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Data Driven Decision Making Series - Part 4

How to make Data - Driven Decision Making in 6 Steps

Today every organization strives to be data-driven. There is not a single organization that says, “Let’s not use the data; our intuition alone will help in arriving at good decisions.” Most leaders and managers understand that – without data – bias and false assumptions can influence decisions and lead to poor decision making. And yet, in a recent survey, more than 58 percent of respondents said that their companies base most of their business decisions on gut or intuition instead of making it data driven.

How do you ensure you’re making data-driven decisions that are void of bias and focused on clear questions that empower your organization?

How to Make Data-Driven Decisions

To effectively utilize data, professionals must achieve the following:

  1. Know the objective The steps outlined can help you find the “why, what, who, where, and when” to make the most of data – for you, for colleagues, and the business. It is important to understand that the entire process is not linear and is mostly iterative in nature. One question often leads to another, which may mean you need to go back to one of the steps again or skip to another – eventually leading to valuable insights.

Step 1 – Identify business objectives:

This step will require an understanding of your organization’s strategic and operational goals. This could be as specific as increasing sales numbers and website traffic or as ambiguous as increasing brand awareness. Ask yourself what the problems are in your given industry and competitive market. Identify and understand them thoroughly. Establishing this foundational knowledge will equip you to make better inferences with your data later on. By determining the precise questions you need to know to inform your strategy, you’ll be able to streamline the data collection process and avoid wasting resources.

Understanding the specific goals will help in the process later to choose the right key performance indicators (KPIs) and metrics that influence decisions made from data—and these will help you determine which data to analyze and what questions to ask so your analysis supports key business objectives. For instance, if a marketing campaign focuses on driving website traffic, a KPI could be tied to the amount of contact submissions captured so sales can follow-up with leads.

Step 2 – Survey business teams for key sources of data:

The data that you require to address the business problem that you are looking to solve might be spread across the organization. You might have to coordinate information from different databases, web-driven feedback forms, and even social media.

Coordinating across various sources seems simple, but finding common variables among each dataset can present a tremendously difficult problem. It can be easy to settle for the immediate goal of utilizing the data for your current purpose alone, but it’s wise to determine whether or not this data could also be used for additional projects in the future. If so, you should strive to develop a strategy to present the data in a way that’s accessible in other scenarios as well.

Valuable inputs from across the organization will help to guide your analytics deployment and future state—including the roles, responsibilities, architecture, and processes, as well as the success measurements to understand progress.

Step 3 – Collect and prepare the data you need:

Accessing quality, trusted data can be a big hurdle if your business information sits in many disconnected sources.

Surprisingly, 80 percent of a data analyst’s time is devoted to cleaning and organizing data, and only 20 percent is spent actually performing analysis. This so-called “80/20 rule” illustrates the importance of having clean, orderly information before you can attempt to interpret what it might mean for your organization. Once you have an idea of the breadth of data sources across your organization, you can start data preparation. The access to quality and trusted data can be a big hurdle when the business information is in many disconnected sources and in multiple formats.

Start by preparing data sources with high impact and low complexity. Prioritize data sources with the biggest audiences so you can make an immediate impact. Use these sources to start building a high-impact dashboard.

Step 4: Perform Statistical Analysis

After the data has been cleaned, you can begin to analyze the information using statistical models. At this stage, you will start to build models to test your data and answer the business questions you identified earlier in the process. Testing different models such as linear regressions, decision trees, random forest modeling, and others can help you determine which method is best suited to your data set.

Here, you will also need to decide how to present the information in order to answer the question at hand. There are three different ways to demonstrate your findings:

  • Descriptive Information: Just the facts.
  • Inferential Information: The facts, plus an interpretation of what those facts indicate in the context of a particular project.
  • Predictive Information: An inference based upon facts and advice for further action based on your reasoning.

Clarifying how the information will be most effectively presented will help you remain organized when it comes time to interpret the data.

Step 5 – Develop insights:

Ask yourself, “What new information did you learn from the collection of statistics?” Despite pressure to discover something entirely new, a great place to start is by asking yourself questions to which you already know—or think you know—the answer.

Many companies make frequent assumptions about their products or market. For example, they might believe, “A market for this product exists,” or, “This is what our customers want.” But before seeking out new information, first put existing assumptions to the test. Proving these assumptions are correct will give you a foundation to work from. Alternatively, disproving these assumptions will allow you to eliminate any false claims that have, perhaps unknowingly, been negatively impacting your company. Keep in mind that an exceptional data-driven decision usually generates more questions than answers.

Step 6 – Visualize and share your insights:

Critical thinking with data means finding insights and communicating them in a useful, engaging way.Visualizing data forms a very key part of Data Driven Decision Making. Visually representing the insights gathered from your data in an impactful way will give you a better chance of influencing the decisions of senior leadership and other stakeholders.

With many visual elements like charts, graphs, and maps, data visualization is an accessible way to see and understand trends, outliers, and patterns in data. There are many popular visualization types to effectively display information: a bar chart for comparison, a map for spatial data, a line chart for temporal data, a scatter plot to compare two measures, and more.

Once you discover an insight, you need to take action or share it with others for collaboration. One way to do this is by sharing dashboards. Highlighting key insights by using informative text and interactive visualizations can impact your audience’s decisions and help them take more-informed actions in their daily work.

The conclusions drawn from your analysis will ultimately help your organization make more informed decisions and drive strategy moving forward. It is important to remember, though, that these findings can be virtually useless if they are not presented effectively. Thus, data analysts must become skilled in the art of data storytelling to communicate their findings with key stakeholders as effectively as possible.



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