Data is now a significant commodity for companies in the constantly evolving digital economy. To ensure that their strategies align with this, they must adopt cultural and technical changes to accommodate rapidly expanding data workloads. Unfortunately, most traditional database systems cannot support the constraint of securely transferring and storing such varied and numerous information.
However, they must develop new approaches to counter competition while maintaining a good level of security. Solving this problem requires organizations to recognize that their data is a priceless asset. To properly manage this asset, they must adopt a data-centric architecture. Although this architecture does not require significant expenses, it nevertheless requires several essential steps to ensure the security and integrity of their data.
What is a data-centric architecture?
A data-centric architecture is designed to store and transfer large amounts of information while providing the speed necessary to support near real-time decision-making. Companies can access and analyze data from disparate systems using a unified platform, a shared database, or a network that links all the company’s resources, thus increasing their competitive advantage.
A data-centric architecture is crucial to ensure compliance with internal policies and external regulations. Moreover, as data is a primary and permanent asset, it must comply with industry standards. Therefore, organizations must ensure that their own data model meets certain standards.
Why should companies adopt data-centric enterprise architectures?
In the past, companies were application-centric, but over time it was realized that these applications can disappear, but data remains. Whether it’s big or small projects, those who create the applications focus mainly on a data model that mimics how the industry for which the application is designed works.
Moreover, this focus on applications has led to a situation where each new solution requires its own access permissions and deep integration, often with additional copies of data.
IT teams are often led to duplicate data and link systems each time they introduce a new solution or feature. This process is slow, time-consuming and carries risks.
Moreover, it makes the company’s systems more fragile, and it is generally advised to avoid modifying old systems for fear of creating new problems. This immobility, caused by the fear of failure, is no longer suitable for the current era. Companies that remain stuck in this approach risk being overtaken by those who are bolder and at the forefront of technology.
Limited business agility
No matter how skilled your IT team or advanced your technology, a company’s success does not go faster than its technology. For application-focused organizations, each new project requires connecting different data sources. This work must be done before the current data can be used in the new system. Unfortunately, this groundwork often takes 50% or more of a project’s budget and time.
Evolution of data management
Data is now treated as an asset, and with regulatory requirements such as the GDPR or the CCPA, organizations must ensure they retain control of this asset.
Moreover, the growing need for businesses in data analysis requires organizations to have access to data quickly and securely. A data-centric approach is the only viable solution to meet these requirements.
Accelerate while improving decision making
By adopting a data strategy, leaders can react quickly to the evolving needs of their customers and teams. This approach provides accurate information to support key decisions.
Thanks to a data-centric architecture, technical specialists can simply explain the data journey. This gives leaders the confidence they need to make informed decisions.
To ensure the best performance and prepare for future challenges, technical managers must develop a data plan. This plan describes how to conduct a large-scale digital transformation.
This method helps organizations make better decisions. Moreover, it makes innovation understandable for those who are not technology specialists. That’s why companies should integrate more data into their daily work. Otherwise, they risk missing out on important long-term opportunities.
Breaking down data silos
It’s not surprising that data silos persist and exist because of the application-focused design. As long as data is connected to the applications that create it, new data sources and data lakes are always needed when setting up new software. Therefore, adding more data silos means moving from small silos to larger ones or creating more legacy systems with redundancy and overlap.
Building a larger data lake can provide temporary relief, but you will still need to improve; constantly filling larger database silos will become tedious and costly. Moreover, the resulting tangled network of connections will hinder the usefulness of the data.
The only foolproof solution is to switch to an enterprise data architecture that keeps your information organized and accessible.
What does a data-centric approach mean for businesses?
In businesses, data and analytics are becoming central to decisions, processes, and essential connections. Instead of relying on isolated sets of information, a unified data system is used across different departments.
This shift is leading to an expansion of data and technology experts’ skills towards management and customer service teams.
By giving employees company-wide access to necessary information, perhaps through the cloud, businesses are moving from a focus on data management to a data-centric paradigm.
Enhancing governance risk and compliance with data privacy protection
A data-centric architecture provides substantial advantages for those involved in governance risk and compliance, particularly as data regulations become more intricate to navigate. For instance, the GDPR empowers consumers with the right to comprehend any algorithm-based decision affecting them. Utilizing a data-centric strategy, this information can be furnished more efficiently and promptly than ever before.
In the current scenario, with the proliferation of data duplicates, responding to these requests has become challenging and nearly unfeasible due to the high volume of copies. To brace for these regulations and upcoming data privacy protections, governance risk and compliance officers must proactively curtail the number of copies, emphasizing on centralized data management. This approach will simplify compliance with data standards significantly.
Unified data across the enterprise
Data-centered architecture is key to a company’s success as it allows for faster and more efficient extraction of information from data. However, due to data silos and independent databases, it is difficult for companies to truly focus on data or access their information from other departments of the company.
By unifying data across different services, companies can access a much larger volume of information and use powerful analytics to uncover new insights. This can give them a competitive advantage, reduce costs, and help them make more informed decisions.
Transition from an app-centric approach to a data-centric approach
By adopting a data-centric approach, organizations can create flexible structures that adapt quickly and change according to current requirements.
Companies can build new solutions in a few days rather than weeks by eliminating data duplicates and integration tasks.
Thanks to this new approach, it is now possible to deploy technology faster, significantly reducing the “time to market” for solutions.
Implementing a company-wide data architecture: some pointers
Before moving to a data-centric architecture, it is essential to plan and define a clear strategy. Understanding your organization’s current state, identifying your data sources, and defining your long-term goals is crucial.
To optimize the security and efficiency of a data-centered approach, successful implementation should include the following elements:
- Data-focused: Data is a priority in this architecture and is the epicenter of all design.
- Data modeling: This architecture requires a precise definition of the data structure and associations to succeed. It is important to note that even though the big data approach allows for the integration of unstructured data (texts, sounds, images, videos…), this does not mean that structuring or normalizing data should be completely abandoned. Both approaches are set to coexist for a long time.
- Separation of concerns (SoC): By separating the responsibilities of data storage and data processing, the architecture can create more reliable and efficient systems that have greater scalability.
- Data access and manipulation: The architecture allows developers to work with information in a user-friendly way. It offers a range of tools and APIs that organizations can use to access and manipulate data. A data catalog should be offered, alongside an API catalog.
- Data integration: The architecture facilitates the transfer of data between various sources. This integration can be achieved using big data management tools, like Apache NiFi, Kafka, or Spark, or using proprietary solutions, like Talend. These tools ensure efficient synchronization and optimal data availability across the enterprise.
- Peak performance and scalability: This architecture is optimized to efficiently process massive data sets (big data).
- Security and privacy: The architecture has specialized security and privacy features to protect confidential data from unauthorized access.
- Adaptive architecture: The design will make your business flexible and agile, allowing you to quickly respond to new business requirements or capitalize on profitable opportunities.
While an effective data-centric architecture includes each of these components, how to put them into practice?
Define the business objectives of the data
Discuss with the relevant people to determine who should have access to which data and how they will use it.
Set the goals of the project, what you need and when you need it, and think about how this will affect your organization.
Use data analysis to find concrete examples where real-time data can help make automatic decisions, increase revenue and reduce costs.
Take Your Data Inventory
To dismantle data silos, creating a unique data map of all data across your organization is essential.
This should include where the data sets are located and what applications rely on them.
Once this is done, choose the data necessary for critical use cases and prioritize those areas that contain this key information.
The development of a data catalog is a recommended approach to ensure the sustainability and effective tracking of your data inventory.
Develop data standards
Creating a consistent naming convention for data and the same format used across the organization will solve consistency and compatibility issues across departments. Moreover, this specific model will allow interoperability in various use cases.
Determine changes to be made to the existing architecture
Consider what changes in new technologies can drive your data structure to achieve your goals. Investigating various cutting-edge data architectures, such as a data hub or a data mesh, can help you choose the appropriate design for your organization’s needs.
Evaluate your data through analysis
Set up key performance indicators (KPI) and perform advanced analysis to evaluate the effectiveness of your data architecture in terms of data integrity. These analyses should include criteria such as accuracy, quality, and performance of data. In addition, understanding the lifecycle of your database will shed light on the changes to be made to the architecture.
Create a roadmap
Data Éclosion consultants can strategically deploy a data architecture and governance plan in three to four areas each quarter. This approach ensures a flexible implementation of the architecture while leaving enough time to test different scenarios.
However, it is recommended to prioritize the development of data-oriented action plans, which are essential to the success of your data architecture.
How Data Éclosion can concretely make your business data-centric
Data centrality is the transformation lever that unleashes the full potential of your data. Complex organizations around the world have already adopted this approach, deploying solutions quickly and gaining agility.
The era of standalone applications is over. To stand out in the modern world, it is essential to move towards a data-centric approach. Not only does it propel your organization forward, but it also paves the way for the adoption of artificial intelligence.
Data Éclosion is your partner of choice in this journey. As a leader in digital strategy and data governance, we will help you navigate the world of big data, driving growth through informed business decisions. Together, let’s prepare for a promising and data-centric future.
