The Future of Data Democratization is Here. Investors Are Poised to Benefit

By Kate DuBois, Skai General Manager, Market Intelligence

There was more data created in 2020 than any other year so far and the world is on track for exponential data growth over the next five years. Hidden in those zettabytes are predictions about the trends and consumer needs that will emerge and represent major opportunities for the right brands. But if organizations aren’t able to digest data and turn it into actionable insights, they’ll make decisions that keep them behind the curve. As investors evaluate which ventures to pursue, they would be wise to carefully consider how data-driven their targets are. The most advanced organizations that represent the greatest potential for success are those that have democratized data across the business to ensure that everyone at every level is making decisions based on the same data.

True democratization of data analytics is in its early stages. But the path forward is clear and similar to the path we’ve all already taken to widespread use of the Internet, with similar opportunities for investors.

How the Internet became democratized, decade by decade

The Internet as we know it today started out as local and global computer networks in the 1960s, used mostly by specialized computer scientists and national agencies. The following decade saw the expansion of computer network interconnectivity through the development of new linking protocols, though the user base remained largely the same. Throughout the 1980s, the PhoneNet system - a network built using dial-up phone lines - made it possible for even more people to access the Internet and send the first international emails, although you still needed expensive computer equipment and reliable connections to access anything. The real democratization began in the late 80s and early 90s: Sir Tim Berners-Lee’s invention of HTML, HTTP, URLs, and the World Wide Web made it possible for the Web to scale rapidly. The first web browser, also invented by Berners-Lee, opened up the Internet to average people with no special computer science skills. By 1995, consumer websites like Amazon, Yahoo, and eBay were live and the World Bank reported that about 9.24% of Americans identified themselves as Internet users. This year, 93% of American adults are online.

The essential forces of democratization: technology & demand

The democratization of the Internet was thanks to two forces working in tandem: technological innovation and user demand. Today, we’re at a crossroads in data analytics where both of these forces are ramping up. There’s more data out there than ever before, and it’s growing rapidly. In response, the data analytics field has progressed to help businesses make sense of vast quantities of unstructured data. Advanced data analytics solutions can collect the external data sources that are relevant to a business, extract meaningful context from them to explain both what is happening as well as why, and then make those insights available and understandable so that leaders can take informed action. Increasingly, those leaders recognize that their entire business would improve if everyone within it had access to the same single view of information. Some of the benefits of data democratization include:

  • Faster, better decision-making. This can result in a first-to-market advantage as businesses capitalize on emerging trends and consumer demands.
  • More cohesive decisions. A unified and widely accessible view of the data means the entire business is making aligned decisions.
  • Employee empowerment. With access to data, teams and individuals can feel more confident about taking ownership of a business problem.
  • Improved operational efficiency. Data scientists spend almost half their time making data usable. Streamlining internal processes and redirecting data teams towards more strategic work can save a lot of time and energy.
  • More ROI from data investment. Empowering everyone in the organization to make data-driven decisions will ensure you make the most out of every data point you purchase.
  • Better understanding of the customer. There’s a wealth of external data out there about your market, customers, and prospective customers. Understanding that data leads to decision-making that’s focused on meeting consumer needs, resulting in a better customer experience and a greater market share.
  • Faster adaptation to new circumstances. When the market or customer changes, you’ll see it in the data. Then you can make proactive rather than reactive decisions.

With these benefits in mind, it’s no wonder that a recent Google Cloud and Harvard Business Review survey of industry leaders showed that 97% of those surveyed believe organization-wide access to data and analytics is critical to the success of their business. However, only 60% believe their organizations are effectively distributing that access today. An Exasol survey of 500 executives and data professionals found that 90% of respondents are prioritizing data democratization for their companies.

The demand is there. But do we have the technological tools to make data accessible to all?

Data democratization challenges

There are good people, process, and technological reasons why many organizations have yet to fully democratize access to their data, including:

  • Organizational silos. In some businesses, teams are set up to work independently. They don’t share the internal and/or external data they collect to make decisions, and there isn’t a strong culture of sharing insights cross-functionally.
  • Reliance on specialists. Many organizations have long relied on data scientists, analysts, and other experts to interpret data. Some of these teams have become so inundated with requests, decision-makers have developed workarounds or have stopped seeking data as part of their process altogether. Changing these ingrained cultural paths can require a complete overhaul of the business process, a significant challenge.
  • Data complexity. New technologies create ever-larger data sets. Unless that data is collected and contextualized, the average person has a hard time understanding it.

Data dashboards and visualizations have popped up as possible solutions to these challenges. The Exasol study mentioned earlier showed that 82% of respondents use dashboards to communicate insights across their organizations. And it’s easy to see why. Dashboards can be introduced to every team’s process, eliminating the siloing of information. Their simplicity means that you don’t have to be a data expert to understand them. But that simplicity also means that the data that’s being shared is pretty shallow, without enough background or context to answer complex business questions. That’s just one reason why many of the Exasol study respondents reported that their organizations routinely disregarded the dashboards they had in place. The other reasons? Too time-consuming to interpret, too much information overall, and not tailored enough to individual needs. These can all be summed up into one criticism: data dashboards don’t tell stories, and the stories are what’s key to communicating data and analytics results.

People are curious, and they think in terms of questions; this is what made the invention of the web browser and the Google search bar so revolutionary in the democratization of the Internet. Internet users could search for the web pages and information that interested them, rather than coming to the Web with expert knowledge of its contents. Data analytics needs a similar search-focused tool to power true democratization.

Three steps to true data democratization

The easy-to-use, data-democratizing tool of the future will combine the power of big data and AI with the usability of Google to deliver data stories and insights in response to direct questions from individual users. Organizations can begin the shift to true data democratization by following three steps toward overcoming current technological barriers:

Step 1: Build a strong data foundation that includes a wide range of internal and external data sources that cover the entire relevant market, not just a single brand or product. Continuously updated data feeds will ensure all information is always relevant and reveal shifts in the market landscape in time for leaders to execute responsive decisions.

Step 2: Use advanced analytics to make data insights understandable. Today, powerful machine learning (ML) and natural language processing (NLP) algorithms can extract context from data by creating simplified representations of text and applying macros (or rules) to those representations to determine semantics. From there, NLP can identify the sentiment behind a data point and pair it with taxonomy values - or details - that are unique to a specific market. This makes it possible to drill down into, say, consumer opinions of a particular skincare ingredient, or recent patent filings for product packaging.

Step 3: Scale the insights within a user-friendly experience. The future of democratized data will follow Google’s path, with tools that allow individuals across a business to access easy-to-understand, data-driven stories that answer questions and solve problems. The key is for these tools to be responsive to individual user needs; that’s what’s missing from today’s data visualizations and dashboards, and it’s what made Internet searching such a game-changer.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.