Data is at the center of today’s businesses. An organization’s ability to survive, let alone make progress, depends on its ability to put data to good use. This is not an easy task considering the 3Vs of data—volume, velocity, and variety. According to a recent IBM article, businesses generate 2,500,000,000,000,000,000 bytes of data every day—2.5 quintillion bytes of data! To give you some perspective, you would need 2.5 million 1TB hard drives to store all that data.
Traditionally, businesses rely on data analysts to access and process large volumes of data. However, this process is time-consuming and often requires an organization’s leaders to make decisions based on stale data. The need of the hour is data democratization, the process of enabling everyone in the organization to access data for decision-making. By democratizing data, organizations can close the great divide between data analysts and decision makers.
Take Amazon for instance: The online retail giant deploys a clever pricing strategy that undercuts several of its competitors by selling many products at the least expensive prices and offering huge discounts. Amazon is known to change its product prices more than 2.5 million times a day, as opposed to Walmart or Best Buy that change their product prices only about 50,000 times a day. This wouldn’t be possible unless the retailers democratized data and decentralized decision-making to people working in different geographies and departments.
Despite the numerous benefits of data democratization, organizations continue to struggle in creating a liberated, data-driven work culture. A 2020 global study highlighted that about two-thirds (68%) of data available to enterprises goes unleveraged. Listed below are four major reasons that contribute to this situation.
1. Antiquated data culture
Organizational culture is the biggest barrier to data democracy. Several organizations prefer to have centralized data analyst teams create reports for functional teams. This structure can lead to delays in decision-making because functional teams often have to wait for analysts to crunch data. While this is acceptable for complex problems, such delays can be avoided for less complex issues if data is democratized and decentralized.
Data democratization frees up data for use by functional teams and empowers them to make day-to-day decisions. They can still rely on data analysts for complex reporting and analysis, but data democratization enables them to make better decisions and have more control over their operations.
Always relying on data analysts to gain insights delays the decision-making process and leads organizations to miss out on potential opportunities. If Amazon had to rely on data analysts to slash product prices in response to competition, it might miss out on crucial possibilities.
2. The myth that data analytics requires specialized skills
With the advent of artificial intelligence and machine learning, the notion that data analysis is a specialized task is outdated. A successful data democratization framework no longer requires extensive coding or advanced math skills. The heavy lifting can be easily delegated to a data analytics tool. So, knowledge workers can focus on generating unique insights that might otherwise be missed had the data analysis been delegated or outsourced to an external entity.
3. Lack of data security and privacy policies
From Microsoft to Estée Lauder, no organization is immune to security threats or data leaks. In 2020 alone, a staggering 36 billion records were compromised due to data breaches. Security concerns have forced organizations to remain skeptical about democratizing data among their personnel.
Implementing a successful data democratization framework involves creating or
updating a company’s data security and data governance policies. With the General Data Protection Regulation and other similar privacy frameworks enacted across the globe, it’s time for organizations to reassess their data governance policies, train their staff, and take advantage of data analytics.
4. Concerns about misrepresentation and duplication
The two biggest worries that plague decision makers are misrepresentation (non-technical users making incorrect assumptions) and duplication (too many users creating duplicate files and rapidly filling up databases). To ensure data isn’t duplicated or misrepresented, organizations should deploy granular access controls, such as read-write, read-only, report authoring, drill-down, and export controls, based on users’ job role, functional hierarchy, and other requirements.
Putting data to good use
The 2021 Digital Readiness Survey by ManageEngine points out that the adoption of business analytics tools has risen by 89% over the last two years. Improved decision-making was cited as the major reason for this increase. As such tools continue to improve their capabilities with augmented analysis and automation, businesses can expect to gain more insights that can be used to increase revenue considerably while slashing operational costs. This is why organizations should prioritize data democratization.