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How Does Data-Driven Decision Making Help Businesses?

Vrinda Mathur
Oct 30, 2023

Using facts, measurements, and data to inform strategic business decisions that are in line with your goals, objectives, and initiatives is known as data-driven decision-making (DDDM). Everyone—whether you're a business analyst, sales manager, or human resource specialist—is empowered to make better decisions with data every day when firms grasp the full potential of their data. The next strategic opportunity cannot simply be found by selecting the right analytics technology, though.

Your company has to establish data-driven decision-making as the standard, fostering an environment that values skepticism and curiosity. Data is the starting point of interactions at every level, and people improve their data abilities via practice and application. A supportive community that uses data to make choices will persuade other communities to do the same.

What is Data driven decision making?

The process of gathering data based on your company's key performance indicators (KPIs) and turning that data into useful insights is known as data-driven decision making.

Business intelligence (BI) reporting technologies, which enable huge data collecting quickly and effectively, can be used during this procedure. These technologies make data visualization simple, enabling data analytics for users without specialized technical knowledge.

A corporation can use data-driven decision-making to analyze the optimal course toward its main objectives. It is simple to comprehend why businesses can employ data-driven approaches to get insightful business information that helps them succeed.

When a corporation relies on data and analytics, it can make decisions that are supported by facts. A data-driven business improves productivity by concentrating on one or more specific tasks with effectiveness and flexibility, allowing for experimentation with additional headroom. Innovation is frequently motivated by this independence.

Making decisions based on data allows you to move more quickly and with fewer errors, which in turn increases profitability. You can determine when your content performs well and learn why it resonates with customers—or doesn't—by gathering and analyzing data. The performance of future content that is similar to that can then be predicted using that data.

Steps for making data driven decisions

Making decisions based on data is a wonderful approach to demonstrate to clients that you value and are concerned about their time and interests. Remembering previous interactions, both positive and negative, enables you to improve the following time. More individualized and personalized experiences for customers are advantageous, and these experiences may encourage greater customer loyalty and spending. Some of the steps for making data driven decisions are:

Understand your vision:

Understanding the future vision of your organization is necessary before you can make decisions that are well-informed. This enables you to make judgments based on information and a strategy. Graphs and figures are meaningless without the supporting context. Use the quarterly team KPIs or the annual OKRs for your organization to make data-driven decisions.

Look for data sources:

Once you've determined the objective you're aiming towards, you may begin gathering information. Depending on the kind of data you're gathering, you'll employ different tools and data sources. Use a universal reporting tool if your objective is to analyze data sets relevant to internal business processes.

Reporting tools provide a single point of reference for monitoring how work is moving throughout your organization. Some reporting applications, such as Microsoft's Power BI, allow you to compile data from a variety of outside sources. One of those tools can be used to examine marketing trends or rival analytics.

Sort your data:

Making good business decisions requires organizing your data to enhance data visualization. It's challenging to make sure you're making the most educated judgments if you can't access all of your pertinent facts in one location and comprehend how it interacts.’

Using an executive dashboard is one method of organizing your data. An executive dashboard is an interface that may be customized and is typically included with your universal reporting tool. Whether your objectives are strategic, tactical, analytical, or operational, this dashboard will provide you the information that is most important to attaining those objectives.

Conduct data analysis:

You can start your data-driven analysis once you've organized your data. You will do this to get useful information from your data that will aid you in making decisions.

Depending on your objectives, you might want to combine data analysis from your executive dashboard with user research like case studies, surveys, or testimonials so your conclusions take the consumer experience into account.

Draw judgments:

Your study of the facts will probably lead you to make inferences about what you see. However, since it's crucial to elaborate on what you find in the data so you can present your findings to others, your conclusions require their own part. What do I notice that I was already aware of regarding this data?

What fresh knowledge did I get from these data? How can I apply the knowledge I've received to achieve my professional objectives? Once you can respond to these inquiries, you have effectively completed data analysis and ought to be prepared to make data-driven business decisions.

Examples of Data driven decision making

Today’s largest and most successful organizations use data to their advantage when making high-impact business decisions. To better understand how your organization can incorporate data analytics into its decision-making process, consider the success stories of these well-known businesses:

Google's Leadership Development:

Google continues to place a strong emphasis on "people analytics." Google collected information from more than 10,000 performance assessments as part of one of its well-known people analytics programs, Project Oxygen, and compared the information with employee retention rates. Google established training programs to cultivate these competencies after identifying the traits of high-performing managers. As a result, managers' median favorability ratings increased from 83 to 88 percent.

E-commerce:

Have you ever been shopping online and questioned the recommendations you are receiving? Well, it's probably because you clicked on a specific item or previously purchased something similar.

Online marketplaces like Amazon monitor customer journeys and utilize measures like bounce rate and click-through rate to determine which products you're most interested in. Retailers can use this information to show you products you might be interested in without you having to look for them.

Starbucks's Real Estate Decisions:

Following the closure of hundreds of Starbucks outlets in 2008, then-CEO Howard Schultz pledged that the company will choose new store locations more analytically going forward.

Starbucks currently collaborates with a location analytics business to identify the best shop locations based on information such as traffic patterns and demographics. Before making choices, the company also takes feedback from its regional teams into account. Starbucks uses this information to assess a location's chance of success before making a new investment.

Finance:

Data is used by financial organizations in a wide variety of ways, from risk assessment to client segmentation. Because risk is so pervasive in the financial industry, it's crucial for businesses to be able to assess the risk before making any critical choices. The most effective way to comprehend possible risks, dangers, and the possibility that they occur is through historical data.

Financial organizations also identify their target market using client data. Financial organizations can determine which clients have the highest lifetime value and target them by categorizing them based on socioeconomic level, spending patterns, and other factors.

Driving Amazon Sales:

Using data, Amazon determines which products to suggest to customers based on their past purchases and search behavior trends. Amazon's recommendation engine is powered by data analytics and machine learning rather than making random product recommendations. According to a McKinsey assessment, the recommendation engine of Amazon was responsible for 35 percent of consumer purchases in 2017.

Transportation:

Data science additionally plays a huge role in determining safe transportation. The U.S. Department of Transportation’s Safety Data Initiative underscores the role that data plays in improving transportation safety.

The report pulls data from all types of motor crashes and evaluates factors like weather and road conditions to discover the source of problems. Using the hard facts, the department can work toward implementing more safety measures.

Benefits of Data driven decision making

When HR and other leaders have access to people's data, can identify trends in the workforce, and make forecasts about it, the company benefits. What about the front-line managers at your organization, though? Making decisions based on data performs better than assuming. We shouldn't ignore the advantages of a proactive, predictive people analytics function that supports organizational development. These advantages consist of:

Enhanced responsibility:

Who are the top performers for you? Which teams are falling short of their goals? Making decisions based on data improves responsibility. According to Paul Rubenstein, CHRO at Visier, "When everyone can see how their particular decisions tie to a plan, it can shift the way organizations think about accountability in DEI, revenue, and changes in the workforce." "You're now relating to the bigger business objectives.

However, in order to get this harmonious result, data ownership must be reallocated, which is not always a straightforward operation. Leaders at all levels can identify which teams are performing well and which ones require coaching so they can make adjustments before issues become serious when everyone has access to people data.

Make assured decisions:

The companies that outlive their rivals do so because they have faith in their capacity for success. A business's decision-makers risk making mistakes, having a high turnover of team members, and using inadequate risk management techniques if they waver in their decisions.

You'll feel confidence in your choices when you use data to inform your team's and your own most crucial business decisions. Higher team morale and improved performance can result from confidence.

Alignment with corporate objectives:

"Data becomes more than simply a single point of information when everyone has access to it. It turns into a North Star," Paul claims. He is excited about what leaders and managers can accomplish when they have access to people's data. "You can take the smallest moments and the decisions that happen every day, at every level, and relate them to a greater plan and a bigger conclusion," says the author. "By having everyone anchored in the same set of data

For instance, it will be more helpful for every department to understand how they fit in and if the company is paying equally depending on demographics if your organization's purpose is to enhance diversity, equity, and inclusion.

Take care to avoid bias:

Data-driven decision-making will prevent any biases among corporate leaders. Internal biases or values might influence your decision-making even though you may not be aware of them.

Making conclusions based solely on data and facts helps to maintain your judgments impartial and fair. It also means you have evidence to support your choices when stakeholders or other team members question them.

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How Does Data-Driven Decision Making Help Businesses?

What is Data driven decision making?

Steps for making data driven decisions

Understand your vision:

Look for data sources:

Sort your data:

Conduct data analysis:

Draw judgments:

Examples of Data driven decision making

Google's Leadership Development:

E-commerce:

Starbucks's Real Estate Decisions:

Finance:

Driving Amazon Sales:

Transportation:

Benefits of Data driven decision making

Enhanced responsibility:

Make assured decisions:

Alignment with corporate objectives:

Take care to avoid bias:

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