Table of Contents Hide
- What is Data-driven Decision Making?
- What Does It Mean to Be Data-Driven?
- Why Is It Important to Use Data in Decision-Making?
- Steps To Make Good Data-driven Decisions
- Data-driven Decision-Making Examples
- Benefits of Data-driven Decision Making
- How To Become More Data-driven
When you have a big decision to make at work, it might be difficult to decide which way to go. You may feel more secure in your choices if you follow your gut instinct, but are those choices suitable for your team members? When you utilize facts to make decisions, you can relax knowing that your choices are founded on data and are intended to maximize the business effect.
Whether it’s outperforming competitors or enhancing profitability, data-driven decision-making is an essential component of modern corporate strategy. The benefits of data-driven decision-making are discussed here, along with advice on how to use them at work with examples.
What is Data-driven Decision Making?
Data-driven decision-making is the process of collecting and transforming data based on your company’s key performance indicators (KPIs) into actionable insights.
Throughout this process, you can use business intelligence (BI) reporting solutions to capture massive data quickly and efficiently. These tools make data visualization simple, making data analytics accessible to those who lack sophisticated technical knowledge.
What Does It Mean to Be Data-Driven?
In a nutshell, the idea of being data-driven refers to using facts, or data, to identify patterns, inferences, and insights to help you with your decision-making.
Being data-driven essentially implies attempting to make judgments without bias or emotion. As a result, you can verify that your company’s goals and strategy are founded on data and trends, rather than what you like or dislike.
Why Is It Important to Use Data in Decision-Making?
Data-driven decision-making is important since it enables you to base your conclusions on facts rather than preconceptions. Making objective decisions is the best method to stay fair and balanced if you’re in a leadership position.
Data that measure your business goals and populates in real time is the key to making the most educated decisions. With reporting software, you can aggregate the data required to spot patterns and make forecasts.
Some data-driven judgments you can make include:
- Ways to Increase Sales and Profits
- How to Instill Good Management Habits
- How to Improve Operations
- Ways to Optimize Team Performance
While not every decision will be supported by data, many of the most important ones will.
Steps To Make Good Data-driven Decisions
These steps can assist you in determining the “who, what, where, when, and why” of data for yourself, your coworkers, and the organization. But, take in mind that the visual analysis cycle is not linear. One question frequently leads to another, which may require you to return to one of these steps or jump to another, ultimately leading to useful discoveries.
Step #1: Determine your business objectives:
This stage will necessitate knowledge of your organization’s executive and downstream goals. This could be as narrow as increasing sales and website traffic or as broad as raising brand awareness. This will assist you later in the process in selecting key performance indicators (KPIs) and metrics that affect data-driven decisions—and these will assist you in determining which data to study and which questions to ask so that your analysis supports key business objectives.
For example, if a marketing effort is aimed at increasing website traffic, a KPI could be linked to the number of contact submissions received so that sales can follow up with leads.
Step #2: Ask business teams for critical data sources:
It is critical to obtain input from employees across the organization in order to comprehend short and long-term goals. These inputs influence the questions people ask in their analyses as well as how you prioritize verified data sources.
Meaningful input from throughout the organization will aid in the direction of your analytics deployment and future state, including the roles, responsibilities, architecture, and processes, as well as the success metrics to analyze progress.
Step #3: Gather and prepare the necessary data:
Obtaining quality, dependable data might be difficult if your company’s data is scattered across multiple sources. Once you’ve determined the scope of your organization’s data sources, you may begin data preparation.
Begin with preparing high-impact, low-complexity data sources. Select data sources with the most people to have an immediate impact. Start with these resources to create a high-impact dashboard.
Step #4: View and examine data:
Data visualization is critical in data-driven decision-making. By visually representing your thoughts, you will have a better opportunity of influencing the decisions of senior leadership and other personnel.
Data visualization, with its various visual features such as charts, graphs, and maps, is an easy way to observe and analyze trends, outliers, and patterns in data. There are numerous popular visualization types for successfully displaying information, including a bar chart for comparison, a map for spatial data, a line chart for temporal data, a scatter plot to compare two metrics, and more.
Step #5: Get insights:
Finding insights and expressing them in a useful, engaging manner is what critical thinking with data entails. Visual analytics is a simple way to ask and answer questions about your data. Determine the opportunities and dangers that affect success or problem-solving.
To make judgments that are important to the bank’s health, JPMorgan Chase adopted a modern analytics solution. JPMC obtains a comprehensive view of the customer journey by analyzing line-of-business relationships (i.e., products, marketing, and service touch points) with customer data. The Marketing Operations team, for example, conducts assessments that affect design decisions for the website, advertising materials, and products such as the Chase mobile application.
Step #6: Act on and share your insights:
Once you have discovered an insight, you must act on it or share it with others for cooperation. Sharing dashboards is one method to accomplish this. Using informative text and interactive graphics to highlight critical insights can influence your audience’s decisions and help them take more educated actions in their everyday job.
Data-driven Decision-Making Examples
When making high-impact business decisions, today’s largest and most successful firms leverage data to their advantage. Consider these examples of success stories of these well-known businesses to better understand how your organization may use data analytics in its decision-making process.
#1. Leadership Development at Google
Google places a strong emphasis on “people analytics.” Google gathered data from more than 10,000 performance reviews and linked it to employee retention rates as part of one of its well-known people analytics programs, Project Oxygen. They used the data to discover common habits of high-performing managers and developed training programs to help them acquire these skills. Their actions increased managers’ median favorability scores from 83 percent to 88 percent.
#2. Starbucks Real Estate Choices
Following the closure of hundreds of Starbucks stores in 2008, then-CEO Howard Schultz pledged that the firm would take a more analytical approach to determine future store sites.
Starbucks now collaborates with a location analytics firm to identify potential shop locations based on data such as demographics and traffic trends. Before making decisions, the organization also takes feedback from its regional teams. Before making a new investment, Starbucks analyzes this data to estimate the likelihood of success for a specific area.
#3. Increasing Amazon Sales
Amazon uses data to determine which products to recommend to customers based on previous purchases and search behavior patterns. Rather than simply recommending a product, Amazon’s recommendation engine is powered by data analytics and machine learning. According to McKinsey, 35 percent of Amazon’s consumer purchases in 2017 could be traced back to the company’s recommendation algorithm.
Benefits of Data-driven Decision Making
#1. You’ll Make More Assured Choices
Once you start gathering and evaluating data, you’ll likely find it easier to make confident decisions about almost any business difficulty, whether you’re planning to launch or discontinue a product, change your marketing message, enter a new market, or something else entirely.
Data serves numerous functions. On the one hand, it serves to benchmark what is currently available, allowing you to better comprehend the impact of any decisions you make on your organization.
Aside from that, data is logical and tangible in a manner that gut feeling and intuition are not. By removing the subjective parts from your business decisions, you can boost your own and your company’s confidence. This assurance enables your organization to fully commit to a specific vision or plan without fear of making the wrong decision.
Just though a decision is based on data does not guarantee that it is always correct. While the data may show a specific pattern or predict a specific outcome, any decision based on the data would be incorrect if the data-gathering procedure or interpretation is faulty. This is why the impact of every company decision should be analyzed and reviewed on a regular basis.
#2. You Will Be More Proactive
When you first deploy a data-driven decision-making process, it will most likely be reactionary. The data tells a story, and you and your company must respond to it.
While this is useful in and of itself, it is not the only role that data and analysis can play in your business. With enough practice and the right types and quantities of data, you can use it to be more proactive, such as identifying business opportunities before your competitors or detecting threats before they become too serious.
#3. Cost Savings Are Possible
There are numerous reasons why a company might decide to invest in a big data initiative and strive to become more data-driven in its processes. According to a recent study of Fortune 1,000 executives done by NewVantage Partners for the Harvard Business Review, the success rates of various projects differ.
According to the survey, one of the most effective initiatives is using data to reduce costs. More than 49 percent of organizations that started projects to cut costs saw a return on their investment. Other initiatives have yielded less consistent results.
“Big data is already being used to improve operational efficiency,” Randy Bean, CEO and managing partner of consultancy firm NewVantage Partners, said when the survey results were announced. “And the ability to make informed decisions based on the most up-to-date information is quickly becoming the norm.”
How To Become More Data-driven
If you want to become more data-driven in your business approach, there are various measures you may take to get there. Here are some examples of how you might approach your daily duties with an analytical mentality.
#1. Search for patterns in unexpected places.
At its core, data analysis is an attempt to discover a pattern within or between multiple data sets. Insights and inferences can be gained from these patterns and relationships.
Making a conscious decision to be more analytical—both in your personal and professional life—is the first step toward becoming more data-driven. While this may appear to be simple, it requires practice.
Look for patterns in the data around you, whether you’re in the office poring over financial accounts, in line at the grocery store, or on the train. Once you’ve identified the patterns, practice extrapolating findings and drawing conclusions about why they occur. This easy activity can assist you in training yourself to be more data-driven in other aspects of your life.
#2. Relate every decision to the data
When faced with a decision, whether business-related or personal in nature, avoid depending on gut feeling or past conduct to determine a course of action. Instead, make an intentional effort to adopt an analytical attitude.
Determine what data you have that can be used to inform your decision. If no data exists, explore how you could gather it on your own. Once you get the data, assess it and apply any insights to your decision. The goal, like with the pattern-spotting exercise, is to get enough practice so that analysis becomes a natural part of your decision-making process.
#3. Show the Data’s Significance
Data visualization is a critical component of the data analysis process. A table of numbers is nearly impossible to interpret. You’ll be able to rapidly discover trends and draw conclusions about the data if you create attractive visualizations in the form of charts and graphs.
Familiarize yourself with common data visualization approaches and tools, and experiment with any type of data you have readily available. This can be as basic as making a graph to represent your monthly spending habits and then drawing inferences from it. You can then use these findings to create a customized budget for the next month. You will have successfully made a data-driven decision after finishing that assignment.
#4. Think About Continuing Your Studies
If you’re not comfortable learning how to incorporate data into your decision-making process on your own, there are a variety of training choices available to help you build the data science skills you’ll need to succeed.
Which solution makes the most sense for you will be determined by your personal and professional objectives. Those planning a substantial career move, for example, may choose to pursue a master’s degree with a focus on data analytics or data science. For everyone else, though, taking an online business analytics or data science course may be sufficient to build the groundwork for success.
While data-driven decision-making has numerous benefits, it’s important to realize that you don’t have to go all in to get there. You may become more data-driven and prosper in your organization by starting small, benchmarking your performance, documenting everything, and adjusting as you go.
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