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Data Architecture: What It Is and Why You Need It

Every organization, of every size and industry, needs a data architecture to become a more data-driven enterprise.

In a digital economy, enterprises of every size and industry strive to maximize the value of data for improved business performance. Data architecture is a key enabler for an enterprise to become data driven. It is the practice of designing, building, and optimizing data-driven systems by incorporating the company’s vision, strategies, business rules, standards, and capabilities to manage the data.

While many progressive and proactive organizations have the data architecture capability within the role of chief data officer (CDO) function, there are still some organizations that have not taken that plunge. To demonstrate the importance and need for a good data architecture in an enterprise, organization should answer two simple questions. First, what would the business gain from a data architecture? Second, what would a business lose by not having a data architecture?

Business Case for Data Architecture

First things first. Why should the business care about a data architecture? At the highest level, data architecture offers solid strategies for companies to manage their data in the entire data lifecycle of data capture, integration, analytics, and visualization. Specifically, data architecture offers three key benefits to the business:

  1. Strong data strategy. Strategy in general involves considering alternatives and making trade-offs to pick the best option. A strong data strategy needs to be underpinned by a flexible and scalable data architecture that aligns with company strategies, compliance requirements, business rules, capability and IT standards reflecting the current and the future states. These aspects are reflected in the data strategy, including all the components of enterprise data management – see the DAMA-DMBOK© for more about enterprise data management.  An effective enterprise data architecture should include a view of the data landscape using an enterprise data model (EDM). An EDM is a holistic representation of the data created and consumed across the entire organization.
  2. Improved communication and collaboration. A typical enterprise has various stakeholders with different roles, needs, priorities, and constraints across multiple lines of business (LoB). In addition, enterprises operate within various assumptions and constraints. All this often results in data silos in the company. So, when stakeholders from various functions, geographies, LoBs and competing needs come together, it is critical to have a data architecture that can provide a common language for improved communication, collaboration, and data literacy.
  3. Optimal information flows. As data architecture provides a holistic view of the data flows in the enterprise — current and future — it provides opportunities for creating lean and optimal information flows by eliminating complexity, reusing data, and minimizing data and system redundancy. This ultimately results in reduced cost, minimized risk and faster time to market for the products and services.

Effects of not Having a Data Architecture

On the contrary, what will the business lose by not having a good data architecture? While data can be an asset, it can become a liability very quickly. There are three situations where data can become a liability to the company and the root cause of it can be traced to a lack of good data architecture:

  1. Undefined purpose. Without a good data architecture, companies often collect data without a clear business objective. Collecting data without a defined purpose will result in increased cost and missed business opportunities. According to Forrester, 73% of data in a company is never used strategically. While the time and effort to acquire, store and secure data is significant, the opportunity cost of not utilizing the data collected in today’s digital world is massive.   
  2. Poor compliance with laws, rules, and ethics. Data architecture provides solutions to address compliance with laws, business rules, industry standards and even ethics. With the rise of cybercrime and data breaches, companies today are faced with the task of ensuring strong data security and privacy. Data security and privacy are key considerations in a data architecture. In 2017, when hackers accessed millions of customer records from the credit reporting agency Equifax, the company spent $1.4 billion to transform the security infrastructure since the root cause of the issue was tied to Equifax’s poor data and security architecture.
  3. Increased costs. Without an enterprise-level data architecture, companies will potentially spend unnecessary time and effort in maintaining redundant data in the data lifecycle, including duplicate customers, products, assets., etc. Apart from labor costs, data management consumes a lot of data center electricity, thereby increasing the carbon footprint of the company. In 2018, data centers consumed about 1.1% of total global electricity.

The volume of data generated by businesses today is unprecedented. As this growth continues, so do the opportunities for organizations to derive business results from their data. Stephen Covey, author of the international bestseller, “The 7 Habits of Highly Effective People,” said, “Start with the end in mind.” Does one build a skyscraper without architecture? In a data-centric business world, the digital journey for the business should start with a solid foundation — the data architecture. The foundation itself has little value to the business; but the foundation helps to build scalable and robust data-driven systems for business productivity and sustainable competitive advantage.

Conclusion

Every organization should design, create, and maintain an enterprise data architecture to ensure data-driven capabilities.  Along with the data architecture, each organization should develop and implement an enterprise data strategy that includes all the components of enterprise data management.  Doing so will ensure the organization’s ability to collect, store, manage, and use data as an enterprise asset.

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Prashanth Southekal

Prashanth Southekal is the Managing Principal of DBP-Institute (www.dbp-institute.com), a Data Analytics consulting firm. He has consulted for over 50 small, medium and large organizations and has solved problems that are at the intersection of data, technology, and business productivity. In addition, he is an Analytics Advisor for SAS-Institute (Western Canada), Evalueserve, and Grihasoft (India). A resident of Calgary, Dr. Southekal is the author of the books Data for Business Performance and Analytics Best Practices.  He is an Adjunct member of the Data Analytics faculty at the University of Calgary (Canada) and IE Business School (Madrid, Spain).

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