5 Key Elements Of Analytics To Consider

Analytics can provide insight into your business and help with making key decisions, but you must know what you’re trying to accomplish.

Tapping into the potential of Big Data is an increasingly high priority, and yet many businesses are pursuing projects without actually understanding it, according to an article on the ReadWrite website.

The article cites a survey by Gartner that found 64 percent of companies are launching or scheduling Big Data projects, but most don’t truly understand what they’re trying to accomplish. “The problem for many of these same enterprises is that they struggle to understand what Big Data is all about, and how to make it work,” the article notes.

The moral of the story is you should decide where you want to go and how to get there before embarking on an analytics journey. Here are five elements of analytics to consider:

1. Understand the purpose behind using analytics: Are you trying to make a decision, create ideas or validate assumptions? For example, you may want to see if there are correlations between key variables. You could take factors such as age, location and weather and see how they correlate to other data you’ve collected.

If you’re trying to make a decision, you must understand what questions you’re asking and how they align with the objectives and strategies of the business.

2. Measure what’s already available, and make it relative and non-precise: You don’t want to get too detailed. Analytics are designed to understand trends, shifts and patterns. From those components you can look at more detailed analyses, defining and measuring what’s really happening and engaging in problem-solving.

3. Define the vocabulary: Agree on the vocabulary you’re going to use for the metric and the math behind it. You would think everyone would be on the same page as to the definition of something as simple as on-time delivery, but it’s surprising how many different variations there are. Make sure everyone involved in the project is using the same standards.

4. Understand data collection accountabilities: Where’s it coming from? Who’s accountable to get that data? You must know the source of the data and make sure it is timely and precise. Otherwise, what’s the point of looking at it?

5. Build dashboards in iterations: Misunderstandings often occur when developing dashboards, so it’s important to approach it from the perspective of the three architects: content, design and technology. The content architect defines what’s going to be measured. The design architect is concerned with how the dashboard looks and whether it visually stimulates cognitive learning. In other words, when you look at it, do you see shifts in trends? The technology architect outlines how the systems interact and extract different types of information from different sources.

Tesco is one company that’s found success in leveraging supply chain analytics. According to an article on the Computer Weekly website, the British grocer and retailer used analytics to gain a wealth of understanding in such areas as demand forecasting and best practices in stores. Its projects paid off, and Tesco saved more than $136 million annually in supply chain costs thanks to its initiatives.

One such initiative was spurred by Tesco’s desire to cut inventory levels. The company understood that it wanted to measure efficiency at its distribution centers, so it used sales data going back four years. Tesco then plugged forecasting data in its models to determine how to optimize inventory.

In the end, while the potential benefits of analytics can be exciting, it’s important to stay focused so that you’re getting the most out of your effort. As an organization, you must know what you’re working with, what you hope to gain from analytics and how to move forward.