How Confident Are You In Your Decisions?

Using an analytical framework will increase your decision-making capabilities

How Confident Are You In Your Decisions?

Whatever your profession, whatever your field, whether you are a leader or an individual contributor, you will be asked to make a decision at some point in your career. This decision should in part, be made based off data. Analytics will help you make these decisions. But which type of analytics do you use and in what situation? It is not a one versus the other approach, but an overarching framework where you may be using multiple techniques as you flow through your decision-making process.

What is Analytics?

Analytics is not limited to a specific field of business, but it can be used everywhere to make decisions if an individual is data literate. Does that mean that those individuals must be statisticians or mathematicians or data scientists? It does not. It does mean that individuals must understand the fundamentals of data, data analysis, and the tools and techniques that are used to inform decisions. Should individuals use random analytical methods that do not take into account the type of data they are evaluating? No, individuals must understand what analytic techniques should be applied based off the decision they are trying to make as well as the type of data they are using.

Analytics can take many forms. It ranges from basic math techniques all the way to a combination of statistics, probability and powerful programming languages to gain valuable insight and deliver it in the form of visualizations.

Four types of Analytics

Descriptive Analytics

Typically, an analysis starts with descriptive analytics, which are designed to give you an overview of your data. This includes looking at both quantitative and qualitative data. Both are critical when it comes to decision making. Qualitative analysis uses data that is not defined by numbers like open-ended survey questions and social media posts. The data must be coded so that items may be grouped together intelligently. Here you are typically looking at descriptive metrics like frequencies. Quantitative data analysis includes looking at things like the median, standard deviation, and other descriptive metrics. A key decision point on what analytics to use later on in your analytic framework is whether or not your data is normally distributed. This is determined during the descriptive analytics process.

Exploratory Analytics

Exploratory analytics is an approach to analyzing data where an individual takes a high-level view of the data and tries to make some sense of it. With descriptive analytics, you are looking at key metrics of each variable of data independent of each other. With exploratory analytics, you look across all your data together to get a feel for the data and use your judgement to determine what the most important aspects of the data are and how they may relate to each other. Where descriptive analytics helps answer what happened, exploratory analytics starts the hypothesis into why it may have happened. Exploratory analytics is not necessarily a set of calculations or techniques, rather it is a process where you visualize your data, look for patterns and trends, and explore other areas of interest.

Inferential Analytics

While descriptive and exploratory analytics describe the data and start to create a hypothesis into why something is happening, it is not sufficient to draw conclusions and make decisions. Inferential analytics use a set of inferential statistics that focus on using the data to make predictions, forecasts, and estimates, in order to make more informed decisions with your data. Inferential analytics use statistical approaches that make generalizations and predictions from a sampling of the dataset.

Predictive Analytics

The last group of analytics is predictive analytics. Predictive analytics is the study of data from the past to predict future behaviors. It uses a combination of several different techniques such as data mining, machine learning and artificial intelligence to understand the existing data and make future predictions. Sometimes the event of interest will be in the future but predictive analytics can be implemented for any type of unknown, in the past, present or future. A good example of this is detecting fraud before it happens. This also helps an organization become proactive which enables it to make decisions based on the data and not on a hunch.

Interested in learning more about data-informed decision making? Take a look at our free Data Literacy and Data-Informed Decision-Making learning modules on the Qlik Continuous Classroom.

Want to improve your decision-making capabilities? Leverage an analytical framework


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