Rethink Your Data Architecture To Raise The Bar On Innovation
Businesses are being overcome by an avalanche of data, with some estimates pointing to creating 2.5 quintillion bytes of data per day. However, what should be an embarrassment of riches has become a lost opportunity because it’s becoming clear that existing data architectures are simply unable to accommodate what is being presented to them. By some accounts, only 0.5% of the data gets analyzed and used. Many businesses have simply been collecting data with no way of deploying it. Now living in a data swamp, businesses lack the infrastructure to clear it.
Unfortunately, time is not on their side to find solutions. We have all noticed that COVID-19 has quickened the pace of digital transformation, with companies rushing to find ways to accommodate new ways of working and serving their customers with innovative new approaches while actively managing their data. As I was talking to a few data architect friends, I started wondering what’s needed to redesign data architecture with a fundamentally different but open approach.
Existing data architectures – how did we get here?
Let’s look back at some of the existing data architecture systems as we evaluate where to go from there.
Operational data started in the ‘80s because businesses could not get hold of their performance and needed numbers to evaluate their key metrics. As this started to gain interaction, there came a saturation point for businesses to find a place to store these pieces of information. This led to the invention of prem-boxes or data warehouses.
One big problem that businesses faced while using these boxes was the lack of scalability. Also, companies then started looking for answers to detailed questions. This was made possible through the use of artificial intelligence and machine learning.
Another breakthrough that came in 2010 was the data lake. It was cheaper, and businesses could throw all their information in there. But when most businesses aimlessly dump such information in the data lakes, they become data swamps making it impossible to analyze the data and its sources.
To handle data of the different business units, data experts started to use data marts for retrieving client-facing data. This gave enough freedom for businesses to work on various data groups without much hassle.
Where do we go from here?
For decades, data architecture has been going back and forth — from data warehouses and data lakes — to build a system that sustains the demand of business needs and upgrades. However, the biggest question is whether they can store information like the future demands and make it accessible for analytics at the desired speed. With the rise in demand for data collection, it will be challenging for businesses to adopt any single method that would complement their style. To help solve this ever-growing problem, businesses will require a channel that scales with them.
In the last few years, we have seen some new approaches to upgrade the existing data architecture. But data replication and the growing number of users demanding the same data inhibit the current architectural scale-up of data marts and warehouses.
