Data lake is a centralized repository that helps to store all the structured and unstructured data at any scale. It offers the storage of data without having to first structure the data, and run different types of analytics. The analytics used within the data lake includes Machine Learning, Predictive Analytics, data discovery, and profiling.
Data scientists, data developers, and business analysts (using curated data) are the major end-users of data lake.
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The need for a platform that can store and manage the large volume of data with respect to customer information and behavior has increased due to the increase in competition across industries. A data lake can combine customer data from a CRM platform with social media analytics, a marketing platform that includes buying history, and incident tickets which can be used to improve customer loyalty toward the company.
Internet of Things (IoT) introduces more ways to collect data related to processes such as manufacturing, with real-time data coming from internet-connected devices. The need for operational efficiency with respect to cost and data analytics has increased across the globe. A data lake makes it easy to store and run analytics on machine-generated IoT data to discover ways to reduce operational costs and increase quality.
The number of professional experts in data science and with the skill set to manage the data lake is comparatively less. The level of HDI is low majorly in Asia Pacific and South America compared to other regions. Therefore, the number of skilled and professional workers is low in these regions. Lack of awareness and low HDI is restricting the growth of the data lake market.
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In terms of region, the global data lake market can be divided into North America, Europe, Asia Pacific, South America, and Middle East & Africa. North America is expected to dominate the global data lake market during the forecast period, as the adoption of AI and machine learning technologies within this region is very high compared to other regions.