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TDWI Upside - Where Data Means Business

The Changing Value of Data and the Need for Data Literacy

Businesses understand the need for faster, deeper insights into their data; the question is how to get there.

Organizations have been collecting, analyzing, and storing data for decades, but the true power of data is just beginning to be valued and utilized. For many organizations, most data is still a vast, untapped field of information with unrealized potential. Many organizations spanning a broad range of sectors -- both public and private -- understand that to succeed and grow they will need to unlock the value of their data faster than ever before.

For Further Reading:

Natural Language Generation: 3 Reasons It's the Next Wave of BI

How to Develop a Data-Literate Workforce

2022: The Year When Humans and AI Work Together to Drive Enterprise Performance

To get to the deeper meaning of their data, data literacy is essential. According to Gartner, “By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.”

The Goal: Creating Business Value from Data

Although mitigating risk is still crucial, creating value with data assets has become the primary mission of the chief data officer (CDO). Companies at the forefront of technological advancements understand that data analytics should be a key driver of business decisions. Understanding their data is critical to every aspect of their businesses. Business intelligence has become the new currency.

A Qlik-Accenture study showed that only 32 percent of business executives surveyed said they can create measurable value from data, and only 27 percent said their data and analytics projects produce actionable insights. Too many organizations are forced to rely on a few experts, experienced analysts, and senior staffers with the skills to translate insights and meaning from their data.

The analysis is often time consuming and can be fraught with inaccuracies based on human error or simply the massive amounts of data being processed by these few experts. Insights gained from the data are also not dynamic.

Businesses understand the need for faster, deeper insights into their data; the question is how to get there. Adding additional data-literate employees is not always feasible, especially in a competitive job market.

What Data Literacy Means to Business Outcomes

Qlik’s 2018 Data Literacy Index found that “data-driven organizations benefited from increased corporate performance, resulting in a higher total enterprise value of 3-5 percent, equating to US$500 million when applied to the organizations included in the study.”

Business leaders do not need to read the research to understand how great an impact faster, more accurate, deeper data insights would have on business outcomes. In addition to allowing for better-informed decision making, data literacy can provide awareness of trends and anomalies in business.

BI Tools and Data Literacy

Organizations have turned to business intelligence (BI) tools to gain deeper insight into their data. BI tools such as Tableau and MicroStrategy combine data analytics, data visualization, data tools, and best practices to help organizations make better-informed decisions. The information is presented through dashboards, charts, and graphs.

Qlik’s research showed that 67 percent of the global workforce access their data using business intelligence tools. However, often only a small group of data scientists and experts can decipher the meaning of data from these tools. It takes expertise to decipher those visuals, and that is where artificial intelligence in the form of natural language generation can help.

Using Natural Language Generation for Organization-Wide Data Literacy

BI tools have brought data literacy to a wider swath of the workforce, but these tools still require knowledge and experience to use effectively. Incorporating natural language generation (NLG) into BI advances data literacy across an organization, allowing all employees to have deeper understanding of data.

NLG translates data and creates a narrative in plain language so that everyone can have understandable insight. It is dynamic, enabling real-time analysis and data understanding. However, NLG itself is not the end point. It enables iterations of analysis where insights that may have been invisible -- including trends, anomalies, and outliers -- can now be explored further.

At each level, the NLG is available -- not only to answer questions during a deeper dive into the data but also to guide where to dig. Artificial intelligence (AI) has been transforming business for decades, and NLG is the next step in using the power of AI to improve decision making and drive accurate business outcomes.

As the value of these ever-growing data streams increases, the importance and urgency of equipping all data users -- not just the analysts -- with the skills and tools to interpret that data is also increasing.

Businesses need to find effective and efficient ways to enable and improve data literacy across the organization. The alternative is to be left behind the competition, unable to harness the true value of the data they are sitting on.

About the Author

Lyndsee Manna is the EVP, strategic partnerships and global business at Arria, a company focused on natural language generation. She works with enterprises to turn data into business actions with AI technology across various industries, including financial services, pharma, and retail.

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