3 Ways Harnessing Data and Leveraging Analytics Can Give Your Business an Edge

Data is the backbone of business these days. Employees are debating the best ways to collect it, analyze it, and use it behind the scenes at companies around the world. In the digital age, tapping into the right data can catapult an unfamiliar operation into a household name. At the very least, leveraging accessible information can make a business more efficient and responsive to customer needs.

But the data is also complex, making it easy to confuse its meaning. Uncovering actionable insights requires knowing how to use the insights while filtering out the nonsense. In data analysis, biases, misinterpretations and insufficient methods can lead a company down the wrong path. When done correctly, however, data analysis has the power to put a business ahead of its competitors. Here are three ways to do it.

Companies rely on well-designed strategies to stay ahead. While business strategies aren't a guarantee of success, they are a roadmap to what business leaders hope to achieve. Ideally, C-suite members do not design these plans based on intuition alone. They also use data to determine which direction to go.

Relational data models are examples of sources that leaders can use to formulate their game plans. These patterns reveal not-so-obvious relationships between different variables. While correlation doesn't always mean causation, discovering connections between variables can lead to more informed strategies.

Let's imagine that a company's customer survey data shows an inverse relationship between loyalty and satisfaction. In other words, the longer customers stay, the lower their overall satisfaction. Intuitively, this doesn't seem logical. But the data points to the need for a different customer retention strategy. To determine what the potential solution should be, leaders should integrate more variables.

DevX, a leading provider of tools and services for the technology industry, highlights the scalability of relational data models. These models are relatively easy to understand and adapt according to a company's needs. The number of variables can increase or decrease as leaders seek to solve problems of varying complexity. For example, the company looking to solve its customer satisfaction problem may need to add data points on agent empathy and language alignment to traditional measures of response time and first contact resolution.

Predictive analytics is as close to a crystal ball as businesses can get. With these tools, employees can identify trends in consumer behavior. Predictive analytics brings companies closer to the customer's mind by revealing how a customer is likely to react to market developments and company tactics. The tools predict the future by examining past data to identify trends and preferences.

For example, historical data shows that people reduce spending when prices rise. But not all market segments bear the brunt of a slowing economy in the same way. In 2023, inflation and rising interest rates have caused consumers to