HomeData EngineeringData NewsDeriving real Business Value through Data analytics

Deriving real Business Value through Data analytics

An often underutilized source of organizational value is business data. Customer interaction data, supply chain data, operational data, financial data, market research data, back-office data—these frequently hidden data sources hold enormous potential for operational insights and value creation, according to Sidharth Mukherjee, chief digital officer of Teleperformance, a global provider of digital business services.

However, it can be difficult to make sense of the enormous amounts of corporate data that are available today. For starters, there is a lot of it, its structure is frequently ad hoc, and it is frequently unidentified or compartmentalized within the organization. In fact, the research firm Forrester named activation of unstructured ‘dark’ data’ as a megatrend in customer service for 2022.

The necessity to utilize this data was brought into sharp relief by the release of enterprise-ready generative AI tools in late 2022. For businesses looking to take advantage of the potential of generative AI, having a solid data strategy has become essential given the previous months’ immense buzz and raised expectations around it.

To enable better decision-making, streamlined back-office procedures, and improved corporate performance, data analytics can fortunately assist organizations in locating and extracting useful insights from this underutilized data. But to pull this off, business and analytics leaders must guarantee data quality and have the necessary leadership, employee buy-in, and a culture that values data.

Advantages of data operationalization

Statista predicts that by 2025, there will be more than 180 zettabytes of data in existence. This covers the huge amounts of data produced by everyday business applications, such as customer interaction logs, supplier connections, conversion tracking results, employee and workforce management data, customer feedback data, research findings, invoice processing receipts, and vendor management. These technologies produce data, much of which is underutilized, ranging from tools for employee onboarding to payroll processing services. This is changing, though, as businesses use data analytics to analyze this data, spot patterns, and build models that present pertinent information and suggestions that might help them make more informed decisions.

Sharang Sharma, vice president of business process services at Everest Group, asserts that data analytics technology has advanced significantly in recent years. The volume of information that some of these programmes can examine and draw conclusions from is truly amazing. According to Gartner research, the analytics and business intelligence software industry is really predicted to double in size by 2025 and reach a value of $13 billion.

Data analytics is already helping organizations find novel and creative methods to operationalize business data. These cross-industry use cases highlight the ability of data analytics to pinpoint underperforming internal operations, particularly in the back office, and optimize them for improved business performance.

To identify the reasons for bottlenecks and delays, a grocery store chain, for instance, would look at its supply chain data. These insights not only help the store address delays and stay ahead of the curve, but they also give warehouse and procurement managers the ability to optimize inventory in ways that can reduce product waste, frustrated customers, and unneeded expenses.

The data produced by human resource management systems may be analyzed by an insurance company to produce fresh operational perceptions. Think about a health insurance provider that spends the time to analyze the data related to its employee onboarding procedure, as an example. It might pinpoint the reasons why certain new workers take longer than others to reach peak productivity, allowing the company to put in place training programmes that are intended to increase output and cut down on turnover. In highly competitive industries and in the present-day tight labor market, these types of applications are, of course, a distinct advantage.

When data analytics technologies are utilized to track contact activity in a customer service environment, operational efficiencies can be attained. For instance, specific data patterns can indicate an unexpected spike in call volume. Organizations that are aware of these patterns can better allocate resources depending on changing demand and get their workforce ready for upticks. Cost savings, better customer service, and increased operational efficiencies are the end results.

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