Data-Driven | 6 mins read

How to Become a Data-Driven Business- Guide for Business Owners

how to become a data driven business guide for business owners
Chloe Henderson

By Chloe Henderson

Being "data-driven" is a term that is often used in science and mathematics fields, such as IT. However, companies can utilize big data from their internal systems and external sources to become data-driven.

Being a data-driven business does not just entail using metrics to monitor performance and profitability. It also provides factual support to enhance decision-making so companies can resolve issues and improve processes.

However, becoming data-driven is not as simple as collecting large volumes of data. Instead, it involves a continuous process of gathering, filtering, and preparing comprehensive information so management can draw informed conclusions.

Therefore, organizations should learn how to become data-driven by utilizing their internal and external resources to improve their performance.

What Does it Mean to Be Data-Driven?

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Being data-driven means a business bases its decisions on tangible statistics and facts, rather than guesswork and personal intuition. It also refers to creating a work culture that fosters logical thinking to develop impactful business strategies and solutions.

Especially in modern markets that continuously fluctuate, data-driven decision-making (DDDS) is essential for companies to adapt to trends. By collecting data, detecting patterns, and anticipating emerging trends, companies can prepare their operations to enhance performance.

For example, if a retailer does not have an advanced point-of-sale system that collects information from each transaction, they are unable to monitor consumer interests. By monitoring customer purchase histories, reviews, and dislikes, retailers can optimize their marketing strategies to drive customer reach and sales.

Otherwise, companies utilizing outdated processes that do not store and analyze data can lose their competitive edge. This gives competitors who capitalize on real-time data the opportunity to capitalize on emerging trends and attract more customers.

Therefore, business owners should consider how making data-driven decisions can improve their strategy development and profitability.

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What Businesses Need to be Data-Driven

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While almost every business strives to be data-driven, some do not have the means to collect the necessary information. In order for a company to be data-driven, they need two essential functions-

Data Collection

With modern management technology, businesses have access to large volumes of data. This means that they must be able to gather and store an extensive collection of information.

Companies must also be able to filter out any irrelevant, inaccurate, and biased data that can compromise their integrity and decision-making. Even the smallest data inaccuracies and biases can negatively sway conclusions.

For example, an incorrect inventory count can lead warehouse management to place re-orders for a specific product line that has already been restocked. This can lead to unnecessary inventory expenses, such as ordering, delivery, and handling costs. It can also take up valuable storage space that can be used to hold high-profit items.

Therefore, companies should take their data science seriously by investing the time to clean and prep data. On average, data scientists spent 80% of the time collecting, cleaning, and prepping data, and 20% analyzing the information to generate models and conclusions.

Businesses that have mastered collecting and cleaning data need to ensure their information is high-quality and impactful. Just because a company has large data inputs, doesn't mean all of their metrics are useful. Management needs to take the time to sift through data and find the most significant figures that represent the business's health and performance.

Data Access

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Once a business finds a system that collects relevant and accurate data, they need to make sure it is accessible to employees and stakeholders. Data must be-

  • Joinable
Data should be formatted to join other business systems so that management can consolidate various data sets into one centralized interface. Without a standardized format, data scientists have to dedicate extra time to reformat information.

By using advanced management tools, such as forecasting software, businesses can integrate their established systems, enabling real-time data entries to update all platforms. System integrators ensure all data is reformatted to allow seamless information exchange.

For example, by integrating the point-of-sale (POS) and forecasting software, the predictive model is able to use real-time transactions and customer data to improve the accuracy of generated projections. This allows retailers to adapt their marketing strategies to capitalize on emerging customer demands.

  • Shareable
Data must also be shareable so all members of an organization can view important information. Again, system integration centralizes data so employees can access real-time metrics on one interface. This prevents having to navigate through multiple systems and manually share data between departments.

Shareable data also enhances customer service as it automatically transfers personal information across different sales channels. Imagine requiring customers to set up separate accounts for the online and traditional stores, rather than creating a universal loyalty member login. Not only would this be frustrating for shoppers, but it would also produce disjointed data sets for the retailer.

Therefore, businesses should ensure that their data is not siloed, as it can significantly inhibit their insights.

  • Comprehensive
Lastly, aggregated data should be comprehensive to allow analysts to generate reports and actionable insights. Whether it's key performance indicators (KPIs) or other metrics, organizations need to be able to see trends and understand the relationship between variables.

Advanced tools, such as predictive software, can anticipate emerging trends by incorporating historical and real-time metrics.

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How to Make Data-Driven Decisions

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In order to become data-driven, businesses can follow five simple steps-

1. Set an Objective

First, businesses should have a set mission statement and long-term goals. These objectives can be based on making improvements to lagging systems or capitalizing on the market's trends. Regardless, management should identify and define goals, so employees can actively make data-driven decisions to progress toward the company's objectives.

This step should be completed before collecting and analyzing data as this gives management time to determine what datasets hold value to enhance their progress.

2. Define Data Sources


Next, management is responsible for pinpointing where to extract reliable data. This could include both internal and external sources, such as databases, online reviews, social media, and management software.

Determining which data sources to pull from requires management to find common variables to connect various datasets. This information should also work towards solving an issue or improving a system.

For example, restaurants can collect customer data from transactions and ratings from online reviews to enhance the customer experience and advocacy.

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3. Organize Datasets

Once the sources are defined and data is collected, analysts must clean and prepare the information for further analysis. This includes eliminating duplicates, prepping raw data, correcting inaccuracies, and finishing incomplete datasets.

This step is often time-consuming but is vital for generating impactful insights that could enhance decision making. To start, management can organize data using spreadsheets or software to group information based on their impact, department, and relevance.

After the information is organized, analysts can begin translating data and developing relationships between variables.

4. Conduct Statistical Analysis

Now that the data has been cleaned, management can begin analyzing the information in building statistical models. Organizations commonly use models such as linear regressions, decision trees, and random forest modeling.

However, businesses that utilize management software can automatically generate reports using pre-programmed models. Reports should focus on data relevant to their defined objectives.

Once the analysis is performed, management needs to decide how to present their findings. There are three ways to report data-

  • Descriptive Information consists of straightforward facts.
  • Inferential Information presents the facts, plus an interpretation to give more context.
  • Predicative Information uses facts to estimate future events and trends to advise how the business should act.

The way data is presented directly impacts how well employees can digest the information. Therefore, if management has poor communication and cannot effectively showcase data, influential trends and insights will go unrecognized. Even if the information is valuable, it is less impactful without proper presentation.

Management should consider using data visualization, such as graphs and charts, to display trends and explain complex metrics.

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5. Develop Conclusions

Finally, management can make a conclusion based on their data collection and thorough analysis. While findings should relate to the objective, not all conclusions will uncover a significant new business element. Therefore, companies should not be discouraged when data suggests making small adjustments.

This step is a great opportunity for organizations to determine if their previous assumptions are correct. For example, many companies try different marketing strategies to improve sales and income. However, data may show that a specific product line is in the wrong market.


Ultimately, these conclusions should help organizations improve their decision-making and strategy development through impactful data support.

Real Examples of Data-Driven Success

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Most modern companies recognize the importance of collecting and utilizing data to make better business decisions, especially large enterprises. Some of the most well-known organizations that are data-driven include-

  • Netflix
Originally, Netflix started as a mail-based DVD rental company. While they didn't find immediate success, with access to impactful data, Netflix capitalized on the growing market of Internet streaming. This led to them becoming one of the most successful entertainment streaming companies today.

Without access to data, Netflix would not have been able to take advantage of the new digital sales channel, which led to their tremendous success. In fact, they may have gone unrecognized as DVDs became outdated and virtual streaming gained momentum.

  • Amazon
Likewise, Amazon started as an online bookstore but eventually transformed into a massive online store for a wide variety of products.

Without access to data, they may not have rebranded themselves and capitalized on the emerging popularity of online shopping. However, they are now among the top e-commerce companies, pulling in over $280 billion in 2019 alone.

Benefits of Being Data-Driven

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Becoming data-driven enables companies to not only enhance their decision-making but also-

  • Promote Transparency and Accountability
By standardizing data collection and sharing, businesses can promote transparency and accountability for members throughout their organization. This not only helps improve performance but enables management to anticipate risks.

For example, by utilizing employee software that tracks verified users' activity, management can determine which workers handle merchandise in the cash registers. Therefore, if registers come up short or inventory goes missing, management can refer back to their internal systems to determine which employee was active during the period it went missing.

Data collection also ensures that companies' systems remain compliant to avoid legal repercussions.

  • Experience Consistent Improvement
Data-based decision management can also help businesses make constant improvements. Many companies can implement and monitor incremental changes based on their data collection. This helps improve overall performance and operational efficiency.

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  • Access Analytical Insights
Collecting relevant data saves analysts time generating actionable insights that management can use to improve internal processes. Detailed analytical insights have the ability to resolve problems, eliminate inefficiencies, and improve performance.

Insights also enable stakeholders to gauge a company's productivity and profitability, streamlining the decision-making process.

  • Access Clear Feedback for Market Research
Collecting big data from outside sources allows businesses to view feedback on their brand, product lines, and service. This helps management to determine how to create new items, impactful campaigns, and unique customer experiences.

Big data also gives forecasting software the information to anticipate emerging market trends, such as customer demand, fluctuating prices, and popular sales channels. This enables businesses to adapt their operations to attract consumers and improve their brand exposure.

  • Enhance Operational Consistency
Data-driven businesses can improve their consistency over time with the ability to monitor metrics and quickly detect malfunctions. While it may take a while to work out all of the kinks within a process, data provides the right tools to find resolutions and implement improvements.


Once operations are enhanced, management can standardize procedures to guarantee consistent results, improving efficiency and productivity.

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