The Advantages of Data-informed Decisions

Data offers information on patterns, anomalies, and signals on behaviours and performance. Studies have shown that companies that invest in analytics and incorporate the intelligence into their decision-making process perform better than competition in their industries.

With abundant data captured on customers, operations, and diverse aspects of a business, it makes sense to explore and glean data for meaningful information. Instead of trialing in the dark, effective use of data helps to guide sound decision-making. There are many advantages to making data-informed decisions.

Customer-oriented products and services

Data from market research provides first-hand input from customers about their preferences and dislikes. Combining that with data on revenue streams and returns, for instance, offer information on actual purchase behaviours that could be used to validate the market research data. The intelligence helps a business develop better products and services, as well as more targeted marketing programs.

Investment in critical operational deficiencies

Processes, systems and people are the building blocks for business operations. Unfortunately, there would be always be areas that require attention. For instance, onerous manual tasks generate a high error rate. By gathering data on the significance of the effects attributable to these errors, it becomes trivial to decide whether an investment is justified to automate the manual tasks. The information enables better prioritization of needs.

Meaningful thresholds for corrective actions

Without data, kneejerk reactions trigger responses to situations that might not be as bad as they look. The business ends up usurping resources from other challenges that deserve more support. For instance, the cost of producing products with zero defect versus the cost of repair. While it might be ideal to produce products with zero defect rate, it could be costly to do so. By tracking defects, analyzing repair cost and investment associated with quality improvement, the business might choose to balance an acceptable level of defects with repair costs.

Prediction of outcomes

Analyzes of the correlations among different data attributes explain their impacts on outcome. For instance, profitability of a store location is affected by the competition and demographics of the target market in the area. Earnings, population age, competition intensity, and leasing costs impact profit. With pertinent data, a model can be developed to select new store locations by predicting the potential profitability. This helps to establish a more systematic approach to store location selection, minimizing the risk of making a poor choice.

Optimization of resource deployment

Decisions get more complex as a business grows. Revenue comes from multiple lines of business. Inventory allocation involves prioritization of sales channels. Shipping costs vary by region. Production of different products, and hence, profitability, is subject to the availability of the input materials. There are opportunity costs associated with each decision due to resource constraints. By building a model to evaluate how best to deploy the various resources, the business would be able to identify an optimal solution. Without analytics, trial and error is a time-consuming approach, which is also costly.

There are many ways businesses can benefit when they leverage the data trove available at their fingertips. Data-informed decisions help to build competitive advantages and accelerate strategic objectives.