If you ask me today, data makes the world go round. We thrive on data, data from our cellular network providers to the very data we create and save every time we use the computer and the web. We freak out at the thought of losing valuable data whenever there is something wrong with our gadgets or when we accidentally delete files we aren’t supposed to erase. Back in the 90s, data warehousing was the hottest tech craze and companies did not think twice to put their money on it.
The problem with this concept is that it did not live up to its promise, specifically to the return of investment entrepreneurs wanted from such a big investment mainly because of the wrong strategy used. Data warehouses should ideally act as a data information hub rather than an aggregation platform for all sorts of data that it turned out to be.
The concept of data warehouse was at its peak of investments and hype in the ‘90s. Back then, enterprises spent a fortune on data warehouse. Seeing the potential that data offered, average investment by organizations ranged from USD 30 to USD 40 million on data warehousing capabilities. Enterprises started building infrastructures to assimilate and consume data on a common platform, but they had no clear idea about its utilization and life cycle management. They eventually used it as a central place for enterprises to collect transactional data and distribute it to those who needed it.
CIOs often ask this, because CFOs question them where the ROI is. When we speak to CXOs, they point out that only forty to fifty percent of their business needs are being met by a data warehouse – this is a low number for such an investment.
Though a centralised repository of data system was put in place, the investment didn’t live up to its promise. About ninety percent of enterprises failed in creating value because they implemented the wrong strategy. A data warehouse was perceived as a central base to collect all types of data across an enterprise. With a lot of unnecessary data coming in, the data overgrew its size. It worked as a data aggregation platform rather than a data information hub. There’s a structural flaw in this design, while data volume, governance and sustenance went for a toss. Enterprises didn’t consider a data warehouse as a strategic infrastructure which meets all the business data requirements of an enterprise.
In the past, data were often housed in enterprise mainframe servers but most of them are stored in the cloud nowadays. This non-volatile collection of data is what helps analysts and executives make practical decisions for the best of the company while at the same time keeping them separately from operational databases.
“We see an accelerated move toward the cloud,” Shamgunov says. “When talking to our customers or attending conferences, we see most enterprises using hybrid environments with infrastructure deployed on premises and in several public clouds. We believe that multi-cloud environments is the future and enterprises will choose to depend on infrastructure that works across clouds.”
“Specifically in data warehousing, we see a shift from expensive on-premises offerings to much more efficient cloud data warehouses,” Shamgunov continues. “Traditionally, moving data has been a hard proposition due to its stickiness, however the cloud proposition is irresistible. Having a solution that works natively in the cloud and with an option of deploy on-premises has become a unique, compelling position for MemSQL.”
Data warehouse comes in handy in banking and financial institutions as well as in the retail and consumer sector. A lot has changed with how data is stored these days, especially that millions to billions of data are stored, managed, and analyzed by the minute. And to keep up with the fast-paced life, companies now need real-time insights about the multitude of these data to help them make smart decisions whenever it is needed.
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