Enterprise Data Architecture
Data Architecture For The Enterprise Overview
Many organizations that I get to interact with are starting to think about what is important to them as they transition towards a modern data architecture. All organizations, regardless of size, industry, geography, and use cases, should understand data architecture best practices and the advantages of developing a formal plan to evolve their organization’s capabilities.
Enterprise Data Architecture is a discipline designed to simplify, streamline, standardize, and enhance the accessibility of your organization’s data. A successful enterprise data architecture plan should cover the policies, procedures, standards, on how data is collected and stored in addition to how data is managed, processed, and used throughout the organization.
Most larger organizations are currently dealing with an extremely fragmented data landscape, with substantial redundancy. In many cases, this landscape has evolved for a variety of reasons, such as rapid growth, limitations with legacy technology, and the lack of investment in managing your organization’s big data assets.
The result is disparate systems with data being stored and managed in silos throughout the organization and an environment that eventually becomes so complicated that no one individual in the organization can understand how it works together. Some organizations start out believing that technology can solve the challenge and that applications such as CRM or ERP systems will solve everything.
The number of applications within organizations is expanding rapidly, along with the volume, variety, and velocity of data that will need to be managed. Organizations are trending towards increased data distribution with many areas of an organization claiming responsibility for subsets of data, which highlights the need to manage the organization’s enterprise’s data architecture more holistically.
I work with our customers to take a holistic view of their data landscape and transform them to leverage a modern data architecture.
Modern data architecture will enable their organization to navigate the fast-paced modern world of data and analytics.
Regardless of your role within the organization at some level, you are responsible for data assets, and you can leverage these basic principles to guide your decisions. Think of them as the foundational elements for building a modern enterprise data architecture that will allow your organization to operate at an optimized level today and support future growth.
A Formal Plan
A formal plan is needed to align the organization and help guide them as they make informed decisions on what’s required. All organizations need to understand the advantages of a formally defined enterprise data architecture and the challenges they will continue to face if they continue to operate without one. At a minimum, your organization should have a formal architectural plan, principles, and process for sharing data.
Processes
The organization must establish rules and processes to drive effective decisions and engage business leaders. Business users should be part of the process and understand changes that may impact their existing solutions for delivering information.
The change control process must be adhered to across functional areas with clear documentation of requests, approvals, and changes.
Project Planning, Management, Governance
Resources should be applied to deal with data architecture problems in a coordinated way to maximize effectiveness. Consolidate projects that have similar objectives and scope to provide a more holistic solution that is more aligned to the business and data strategy maximizing value for the organization.
They should prioritize strategic items that can serve the needs of the larger organization as opposed to falling into the trap of the first-in, first-out approach.
Data is a Shared Interest
Data is one of your organization’s most valuable assets, and it should be managed appropriately. Fast, accurate data is critical to support fact-based decision making. Data is the foundation for an organization and should be carefully managed to deliver additional value to the enterprise. Both the business and IT teams must understand the importance of data and how it is imperative to increasing the organization’s performance.
As a shared asset, we must work with other areas of the organization to abide by a common set of policies, procedures, standards, and definitions to work towards a single source of the truth. KPI’s should be shared across the organization.
Data Security and Access Controls
Data Security and access control have become more of a challenge for organizations as they transition into building modern data platforms.
You must carefully think about how you can implement access controls at the raw data level to remove the need to have ad-hoc security further down your data pipeline. Today users are demanding real-time data, but for data governance purposes, making it highly secure as they are enabling self-service access to this data becomes a requirement.
Enterprise Data Definitions
The lack of enterprise data definitions is a common challenge in the organization. No data ownership or data stewards exist, and this lack of ownership prohibits leveraging consistent terms and definitions, with the result being many groups using the same or similar terminology in different contexts. A common vocabulary is vital to the success of modern enterprise data architecture.
Data Lifecycle
Do you have organization-wide policies in place around successfully managing the preservation of data for use and reuse? Data needs to be delivered in a more timely and useable format to drive a data- driven culture.
Minimize Data Movement and Redundancies
Modern Data Platforms can scale as data volumes increase, enabling organizations to centralize and consolidate data sources.
Data movement compromises the timeliness and quality of the data and should be minimized where applicable. The ability to integrate and successfully leverage data will continue to be a significant challenge for organizations that rely on numerous manual processes resulting in redundant data silos throughout the organization.
Data Architecture Best Practices Recap
Modern Enterprise Data architecture covers a broad spectrum of roles throughout an organization. Naturally, the enterprise data architect maps the entire data landscape and aligns this to meet the organization’s requirements in addition to being responsible for developing the high-level plan. Data engineers are responsible for implementing data architecture best practices for maintaining quality throughout the process. Further down the data lifecycle, the data analysts are doing statistical analysis and building reports for data stakeholders in the business. Some organizations have data scientists who mine data to solve complex challenges and drive advanced business insights.
A formal modern enterprise data architecture can enable your organization to understand its valuable data assets and provide the foundation to support turning data into actionable intelligence. This foundation supports the business necessary to meet the needs of the business. Of these, the most important resource is the information asset.
