Democratizing data for human and machine consumption

Solving today’s data problems and capitalizing on the wealth of data opportunities requires a modern approach to data management.

Data is one of the most important assets in today’s knowledge economy, and most organizations would agree that unlocking the value of their data paves the way for better, smarter and faster business decisions — driving a critical competitive advantage. Used across all facets of an organization, data plays a key role in automation, and when automation and intelligence come together, businesses thrive.

Keeping up with the pace of change and innovation that data can influence can be challenging, and some traditional companies struggle as “data laggards.” Due to the growing volume and complexity of data, including different types and formats, modern data management is essential for organizations to democratize data for human and machine consumption — enabling insights-based decision-making.

Traditional data management

Traditional data management practices have focused on basic analytics, and most enterprises are currently in this phase. The introduction of traditional data stores and warehouses provided a central structured environment that consolidates and conforms disparate data sources in a semantic view that supports analytics.

As data volumes and velocity increased, data lakes evolved through cloud computing advancements and lower storage costs to acquire and store all data available to an organization. This data includes unstructured, log, and IoT data that traditional data stores and warehouses do not consider. While data lakes focused on building out discovery platforms to support data science and machine-driven models, they failed to meet the needs of end users to present data in a single structured format.

Thus, to satisfy both human and machine use came to the development of the data lakehouse, which provides the benefits of both the data lake to acquire and persist data along and a data warehouse to conform the data into a structure that supports analytics.

The ever-increasing need for organizations to mature analytical intelligence and deliver insights with the lowest level of latency, and ultimately in real-time, has been the driving force behind the evolution of data management.

Common challenges with traditional data management

Traditional data management, which has focused on persistent data for analytical use, resulted in moving and integrating data, adding to high storage costs. This one-sided focus on the analytical plane has also failed to account for supporting operational needs such as applications and portals.

The cycle time to model and integrate new data sources has taken too long for end users, resulting in a lack of agility and scalability. Integrating data into structured databases has ended in data model rigidity that does not meet the expanding data use cases that require more flexible integration.

Adding to the complexity is the lack of ownership and adoption, which have led to data literacy challenges or the inability to access the data needed to produce analytics. Teams have struggled with ownership and accountability of the data model as it continues to evolve with new data sources and subject areas and the lack of realistic and practical data governance.

Why you need modern data management

A modern approach to data management allows organizations to turn data into their strongest asset, delivering insights to optimize decision-making. Today’s solutions can help increase analytical agility without impacting the data quality by becoming systems of intelligence that enable data democratization and self-service analytics. Through active metadata and knowledge graph technologies, data pipelines are automated to allow for real-time analytics for consumption by both end users and machine-driven models.

For example, the emerging design concept called “data fabric” is a centralized approach to modern data management that leverages both human and machine capabilities to access data and continuously identify and connect data from disparate applications. It aims to discover business-relevant relationships between the available data points for optimized decision-making.

Another hot topic in data management is a new de-centralized approach called “data mesh,” which is based on a modern, distributed architecture for analytical data management. Data mesh makes data more accessible and available to business users by distributing data ownership to domain-specific teams that manage, own, and serve the data — to improve business outcomes of data-centric solutions.

Both data fabric and data mesh, are in the adoption phase of the hype cycle and will continue to evolve. They require maturity in an organization’s current data management practices as well as the following characteristics to be established disciplines:

• Curated data catalog with rich metadata
• Data governance enforcing data standards
• Master data management
• Data quality and proactive observability
• Data literacy

It is important to note that adopting modern data management requires a cultural shift because implementing a modern architecture alone is only one part of the approach. Organizations must align their vision, people and processes to solve business problems with a data lens. It is imperative to tightly integrate data management technology solutions with the business strategy and adopt a domain-based approach of cross-functional teams across both IT and business.

How to get started with a modern data management approach

Achieving modern data management begins with defining your goals and understanding where your organization is today. What do you want to achieve? What metrics are you trying to optimize, and how do you want to measure them?

Start with a readiness assessment to determine the maturity of your organization to shift to a modern data management approach, then develop a value-driven target roadmap that prioritizes the necessary data management initiatives to maximize business value.

We recommend starting with a small domain-specific use case as a proof of concept before embarking on a more extensive implementation — focusing on the basics of data management and a simple use case with many manual touch points that can be automated. Over time, a modern system should allow you to foster a fail-fast, iterative approach to development and lean on retrospectives to continue to adapt and mature easily and cost-effectively.

Ready to take the next step?

Modern data management paradigms are still in their infancy and will continue to evolve and mature in the coming years. Adopting such an approach will take your organization one step closer to data democratization for human and machine consumption, enabling your data to become your organization’s most vital asset.

CGI’s Data2Diamonds framework is based on a powerful methodology for designing and implementing data-driven insights. We help organizations identify the focus areas for building a solid data foundation that produces analytical insights - driving continual improvements and innovation for a sustainable competitive advantage.