Solving today’s data problems & capitalizing on the wealth of data opportunities requires a modern approach to data management.Bạn đang xem: Cgi advantage insight
Data is one of the most important assets in today’s knowledge economy, và most organizations would agree that unlocking the value of their data paves the way for better, smarter & faster business decisions — driving a critical competitive advantage. Used across all facets of an organization, data plays a key role in automation, và when automation and intelligence come together, businesses thrive.
Keeping up with the pace of change & innovation that data can influence can be challenging, and some traditional companies struggle as “data laggards.” Due to lớn the growing volume and complexity of data, including different types & formats, modern data management is essential for organizations to democratize data for human & machine consumption — enabling insights-based decision-making.
Traditional data management
Traditional data management practices have focused on basic analytics, & most enterprises are currently in this phase. The introduction of traditional data stores và warehouses provided a central structured environment that consolidates & conforms disparate data sources in a semantic view that supports analytics.
As data volumes and velocity increased, data lakes evolved through cloud computing advancements & lower storage costs to acquire and store all data available to an organization. This data includes unstructured, log, và Io
T data that traditional data stores & warehouses vì not consider. While data lakes focused on building out discovery platforms to support data science and machine-driven models, they failed khổng lồ meet the needs of kết thúc users to lớn present data in a single structured format.
Thus, to satisfy both human & machine use came to lớn the development of the data lakehouse, which provides the benefits of both the data lake to lớn acquire & persist data along & a data warehouse khổng lồ conform the data into a structure that supports analytics.
The ever-increasing need for organizations to lớn mature analytical intelligence và deliver insights with the lowest màn chơi of latency, & 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 và integrating data, adding to lớn high storage costs. This one-sided focus on the analytical plane has also failed to account for supporting operational needs such as applications & portals.
The cycle time to model and integrate new data sources has taken too long for over users, resulting in a lack of agility & scalability. Integrating data into structured databases has ended in data mã sản phẩm rigidity that does not meet the expanding data use cases that require more flexible integration.
Adding khổng lồ the complexity is the lack of ownership and adoption, which have led lớn data literacy challenges or the inability lớn access the data needed khổng lồ produce analytics. Teams have struggled with ownership & accountability of the data mã sản phẩm as it continues to evolve with new data sources and subject areas & the lack of realistic và practical data governance.
Why you need modern data management
A modern approach khổng lồ data management allows organizations khổng lồ turn data into their strongest asset, delivering insights to lớn optimize decision-making. Today’s solutions can help increase analytical agility without impacting the data chất lượng by becoming systems of intelligence that enable data democratization và self-service analytics. Through active metadata and knowledge graph technologies, data pipelines are automated to lớn allow for real-time analytics for consumption by both end users and machine-driven models.
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For example, the emerging thiết kế concept called “data fabric” is a centralized approach to lớn modern data management that leverages both human and machine capabilities to access data và continuously identify and connect data from disparate applications. It aims lớn 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 & available to business users by distributing data ownership to domain-specific teams that manage, own, và serve the data — lớn improve business outcomes of data-centric solutions.
Both data fabric & data mesh, are in the adoption phase of the hype cycle and will continue to lớn 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 metadataData governance enforcing data standards
Master data management
Data chất lượng 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 và processes khổng lồ solve business problems with a data lens. It is imperative to tightly integrate data management giải pháp công nghệ solutions with the business strategy và adopt a domain-based approach of cross-functional teams across both IT and business.
How khổng lồ get started with a modern data management approach
Achieving modern data management begins with defining your goals & understanding where your organization is today. What bởi you want khổng lồ achieve? What metrics are you trying to lớn optimize, and how bởi vì you want lớn measure them?
Start with a readiness assessment khổng lồ determine the maturity of your organization to shift khổng lồ 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 cảm biến points that can be automated. Over time, a modern system should allow you to foster a fail-fast, iterative approach khổng lồ development & lean on retrospectives to lớn continue to adapt and mature easily & cost-effectively.
Ready lớn take the next step?
Modern data management paradigms are still in their infancy & will continue khổng lồ evolve và mature in the coming years. Adopting such an approach will take your organization one step closer lớn data democratization for human & machine consumption, enabling your data to become your organization’s most vital asset.
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Driving more value from data
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Analytics critical information bản đồ - Working together to identify the information that is critical to lớn the business and ensuring its qualityAnalytics idea exploration - Workshop using the critical information and additional “big data” sources to identify real business value through specific use cases và applicationsAnalytics sprint - Taking one of the prioritized use cases & using data và advanced analytics to lớn prove the value
Analytics advanced technology - Continuous development và innovation by assessing new data sources and using advanced analyticsAnalytics production & scale - Building a future-proof data analytics platform that is secure, scalable và enables innovation
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