In today’s data-driven landscape, the concept of data democratization emerges as a beacon of empowerment. Imagine an environment where every team member, from the technical experts to the non-technical visionaries, has the ability to access, analyze, and harness data to drive decisions. This is the essence of data democratization – a transformative approach that has the potential to reshape the way organizations operate and thrive. Data democratization isn’t merely a buzzword; it’s a strategic initiative that seeks to break down the traditional barriers surrounding data access and utilization.
What is the primary purpose of data democratization?
The primary purpose of data democratization is that it ensures an efficient method of analyzing big data to influence data-driven decisions via readily accessible and reliable data for different teams and even non-technical users. This eliminates the frustration of requesting access, sorting information or reaching out to IT. In fact, data literacy is embedded throughout the organization, where everyone can learn, access, and understand data to leverage it for data-driven business decisions. As a result, this enables organizations to deliver entrusted data rapidly to data consumers with higher quality and quantity.
What are the risks of data democratization?
Like everyone else, we’re excited about the promise of democratizing data and improved decision-making, but there are concerns organizations should mitigate to feel confident utilizing data and more importantly, accessing and trusting the right data. Below we review six challenges to achieving data democratization.
Six challenges to data democratization
Over the past few years, while CDOs have been aspirationally talking about data democratization, the truth is they have faced a few challenges: from promise to reality. From data sovereignty to data access, data leaders will need to have a plan in place to facilitate and incentivize data sharing. Here are a six challenges to data democratization:
1. Data quality drives confidence in data-driven decisions
Data quality entails qualities such as integrity, completeness, accuracy, consistency, and lineage. Meanwhile, as much as we invested and believed in the possibilities of a single source of truth, data warehouses struggled to create value from the massive data sprawled across the enterprise. By the time data engineers transform the data between data sources and data warehouses, data quality undermines the overall efforts and efficiency of the single source of truth.
2. ETL data pipelines create friction
Traditionally, data engineers are tasked with creating ETL pipelines to turn raw data from data sources into structured data, which is then used for dashboards, analytics, data science projects and so much more. Whether you’re building or patching ETL data pipeline, it can be a slow, cumbersome and complex process, which ultimately impacts the speed in which analysts and data scientists access their organization’s data. ETL is necessary in a lot of cases, but it is becoming an over-utilized strategy as the modern data stack evolves. As such interactive analytics/query engines can be used to mitigate ETL and have recently proven value-driven in terms of providing quick insight that ETL cannot provide.
3. Migration projects are disruptive
Many organizations don’t have the resources to redesign their data architectures or afford yet another disruptive enterprise migration exercise. To win the digital race, organizations need to leverage the architectural investments that they already have in place. Migration projects require planning as it’s vital to eliminate risk associated with moving critical business data, ensure data integrity, ensure permissions remain the same after the migration, and overcome the challenges of moving cross-platform. Finally, keep a close eye on costs: productivity and downtime.
4. Data silos is a challenge
Data that is stored in various silos, makes it difficult to pinpoint the source of data that people can trust. Siloed data and minimal data sets involve a great amount of inconsistencies across the organization that prohibits users from taking advantage of the information. To resolve these inconsistencies, they would need to be recovered to transfer into a singular format that can be utilized.
In today’s modern data analytics architecture, various roles attempt to access data in different locations to run different queries to accelerate data-driven insights. Not only are there different roles, there are also different tools that access the data such as BI, ML and AI. This results in conflict and challenges that are often isolated from the multitude of data sources available.
5. Overcoming data access limitations
A key security principle is least privilege where it limits users’ access rights to only what is required to do their job. More often than not, data is simply locked. Ask IT how many requests they get to provision access every day? The answer: countless. Leading data-driven organizations with high performing analytics teams must have automatic, instant access to the data they need. Compliance and risk management controls continue to be vital, but data leaders understand the business value from unlocking data.
6. Data sovereignty and data privacy
As data is stored worldwide, regulators believe that the best way to protect citizen data while encouraging data-driven innovations is to ensure that data resides in local servers and is subject to the laws of the country in which it is processed. This idea is bound by the protections of GDPR. With countries that have data sovereignty laws in place, siloed data cannot be easily integrated with other dashboards or analytics. Without complete data, organizations are operating at a huge disadvantage.
Following compliance within data while leveraging complete data serves challenges within an organization. Many of the issues pertaining to IoT vulnerabilities, as issues are posed within the system causing breaches and hacks that must be closely monitored. The organization can place their employees under attack through data breaches and privacy regulations. Security and governance of data is innovating towards an era of protection to protect their employees and consumers.
Related reading: Enabling Data Sovereignty with Starburst Stargate
Six benefits of data democratization with Data Mesh
In reality, many organizations have implemented some aspect of Data Mesh and in parallel democratized data to meet their digital transformation goals and evolved with agility under an uncertain economic climate. Businesses and data leaders recognize that data democratization is no longer limited to a select few, but a necessity.
1. Data Mesh is a data management strategy that’s here to stay
When data is growing exponentially, when you’re faced with data availability and accessibility, and when you need quality data, Data Mesh provides a framework with an analytics tool that’s based on a modern, distributed architecture for analytical data management. The result for businesses include: an increase in time to insight, empowering organizations to quickly validate ideas, drive revenue decisions, and allocate more effective use of resources.
With a proper data platform, data democratization increases overall workplace productivity, while decreasing expenses. Without the complications of storing or migrating data, delayed decision responses, and applying additional resources.
2. Eliminate data silos: accelerate time to insight with a single point of access
Breaking down data silos with a single point of access, enables organizations to work efficiently and increase workflows. Gaining access to this information becomes much easier. Self-service analytics provides the tools to focus solely around a customer centric ideology, through removing barriers and restrictions. The ideology of self-service data is to enable every function of each domain to maintain the structure of high-quality data to increase business value. This excludes the probation of an IT section to overshadow the entire process.
3. Empowering data consumers
Data democratization supports and empowers data consumers that specialize in data, such as software engineers, developers, and data analysts. The extent of data democratization does not stop there, the application empowers numerous end-users through providing the right tools and resources to ensure success in their path. Employees can utilize these resources to collect information and process the analysis themselves, without any IT intervention. Empowering data consumers begin with producing an achievable data democratization for the workplace.
4. Data quality is the backbone of data confidence for data leaders
Data democratization doesn’t mean you’ll no longer have data warehouses or data lakes. However, data sprawl is real. We accept it while protecting our existing investments. The more important point is to ensure quick, reliable and accurate data analysis without or limited data movement. Once you are able to ensure data quality and enable access, applied data and analytics becomes much more achievable.
For instance, reusability of a data product demonstrates confidence in data quality. Data quality is no longer an isolated, independent process, but nurtured by domain owners and upvoted by other data consumers because they have confidence in the data product.
5. Data literacy empowered data consumers
Increasing the data literacy in a workspace through self-service analysis and value creation implements advancement, as a whole, in an organization. Workers are gifted with the establishment of being able to analyze, communicate, and strategize with data. Organizations will be empowered to lead business driven decisions.
6. Data Products: the heart of Data Mesh
Data products are fundamental to a robust data strategy and the stepping stone towards innovating the business, reducing operational costs and minimizing risk. Creating data products is a powerful capability as you’ve enabled your data consumers to very quickly move from discovery to ideation as well as to insight. We go into more detail below about creating valuable data products.
Related reading: Data Products For Dummies, Starburst Special Edition
What is the best way to democratize data?
Data Mesh is an innovative monolithic system of refining organizational agility through the leverage of a domain-specific, self-analysis platform. By eliminating the bottlenecks of data lake or data warehouse, organizations can be empowered with the accessibility to query data.
Sure, this new data management strategy will require a cultural shift, but data mesh was designed as a faster and efficient path to meet business objectives, versus monolithic and centralized data management structures of the past which created bottlenecks and delays. It’s a strategy that will not only include technology, but people and culture as well as process.
Savvy data management and analytics pros know that you cannot buy a Data Mesh technology to meet their data democratization goals.
Related reading: Data Mesh For Dummies, Starburst Special Edition
Reimagining the future of business with data democratization
What is the sole purpose of data democratization? Stakeholders and data consumers will gain a sense of independence, regardless of their technical background, to produce value in their own respective data ecosystem.
By leveraging existing governance processes in place, users can safely and securely access data and embark on their self-service journey. Furthermore, with complete training in handling the information. The new digital age of data democratization has the potential to revolutionize an organization’s bottomline with empowered data consumers with access to data.
Data platform: How Starburst helps to democratize data access
At Starburst, we offer several services to help different departments (i.e. sales ops, marketing teams, accounting, IT departments, etc) meet their KPIs and metrics regardless of skill set. Our main goal is to ensure a smooth transition to the implementation of the most efficient and effective data processing approach.
From single source of truth to single point of access
Starburst has a vision for data access, a single point of access, no matter where it lives. We are shifting the industry of a rigid and slow single source of truth to a flexible and real-time source of truth. The transition opens many opportunities for data consumers, such as creating new possibilities for analytics or overall increased productivity, speed to insight, and real-time actionable insights.
Creating valuable data products
Data products, the heart of Data Mesh, is also a feature of the Starburst Enterprise platform enabling data consumers to discover, produce, maintain, understand, and utilize data products. The platform is designed to alleviate the function of producing, sharing, and consuming data products through the built-in workflow and query editor. Customers have the ability to build the data products themselves through implementing queries, functioning views, and much more. On the back end, Starburst’s data products configuration captures any exchanges between data producers and consumers, through SQL, to define and consume the data products. This enables time spent to create and refine business insights and strategies based on data.
Address complex data privacy regulations
Data privacy is our goal and commitment. We have a consistent governance of secure data products and access control, from source level to data products. By way of least privilege, users will only have access to what they should have access to through merging governance with the datasets. The challenge of transporting sensitive data across borders is through diffusing Starburst clusters to where the data lives. Other strategies include the Starburst Stargate connector that enables users to connect data catalogs and data sources from remote clusters. If users decide to process queries as if they were in the same location, we have implemented filtered data within the Starburst cluster that can help them with that.
