In today’s highly competitive landscape, there are few things more valuable to a business than information. Modern organizations identify, collect, produce, maintain, and share a vast amount of data. This data is then used to further business growth, identify customer preferences and tendencies, extract strategic insights, and maintain a demand and supply of existing or new products and services.
Traditionally, such data has resided in silos across the organization. This presents a challenge for extracting trends and insights, especially when dealing with big data. Enterprises thus need a next-generation solution for extracting business intelligence — data lakes.
A data silo is a repository of data that remains under the authority of a single department, isolated from other departments. Data silos exist because of different factors:
One of the biggest disadvantages of data silos is that they inhibit productivity besides promoting disharmony in different departments of a single organization. Data silos can create confusion when there are two or more silos for the same data but with different content. This could translate to the latest data getting accidentally overwritten with outdated data.
Imagine the R&D team of an organization refusing to share its latest product design tests with the product management department or the sales team refusing to share its CRM data with the marketing department. This bunker mentality shouting “It’s only mine!” can dent the growth and prosperity prospects of an organization.
A data lake is a new, smart approach to information management and enterprise reporting that stores all information on a single interface. It is a centralized repository to store all structured and unstructured data, which can be used to derive analytics for business. Designed for big data analytics, data lakes effectively solve the challenges commonly associated with data silos.
Data lakes are extremely beneficial in extracting invaluable insights in a single and comprehensive IT infrastructure. A data lake environment primarily eliminates data silos by facilitating the centralization of information. It eliminates two of the biggest disadvantages of data silos: inhibited productivity and wasted resources.
Central to the success of big data projects are two elements — a knowledge of the actionable data to achieve desired outcomes and access to the right data to achieve such outcomes. Traditional data management approaches can’t handle big data analytics that rests upon an identification of correlations between different data sets. When such data sets sit in entirely different systems, analysis is impossible.
Data lakes hold the potential to improve data sharing with enhanced availability. IT departments can minimize time-to-value because of scalable computing platforms and high bandwidth access along with accelerating analytics that would have taken weeks and months with data silos to just a few minutes. Because data silos can’t manage the rapid influx of big data, businesses need data lakes to cross-correlate data to extract greater insights and house data in a single IT infrastructure. Data lakes are thus a way to end data silos in an increasingly unstructured big data universe.
Data Lakes solve the issues associated with traditional storage technologies:
Data lakes help organizations collect, produce, use, maintain, and share complex data to simplify the flow of information and data. Businesses can thus analyze trends, detect patterns, and extract actionable intelligence. Data lakes are extremely effective to change lead times and minimize IT investment costs. Organizations looking to gain cutting-edge data insights that promote quick, responsive, and targeted business decisions while realizing cost savings and furthering business growth must thus move away from silos to data lakes.