More data was created in 2020 than any other year to date, and the world is on the path to exponential data growth over the next five years. These zettabytes hide predictions about trends and consumer needs that will emerge and represent significant market opportunities for the right brands. However, when organizations are unable to process data and turn it into actionable insights, they will make decisions that will keep them behind the curve. When assessing which businesses to start with investors, it is wise to carefully consider how data will be handled. The most advanced organizations with the greatest potential for success are those that have democratized data across the organization to ensure that everyone at all levels makes decisions based on the same data.
The true democratization of data analytics is just beginning, but the path forward is clear and similar to the path we have already taken for widespread internet use, with similar opportunities for investors.
How the Internet became democratized, decade by decade
The essential forces of democratization: technology & demand
The democratization of the internet was due to the interaction of two forces working in tandem: technological innovation and user demand. Today we are at a crossroads in data analysis where both forces are increasing. More data is available than ever and it is growing rapidly. In response, the field of data analytics has evolved to help organizations understand large amounts of unstructured data. Advanced data analytics solutions can capture the external data sources relevant to an organization, extract meaningful context from them to explain what is happening and why, and then make those insights available and understandable so that executives can take informed action. Executives can see that their entire business is improving would if all members had access to the same single view of information. The benefits of data democratization include:
Faster, better decision-making. This can result in a first-market advantage as companies benefit from emerging trends and consumer demands.
More cohesive decisions. A unified and generally accessible view of data means that the entire company makes coordinated decisions.
Employee empowerment. With access to data, teams and individuals can feel more confident about dealing with ownership of a business problem.
Improved operational efficiency. Data scientists spend almost half of their time making data usable. Optimizing internal processes and redirecting data teams to more strategic tasks can save a lot of time and energy.
More ROI from data investment. When you empower everyone in your organization to make data-driven decisions, you will ensure that you are getting the most out of every data point you purchase.
Better understanding of the customer. There is a wealth of external data about your market, customers and prospects. Understanding that data leads to decision making focused on meeting consumer needs leadsto a better customer experience and greater market share.
Faster adaptation to new circumstances. When the market or customer changes, you can 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 survey of industry leaders by Google Cloud and Harvard Business Review found that 97% of respondents believe that having enterprise-wide access to data and analytics is critical to the success of their business. However, only 60% lieve their organizations are effectively distributing this access today. An Exasol survey of 500 executives and data professionals found that 90% of respondents prioritize data democratization for their organizations.
The need is there, but do we have the technological tools to make the data accessible to everyone?
Data democratization challenges
There are good people, processes, and technical reasons why many companies haven’t fully democratized access to their data, including:
Organizational silos.Â
In some companies, teams are formed 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 cross-functional knowledge sharing.
Reliance on specialists.
Many companies have long relied on data scientists, analysts, and other experts to interpret data. Some of these teams have become so overwhelmed with requests that decision makers have developed workarounds or stopped looking for data as part of their process. Changing these ingrained cultural paths can require a full review of business processes, which is a significant challenge.
Data complexity.
New technologies generate ever larger data sets. If data is not collected and contextualized, it is difficult for the average person to understand.
Dashboards and visualizations have emerged as possible solutions to these challenges. The Exasol study mentioned above showed that 82% of respondents use dashboards to communicate ideas across their company and it’s easy to see why. Dashboards can be integrated into any team’s processes eliminating the soiling of information. Their simplicity means 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. This is just one reason why many of those surveyed in the Exasol study reported that their companies routinely ignored the dashboards they set up. The other reasons: too long to interpret, too much information in general and not adequately adapted to individual needs. In a nutshell: Dashboards don’t tell stories, and stories are the key to communicating data and analysis results.
People are curious and think in terms of questions; This made the invention of the web browser and the Google search bar so revolutionary for the democratization of the Internet: Internet users could use expert knowledge to search for the websites and information of interest to them instead of going to the web for its content. Data analysis needs a similar search-driven tool to drive real democratization.
Three steps to true data democratization
The easy-to-use data democratization tool of the future will combine the power of big data and artificial intelligence with the ease of use of Google to provide data stories and insights in response to direct questions from individual users. Organizations can begin the transition to true data democratization by taking three steps to overcome today’s technology barriers:
Step 1:Â Â
Build a robust database 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 sources ensure that all information is always relevant and show changes in the market landscape in good time so that managers can make responsive decisions.
Step 2:
Use advanced analytics to make information in data understandable. Today, powerful machine learning (ML) and natural language processing (NLP) algorithms can extract context from data by creating simplified textual representations and applying macros (or rules) to those representations to determine semantics. From there, NLP can identify the sentiment behind a data point and match it with values ​​or taxonomy details that are unique to a particular market. For example, you can examine consumer opinions on a specific skin care ingredient or a recently published patent for product packaging presentations.
Step 3:
Scale your knowledge in one easy-to-use experience. The future of democratized data will follow the path of Google, with tools that enable the people in 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 missing in today’s data visualizations and dashboards, and that’s what made internet search so groundbreaking.
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