How Businesses Benefit from a Data-Driven Culture

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A company employees using data analytics to identify problems and power growth.

Globally, data is growing — from 45 zettabytes in 2019 to a projected 175 zettabytes by 2025, according to IDC. According to Our World in Data, drivers behind worldwide data growth include increased internet access, broadband access, mobile phone use and social media use. What matters most for business is not the volume of data but, rather, knowing how to use it.

Organizations, seeing how data trends are disrupting whole markets, are adopting a data-driven culture to avoid falling behind their competitors and to see results. According to a Forrester report, data-driven companies “are growing at an average of more than 30% annually.”

What is a data-driven culture? It is one that embraces the use of data and analytics to get business insights that lead to improvements. It provides data-driven organizations with a competitive advantage.

What Does Data-Driven Mean?

Taking risks in business often pays off, but this does not mean that companies should pursue opportunities blindly. Enter the data-driven approach. What does data-driven mean? Data-driven describes a strategic process of leveraging insights from data to identify new business opportunities, better serve customers, grow sales, improve operations and more. It allows organizations to use evidence-based data to make decisions and plan carefully to pursue business objectives.

A data-driven decision is based on empirical evidence, enabling leaders to take informed actions that result in positive business outcomes. The opposite of a data-driven process is to make decisions based solely on speculation. For data-driven business leaders, listening to their gut may be part of their decision-making process, but they only take specific actions based on what the data reveals.

Business leaders in data-driven organizations understand the benefits of relying on data insights to make wise business moves. As MicroStrategy reports based on results from a McKinsey Global Institute study, data-driven businesses are 20-plus times more likely to acquire new customers and six times more likely to keep them. Those leaders, however, must be able to rely on knowledgeable data professionals and technology tools that can uncover the value in the data. Data professionals can also provide insights into the best ways to collect, store, analyze and protect valuable business data.


Check out these resources to learn more about what it means to be data-driven.

Eight common traits of data-driven organizations.

What Is the Value of Data?

Data in and of itself offers little to no value if it does not reveal anything. The value is not the data; it is what is done with the data. Data-driven organizations find value through data analytics, which is the process of analyzing data to acquire business insights. The data can then help add business value, like solving business problems or improving processes. The value of data is that it enables business leaders to make informed decisions that can lead to enhanced business performance, streamlined operations and stronger customer relationships.

Executives see value in a data-driven culture, especially when they realize that their competitors use data to their advantage. According to findings from NewVantage Partners, of the 91.6% of leading companies accelerating their big data and artificial intelligence (AI) investments, 75% mention fear of disruption from competitors as a driver for their data investments.

Benefits of Data-Driven Decision-Making for Business

From improving operations to driving sales, data-driven decision-making offers many advantages for businesses. The following examples highlight some of the many benefits of data-driven decision making for businesses.

Better Serve Customers

Organizations can use data to determine what consumers prefer. For example, in the customer support center, data can help organizations learn the most cost-effective way to address customer questions and issues, reducing problem resolution times and improving customer experiences.

Identify New Business Opportunities

Data can reveal insights that help businesses create additional revenue streams by innovating and developing products and services that meet consumer demands. For example, a retailer of women’s shoes can identify trends that indicate a popular style or brand of shoes. They can then swiftly respond and tailor their products and services accordingly.

Grow Sales and Improve Processes

Every business wants to maximize revenue growth. In a competitive global marketplace, data plays a crucial role in identifying and translating data into revenue opportunities. For example, slower sales growth can be a sign of mediocre sales team performance. By digging into the data, a leader can identify problems and develop sales and marketing strategies that can improve performance and grow revenues.

Create a First-Mover Advantage

Data and analytics can help organizations respond to market changes faster. By harnessing data analytics, businesses can predict future trends, identify consumer behaviors and detect new business opportunities more quickly, creating the potential for obtaining a market advantage.


The following resources provide additional insights describing the business benefits of data-driven decision-making.

There are 12 steps for building robust data analysis systems.

How Can Businesses Use Data to Gain Valuable Insight?

Businesses can use data in different ways, depending on their industry. For example, in manufacturing organizations, operational data sourced from systems and machines can reveal insights such as productivity and efficiency performance. By understanding which processes cause delays, leaders can develop strategies to streamline critical operational areas.

In another example, health care facilities can use patient data, collected during patient intake processes and stored in systems such as electronic health records (EHRs) to understand their patients’ needs better. This data helps health care providers become more responsive to their patient populations and improve health outcomes. Health care organizations can also leverage public data, such as claims data, which includes standardized, structured information about patient demographics, diagnosis codes and health care service costs. From a revenue standpoint, this information can help facilities improve their payment models.

Data can also help business functions within organizations. For example, human resources departments can leverage data in their applicant tracking systems to improve hiring practices. Other system data can uncover insights that help HR managers develop strategies to help retain top talent and build more productive and happier workforces. Marketing organizations can use data to reveal insights about customer behaviors and implement cross-functional activities with sales and customer service groups to elevate customer experiences.

Data-driven cultures can create plenty of business opportunities. The question is: How can businesses use data to gain valuable insights and gain advantages? The following 12 steps can provide guidance.

1. Understand the Business Problem

Problems come up in business. Knowing how to solve a problem requires understanding it. This means asking questions, determining the needs of the company and specifying the desired outcomes. Start with a root cause analysis, which helps determine the source of problems, whether caused by people or organizational factors. Unless the root cause of a problem is identified, it can recur or cause different issues in other areas.

2. Develop a Data Approach to Solve the Problem

Data analytics is a systematic approach to find insights into data. Developing a data analytics approach that provides executives and leaders with the confidence that the information is accurate enables them to make informed business decisions and solve problems. The early stages of this data approach include defining a problem statement. This information serves as a baseline and clears the path for the next steps.

3. Determine the Type of Data Needed

Different business activities create different types of business data. For example, transactional data is a type of data in a business or organization that refers to core activities. An online brokerage firm may have data on their client’s investment and stock purchasing and selling activities, while an industrial manufacturer may have production data. Other forms of data include machine data, metadata, master data and operational data. Understanding the characteristics of the types of data will help determine the right type to choose.

4. Establish Methods for Data Collection

Before selecting a data collection method, also known as a data mining method, analysts must know which form of data they seek: quantitative or qualitative. Quantitative data is numerical, while qualitative data is descriptive. A quantitative method of data collection includes experimental research, which involves manipulating one or more variables and measuring their impact on other variables. A qualitative method includes interviews or surveys, which require asking people questions in person, over the phone or online.

5. Ensure Quality Data Inputs

Data can be unstructured and structured. Unstructured data typically comes in raw form. Quantitative methods are mainly used to find insights into unstructured data. Structured data, on the other hand, is organized and categorized, making it easier for analysis. However, even structured data can be inconsistent across systems. For example, the spelling of a customer’s name in one database can differ from the spelling in another within the same company. This is often a result of siloed systems and processes. Therefore, to ensure quality in the data, data inputs must be reliable and interconnected.

6. Prepare the Data for the Modeling Stage

Data preparation is an essential step for building reliable and accurate models. Spotting issues early in the process requires human discretion and decision-making. Activities can include eliminating inaccuracies in setting up prediction models and data cleansing. Another common activity in this stage is data integration, which helps to combine different data sets to eliminate silos.

7. Model the Data to Aid in Communicating Insights

Data models are designed in three different ways: conceptual, logical and physical. The conceptual data model offers a high-level view of the data; it shows what the business wants. The physical model presents the technical framework for storing that data. The logical framework communicates business requirements and how the data will be technically implemented. Developing a data model with diagrams, graphs, symbols and textual references to show data structures and the interrelationships between different data objects aids in communicating insights to business leaders.

8. Validate Data Integrity

A decision based on data does not guarantee that the outcome will always be on target. Data can reveal patterns and even help predict results, but if there is a flaw in the data collection process or if the data is incorrect, any decision based on that data will also be flawed. Data integrity can also be compromised when an error is made by a human or machine, through misconfigured systems or malicious activity such as hacking. This is why validating data integrity through monitoring and measuring is essential for a thriving data-driven culture.

9. Verify the Effectiveness of the Data

Determining the effectiveness of data requires measuring the impact of the data on helping meet business goals. For example, if a data analytics effort aimed to improve operational performance, then the following questions should be asked: Did we succeed in improving operational performance? What role did the data play in achieving this aim? An effective data effort also helps to encourage business leaders to embrace a shift toward a data-driven culture.

10. Share the Data with Decision-Makers

Data analysts are comfortable with data and understand its intricacies. Business leaders are less familiar with it but are interested in what the data is saying. For them, answers to two questions are critical: What is the business problem? How can they solve it? For example, a financial executive may want to see what the data says about the bottom line. A marketing manager may want to see which marketing campaigns worked and which ones failed and why. Filling the gap between data analysis and decision-making requires sharing insights in the decision-maker’s preferred language. The data should also be easily accessible via a dashboard where leaders can revisit it as needed.

11. Address Trust Gaps Through Greater Transparency

Only a third of companies surveyed indicated enough trust in their data “to use it effectively and derive value from it,” according to Accenture. Siloed data and poor data quality can become obstacles to adopting a data-driven culture. On the other hand, greater transparency on the condition of the data enables business leaders to understand its potential value. Business leaders feel more confident in the data to make decisions when the data is anchored in integrity, quality, resiliency and effectiveness.

12. Create a Feedback Loop

A feedback loop provides insights into whether something is working or not. For example, in a customer feedback loop, customer feedback (input) influences business decisions on what changes will go into the next version of a product (output). In a data-driven organization, the implementation and use of data are measured by business leaders who provide feedback on their effectiveness. If the data proves effective, it remains in the feedback loop, where it undergoes continuous monitoring and improvements as needed.

Tools and Software Resources

The following resource list includes popular tools and software used by business analytics professionals and executives to gain insight from business data.

How to Create a Data-Driven Culture

For organizations looking at how to create a data-driven culture, key strategies for garnering organization-wide adoption include the following:

  1. Make data a core component of the overall business strategy. This may involve shifting and aligning responsibilities.
  2. Take an audit of the current situation in an organization. This effort can reveal barriers to change, such as outdated technology, cultural biases and entrenched beliefs.
  3. Identify and determine gaps across systems and processes. Then establish a reliable single source of truth, such as a centralized dashboard that ensures transparency and accuracy and eliminates data silos.
  4. Be prepared for change. Adding technologies to the mix and replacing legacy systems may enhance value to existing infrastructure.
  5. Develop and promote strategies to ensure everyone is on board with the data-driven culture; this can include creating a data dictionary, training and skill-building programs.
  6. Show flexibility to adapt. Assessing data programs is essential for measuring success, but organizations must be ready for change processes to deliver improvements.


The following resources provide additional information on steps for creating a data-driven culture.

When faced with the decision to adopt a data-driven culture, executives often cite a lack of guidance. It is up to data professionals to present the benefits of data and persuade executives about the value of a data-driven culture. By connecting emotionally with stakeholders, they can gain buy-in and help ensure business leaders understand the benefits of a data-driven culture.

Infographic Sources:

CIO, “Executives Still Mistrust Insights from Data and Analytics”

CIO, “What Exactly Is a Data-Driven Organization?”

Forbes, “The Four Key Pillars to Fostering a Data-Driven Culture”

Gartner, “Create a Data-Driven Culture by Influencing 3 Areas”

KPMG, Building Confidence in Your Data

Towards Data Science, “Data Science Methodology 101”

Towards Data Science, “The Empowered Data-Driven Organization”