Market Research Report
Global Data Lakes Market - Segmented by Deployment Type (On Premise, Cloud), End User (BFSI, Retail, Entertainment and Media, Healthcare, IT and Telecommunications, Manufacturing), and Region - Growth, Trends and Forecasts (2018 - 2023)
|Published by||Mordor Intelligence LLP||Product code||670013|
|Published||Content info||113 Pages
Delivery time: 2-3 business days
|Global Data Lakes Market - Segmented by Deployment Type (On Premise, Cloud), End User (BFSI, Retail, Entertainment and Media, Healthcare, IT and Telecommunications, Manufacturing), and Region - Growth, Trends and Forecasts (2018 - 2023)|
|Published: July 19, 2018||Content info: 113 Pages||
Global Data Lakes Market was valued at USD 3.24 billion in 2017, and is expected to reach a value of USD 14.01 billion by 2023 at a CAGR of 27.4%, over the forecast period (2018-2023). The scope of the report is limited to deployment type which include Cloud, On-premise. The End Users considered in the scope of the report include BFSI, Retail, Entertainment and Media, Healthcare, IT and Telecommunications and Manufacturing.
Data Lakes have become an economical option for many companies rather than an option for data warehousing. Data warehousing involves additional computing of data before entering the warehouse, unlike Data Lakes. The cost of maintaining a data Lake is lower than maintaining a data Lake owing to the number of operations involved in building the database for warehouses. The speed of data retrieval is also better for data lakes compared to data warehouses which have proved them to be a viable option compared to warehouses. According to O'Reilly Data Scientist Salary Survey, it has been identified that about one-third of the data scientists spend time for doing basic operations such as basic extraction/transformation/load (ETL), data cleaning, and basic data exploration rather than true analytics or data modeling which reduces the efficiency of the process. The growing use of IoT in many offices and informal spaces has further emphasized in the need for data lakes for quicker and efficient manipulation of data.
According to IBM most of the companies in the United States have about 100 Terabytes of data stored. Companies have been struggling to manage such data as increasing the storage capacity, and processing power of the existing systems involves in high cost and not a sustainable option. Data lakes have emerged as a practical solution to exponentially increasing data. The variety of data emerging in the present day scenario has been broader in range as many developments such as modern cars alone have about 100 sensors per car, NSE has nearly 1TB of trade information per session, according to IBM. Owing to such developments the use of data lakes aids in improving the agility of organizations such as analytics firms to retrieve, store and manage data in a better manner.
Banks have been increasing the use of data lakes to integrate data across various domains to create a central database. Australia and New Zealand Banking Group (ANZ) has been implementing a project to aggregate all the data ponds across its domains to create a central data lake for the bank which will allow the bank to shift from the typically used data warehouse architecture. In the present scenario, the implementation of data lakes in the domain has not been effective as many data lakes have been changing into data swamps. Customers are unable to access data with ease, and data lakes have become the bottlenecks for organizations. Banks have been investing in data engineers to provide more responsive data lakes to tackle with consumer requirements. Banks have been trying to increase the utility of data for on the go solutions. State Bank of India (SBI) has been providing data lakes, apart from the typically used data warehouse, to bank executives, deputy managing director and chief information to deliver on the go analytics.
According to Capgemini, more than 60% of the financial institutions in the United States believe that big data analytics offers a substantial competitive advantage over the competitors and more than 90% of the companies believe that the big data initiatives determine the chance for success in the future. Data Lakes are needed for the use of Smart meter applications. In Canada, BC Hydro uses an EMC data lake for analyzing data aggregated by various smart meters. The data then enables in detecting discrepancies in the system. This has aided in achieving savings of 75% of the electricity due to theft. The number of Smart Meters in the region have also been growing in usage. Owing to increase in the usage of smart meters a huge amount of data is being generated which needs the use of Data Lakes. In the United States, a total of 70,823,466 smart meters have been installed according to U.S Energy Information Administration.
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