Data democratization is the practice of making digital data available to the average non-technical user of information systems without requiring IT’s assistance.
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End of a reign
A few data analysts with the knowledge and skills to properly arrange, crunch, and interpret data for their company had wielded enormous power over organizations. This happened due to necessity – most employees were uneducated on employing the growing tide of data effectively. With the advent of technologies that enable data to be shared and interpreted by non-data experts, things have changed. Data democratization allows data to flow freely from the hands of a few experts into the hands of countless employees throughout a business, acting as a foundation for self-service analytics.
A recent Google Cloud and Harvard Business Review poll showed that 97% of the industry leaders believe free access to data and analytics throughout an organization is essential to success. However, only 60% of respondents think their companies presently distribute access equally. According to Exasol’s findings, 90% of CEOs and data specialists are focusing on data democratization for their businesses.
What is data democratization and why is it important?
Data democratization implies that everyone has access to data. The objective is for anybody to utilize data in any manner to make smart judgments with no limits on access or comprehension.
Data democratization entails that everyone has access to data, and there are no gatekeepers preventing people from accessing it. It necessitates that we provide easy access to the data and instructions on how to interpret it so that individuals may utilize it to hurry decision-making and uncover possibilities for an organization. The objective is for anybody at any time to use data.
Until recently, IT departments controlled the data. Marketers, business analysts, and executives used the data to make commercial judgments, but they had to go through with the IT department to obtain it. This was how it’s been for most of five decades, and there are still a few people who believe it should stay that way. However, data democratization aims otherwise.
The advocates of data democratization believe that allowing everyone accesses to the same data across all business teams gives your company a competitive edge. More individuals with diverse expertise who have easy and quick access to the data can help your business discover and act on key business insights. Many experts think that data democratization is a game-changer.
The capacity to access and comprehend data instantly will lead to faster decision-making, which will result in more agile teams. Those companies with an advantage over slower data-stingy organizations would have a leg up on the competition.
When a business gives data access to all levels of the organization, it allows individuals of all ownership and responsibility to utilize such data in their decision-making. Team members are more data-driven when data democratization encourages them to go around data to accomplish tasks on time. When bad or good events occur, the responsible professionals are promptly informed, and they can examine and comprehend those anomalies to help them be proactively aware.
Finally, data democratization is a must for marketers trying to deliver the best customer experience possible. The question they should be asking isn’t whether data democratization is a necessity; rather, it’s how they can get it implemented quickly and effectively for their company.
How to democratize data?
Data democratization implies a financial, software, and training commitment from management. But data democratization can’t be dissociated from data governance. Data democratization is an act of a data governance strategy.
Breaking down data silos is a necessary step to user empowerment. This can’t be done with generic analytics tools that can desegregate and link formerly segmented data, making it easier to access from a single location.
Ideally, according to their position, the tools will filter the information and visualizations supplied to each individual — whether they are a senior executive, a director, or a designer. Marketing managers, for example, will require data that lets them analyze customer groups leading up to a new campaign. On the other hand, CMOs will need data to evaluate marketing ROI as they create next year’s budgets.
For the most part, organizations place a high value on employee data visualization. These tools need to help people make sense of their data. Customers must understand how the information is represented graphically. These visualizations must be in line with corporate KPIs, such as metrics, goals, targets, and objectives aligned from the top that enable data-driven decisions.
Team training becomes the next crucial step with the appropriate tools in place. Because data democratization is based on self-service analytics, every team member must be trained to a certain level of competence with the technology, ideas, and procedures required to participate.
Finally, you can’t have a democracy without checks and balances, which is the final component of data governance. Data can be misused or mishandled in a variety of ways. As a result, setting up a data center of excellence is necessary to ensure that data usage is kept on track. Companies should encourage the adoption of data usage in line with their capacity to own data accuracy.
Steps for a successful data democratization
Three simple actions may be taken by businesses to begin the process of data democratization:
- Build a robust data foundation that comprises an extensive range of internal and external data sources across the entire market, not just one brand or product. Data feeds that are constantly updated will guarantee that all information remains up to date, allowing leaders to make timely decisions based on changes in the market landscape.
- Make data insights understandable by utilizing advanced analytics. Today, sophisticated machine learning (ML) and natural language processing (NLP)” algorithms can extract context from data by generating simplified representations of text and applying macros (or rules) to those representations to determine meanings. NLP can analyze a data point’s tone and connect it with taxonomies’ unique characteristics, allowing you to go deeper into the information.
- Scale the insights within a user-friendly experience. The future of data accessible to everyone is accompanied by tools that enable individuals across a company to access simple-to-understand. These data-driven narratives address issues and solve problems. The key is for these tools to be attentive to user requirements; this is lacking in most of today’s data visualization and dashboard tools.
Benefits of data democratization
The advantages of data democracy become more apparent as organizations comprehend and effectively tackle the risks listed above:
- Improved decision-making: Businesses can benefit from a first-to-market position by taking advantage of current trends and consumer needs. The data is accessible to all employees, which allows the entire organization to make comparable and aligned judgments.
- Employee empowerment: Teams and individuals can have greater confidence in taking on a company problem with access to data. Data scientists devote about half of their time to making data usable. Reducing internal processes and diverting data teams toward more strategic activities may save time and effort.
- More data investment ROI: Empowering everyone in your company to utilize data to make informed judgments will guarantee you get the most out of every data point you invested.
- Better customer insights: There’s a plethora of external data on the market and customers. Understanding this data allows you to make better consumer-centric decisions that lead to a superior customer experience and greater market share.
- Unparalleled flexibility: When the market or consumer changes, the data will reflect it. Then you will be able to make proactive rather than reactive judgments.
Why do some organizations approach data democratization with caution?
Some organizations are still concerned that non-technical team members could misinterpret data, and these staff would make poor judgments due to their incorrect understanding of the data.
Another argument supports the notion that as the number of people who have access to data rises, the risk of data security breaches and difficulties in maintaining data integrity increases.
Although there has been significant progress in recent years, data silos still exist. This reality still makes it difficult for people from various departments to access information and view it.
Another worry about data decentralization is the potential for duplication of effort across several teams, which might be more expensive than a centralized analysis team.
Once a silo user, always a silo user?
Changing company cultures is easier said than done. A significant part of the difficulties that complicate data democratization stems from employee and team habits, which can be evaluated within the scope of company culture. Moreover, this situation often arises from the past decisions and approaches of the management. Teams are sometimes organized independently. They don’t share internal or external data to make decisions, and there isn’t a strong culture of sharing insights across functions.
These ongoing habits have increased the need for data scientists, analysts, and other technical experts to interpret data for many companies. Some of these companies have been so clogged with requests that decision-makers have come up with workarounds or stopped looking for data as part of their procedure. It may be tough to transform entrenched cultural habits, which will require a comprehensive overhaul of the company process.
Finally, as the technology gathers more and more data, the quantity of data sets has increased. Unless that data is gathered and contextualized, most people will not comprehend it. Data dashboards and visualizations have popped up as possible solutions to these challenges.