use case, application and resulting value.”
Today, your employees are essentially data consumers. There are three technological advances driving this data consumption and, in turn, the ability for employees to leverage this data to deliver business value 1) exploding data production 2) scalable big data computation, and 3) the accessibility of advanced analytics, machine learning (ML) and artificial intelligence (AI).
The confluence of this data explosion has created a fertile environment for data innovation and transformation. As a result, we’re seeing the rise of the “citizen analyst,” who brings business knowledge and subject-matter expertise to data-driven insights.
Some examples of citizen analysts include the VP of finance who may be looking for opportunities to optimize the top- and bottom-line results for growth and profitability. Or the product line manager who wants to understand enterprise impact of pricing changes.
David Loshin explores this concept in an erwin-sponsored whitepaper, Data Intelligence: Empowering the Citizen Analyst with Democratized Data.
In the whitepaper he states, the priority of the citizen analyst is straightforward: find the right data to develop reports and analyses that support a larger business case. However, some practical data management issues contribute to a growing need for enterprise data governance, including:
- Increasing data volumes that challenge the traditional enterprise’s ability to store, manage and ultimately find data
- Increased data variety, balancing structured, semi-structured and unstructured data, as well as data originating from a widening array of external sources
- Reducing the IT bottleneck that creates barriers to data accessibility
- Desire for self-service to free the data consumers from strict predefined data transformations and organizations
- Hybrid on-premises/cloud environments that complicate data integration and preparation
- Privacy and data protection laws from many countries that influence the ways data assets may be accessed and used
Data Democratization Requires Data Intelligence
According to Loshin, organizations need to empower their citizen analysts. A fundamental component of data literacy involves data democratization, sharing data assets with a broad set of data consumer communities in a governed way.
- The objectives of governed data democratization include:
- Raising data awareness
- Improving data literacy
- Supporting observance of data policies to support regulatory compliance
- Simplifying data accessibility and use
Effective data democratization requires data intelligence. This is dependent on accumulating, documenting and publishing information about the data assets used across the entire enterprise data landscape.
Here are the steps to effective data intelligence:
- Reconnaissance: Understanding the data environment and the corresponding business contexts and collecting as much information as possible
- Surveillance: Monitoring the environment for changes to data sources
- Logistics and Planning: Mapping the collected information production flows and mapping how data moves across the enterprise
- Impact Assessment: Using what you have learned to assess how external changes impact the environment
- Synthesis: Empowering data consumers by providing a holistic perspective associated with specific business terms
- Sustainability: Embracing automation to always provide up-to-date and correct intelligence
- Auditability: Providing oversight and being able to explain what you have learned and why
Data Literacy: The Heart of Data-Driven Innovation
Data literacy is at the heart of successful data-driven innovation and accelerating the realization of actionable data-driven insights.
It can reduce data source discovery and analyses cycles, improve accuracy in results, reduce the reliance expensive technical resources, assure the “right” data is used the first time reducing deployed errors and the need for expensive re-work.
Ultimately, a successful data literacy program will empower your employees to:
- Better understand and identify the data they require
- Be more self-sufficient in accessing and preparing the data they require
- Better articulate the gaps that exist in the data landscape when it comes to fulfilling their data needs
- Share their knowledge and experience with data with other consumers to contribute to the greater good
- Collaborate more effectively with their partners in data (management and governance) for greater efficiency and higher quality outcomes
Then all enterprise stakeholders – data scientists, data stewards, ETL developers, enterprise architects, business analysts, compliance officers, citizen analysts, CDOs and CEOs – can access data relevant to their roles for insights they can put into action.
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