6 Steps to Efficiently Make Data-Driven Decisions in 2022

The use of facts, measurements, and data to influence strategic business decisions that correspond with the goals, objectives, and projects is known as data-driven decision-making (DDDM). When businesses see the full value of their data, everyone—whether a business analyst, a sales manager or a human resource specialist—is empowered to make better data-driven Decisions choices on a daily basis. This, however, cannot be accomplished just by selecting the right analytical tool to uncover the next strategic opportunity.

Make data-driven decision-making the norm in any company by cultivating a culture that values critical thinking and curiosity. People at all levels engage in data-driven conversations, and they hone their data abilities via practice and application. This necessitates a self-service paradigm, in which users may access the data they desire while maintaining security and governance. It also necessitates competency, necessitating data training, and growth options for personnel. Finally, executive advocacy and a culture that supports and makes data-driven decisions will inspire others to follow suit. 

These core capabilities help in encouraging data-driven decision-making across all job levels in order to regulate questions and investigate information for discovering powerful insights that drive action. 

Importance Of Data-Driven Decision-Making

What makes it more difficult for organizations is to gather the collected amount of information which thereby makes it difficult for organizations to manage and analyze their data. Many businesses are establishing three essential characteristics in order to become data-driven decisions: data competence, analytics agility, and community. It’s not simple to change the way a firm makes choices but putting data and analytics into decision-making processes is where it sees the most significant changes. This kind of change necessitates a focused approach to the development and refinement of the company’s analytics program. 

Organizations are gradually realizing the importance of data-driven decision-making across all departments and roles, thanks to contemporary business intelligence. Here are a few instances of companies that are making good use of the technology. 

6 Steps to Effectively Make Data-Driven Decisions

These stages can assist in determining the “who, what, where, when, and why” of data for the employees and the company. However, take in mind that the visual analysis cycle isn’t a straight line. One inquiry frequently leads to another, requiring a return to one of these phases or a jump to the next, ultimately leading to useful discoveries.

1. Determine the company’s goals

This stage will need knowledge of the organization’s executive and downstream objectives. This might be as precise as increasing sales and website traffic, or as general as raising brand recognition. This will aid in selecting key performance indicators (KPIs) and metrics that affect data-driven choices, as well as determining which data to analyze and what questions to ask so that the analysis supports important business objectives. For example, if a marketing effort is aimed at increasing website traffic, a KPI may be linked to the number of contact submissions received so that sales can follow up with leads.

2. Conduct a survey of business teams to identify critical data sources

It’s critical to obtain input from employees across the company to understand short and long-term goals in order to achieve success. These inputs aid in informing the questions that individuals ask throughout their research and analysis. The company’s analytics deployment and future state will be guided by valuable inputs from throughout the business, including roles, responsibilities, architecture, and procedures, as well as success measures to analyze progress.

3. Gather and prepare the required information

If a company’s data is scattered among several sources, getting access to high-quality, reliable data might be difficult. The employees might begin data preparation once they have a sense of the scope of data sources available throughout the firm. Beginning with preparing high-impact, low-complexity data sources and having data sources with the largest audiences as the top priority can make an instant effect. This might help to start developing a high-impact dashboard using these resources.

4. Examine and investigate the data

DDDM requires the company to visualize its data. This will have a higher chance of influencing senior leadership and other employees’ actions if the employees communicate their findings in a visually appealing manner. Data visualization, which uses a variety of visual components like charts, graphs, and maps, is an easy method to observe and comprehend trends, outliers, and patterns in data. A bar chart for comparison, a map for geographical data, a line chart for temporal data, a scatter plot to compare two measurements, and more are all common visualization methods for successfully displaying information.

5. Developing Insights

Finding insights and expressing them in a meaningful and interesting way is what critical thinking with data entails. Visual analytics is a user-friendly way of asking and answering questions about the data. Determine whether there are any possibilities or hazards that might affect the success or ability to solve problems of the company.

6. Take action and share what is learned

Upon discovering an insight, any employee must act on it or share it with others in order to collaborate. Sharing dashboards is one method to do this. Using informative text and interactive graphics to highlight critical insights can influence the company’s audience’s decisions and help them make more educated actions in their everyday job.

Data-driven decision-making is a game-changer. When everyone in a business embraces visual analytics, data becomes a valuable organizational asset. Data-driven decision-making becomes a corporate purpose, not a headache, with a contemporary business intelligence system. As a result, quicker and more informed judgments are made. These decisions will thereby generate a stronger bottom line along with greater creativity and commercial success, and more engagement and collaboration from employees.

Examples Of Data-Driven Decision-Making

When making high-impact business choices, today’s largest and most successful firms leverage data to their advantage. Consider the success stories of these well-known companies to better understand how any company may use data analytics in its decision-making process.

Leadership Development at Google

Google continues to place a strong emphasis on “people analytics”. Google gathered data from over 10,000 performance reviews and linked it to employee retention rates as part of Project Oxygen, one of its well-known people analytics programs. Google analyzed the data to identify high-performing managers’ common habits and construct training programs to help them improve these skills.

Real Estate Decisions at Starbucks

Following the closure of hundreds of Starbucks outlets in 2008, then-CEO Howard Schultz pledged that the firm would adopt a more scientific approach to locating new stores. Starbucks has teamed up with a location-analytics firm to identify potential shop sites based on demographics and traffic trends. Before making decisions, the organization consults with its regional teams. Starbucks utilizes this information to assess the chances of a location’s success before making a fresh investment.

Driving Sales at Amazon

Amazon utilizes data to determine which goods to recommend to consumers based on previous purchases and search activity trends. Rather than randomly recommending a product, Amazon’s recommendation engine is guided by data analytics and machine learning. According to McKinsey, 35 percent of Amazon’s customer purchases in 2017 might be attributed to the company’s recommendation algorithm.

Advantages Of Data-Driven Decision Making 

  • Delivers greater transparency and accountability.
  • Continuous improvement.
  • Ties business decisions to analytics insights.
  • Provides clear feedback for market research.
  • Enhances consistency.
  • Makes more confident decisions.
  • Provides cost savings.

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