Data is becoming more integral in today’s business landscape. Organizations rely on data in the decision-making process. They look at historical data and market trends to make pivotal decisions.

As a result, CIOs are seeing a shift as they move from infrastructure managers to data brokers within their organizations. They must learn to collect information, analyze it, and determine how it can move their company forward. The shift requires adopting new skill sets that will eventually benefit business.

The Importance of Data

• Helps with Decision Making: Data is imperative in the decision-making process. Teams often review historical data to understand market trends and customer behavior. They use this information to predict how current decisions will impact growth.
• Improves Problem-Solving: Data provides performance metrics that could indicate underlying issues that may contribute to problems within the company. It also offers insights that help companies solve problems effectively.
• Allows You to Understand Performance: Data offers insights into company performance so teams can see where they excel and where to make improvements. Companies can use the information to become more efficient, provide improved products and services, and become leaders in their industry.
• Improves Processes: Companies can use data to identify inefficiencies and make processes more efficient. Increased efficiency saves money and reduces time spent on marketing, improving customer service and order fulfillment.
• Helps Companies Understand Customers: Data provides insight into customer demographics and behavior. Organizations can use that information to develop better products and produce relatable marketing materials.
• Useful for Financials: Companies can use data to create budgets and determine pay and wages.

How to Translate Data

CIOs that utilize data in their business decisions must understand how to translate and apply it to achieve their business goals. They must implement the following processes:

1. Identify Your Goals: CIOs must first understand what they hope to accomplish with the data collected. Are they hoping to improve processes? Design better processes? Deliver more effective marketing and customer service? Their goals will help them determine what type of data to collect and the insights to examine.
2. What Does the Data Mean? CIOs must go beyond reviewing data. They must analyze it to understand how it will impact their company. The data should provide insights that determine their next steps.
3. Understanding the Why: An analysis is incomplete until you know the why. For example, the rise in beach item sales during summer is easily explainable. However, other trends and behaviors may be harder to define. CIOs must consider which factors contributed to the outcome to determine what their company can do to produce a similar or different outcome.
4. Review Multiple Data Sources: Multiple data sources lead to more insightful outcomes. For example, an e-commerce business may review its transactions, marketing data, reviews, and social media interactions to understand how its strategies pay off.
5. Communicating Insights: CIOs must share their data with other stakeholders to gain support and input. Make your presentation engaging by using visuals that provide a complete picture. Propose an action plan to motivate others to act on the data provided.

What are the Different Types of Data Analytics?

Data analytics are typically divided into four basic types as follows:

• Descriptive Analytics: These analytics describe trends over a specific amount of time. For example, businesses can use this data type to determine if sales went up or down over a month, quarter, or year.
• Diagnostic Analytics: Diagnostics focus on why something happened. A company may use diagnostic analytics to determine whether foot traffic decreased due to rain or winter products sold more in the fall.
• Predictive Analytics: Predictive analytics predict what will happen in the future based on historical data. For example, if a company’s sales typically slow during the summer, they may expect slower sales when temperatures heat up.
• Prescriptive Analytics: Prescriptive analytics suggest a course of action. A company that is slow in the summer may decide to reduce staff during the summer months, or they may choose to offer a product that is in demand during the summer to compensate for slower sales.

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