How to Create an effective data architecture for your organization

Chief data officers (CDOs) and chief information officers (CIOs) are in charge of putting order to the chaos when it comes to corporate information.

Businesses are under increasing financial pressure to make more use of the data they have as well as regulatory responsibilities for maintaining data, particularly when it comes to handling data related to customers.

The variety of methods available for storing and managing data, including data lakes, data hubs, object storage, machine learning (ML), and artificial intelligence (AI), further complicates the matter (AI).

A study by storage company Seagate found that up to 68% of enterprise data is wasted. As a result, businesses are skipping out on the benefits that data should provide. Organizations also run the danger of breaking regulatory and compliance rules if they are unsure of what data they have and where it is kept.

Companies need to examine their data architecture in order to deal with this complexity and make data "work" for the business. A data architecture, at its most basic level, is a diagram that shows where the organization's data is located and how it flows through it. There is no one set method for doing this, though, given the wide variety of data sources and uses that data might be put to.

Each organization will have to create a data architecture that suits its own requirements.

Tim Garrood, a data architecture specialist at PA Consulting, states that "data architecture involves many things to many people and it is simple to drown in an ocean of ideas, processes, and ambitions." He continues by saying that businesses must ensure that data architecture projects offer value to the company, and doing so requires both knowledge and technical expertise.

The fact that technology is increasing the complexity of data management and how it is used is a problem for CIOs and CDOs. In a 2020 study, the business consulting firm McKinsey stated: "Technical enhancements have significantly increased the complexity of data architectures, including data lakes, customer analytics platforms, and stream processing." Due of this, it is more difficult for businesses to maintain their current data and provide new capabilities.

Organizations now have the flexibility to do far more with data than ever before thanks to the transition away from traditional relational database systems to considerably more flexible data structures and the capability to gather and handle unstructured data.

Making a connection between that opportunity and the demands of the business is a challenge for CIOs and CDOs. It should be more than just a compliance or housekeeping operation to build a data architecture.

According to Garrood of PA Consulting, "I prefer to address the question, 'what are we able to do with better data, what is it that could be different?'" The next step is to that if there isn't a clearly stated business challenge. Following that are physical data architecture, data flows, and the integration of data sources and applications.

What is a data architecture?

A data management plan is a common way to define data architecture. Undoubtedly, an efficient data architecture has to map the information flow within the company.

In turn, this depends on having a solid understanding of the data that is being gathered and stored, the systems in which it is stored, and the regulatory, compliance, and security regimes that are applicable to the data.

Additionally, businesses need to know which data is valuable and essential to operations. This becomes increasingly crucial as organizations store and process more data. In certain cases, it is more of an art than a science.

Tim Bowes, assistant director for data engineering at data consulting firm Dufrain, describes the art of data engineering as "knowing that there are few rules you actually need to stick to, and understanding which data is critical to the organization." Organizations have a ton of data flying around, but not all of it is essential for efficient operation. Understanding which data is important is essential.

Data architecture depends on good data management as well as the organization's data strategy and data lifecycle.

Data supply and data consumption or exploitation are two common divisions that organizations make in their data architecture.

CIOs and CDOs need to consider data sources on the supply side, such as transactions, business applications, customer behavior, and even sensors. Businesses are examining their reporting, business intelligence, advanced analytics, and even ML and AI capabilities on the consumption side. Some businesses may also seek to profit from data by selling it or using it to develop new goods.

The data architecture will be shaped by the relative importance of these components.

For instance, the consulting firm KPMG has a framework for data architecture that it refers to as the "four Cs": generate, curate, consume, and commercialize.

The company's UK head of data and analytics, Nick Whitfeld, claims that creation and curation belong on the supply side, apart from consumption and commercialization. Perhaps each side need an own data architecture.

No organization, he asserts, "can have a single, homogeneous data architecture." "In my opinion, there are several sorts of data architecture for various purposes.

It's not just a data model, either. It consists of a set of procedures, a governance structure, enabling technology, and data standards. Together, these things make sure that data is properly organized and regulated so that it accurately flows through your business operations.

Next articel about Why and how to implement a data architecture.