In today’s world of data-driven decision-making, businesses rise and fall by the quality of their data. Data platforms, in this context, play a key role in driving business intelligence across all organization functions. Nevertheless, building a reliable platform requires careful planning, design, and implementation. From selecting the right tools to deciding on its architecture and data models, these systems take time to build.
As a result, many companies opt for a traditional model that builds the whole ecosystem end-to-end in a “Big bang” approach. At Quest Global, however, our 20 years of engineering experience have shown us a much faster and cost-effective approach to platform-building.
The “Big Bang” approach requires you to consider all your business KPIs (key performance indicators) before building the data platform. This model is, however, fraught with risks.
Incorporating all your functions into the platform in a single move means that you’re taking a massive risk without knowing if the system will work. It also significantly extends your time-to-market. Say, for instance, that building the complete platform takes two years. You won’t be able to use it until all areas are completed, which means you’d be potentially leaving money on the table. Lastly, it can lead to data mistrust and poor data governance. Since nobody owns the data, nobody feels accountable for it, either.
The alternative is the incremental approach of DataOps. At Quest Global, we strongly believe in the “think big, act small” philosophy where we’ve seen time and again how small steps can lead to huge benefits.
Instead of committing to incorporating all areas into the platform at once, you identify specific use cases and implement them one at a time. Once that one is validated, you move on to the next. Issues are addressed as they arise, minimizing the risk to your business.
Other than its flexibility, there are numerous benefits:
When you rely on DataOps, the manager of each business function is responsible for the quality of the data they provide. This inspires data ownership and data-driven decision-making across all parts of the business.
With DataOps, the scope is much smaller, so you don’t need massive upfront investment. Likewise, the end solution will be less costly since you will iterate and improve along the way.
As soon as you incorporate one function of your business into the platform, you can start using it. This means that you don’t have to wait until all other areas of the business are implemented before you start benefiting from it.
Data governance is an integral part of any data-driven organization, and getting it right can be a game-changer. While it can be a daunting task, involving more people in the data collection and management process can inspire trust, accountability, and data ownership, which are the bedrock of solid data governance.
Adopting a DataOps approach can help streamline the process. It begins with identifying your biggest pain point and creating a strategic plan forward. Once you have your proof-of-concept, you can gradually add all functions of the businesses until you have a fully integrated and streamlined data platform. Embracing DataOps and implementing a strong data governance system is a journey, but the benefits are well worth the effort.