Even though organizations have figured out how to build data lakes, they still face difficulty in monetizing the data as quickly as they had desired. It is because they are not prepared to answer questions on different data assets.
Fremont, CA: In today’s digital market, customers demand relevant, immediate, and personalized interactions with the organization they choose to do business with. In 2019, the CIO Tech Poll study both data and analytics achieved the top position for the highest expected budget increase over the year.
How data analytics helps organizations reap benefits?
Even though organizations have figured out how to build data lakes, they still face difficulty in monetizing the data as quickly as they had desired. It is because they are not prepared to answer questions on different data assets. It is important to modernize the strategy encompassing goals, vision, and mission around Technology/Tool, Process, and People.
Build Intelligent Data Lakes
Data explosion in data lakes would make it nearly impossible for one to explore and monetize data since data lakes are incapable of delivering anything by themselves. Hence, it is better to design and build data lakes with automated data crawlers and AI abilities that continuously recognize patterns and create the data web connecting layers across distributed data assets. This creates a platform that allows for faster and greater analytics validation and monetization.
Though enterprise data lakes emphasize on connecting dots, it is essential to have embedded analytics. In fact, it is better not to design and build any business workflow, function, or microservice without embedded analytics focus. Every business or system event should be measurable for its value in real-time and should be subjected to continuous improvements. Nowadays, business decision-makers have access to increasingly innovative and accurate means of collecting data and translating it into organization value to reap these benefits — not to forget the improvements to customer experience, such as machine learning.