In an era where data is a powerful undercurrent driving all aspects of a business, its accessibility and comprehension are paramount. Business owners must understand the importance of data democratization in modern organizations to support positive outcomes. What is data democratization?
What is Data Democratization?
Data democratization is the process of ensuring data is readily accessible and understandable to everyone in an organization. Data democratization takes into account various levels of data literacy and aims to empower every employee to gather and analyze data. With access to corporate data, employees can make educated decisions without bottlenecks. Organizations may include self-service analytics tools in their data democratization strategy to encourage problem-solving and boost efficiency.
This guide takes a detailed look at data democratization, shares the benefits, addresses challenges, and outlines steps to implement this process.
Core Principles of Data Democratization
Data democratization can be broken down into three primary principles: accessibility, data literacy, and governance and security.
Accessibility
Accessibility is a key component of data democratization because it increases the accuracy and efficiency of decision-making in modern businesses. Data democratization seeks to ensure data is available to all team members, regardless of technical background. This means data should be accessible to marketing, product, engineering, sales, executive, and all other teams. Different teams vary in how they work with data and in their technical know-how.
With data democratization, it doesn’t matter what team employees are on — they should be able to access and read data and act upon its insights.
Data Literacy
Data literacy refers to an individual’s understanding of reading, interpreting, and acting on data. Data democratization efforts include encouraging a culture of data understanding across an organization. Specialists and leadership alike should feel comfortable asking data-related questions.
Not everyone is a data expert. Non-technical employees should feel confident enough through data democratization to locate, read, and find answers to queries. An organization must continuously refine data democratization to empower every employee to make informed decisions.
Governance and Security
The last principles of data democratization are governance and security. When implementing data democratization in an organization, leaders must balance accessibility with security. The goal is to achieve appropriate levels of data access integrated with robust security to ensure confidential data doesn’t get into the wrong hands.
To securely access data, implement role-based access controls so individuals can only access the data they need for their role. This prevents unauthorized or accidental access to sensitive data. Consider (2FA) for data warehouse access as an extra layer of protection.
Benefits of Data Democratization
Data democratization is a powerful process that affects every employee of an organization. The benefits range from stronger internal decision-making to elevated customer experiences. Read on for an in-depth review of these benefits.
Empowered Decision-Making
Data is the driver of most critical business decisions. Company leaders can break down silos with corporate data democratization. With access to all the information they need in an easily digestible format, teams can make informed, data-driven decisions.
Data-driven decision-making has been shown to improve customer retention and attrition, as well as employee satisfaction. Not to mention, many employees rely on data to effectively perform their job duties. Whether an employee works in marketing, customer service, or engineering, data often plays an important role. Continuously improving data democratization supports teams in making decisions that accurately reflect business insights.
Increased Efficiency and Collaboration
Business leaders can facilitate cross-departmental collaboration by investing in data democratization. Employees can connect over shared insights by enabling team members from different departments to access and understand data. For example, a product development team can collaborate with a paid advertising team to update ad creatives based on A/B testing data.
With data democratization, employees should feel comfortable interpreting and using data to have informed and strategic conversations with colleagues. After removing barriers, organizations may notice increased collaboration and efficiency among teams.
Enhanced Customer Experience
Data — especially consumer data — is critical to improving personalization and customer satisfaction. Teams should use accessible data, such as purchasing behavior, preferences, and patterns, to create highly personalized customer experiences. Marketing, engineering, and sales teams can utilize insights and work together to deliver customized consumer experiences.
Customer data is only useful if teams can read and interpret it accurately. Data democratization aims to remove access barriers and educate teams on using the data available to them. Data and analytics can elevate customer experiences by enabling teams to make decisions based on real-time insights.
Challenges in Implementing Data Democratization
Data democratization is a process that takes time and should be continuously monitored by organizations. The challenges of data democratization range from integrating data sources effectively to maintaining data literacy among employees.
Data Silos
When data is stored within different departments, teams may make decisions based on incomplete or inconsistent data. Fragmented data access causes inefficiencies. To overcome this challenge, organizations can integrate tools that aggregate data from various sources into a centralized data platform.
Security Concerns
Another issue organizations face is addressing data security and compliance concerns while simultaneously providing access to end-users. Data security concerns cause some organizations to hold back from data democratization. When IT teams are in charge of data and hold the keys to access it, organizations may feel less risk of data leaks or breaches. However, a data democratization strategy should implement checks and balances to ensure confidential information is not at risk.
Data Literacy
Organizations may also struggle with building and maintaining data literacy among non-technical employees. With limited data literacy, teams can experience data misinterpretation or mistrust, leading to uninformed decision-making. Organizations must consider their current analysis tools to ensure that non-technical employees can access and use data.
Organizations should continuously encourage frequent data literacy training for employees across the board. Employees can collaborate with colleagues to learn how to use an organization’s tools and software effectively to analyze data. Businesses should gather feedback and conduct employee check-ins to get a pulse on where employees’ data literacy stands.
Steps to Achieve Data Democratization
Understanding data democratization is one thing — achieving it is another. To create a balanced playing field where all employees feel confident with business data, organizations should follow these steps:
1. Develop a Data Strategy
First, business owners should evaluate organizational goals for data use. These goals could include:
- Improving decision-making processes
- Enhancing customer personalization and satisfaction
- Driving innovation through predictive analysis
- Boosting operational efficiency
When developing a data strategy, business leaders should ensure data use aligns with broader organizational goals and objectives. Next, identify the KPIs that are most relevant to achieving the goals.
Teams should also set clear policies and expectations surrounding data use. Setting data policies and standards protects data from being misused or mishandled. Organizations should designate a team or a few individuals to oversee data governance, security, and accuracy. Additionally, organizations should implement guidelines to comply with data protection regulations such as GDPR and HIPAA.
2. Invest in Data Tools
Leadership teams should introduce user-friendly platforms to simplify data access and analysis to further enhance an organization’s data democratization. Advanced AI/BI tools can be used to broaden access and create easy-to-understand data visualizations. Centralize data so that end-users only need to access a single source of truth platform. By centralizing data, teams can prevent silos and streamline collaboration.
Cloud-first enterprises can achieve analytics democratization by utilizing a semantic layer platform. A semantic layer consolidates data across an organization to deliver accurate, easy-to-understand corporate data without technical expertise.
3. Educate and Train Employees
Educating and building a data-literate workforce is key to achieving data democratization. Leadership teams can promote skill growth among their workforce through ongoing training and support. Technical experts may need to conduct training sessions to ensure non-technical employees understand how to utilize an organization’s data tools.
Additionally, educate employees on general data awareness to support a holistic view of how the organization handles data. Employees at various levels should feel confident handling any required data securely. Training sessions allow non-technical employees to ask questions and learn from their more experienced technical counterparts.
Tools and Technologies for Data Democratization
Different platforms can help facilitate data access, such as data visualization tools, BI platforms, and semantic layers. Tools like AtScale centralize data, making insights available to all members of an organization while ensuring secure and governed use.
With data visualization tools, organizations transform data points into graphs and dashboards that non-technical employees can easily understand. Popular data visualization tools, like Looker and Tableau, enable teams to create customized dashboards to view the data they need.
BI platforms, such as Qlik and Power BI, simplify querying and analyzing data from multiple sources. These platforms promote increased collaboration between teams by their ability to quickly share reports and insights. Predictive modeling is a common feature of BI platforms, enabling teams to find opportunities, forecast trends, and evaluate risks.
A semantic layer translates complex database structures into business terms that the average end-user understands. This powerful tool standardizes metrics and creates a single source of truth for employees to reference. End-users can even query data using non-technical business language to find the necessary information.
Real-World Examples of Data Democratization
Airbnb
In 2017, Airbnb introduced its Dataportal to “democratize data and empower Airbnb employees to be data-informed by aiding with data exploration, discovery, and trust.” This online homestay company noticed employees having difficulties navigating the company’s data landscape. The Airbnb team developed an integrated data space to empower employees to access critical data. The Dataportal offers a unified search feature that enables non-technical employees to quickly find exactly what they are searching for. This data democratization helped Airbnb deliver stellar customer experiences by breaking down barriers to essential data.
Netflix
Netflix’s approach to data democratization is also admirable. This streaming platform giant utilizes open-source tools and custom-built solutions to create an accessible data landscape for employees. Employees can easily analyze viewer behavior data to make informed decisions on profitable content acquisition. With Netflix’s focus on data democratization, employees can strategically use data to enhance subscriber satisfaction.
How We Can Help
In today’s data-driven landscape, organizations should understand the vital role data democratization plays in business success. Organizations can support informed decision-making by empowering all employees to confidently access and interpret data.
With AtScale’s semantic layer, businesses can view corporate data pulled from AI/BI tools quickly and securely, simplifying data access by translating technical data and code into user-friendly metrics into a single source of truth. Users don’t need SQL or database architecture expertise to access and analyze data with AtScale.
Additionally, we offer live data access without the need for movement or preprocessing, allowing organizations can unify their data ecosystem by using multi-protocol deep integrations that seamlessly converge with BI platforms and visualization tools.
Explore how AtScale’s semantic layer platform helps democratize data and provide secure access to actionable insights. Request a demo.
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