Manish Mistry September 10, 2020 
It may seem inconceivable today, but there was once a time when all an organization had to worry about was simply collecting data. Volume was the name of the game, and more data usually led to better decision making. This is still true on a fundamental level, but the increasing diversity and complexity of data out there have forced organizations to re-evaluate their approach.
Data-backed decision-making is still crucial, but those decisions now have to be lightning-fast with no margin for error. Data sources have increased exponentially, and the amount of information out there is so disparate and disconnected that organizations can no longer afford to play the volume game alone. The age of connectivity has brought with it an immense amount of pressure to get the right data to the right place at the right time, with levels of contextual insight that may have seemed insurmountable just a few short years ago.
Where once organizations gathered data, today they engineer it.
A decade ago, data engineering wasn’t even a concept. Engineers design and build things that tend to make processes easier, and that’s precisely what data engineering strives to achieve. Data engineers design and build data pipelines that ultimately transform data into its most useable and useful format. If data science was a thread, with artificial intelligence at one end and the raw gathering of data at the other, data engineers would occupy a critical role somewhere in the middle, controlling the process and enabling the seamless conversion of one to the other.
Much of this boils down to data integrity. Data integrity is still arguably the biggest problem that many enterprises don’t even know they have. Many of them are still in the mindset of “more data = better,” but if that data is inaccurate, incomplete, or inconsistent, any competitive advantage that can be gained from agile decision-making is immediately lost. Just as a lack of data can present problems, having too much data can present needless inconsistencies and result in the relevant information getting lost. If critical data isn’t present or is incomplete, production lines can be halted, resources can be wasted, and poor business decisions become increasingly likely. And even if all of these boxes are ticked, and catastrophe is seemingly avoided, there are still strict rules, standards, and regulations around the use of data that must be adhered to throughout the design and manufacturing cycles of any product or service.
Without an architecture in place to structure and format ever-changing sets of data, scientists, designers, and manufacturers would find their jobs significantly more difficult. That’s where data engineering fits in, and it’s rapidly becoming the cornerstone of digital transformation.
By not only gathering data but engineering it and leveraging it in the right way, organizations are able to capitalize on their resources like never before. From development, testing, and QA right through to administrative processes like sales and HR, businesses can use data to streamline their operations and make themselves more agile in an environment that can, and often does, transform overnight.
Infostretch takes this principle to heart in its “Go, Be, Evolve Digital” approach. Using this method, we are able to help clients at any stage on their journey toward data success and digital maturity. As any business owner will attest to, this is a journey that never ends. From strategy, planning, and migration, right through to production scaling and automation, there is always work to be done, and always enhancements that can be made.
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