Data Democracy: What Is Preventing It

Image credit: iStockphoto/Johnny Valley

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. It is not an easy task considering the three V’s 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 organization leaders to make decisions based on stale data. The need of the hour is data democratization, 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 high 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 high frequency of price changes wouldn’t be possible unless the retailers democratized data and decentralized decision-making to people working in different geographies and departments.

The barriers to data democratization

Despite the numerous benefits of data democratization, organizations continue to struggle in creating a liberated, data-driven work culture due to one or more reasons below:

Antiquated data culture

Organizational culture is the most significant 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, you can avoid these delays for less complex issues if the 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.

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. You can easily delegate the heavy lifting to a data analytics tool. So, knowledge workers can generate unique insights that might otherwise be missed had the data analysis been delegated or outsourced to an external entity.

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 globally, it’s time for organizations to reassess their data governance policies, train their staff, and take advantage of data analytics.

Concerns about misrepresentation and duplication

The two biggest worries plaguing 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.

The role of IT in data democracy

With automation handling the bulk of redundant IT tasks, IT teams can finally contribute to their organization’s sustenance and growth actively. The candidates most suited to serve as gatekeepers of data within the organization, IT teams, can manage the task of maintaining data and creating secure data democratization processes that align with the organization’s data governance policies and rules.

Sailakshmi Baskaran, product consultant at ManageEngine, wrote this article.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/Johnny Valley