Enterprise big data solutions have seen a major transformation in recent years due to the information explosion upon us. The need for data-driven business insights has increased, and big data analytics will significantly impact how businesses operate in the future. For enterprise-level companies, this means concentrating their efforts on customized solutions created from scratch to fit their ecosystem and address their concerns.
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What is big data?
In addition to combining unstructured, semi-structured, and structured data types, big data can take many different forms. It originates from various sources, including social networks, traditional databases, sensors, log files, logbooks, GPS systems, and streaming data systems. Some of these resources may add or modify data as frequently as a million times every minute. With big data, size is one of many factors to consider. Some of the V’s of big data is data volume, yet handling it is one of the more straightforward problems to resolve.
Methods for Enhancing the value of big data
Creating transparency
An organization becomes increasingly open and eliminates departmental silos when it uses data to inform decisions about the future. Enterprise big data solutions can reveal a range of inefficiencies because it is studied across boundaries. Big Data, for instance, can assist in identifying possibilities for enhancement across R&D, engineering, and production divisions in manufacturing firms to hasten the release of new products.
Segmentation and customization
Enterprise big data solutions offer a better chance to tailor product-market offerings to specific client categories to boost sales. Building multiple profiles of customers that can be addressed appropriately is made feasible by user or customer activity data. Online businesses, for instance, can improve their conversion rates by customizing the product selection on their web pages to match the present customer.
Social Analytics
The enormous amount of enterprise big data solutions that exist nowadays is measured by social analytics. This information can be found on social media sites like Facebook, Twitter, and Yelp through conversations and reviews. Three significant areas are measured by social analytics: recognition, participation, and referrals or reach. Metrics like the number of views for a video and the number of followers or supporters are frequently used to measure awareness, which examines the exposure to or mention of social content.
Engagement gauges the degree of contact and activity among platform users, including how frequently user-generated material is published. More recently, location-based data from mobile apps and platforms like Foursquare, which recognizes active users with badges, allows businesses to assess brand recognition and engagement. These data include the quantity and frequency of check-ins.
Data Exploration
Data exploration heavily relies on statistics to experiment and find answers to problems that managers may not have previously considered. This strategy uses predictive modeling tools to forecast user behavior in light of the customer’s previous purchases and preferences. Using cluster analysis, customers can be divided into groups with comparable characteristics that might not have appeared on analysts’ radar screens.
Once they have identified these groups, managers can take specific measures, such as tailoring marketing messaging, improving service, and cross- and up-selling to each distinct group. The ability to foresee which set of users will “drop out” is another everyday use case. With this knowledge, managers may proactively devise plans to keep this user category and reduce the churn rate.
Enterprise big data solutions’ remarkable growth over the past few years is primarily because it offers long-term business value. Value is realized as an immediate social or financial benefit and a tactical competitive advantage. Contact us to find out more about developing a big data plan.
