Data analytics has gained momentum with the growing popularity of companies like Facebook, Google, eBay, and LinkedIn. Such organizations, instead of merging their huge data with traditional IT infrastructures, rather focus on building standalone technology architecture. Big data is expanding each year at the rate of 50%, as companies are increasingly using video footage, text and voice methods for their sales and marketing activities. Also, data analytics is considered growing across sectors like telecom, travel, retail, healthcare, manufacturing, and financial services.
Traditional Way of Managing Data Volumes Is Fading Away
Though big datasets organization can be new for start-up online firms, however, large companies also have their own set of challenges to manage it efficiently via the traditional methods.
Traditional means of data management is day by day losing its importance because of its incapability to deliver large scale benefits of data analysis & organization. Neither it can detect, prevent and remediate financial fraud, nor calculate risk on large portfolios or improve faulty collections.
A rise in data velocity and volumes requires an improved analysis of customer information creating a demand for better methodology and thereby fading traditional means, such as web analytics, business intelligence, and customer survey. Hence, these tools are proven ineffective in generating real outcomes for businesses.
Contemporary and Effective Data Management Solution
One of the main objectives of handling big data is the proficiency to analyze multiple data types. Captive centers across the globe, integrated BPO/ IT players as well as companies specializing in data analytics are leveraging this new wave.
Call centers facilitate effective data capturing and consolidation of customer information followed by converting the data into relevant information that can be further presented as dashboards and reports. A majority of big data providers use cloud-based architecture to manage clients’ data to bring scalability and flexibility in the process
Next is the statistical algorithm which is applied to convert raw data into valuable information, which may include enterprise processes, consumer purchasing patterns and trends in consumer behavior.
On the other hand, some of the providers use sentimental analytics to leverage customers’ data to make informed decisions, for e.g., a travelling business can use this method to evaluate the real-time response from their marketing campaigns.
Hence, companies across the globe are integrating their data warehouse with call centers to move to leverage big data. This is helping them to reduce customer churn ratio as well as drive business through cross-sell and up-sell. Moreover, logs provided by the call center offer valuable insights into consumers’ needs and purchasing behavior.