As organizations move ahead in the journey of digital transformation, the massive tail of data from digital transactions is increasing steadily. Still, for a lot of enterprises deriving intelligence from data continues to be a pipe dream. According to a recent report of International Data Corporation (IDC), business and customer data has been accumulating at a CAGR (compound annual growth rate) of approximately 23% since last year, with a CAGR of 28% attributed to organizations, and is foreseen to reach 180 zettabytes by 2025.
As the digital business has become an enterprise priority, the volume of data generated by digital transactions is rapidly increasing, making data storage and management a growing concern. Traditional data management approaches are not always fit for purpose, especially when it comes to digital transformation. Data pools can cause more problems than they solve, and trying to manage data in a Hadoop environment without a clear understanding of the data lifecycle can lead to serious issues. These days, data is growing exponentially, and keeping up with this growth is a constant challenge, especially when organizations increasingly struggle to find the correct information to enable digital transformation. A lot of organizations have reached a position where it is clear to them that the data they possess neither offers a constant competitive edge nor empowers them to unlock value from it.
So, what can be done to maintain control of data in a digital world? The answer is not as simple as you might think, but it lies in a batter data management strategy.
Toward a Reliable Data Management Strategy
As the volume of data continues to grow, organizations will have no choice but to develop an efficient and effective data management strategy. And one of the best ways to ensure that your data is “fertile” for use is to digitize everything. Doing this will enable organizations to add metadata and use data for predictive, prescriptive, and real-time applications.
The data engineering practices of most organizations are based on a very weak data foundation, and these foundations are established on the ETL methodology; extract, transform, and load. This data transformation process can be compute-intensive and somewhat complicated. Also, it can take significant time as the process requires a lot of input/output activity, data parsing, and string processing.
A better data management approach starts with shuffling the characters “ETL” a bit and employing a method that begins with the wrenching of the data and then loading it in specific data repositories that transform it individually into a more valuable and appropriate form.
Instead of employing a single ETL server/engine to modify all the raw data, organizations may use the ELT approach segments to channelize specific cloud data repositories where the portions can be individually transformed. This can result in less input/output time and faster explicate.
Less Chaos And More Intelligence
Data has become the driving force behind innovation, the lifeblood of every industry, and the foundation for the digital economy. On the basis of ELT structure, the future state data architectures will focus on developing a reliable data foundation tier and a platform‐based strategy to present an all‐incorporating data management solution for the organization. Whether it is business metrics, sales and marketing intelligence, IoT data, user analytics, or clickstreams, the future architectures will count on a coherent platform to decrease the gap between the procuring of data and unlocking value.
Migration and Transformation
The aim of future state architectures is to reduce long‐running inquiries and join with business data by possessing data components that are compute‐ready and lead to the highest usage of processing resources and data storage. This will reduce the amount of data stored to a fraction of what we store as of now, and will also enhance the speed at which organizations can unlock valuable and actionable business intelligence.
