Data democratization, much like the term digital transformation five years ago, has become a popular buzzword throughout organizations, from IT departments to the C-suite. It’s often described as a way to simply increase data access, but the transition is about far more than that. When effectively implemented, a data democracy simplifies the data stack, eliminates data gatekeepers, and makes the company’s comprehensive data platform easily accessible by different teams via a user-friendly dashboard.
Beyond the technical aspects, the goals are far loftier. When done well, data democratization empowers employees with tools that let everyone work with data, not just the data scientists. It can spark employees’ curiosity and spur innovation. When workers get their hands on the right data, it not only gives them what they need to solve problems, but also prompts them to ask, “What else can I do with data?” through a truly data literate organization.
In this article, we’ll explore the benefits of data democratization and how companies can overcome the challenges of transitioning to this new approach to data.
What is data democratization?
Data democratization helps companies make data-driven decisions by creating systems and adopting tools that allow anyone in the organization, regardless of their technical background, to access, use and talk about the data they need with ease. Instead of seeing data given with consent as the output of workers clients and prospects, it’s now the company’s gateway to strategic decision-making.
For true data democratization, both employees and consumers need to have data in an easy-to-use format to maximize its value. It also requires data literacy throughout the organization. Employees and leaders need to trust the data is accurate, know how to access it, as well as how it could be applied to business problems. In turn, they both must also have the data literacy skills to be able to verify the data’s accuracy, ensure its security, and provide or follow guidance on when and how it should be used.
Data democratization is often conflated with data transparency, which refers to processes that help ensure data accuracy and easy access to data regardless of its location or the application that created it. Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.
Architecture for data democratization
Data democratization requires a move away from traditional “data at rest” architecture, which is meant for storing static data. Traditionally, data was seen as information to be put on reserve, only called upon during customer interactions or executing a program. Today, the way businesses use data is much more fluid; data literate employees use data across hundreds of apps, analyze data for better decision-making, and access data from numerous locations.
Data democratization uses a fit-for-purpose data architecture that is designed for the way today’s businesses operate, in real-time. It’s distributed both in the cloud and on-premises, allowing extensive use and movement across clouds, apps and networks, as well as stores of data at rest. An architecture designed for data democratization aims to be flexible, integrated, agile and secure to enable the use of data and artificial intelligence (AI) at scale. Here are some examples of the types of architectures well suited for data democratization.
Data fabric architectures are designed to connect data platforms with the applications where users interact with information for simplified data access in an organization and self-service data consumption. By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance.
Data within a data fabric is defined using metadata and may be stored in a data lake, a low-cost storage environment that houses large stores of structured, semi-structured and unstructured data for business analytics, machine learning and other broad applications.
Another approach to data democratization uses a data mesh, a decentralized architecture that organizes data by a specific business domain. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata. Then, it applies these insights to automate and orchestrate the data lifecycle. Instead of handling extract, transform and load (ETL) operations within a data lake, a data mesh defines the data as a product in multiple repositories, each given its own domain for managing its data pipeline.
Like microservices architecture where lightweight services are coupled together, a data mesh uses functional domains to set parameters around the data. This lets users across the organization treat the data like a product with widespread access. For example, marketing, sales and customer service teams would have their own domains, providing more ownership to the producers of a given dataset, while still allowing for sharing across different teams.
Data fabric and data mesh architectures are not mutually exclusive; they can even be used to complement each other. For example, a data fabric can make the data mesh stronger because it can automate key processes, such as creating data products faster, enforcing global governance, and making it easier to orchestrate the combination of multiple data products.
As more organizations seek to evolve toward a culture of data democratization and build the architecture to support a data literate culture, they’ll realize several benefits—and encounter a few challenges along the way. Here are some advantages—and potential risk—to consider during this organizational change:
Many companies look to data democratization to eliminate silos and get more out of their data across departments. The necessary data integration it requires reduces data bottlenecks, enabling business users to make faster business decisions and freeing up technical users to prioritize tasks that better utilize their skillsets. The result is greater efficiency and productivity.
Data security is a high priority. Data democratization inherently helps companies improve data security processes by requiring deliberate and constant attention to data governance and data integrity. There is a thoughtful focus on oversight and getting the right data in the hands of the right people resulting in a more comprehensive data security strategy.
Risk of data swamps
A data swamp is the result of a poorly managed data lake that lacks appropriate data quality and data governance practices to provide insightful learnings, rendering the data useless. Too many businesses struggle with poor data quality; data democratization aims to tackle this problem with comprehensive oversight and data governance. By recognizing data as a product, it creates greater incentive to properly manage data.
Agile data use
Data democratization counteracts the problem of data gravity, or the idea that data becomes more difficult to move as it grows in size. Things like massive stores of customer data are approached more strategically, allowing companies to maintain access as the company scales.
Data democratization seeks to make data more accessible to non-technical users, in part, by making the tools that access the data easier to use. This includes tools that do not require advanced technical skill or deep understanding of data analytics to use.
How to get started with data democratization
As with any major change in business operations, companies should develop a comprehensive data strategy to reach their data democratization goals. Key steps include:
Define business and data objectives–What are your company’s goals? What are your data and AI objectives? The alignment of data and business goals is essential for data democratization. By tapping the expertise of stakeholders, you can ensure your objectives are inclusive and realistic.
Perform a data audit–How is data managed today? Examine what’s working, what is not and identify bottlenecks and areas where better tools and increased access are needed. Understanding the current status of your data management helps you understand what changes the organization needs to make.
Map a data framework–When you achieve full data democratization, what will that look like? Design a path toward that goal, defining where application modernization, data analysis, automation and AI can help get you there.
Establish controls–This is where you lean on data allies to help with compliance across the organization. How will data standards and process be communicated and enforced? Use this step to create and implement data governance policies.
Integrate your data–It’s common for organizations to suffer from a lack of visibility between departments. Implementing data democratization means breaking down these siloes and designing a way to effectively integrate processes in a way that encourages adoption.
Train and empower employees–Successful implementation of data democratization requires employees to have the right level of data literacy to access and use the data effectively. Look to data leaders to drive adoption and make data literacy part of the new hiring process. Train employees on how data democratization can improve their work outcomes and improve customer experience.
Use data democratization to scale AI
Once your data democratization journey has begun, teams can begin to look at what this new data paradigm can bring, including advancing new tools like AI and machine learning. Here are some ways companies can use data democratization to enable wider AI implementation:
Define AI use cases
Discuss business analytics and automation priorities and decide where to implement AI first. For example, you may want to invest in analytics tools to develop internal business intelligence reports, real-time customer service chatbots and self-service analytics for different business teams. It’s likely you can’t manage implementing these AI tools all at once, so define the best areas to use AI first.
Identify data sets
Not all data within your company is right for AI, or use cases for that matter. Examine your data sets and determine which ones are right for further research to see if they will help you tackle relevant use cases. With data democratization in place, your company should have greater insights into the quality and availability of data to drive this process, and the ROI for each use case.
Use MLOps for scalability
The development of machine learning (ML) models is notoriously error-prone and time-consuming. MLOps creates a process where it’s easier to cull insights from business data. It also optimizes the process with machine learning operations (MLOps) which uses prebuilt ML models designed to automate the ML model-building process.
Make AI transparent
Data democratization ensures data collection, model building, deploying, managing and monitoring are visible. This results in more marketable AI-driven products and greater accountability.
IBM and data democratization
There are two key elements for data democratization: it starts with the right data architecture, but is amplified by the right automation and AI solutions. IBM offers a modern approach to designing and implementing a data fabric architecture that helps organizations experience the benefits of data fabric in a unified platform that makes all data—spanning hybrid and multicloud environments—available for AI and data analytics.
Watsonx is a next generation data and AI platform built to help organizations multiply the power of AI for business. The platform comprises three powerful components: the watsonx.ai studio for new foundation models, generative AI and machine learning; the watsonx.data fit-for-purpose store for the flexibility of a data lake and the performance of a data warehouse; plus, the watsonx.governance toolkit, to enable AI workflows that are built with responsibility, transparency and explainability.
Together, watsonx offers organizations the ability to:
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