Making Data Architectures Work Smarter, Not Harder

Architectural detail of layer of triangles

When my brother and I were growing up, my Dad had unspoken, but well-known, rules for the weekend: First, you didn’t leave the house without making sure there wasn’t any work to be done, and second—whatever the work was—it had better be done right.

If it was done to his satisfaction, we were free to spend the remainder of the day with our friends. Even so, there was one thing that consistently drove my father crazy…my brother’s knack for doing everything the hard way. If he had to pick up a pile of leaves, he would do it with his hands instead of a rake. If he had to trim the bushes, he would use pruners instead of the hedge trimmers. “Work smarter, not harder!”, he’d yell, like clockwork. Although his frustration was lost on my brother, I am my father’s daughter, and suffice to say, the phrase turned into a guiding principle in my life.

In the business world, the same can be said for data architecture. The need for immediate access to significant amounts of data leads to a race to pull together whatever data is available and accessible, without fully understanding what is actually needed. In asset management organizations, data is often collected, stored, and distributed in silos which has resulted in inconsistencies in data, multiple versions of the same data, and no clear view into who is using the data and for what purpose.

To make matters worse, with increasing demands on data, organizations continue to expand upon legacy technology and/or bolt on additional solutions, thereby increasing the complexity. This not only impacts the ability to effectively manage an existing data architecture, but also greatly reduces the capacity to effectively deliver new capabilities.

Work smarter, not harder.

To achieve a smarter data architecture, we first need to rethink the way we look at data architecture. The following is a list of six key components for consideration:

  1. Is there a collective vision for data to be viewed as a shared asset across the organization?
  2. Is there a clearly defined data strategy, driven by business objectives, and supported by a shared data architecture that centralizes all data, regardless of data type, source, or business need?
  3. Does the architecture support access and management of a continuous flow of real time data from source to business users, with uniform standards and policies for data sourcing, collection, distribution, and governance?
  4. Is there opportunity to simplify the architecture by retaining only those data technology capabilities that correlate directly with business needs, and/or retiring data technologies that are less than optimal or cost too much to maintain?
  5. Does the architecture include best of breed modular components and tools that can easily adapt to, or integrate, data solutions as new technologies continue to evolve?
  6. Does the architecture include cloud-based tools and technologies for low-cost scalability, increased automation and faster integration and delivery of requirements on demand?

As data technologies continue to develop at a rapid pace, it is becoming harder and harder to meet demands for data within the confines of rigid, complex data architectures of the past. The path to move towards a smarter data architecture may seem daunting but those who do nothing will be left behind with outdated technology, increasing costs, and a data architecture that can no longer provide the support required for continued growth and improvement.