Data Strategy vs. Data Architecture

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Data Strategy vs. Data Architecture

“Data Leadership is about understanding the organization’s relationship with data and seeking ways to help the organization meet its goals using whatever tools are available,” said Anthony Algmin, of Algmin Data Leadership in a DATAVERSITY® interview. Within that overall Data Leadership Framework, sit Data Strategy and Data Architecture as individual disciplines.

Data Strategy

Companies often develop their Business Strategy, and then mandate that a Data Strategy be created to address that Business Strategy. “Developing a Business Strategy independent of the key mechanisms by which you will need to realize that Business Strategy is a sub-optimal approach,” remarked Algmin. Instead, a Data Strategy should be treated as a functional view of the Business Strategy, developed in tandem with it.It’s really a subset—not an independently developed data-focused thing.”

Data Strategy, at its core, should work toward maximizing business impacts by aligning with Business Strategy. At the same time, the Business Strategy needs to work from the premise that data is an important tool for reaching desired outcomes. “It’s really about asking, ‘How can we use data to drive better business?’”

Algmin said he’s a big advocate for understanding data value, which he defines as the differential in business outcomes across three dimensions: increasing revenue, decreasing cost, or managing risk. “That ‘differential in business outcome’ is the difference between what the business would do with it versus what the business would do without it.” In the absence of doing X, Y, or Z with the data assets that we have, what would that business outcome be? “Your whole point is to create business outcomes, and you don’t do that by doing Data Strategy. You do that by doing Business Strategy.”

Data Architecture

“The Data Architect has the ability to give energy to business process.” Data Architecture is all about building the infrastructure to create those business impacts that are identified in the Data Strategy. “We now know what the business outcomes are, we now know about the differential in business activities across those three dimensions,” remarked Algmin.

In order to be effective with Data Strategy, he said, a baseline set of measurements must be put in place to measure results. “We have to understand the fundamental measurements of what we’re doing and compare them to those things that we would like to be doing.” This process creates accountability and provides a clear picture of the effectiveness of initiatives taken to meet goals. What data are we going to measure and then what do we need to do to that data to impact other business systems to achieve these data-driven business outcomes?

The Changing Role of the Data Architect

Algmin said that Data Leadership is largely made of Data Architecture, but it’s also about becoming part of the business as well as providing support for behavior change; “Things that go above and beyond what Data Architecture is.”

The role of the data architect is changing significantly, he said. “It used to be simple. We had databases, we had some ETL, and then we’d shoot out a report and that would be cool.” Because of the scale and number of options for working with data, along with a simultaneous level of granularity inherent in IoT, being a data architect is no longer synonymous with being a database developer or modeler building data flows for reporting purposes.

The line between the data architect and the enterprise architect has become less clear. Enterprise Architecture has largely fallen by the wayside in many organizations because many enterprise architects expected the business to work within the technological parameters established by IT, rather than tailoring the technology to the needs of the business.

“A lot of enterprise architects, in my opinion, [became] too fond of the idea that they mattered by themselves. But really, whether you’re talking Data Architecture or Enterprise Architecture, until you’re creating business impact, you don’t matter at all.”

By being abstracted from the problem solving and planning process, enterprise architects became unresponsive, he said, and “buried in the catacombs” of IT. Data Architecture needs to look at finding and putting the right mechanisms in place to support business outcomes, which could be everything from data systems and data warehouses to visualization tools.

Data architects who see themselves as empowered to facilitate the practical implementation of the Business Strategy by offering whatever tools are needed will make decisions that create data value. “So now you see the data architect holding the keys to a lot of what’s happening in our organizations, because all roads lead through data.”

Algmin thinks of data as energy, because stored data by itself can’t accomplish anything, and like energy, it comes with significant risks. “Data only has value when you put it to use, and if you put it to use inappropriately, you can create a huge mess,” such as a privacy breach. Like energy, it’s important to focus on how data is being used and have the right controls in place. “The downside risk alone is justification for responsible and proactive management, because in absence of it, you’re going to blow something up.” Ignorance is no longer a justifiable reason to have a data breach, he said.

Data Architecture supports Data Strategy. “I really don’t think you can do Data Strategy without Data Architecture,” he said, and if Data Architecture is put in place without a strategy, “you’re not going to be as valuable as you should be.” Data Strategy and Data Architecture are different facets of a tremendously complicated ecosystem, where Data Architecture serves as a way to execute Data Strategy.

Building a Data Strategy

To build a successful Data Strategy, Algmin commented that it’s important to have the knowledge of what’s possible from a technological point of view, as well as what it takes to make that possibility into reality. Also key is an ability to understand business-side challenges, a desire – and an ability – to interact with other business leaders, as well as a willingness to let go of the mentality that IT people are somehow different from other people in the business.

People in the IT department have a functional skillset that benefits the greater whole, but they need to be considered part of the business, he said. That mindset starts with IT leadership:

“Your relevancy as a data architect is in how you impact the business. It’s not how well you do your thing. It’s how your thing helps your business be successful.”

To truly be an effective part of the business, the data architect should understand the answers to these questions:

  • What is our business operation’s goal?
  • What are we trying to accomplish as an entity?
  • What is the thing we should be doing as a business fundamentally?

Answers to these questions lead to more detail about how to accomplish those goals:

  • How do we source our product?
  • How do we take those products to market?
  • How do we connect to customers?
  • How do we deliver products?

Next, an understanding of how data can support both the overarching goals and the processes used to reach them:

  • How do we leverage the data that we have today?
  • How do we create more data?
  • How do we use data to support all of these processes, measure them, and then improve them from a business perspective?

“All of these things are tied together. Answering these questions will help create your blueprint for the architecture,” he said, with high-level scaffolding for the overall concepts and building blocks for the details underneath.

Best Practices

Best practices are usually focused at the detail level, he said, but best practices really should start at a higher level. They should also evolve over time, yet by identifying something as a “best practice,” it is less likely to be challenged over time, even when it ceases to be a best practice. Algmin prefers the concept of “guiding principles,” such as data value and business impact, which allow for more flexibility and responsiveness to each unique situation.

Data: A Proxy for Truth

The role of data in an organization is to cast light in all directions and fully illuminate a situation, to unveil truth. “It’s really the best proxy for truth we have,” he said. Without a transparent view of reality, it’s impossible to know which choices or initiatives will lead toward success or when to change course.

“Logically you cannot be as capable if you don’t have a full view into what’s around you,” he said. Data Strategy and Data Architecture are not the only important pieces in Data Leadership, but without understanding the roles that they play, a business won’t be able to leverage that truth to its advantage.

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