The cloud approach for your IT management: Webinar series

  • 07 July, 2021


Boosting Productivity and Achieving Business Visibility Through Data Democracy
- Sailakshmi Baskaran, Product consultant, ManageEngine

Data is at the center of today’s businesses. An organization’s ability to survive, let alone make progress, depends on its ability to utilize data effectively. This is not an easy task considering the 3Vs of data—volume, velocity, and variety. According to a recent IBM article, businesses generate 2.5 quintillion bytes of data every day; to give you some perspective, you would need 2.5 million 1TB hard drives to store all that data.

Traditionally, businesses have relied 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 topline need for businesses is data democratization, or the process of enabling everyone in the organization to access data for decision-making. By democratizing data, organizations can close the ever-present 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, who change their product prices roughly 50,000 times a day. This wouldn’t be possible without the retailers’ democratized data and decentralized decision-making for people working in different geographies and departments.

With all the potential value hidden within data, it’s no wonder organizations are looking for ways to effectively leverage their data. One way to do this is to clean, transform, and model data for visualization and analysis, which requires collaboration with data analysts and database administrators. While this system works to some extent, it creates a bottleneck where business leaders and strategists have to wait in line for information, during which time they may miss out on opportunities to increase revenue, productivity, quality, etc.

A more feasible, long-term strategy is to democratize data and leverage user-friendly analytics tools, so teams can access the required data when they need to build their own models. Business intelligence and analytics vendors offer a variety of tools—powered by artificial intelligence, machine learning, and predictive analytical capabilities—that can simplify and automate data analysis, so that knowledge workers can become data scientists and actively engage, visualize, and explore data to seek solutions to resolve day-to-day problems.

Barriers to Data Democratization  
Despite the numerous benefits of data democracy, Inc. reports that, on average, 60 to 73 percent of an organization’s data is not analyzed due to one or more of these reasons:

  • Outdated data culture and resistance to move away from traditional data analysis methods: Organizational culture is the biggest barrier to data democracy. Many organizations prefer to have centralized data analyst teams create reports for functional teams, which 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. With data democratization, data can be easily used by functional teams who are then empowered to make day-to-day-decisions and have more control over their operations.
  • Deep-rooted belief that data analytics is a specialized skill: 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.
  • 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.
  • Concerns about misrepresentation and duplication of data: 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.

IT Teams as Gatekeepers of Data  
Truth be told, organizations are fully aware of the benefits of data analysis. Many are just wary of the risks involved in giving employees free rein of organizational data. What organizations need is a simple solution that lets users securely see, interact with, analyze, and absorb information from data without physically moving it.

Traditionally, IT teams were tasked with deploying, maintaining, and updating business applications and services. But with the advent of Web 2.0, IT teams have evolved to handle the data and services of customer-facing applications. In fact, they’ve essentially become the gatekeepers of data in organizations. This makes IT teams the ideal candidates to facilitate data democratization. These teams should be in charge of maintaining a single source of truth, acquiring the right tools for data analysis, and creating a secure data distribution process that aligns with the data governance policies and compliance mandates required by the organization as a whole.

Putting Data to Good Use  
As BI and analytical tools continue to improve their offerings 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.

Below is a four-step approach to democratizing data for knowledge workers within your organization.

1. Create a Single Source of Truth
An organization’s data is often siloed in several stacks of applications and database layers. Create a single, reliable source of data storage to which only a few select administrators have the authority to make changes. Creating this primary data source can be a challenge and should be carried out by pooling data from various sources over time rather than all at once.

2. Choose a Data Democratization Model
To democratize data broadly, you need a model that’s flexible and adaptable to your organization’s governance and compliance policies. A few common models are:

  • The liberal model where users in an organization are given complete access to all non-personal company information. Users are provided adequate training on data analytics tools and taught data governance and compliance rules.
  • The conservative model where individual users are provided access to raw, segmented data on a per-request basis. In this model, users must receive approval from the data team or IT team to access the data. This model is usually adopted by organizations with access to extremely critical customer data, such as law firms and financial organizations.
  • The POC model where one member from each major team in an organization is appointed as the point of contact (POC) with regards to access and analysis of data. These members are tasked with ensuring secure access to data for the rest of their team members as well as ensuring users are correctly using the data. POCs are trained to work with and ensure the security of data, so they don’t often have specialized data analysis skills.
  • The hybrid model where a skilled data specialist is appointed to each major team and entrusted to act as the liaison between data and the members of the individual teams. These data specialists help team members analyze data and ensure it isn’t duplicated or misused.

3. Acquire, Set Up, and Distribute Data Analytics Tools

Self-service analytics tools allow novice users to create reports and gain actionable insights in a few clicks without seeking expert help. Some even feature built-in data governance and security controls that ensure data is available for consumption but remains secure.
Investing in self-service analytics tools will bridge the gap between data and the people in your organization, enabling them to resolve problems and discover opportunities themselves.

4. Empower Users to Access Data with Fine-Grained Access Controls
The final step in data democratization is to give users access to data sets relevant to their functions. Regardless of the democratization model you’ve chosen, it’s important to ensure data is secure and isn’t duplicated or misrepresented. This can be achieved by providing users access to data with fine-grained access controls such as read-only, read-write, report authoring, drill-down, and export.

By giving users forked access, they’ll have the freedom to explore and manipulate data without wondering if the information is accurate or worrying about accidentally deleting or modifying it, letting them learn more from their data without putting data integrity at risk.

Once Users Have Access to Data, Will They Use It?  
Deploying a self-service analytics tool and providing users with access to data is easy. However, it’s not as easy to create a data-driven culture where users turn to data instead of gut instinct to make decisions. That leaves organizations with the Herculean task of making data the cornerstone of problem-solving and decision-making.

With automation handling the bulk of redundant IT tasks, IT teams are finally in a position to actively contribute to their organization’s sustenance and growth. IT teams, which are most suited to serving as gatekeepers of data within the organization, can manage the task of maintaining data and creating secure data democratization processes that align with the data governance policies and rules of the organization. In turn, a top-down approach with C-suite executives and leaders pushing for autonomous, data-driven decision-making combined with data democracy can go a long way.

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The cloud approach for your IT management: Webinar series