Data-driven decision-making is less common in companies than one might expect. To explore how organizations can use their data effectively and build data-driven processes, we spoke with Jakub Novák, a data and analytics expert at Ness Czech.
What Is a Data-Driven Company?
The term Data-Driven originates from English and translates to data-driven management or, more specifically, data-driven decision-making. Surprisingly, many companies still rely on habits or intuition to make decisions, using data only sparingly.
“Most companies have more data than they realize, yet they fail to leverage it effectively,” explains Novák. “Large or fast-growing companies are often better at adopting this approach. For instance, retailers, especially e-shops, use historical sales data to optimize cross-selling and up-selling, while automotive companies analyze data to minimize production inefficiencies and predict equipment failures.”
Is Data-Driven Decision-Making Enough?
“Absolutely not,” says Novák. “Data alone is often meaningless without context.”
For example, if a company reports 10 sales, is that a lot or a little? The answer depends on factors like the product, location, and timeframe. Selling 10 luxury properties in a month is different from selling 10 books over the same period.
“The more knowledge we have about the environment—context, business processes, and market conditions—the more we can transform raw data into actionable information,” Novák adds. “Investing time in understanding and visualizing business processes bridges the gap between business and IT.”
At a recent client project, visualizing processes and unifying terminology immediately improved communication and understanding between teams, enabling better decision-making.
How Can You Identify a Data-Driven Company?
There’s no official certification to label a company as “Data-Driven.” According to Novák, being Data-Driven is more about a company’s mindset and approach to using data.
“Data-Driven organizations focus on using data as a tool to gain unbiased feedback on their operations. This mindset accelerates data utilization and helps companies refine processes, explore opportunities, and stay competitive,” says Novák.
Companies can start by benchmarking against industry trends, consulting with experts, or conducting audits of their Business Intelligence (BI) and data solutions to identify gaps and opportunities.
Real-World Applications of Data-Driven Practices
Every company offering goods or services has its portfolio categorized by segments, regions, or timeframes. A data-driven approach involves regularly analyzing these segments to assess profitability.
For example:
- Identify unprofitable product categories and evaluate whether to retain or phase them out.
- Use data to inform strategic decisions, such as subsidizing certain products to meet broader goals.
“This clarity enables companies to make informed decisions and incorporate insights into future planning,” Novák emphasizes.
How Do Big Data and the Cloud Relate to the Data-Driven Approach?
Novák explains the distinction:
- Data-Driven Approach: Focused on using data to make decisions, increase efficiency, and drive revenue growth.
- Big Data: A technical domain that deals with handling vast amounts of data, often involving unstructured or semi-structured formats like logs, images, or videos.
- Cloud: Facilitates faster implementation and management of data solutions, whether a company uses Big Data tools or traditional BI systems.
“Big Data and cloud technologies are accelerators for the Data-Driven approach, but they aren’t mandatory,” Novák clarifies.
Does Big Data Replace Business Intelligence?
“No,” says Novák. “Business Intelligence and Big Data are complementary services.”
BI focuses on structured data, producing reports and analyses that are essential for most companies. Big Data handles larger, more diverse datasets and can uncover deeper insights, but it comes at a higher cost.
“The key is to evaluate individual cases and determine which approach aligns best with the company’s needs and budget,” Novák advises.
What’s the First Step Toward Becoming Data-Driven?
Preparation is critical.
- Identify areas where data tracking will provide meaningful insights.
- Focus on specific use cases to measure effectiveness.
- Leverage existing tools and technology to start small and expand incrementally.
“It’s not enough to declare yourself a Data-Driven company,” says Novák. “Companies must carefully evaluate costs, avoid dead ends, and focus resources where they’ll have the most impact.”
What Are the Costs of a Data-Driven Transition?
Costs can be divided into:
- Obvious Costs: Infrastructure, licenses, operational expenses, and staffing. Many companies now use cloud solutions to reduce upfront capital expenditure.
- Hidden Costs: These include security, data governance, and architectural alignment. Neglecting these areas can lead to significant expenses, such as data breaches or system inefficiencies.
“Investing in proven, secure solutions ensures long-term sustainability and avoids costly pitfalls,” Novák stresses.
The Role of Proof of Concept (PoC)
A Proof of Concept is a crucial step in testing assumptions before full-scale implementation.
Steps include:
- Simplify the environment using sample data.
- Validate the feasibility and value of the use case.
- Apply the Fail Fast approach—quickly determine if changes are needed.
For instance, Ness Czech helped a financial institution prototype an anti-fraud solution using cloud services, delivering actionable insights within one week.
If successful, PoCs lead to the “legalization” phase, where solutions are standardized, documented, and rolled out to production.
Is Investing in Data Worth It?
“Absolutely,” says Novák.
“Data provides a fresh perspective, eliminating biases and simplifying complex decisions.” With modern tools, companies can combine structured and unstructured data from diverse sources to gain deeper insights.
Data-driven approaches streamline processes across all areas of a business, making them adaptable to rapidly changing market demands.
“Those who leverage data effectively will not only survive but thrive in an increasingly competitive landscape,” Novák concludes.