An Agile Foundation for Building the Data-Driven Enterprise

The data-driven enterprise is the cornerstone of modern business.

In recent years, we’ve seen startups leverage data to catapult themselves ahead of legacy competitors. Companies such as Uber, Netflix and Air BnB have become household names and, although the service each offers differs vastly, all three identify as ‘technology’ organizations. This is because data is so integral to the way the business operates.

Data-Driven Business

As with any standard-setting revolution, businesses across the spectrum are now following these examples. But what these organizations need to understand is that simply deciding to be data-driven, or to “do big data” isn’t enough.

As with any strategy or business model, it’s advisable to apply best practices to ensure the endeavor is worthwhile, and operates as close to maximum efficiency as possible. In fact, it’s especially important with data, as poorly managed data will only lead to inaccurate analysis and poor decision making down the line. This typically leads to slower time to markets due to inaccuracy in the planning stages, false starts and wasted cycles.

Essentially – garbage in, garbage out.

This is why it’s so important for businesses to get their foundations right. In order to build on something, you need to know exactly what you’re building on to understand the best way to progress.

The foundations should be built on strong data management initiatives, to:

  • Enable data fluency and accountability across diverse stakeholders
  • Standardize and harmonize diverse data management platforms and technologies
  • Satisfy compliance and legislative requirements
  • Reduce risks associated with data-driven business transformation
  • Enable enterprise agility and efficiency in data usage.

The best way to achieve these aims is to leverage data modeling (DM), enterprise architecture (EA) and business processes (BP) to build a complete, well rounded business platform.

But in order for the platform to be complete and well rounded, the three disciplines must be allowed to work with one another, rather than being siloed. Siloing the practices is usually born out of the employment of disparate tools, that don’t enable collaboration between the relevant persons responsible for the individual data management initiative. This stifles the potential of data analysis and considering the current market challenges, this is something businesses cannot afford to accept.

Businesses operating in highly competitive markets need every advantage of growth, innovation and differentiation. Organizations will also need to leverage a complete data platform as the rise of data’s involvement in business and subsequent frequent tech advancements, mean market landscapes are now changing faster than ever before.

In turn, this has created an environment where businesses have to reevaluate, and even reshuffle their technology stacks far more frequently as new tech has to be brought in to accommodate the disruptions. More tech typically means new tech silos, and redundancies/duplication in tech and process.

Given this, it’s clear that the need for a comprehensive, collaboration enabling approach to data management is more prevalent now than ever.

In our next post, we’ll look into how these three fields tie in together, and why they’re better for it.

Check back with us, or follow us on Linkedin and Twitter to be updated when part two arrives!

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