With increasing volumes of data to manage and analyze, enterprises are seeking a range of infrastructure to undertake data analytics. Business intelligence and analytics are growing in popularity as a means to drive business transformation through big data.
In the present da scenario, there are multiple data sources ranging from IoT devices to clickstreams, a data lake makes it easy run to analytics on such data, reducing operational costs and increasing analytical prowess. In simple terms, a data lake is a repository holding a large amount of unstructured and raw data, making it much richer than a data warehouse, which holds only structured data.
A data lake’s power lies in its ability to combine data from various sources for easy analysis. The benefits of data lakes — previously available only to the most resource-rich companies like Google, Yahoo, and Facebook — are now becoming increasingly accessible. Data can be stored as is, without needing to be structured. It can then be used to run an array of analytics — from dashboards and visualizations to big data processing, real-time analytics, and machine learning — to guide business decisions. Data lakes allow for the storage of relational data — operational and real-time databases, and data from a line of business applications — and non-relational data — mobile apps, IoT devices, and social media.
Businesses produce and consume a vast amount of data, which emanates and dissipates from a variety of sources. Without the usage of the right tools, it is difficult for employees to glean the right insights. To support productivity and better performance, data from a multitude of sources must be consolidated and streamlined. Extracting data relevant for analysis needs a pool of rich data. For example, a bank’s business objectives would include improving customer experience, selling more products, launching new services etc. To achieve these goals, the bank would not only combine data regarding a customer such as the frequency of usage of debit and credit cards at various merchants, call center inquiries, and transactions carried out at the bank but also unrelated data pertinent to the weather and traffic, through which underlying correlations could be made. In this way, data lakes speed up data analytics as they leverage large quantities of data in a consistent manner.
The traditional system of data warehousing, that tackles structured data, is not competent to meet the evolving requirements of the business. It is not adequate to handle the vast amount of unstructured data that an organization produces. Businesses today need on-the-go analytics, which is not possible with data warehouses. Finding correlations between different data sets in real-time becomes impossible when data sits in entirely different systems. Data lakes, designed for big data, real-time analytics, effectively solve this challenge.
Data lakes can be used to offer real-time business insights with rich visuals across devices, helping users stay informed, spot trends quickly, and make better decisions. The improved speed and accessibility that comes with on-the-go analytics improves agility and helps users rapidly respond to real-time events. Data lakes are therefore fit to leverage big quantities of data with algorithms that drive real-time analytics. Using data lakes, businesses can make information easily accessible through various platforms. With gauges, dashboards, scorecards, and key performance indicators available on smartphones and tablets, data lakes can be effectively leveraged to extract analytics on the go.
State Bank of India (SBI) recently moved from a data warehouse and created a data lake to offer on-the-go analytics to bank executives in a bid to eliminate data silos and maximize value from data. This was done after the institution realized the limitations of data warehouses in delivering on-the-go insights with data volumes growing each day.
Organizations that successfully generate and extract business value from their data outperform their peers and competition. They are able to gain from novel methods of analytics like machine learning using log files, data from click-streams, social media, and IoT devices stored in the data lake. This enables them to identify and act upon opportunities for business growth by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.
A data lake can be leveraged to combine customer data from CRM tools with social media analytics to understand customer behaviour and patterns. Various platforms like Waterline Data, AWS, Azure can be deployed for on-the-go analytics to uncover new insights from unique data combinations. Additionally, visualization tools can be created to display data on smartphones and tablets, enabling employees to apply the right information at the right time. Ultimately, real-time analytics enable organizations to make smarter, data-driven decisions based on a 360-degree view of the customer and business.