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Data Democratization

Data Democratization Definition

Data democratization refers to the process of making digital information easily accessible for each member of an organization. This process is driven by solutions such as self-service BI applications, citizen scientists using AI, data visualization and cloud migration in general. The goal is to empower non-specialist users to access data independently to make better, data-driven decisions.

Data Democratization FAQs

What is Data Democratization?

What does data democratization mean? Data democratization is the continuous process by which all of the members within an organization are empowered not only to access, but also understand and leverage the full power of all the data at their disposal. The goal of democratization of data is to improve average users’ data literacy and provide them with the tools they need to confidently and comfortably explore data. Whereas data traditionally has been owned by IT groups, democratization puts data into the hands of everyone on every tier of an organization so that they gain new insights and let the data inform their decision-making process.

Why is Data Democratization Important for Businesses?

The volume of data to which businesses have access is enormous, and growing by the day. But more data is not always better if you don’t have the tools or sufficient skills to manage and understand big data. Only businesses that equip all of their employees with the tools they need to fully harness the power of their data can maintain a competitive advantage. If business users must first put in a request to the IT team to have access to certain data and then wait for the results, then they are at the behest of the IT team’s bandwidth and will be wasting precious time waiting for the results, which no business can afford in the era of real-time results. 

Democratization removes that barrier by fostering a culture in which everyone, not just highly technical users, is able to and comfortable with using data to discover new opportunities and expedite decision-making. When a great number and wider variety of people with varying skills and backgrounds are given the ability to access data, unique perspectives can be explored and quicker action can be taken on crucial business insights. While data democratization requires training and may require a culture shift, it benefits the business in the long run as it facilitates organizational agility and solves the bottleneck issue associated with outdated data silos.  

What is the Data Democratization Process?

The main function of the democratization of data is to solve problems. In a business setting, the democratization process involves helping team members: access the data that they need, access data that is trustworthy, develop the necessary skills to find answers in the data, develop the confidence to independently access and analyze data, and access tools that are designed to handle their particular interests.

Businesses can start democratizing data by first assessing the data literacy of different members and identifying the data needs of each team. It’s not a one-size-fits-all approach – different data needs require different levels of data literacy.

Once you’ve made these assessments, you can determine the most effective tools for each team. In business, there are typically seven different divisions that have very different data needs: Marketing, Growth, Product & Engineering, Support, Customer Success, Sales, and Executive:

  • Marketing teams need access to behavioral data and tools that will help them create content that drives conversions.
  • Sales needs intent and technographic data to determine the likelihood of a prospect converting.
  • Executives need access to performance data in order to determine where the business is falling short and where to make future investments.
  • Support needs to be able to see historic and real-time data related to user experience within a product.
  • Customer Success needs usage pattern data in order to ask customers the most relevant and helpful questions, and improve the overall customer experience.
  • Product & Engineering teams use Quantitative Product Metrics and qualitative customer feedback data in order to determine which features to build and maintain and which features to scrap.
  • Growth teams need a combination of product usage data and qualitative data analysis to run experiments and apply to things like conversion optimization, Search Engine Optimization, product retention, and pricing strategies.

Once you have a solid grasp of the data your teams are working with, you can start looking to invest in a set of compatible tools. You’ll also need a data specialist or team dedicated to the ongoing process of implementing and maintaining modern data stacks. 
Most importantly, a data democracy can only be built and thrive within a business whose culture supports it. Ongoing education, encouraging independence, and opportunities for everyone to make meaningful contributions are all crucial tenants of an effective data democratization strategy.

What are the Data Democratization Benefits?

If your business embraces the culture of democracy, actively participates in ongoing education, and implements the most relevant and intuitive tools, the benefits of data democratization that your teams can enjoy are numerous and varied. Those benefits include: 

  • Empowers team members: People with the confidence, technical know-how, and right tool at their disposal can truly get the most out of the data available to them.
  • Improves information access: Data that was once siloed in data lakes and unavailable to the average user is unlocked when you democratize data access.
  • Alleviates resource limitations: Valuable IT resources are freed up when they are no longer the sole point of contact for fulfilling all data and reporting requests. 
  • Better collaboration: Increased data literacy means better communication, which leads to more diverse perspectives, better innovation, and more creative problem solving. 
  • More trustworthy data: Data literacy in combination with the right tools means access to the most accurate, relevant, and recent data. 
  • Cost saving: Data democratization increases efficiencies throughout the business, which saves money in the long-run.  
  • Time saving: Swapping manual sourcing and integration of data with automated tools allows team members to focus on more valuable tasks. 
  • Data-driven decisions: Data democratization increases the number of decisions and strategies that can be informed by data. 

What Should Data Democratization Include?

One of the most crucial elements that must be in place in order to democratize data is data governance. Data governance ensures that internal data standards and policies are applied to the availability, integrity, security, and usability of the data in an enterprise. This process establishes data access controls and ensures that data quality is trustworthy, consistent, and available to the right people before any users are granted access. Without data governance, data is very likely to fall into the wrong hands and could have severe consequences.

What are the Steps in Data Democratization?

No matter what kind of business you’re in, the data democratization architecture is composed of the same general principles. Here’s an approximate overview of the steps:

  • You want all members of your organization to have access to valuable data – but which data, and where? The first step in democratizing data is being able to identify where the desired data lives: in the cloud, on-premise, or a combination? One should also be able to take inventory of the software and technologies being used to capture, store, and analyze the data. If a team member is unable to articulate what kind of data they need and from where they need it, then that is an opportunity to improve data literacy. 
  • Next, assess the data literacy of the members of your organization. Different groups in an organization require different levels of data literacy. Some groups may require a simple quiz to determine if they grasp the basics. Other groups may need an assessment from a reliable third-party consultant to determine if they possess sufficient data engineering knowledge. 
  • Now you can assess potential data solutions. Different teams require different tools for data democratization. Marketing, Sales, Customer Success, Support, Product & Engineering, and Executive teams all require different data in different formats at different times. Technology review sites, testimonials from informed peers, and product demos are great ways to assess different tools. Be sure to keep budget, market prominence, reputation, and scalability in mind when choosing the best tools for your teams. 
  • Finally, ensure you are investing in proper training and continuous education. Once your teams have integrated new data democratization tools and technologies, make sure ongoing education is available and frequent check-ins are performed so that team members are deriving maximum value from these data tools. Effective ongoing training will result in empowered employees and a strong return on your investment.

Data Democratization Best Practices

Whether you’re part of a smaller organization or a massive enterprise, there are a few data democratization best practices that can be applied to any team to ensure they’re getting the most out of their data:

  • Leverage software solutions: Even in a small organization, a considerable volume of data is generated each day and spread out and siloed across the company’s landscape. Leverage software for data democratization like data catalogs to enhance data discoverability. Data catalog software bridges the gap between IT and average users, sets controls for who can discover registered data assets, and enables users to store data where they want it and connect data with the tools they want. 
  • Choose flexible products: Agnostic solutions are compatible with different systems and platforms and can evolve with your organization. Invest in data integration tools that can combine legacy data with modern infrastructures, and offer instant API connectivity to both popular databases and also cloud-based systems. This ensures interoperability. AI-powered data democratization tools not only make data accessible, but also provide critical context. 
  • Prioritize tools with longevity: While short-term profits are attractive, be sure to invest in solutions that can stand the test of time and grow with your organization. Many data tools are not self-service, so make sure you invest in a solution that is intuitive, encourages independent use, and will benefit future projects and team members.  
  • Get employee input: The best way to get your team members on board with data democratization is to involve them in the process. A team is only as good as the tools in their kit, so make sure you canvas your teams to determine if they find the tools at their disposal valuable and will actually use them. 
  • Choose an Intuitive UI: One of the most important features of a data analytics tool is its usability. A tool is only as useful as your team’s willingness to use it. Schedule demos of different products so that the team members can try it out and decide on which user interface it is easiest to perform analysis, run reports, and navigate.  

What are Data Democratization Examples?

A great example of the democratization of data and analytics in practice is Airbnb’s Dataportal. Dataportal, Airbnb’s home-bred data discovery solution, is a self-service tool that enables all members throughout the company to discover, understand, and use data assets to make better, informed business decisions. The goal of Dataportal is to help users navigate the explosion of available resources, which comes in a wide variety of complexity, quality, relevance, and trustworthiness. The Dataportal provides a framework for best practices with data, and provides transparency to Airbnb’s complex and obscure data landscape. The main features and capabilities of Dataportal consist of Search functionality, Context & Metadata options, Employee-centric Data, and Team-centric Data. Read more about the technical specs on the Airbnb Tech Blog.

Even retail giant Walmart has engaged in data democratization. Walmart launched Data Cafe, a state-of-the-art analytics hub where teams from any part of the business are invited to find answers to complex business questions from over two hundred streams of internal and external data, which can be modeled and visualized on advanced “smart boards.” Time to insights is decreased drastically, and with 2.5 petabytes of data processed every hour, time–saving strategies are incredibly valuable. Team members are empowered to go to the Data Cafe and drill down on specific data to pinpoint the cause of shortcomings in their department. This information can then be used to inform their response strategy.  

What are Data Democratization Tools?

The tools involved in democratizing data in an organization include:

  • Data warehouse: data warehouses like Snowflake, BigQuery, and Firebolt help users access data for analysis and activation
  • Data mart: A data mart is a subset of data warehouse that enables specific users to access specific data so that they can quickly access these datasets without having to search the whole data warehouse. 
  • Business Intelligence (BI) tool: BI tools sit on top of the data warehouse and facilitate self-service analytics.
  • ELT tool: like Airbyte, Fivetran, or Meltano to move data from third-party tools (like the ones mentioned above) into the warehouse
  • Reverse ETL tool: Reverse ETL tools move modeled data back from the warehouse to third-party tools.
  • Data catalog: A data catalog is a collection of metadata that, in combination with data management and search tools, provides managed and scalable data discovery and metadata management capabilities to empower users to discover and acquire data for downstream analytics tools.
  • Metrics catalog: A metrics catalog is a self-service centralized store for all of an organization’s KPIs that every user can access to track their own metrics and conduct a democratic data exchange with others. This provides superior visibility and transparency into KPIs for all team members, allows disparate teams to use the same metrics, and enables diverse users to base all key decisions on the same foundation.
  • Database security: That provides the authorization, authentication and auditing controls to allow appropriate access to the data given its importance to the organization and appeal to bad actors.

Who is Responsible for Data Democratization?

Democratizing data is a collaborative group effort, so, really, everyone is responsible for its success. Only with full participation from everyone in the company can a data democracy succeed. As the name suggests, the goal of a democracy is to give power to the people. While it is the responsibility of the executive team to encourage independence and provide ongoing training and tools, it is the responsibility of all the members of the organization to stay informed, and really take advantage of all available tools and data in order to solve problems quickly and creatively, and make data-driven decisions that will give the company a competitive edge.

Does Cyral Offer a Data Democratization Solution?

There is a growing need for data governance and data security control technologies that are designed for the self-service, democratized data experience. Without first establishing proper data governance and data security in an organization, data democratization can result in real chaos. Cyral’s enterprise solutions ensure that the right people are accessing the right data without slowing down workflows or creating unnecessary frustrations for team members trying to participate in democratizing data. 

Cyral accelerates data democratization by helping to create guardrails – not speed bumps – for today’s data consumers. To truly harness the benefits of data democratization, IT, DevOps, and data security teams must learn how to properly govern access across their growing ecosystem of databases, data lakes, and data services landscape. Cyral provides a Security as Code approach to easily govern all access to the Data Mesh. Cyral’s cloud-native service is built on a stateless interception technology that provides real-time monitoring of all data endpoint activity and enables unified visibility, identity federation, and granular access controls. 
Cyral’s Identity-First approach to data security and data access governance ensures that you can always see, control, and protect every piece of your data in databases and data lakes. This approach fosters trust and confidence – crucial components in a data democracy. Find out more about Cyral’s Data Democratization solution here.

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