Data-driven decision-making means making choices based on systematically collected and analyzed data rather than intuition. A company is ready for data-driven practices when it has sufficient high-quality data, clear business objectives, committed leadership, a solid technical infrastructure, and a healthy data culture.

The 5 Cornerstones of Data-Driven Leadership:

1. Quality data available from various sources
2. Clear business objectives and measurable KPIs
3. Committed leadership and evolving data culture
4. Technical infrastructure and the right tools
5. Skilled people and continuous learning

An increasing number of companies' management teams are considering how existing data could be utilized more effectively. But when is a company truly ready to transition to data-driven leadership? And even more importantly: how do you know whether to invest in data-driven decision-making now rather than a year from now?

Data-driven leadership is not a technology project, but a business transformation. Timing is crucial. A project launched too early easily leads to frustration and wasted resources, while a project started too late can mean loss of competitive advantage.

Data-Driven Decision-Making and Business Intelligence

The following five signs indicate that your company is mature enough to take the next step toward data-driven decision-making and leveraging Business Intelligence solutions.

Sign 1: Data exists, but it's scattered across different systems

Your company generates abundant data daily, but it's scattered across different systems: sales figures are in the CRM, financial figures in the ERP, customer feedback in survey tools, production data in its own database, and marketing data in analytics tools. When the management team inquires about the true costs of customer acquisition, the answer can't be found in any single unit. Each team only has access to its own limited and siloed view.

However, this is not a bad thing, quite the opposite. The absence of data would mean it's not yet time to transition to data-driven leadership. If naturally accumulated business data already exists, it can be combined and refined intelligently. This is precisely what indicates that the company is technically ready to take the next step.

Readiness Assessment

• At least 3 different systems that produce business data
• Data is stored in digital format (not just paper reports)
• Attempts have been made to combine data from different systems, but it's laborious
• It's known where the needed information is located, but retrieving it takes time

At least three items on the list are met: Your technical readiness is good. The challenge is integration, not lack of data.

Sign 2: Analytics is needed to support decision-making

In management team meetings, decisions are often made based on experience. Discussions include assessments of customer preferences, investment needs, or competitive situations based on personal observations. The decisions aren't wrong, but in hindsight, it may be realized that it would have been worthwhile to check the numbers beforehand. For example, a company might decide to invest in product A based on customer feedback, but later discover that product B generates 70% of revenue, even though it only receives 15% of feedback. Dissatisfied customers often provide more feedback.

Experience-based decision-making is not a weakness, but a sign of strength. An organization's ability to evaluate and question its own decision-making processes indicates readiness to develop. The best data-driven leadership emerges when the insight brought by experience is combined with data.

Readiness Assessment

• Decisions made haven't produced expected results
• Leadership needs data to support decision-making
• Desire to test assumptions before larger investments
• Competitors appear to be making data-based decisions

At least three items on the list are met: The organization has the right attitude. Change is not resisted.

Sign 3: Reporting and Excel spreadsheets take too much time

In most companies, there's someone who maintains massive Excel files with dozens of sheets and complex formulas. This person spends hours each week copying data from one system to another and is the only one who understands how certain reports are created. Possibly, they're also the only person in the company who knows how to update the reports. A concrete example describes a controller's week: Monday gathering numbers from the ERP, Tuesday fetching sales figures from the CRM and manually combining them, Wednesday correcting errors and checking totals, Thursday creating charts and formatting the report, and Friday sending the report to management. The next week, the same routine starts over.

However, this Excel hell reveals three things:

1. Reporting is needed, as it's valuable to the business
2. Expertise exists, as the company has a person who can process and refine data
3. The current situation is unsustainable, as efficiency suffers

Readiness Assessment

• Creating reports takes at least one day per week of one person's work time
• Report completion takes days or weeks
• Reporting responsibility rests with one person (what happens if the key person gets sick or changes jobs)
• Manual errors occur regularly

At least three items on the list are met: Automation pays for itself quickly. ROI is clear.

Sign 4: Decision-making shifts from past to predictive analytics

Management team questions have evolved from merely reporting the past toward predictive thinking. Previously, discussion focused on what last month's revenue was, how many customers the company had, or how much money was spent on marketing. Now, questions emphasize anticipating the future: what would be the impact on sales if the marketing budget is increased by 20%, which customer segments should be prioritized in the next quarter, and what means would best improve efficiency in processes.

This change indicates the organization's readiness to leverage predictive analytics and advanced business intelligence tools. Simply answering basic questions is no longer sufficient; instead, there's a desire to predict, simulate, and optimize decisions. As a concrete example, a company no longer asks how much was sold last week, but instead considers what would be the impact on total sales and profitability if product X's price is changed by 5%, taking into account customer price sensitivity and substitute products.

Readiness Assessment

• Leadership has more predictive questions than descriptive ones
• There's a desire to test different scenarios before decisions
• Simulations and modeling are needed
• Ad hoc analysis requests have become more common

At least two items on the list are met: The company needs predictive analytics, not just reporting.

Sign 5: Resources and organization support development

The company's financial situation enables an investment that pays for itself in 6–18 months. The organization has a designated person or team that can own the project, and leadership is committed to the change. Additionally, the IT infrastructure is modern enough to integrate new tools. As a concrete example, consider a company that has grown from 50 to 150 people in five years. Previously functional Excel solutions are starting to break down, but now the company has people who understand the need and can successfully carry the project through.

This is a good sign, as data-driven leadership requires investment both financially and in terms of time. If the organization isn't ready to invest in both, the project will fail. When both resources are available, the probability of success increases significantly.

Readiness Assessment

• The company can allocate a budget of €20,000–80,000 for a development project
• The company has a person who can own the project
• Leadership is willing to actively participate in requirements definition
• The company's IT systems support integrations (APIs, modern technologies)

At least three items on the list are met: Both personnel and financial resources are sufficient to start the project.

Is Data-Driven Leadership Worth It?

Based on the assessment, you'll get an idea of where your organization is on the data-driven leadership journey. If most points are met, your readiness is strong: data exists, motivation and resources are in order, and the organization has the right attitude toward change. In this case, it's worth starting with a pilot project that produces visible results quickly.

If most readiness factors are in place but a few areas still need work, it's worth identifying bottlenecks and strengthening them before making a larger investment. This could be, for example, scattered data, attitudes, or budget.

If only limited readiness factors are met, data-driven leadership is probably not yet timely. In this case, it's worth focusing on basics: process digitalization, systematic data collection, and strengthening leadership commitment. The topic can be revisited later when the foundation is in order.

Most Common Challenges and How to Avoid Them

Even if the foundation is in order, starting data-driven leadership can fail for the following reasons:

Challenge 1: Implementing everything at once

The challenge arises when a ready-made BI platform is acquired with the expectation that it will solve all problems immediately. The project swells, the budget is exceeded, and ultimately no one uses the system. The most sustainable way to proceed is to start with one concrete business question, such as How could we predict sales for the next quarter with 10% accuracy? or Which of our customers are at greatest risk of switching to a competitor? When the first use case works and produces clear value, the solution can be systematically expanded to the next needs.

Challenge 2: Lack of collaboration between business and IT

The challenge arises when the project is completely delegated to the IT department without business ownership. In this case, the end result may be technically functional but useless in practice—a solution that doesn't address real needs. Data-driven leadership projects are primarily business projects, where IT acts as an enabler. Therefore, ownership belongs to the business. The best setup emerges when the team has a clear business owner (such as a controller or sales manager), a data specialist responsible for analytics and modeling, and IT, which ensures integrations and technical functionality.

Challenge 3: Poor data quality

The challenge arises when analytics solutions are built before the data is in order. The result is the familiar "garbage in, garbage out." Therefore, about 30–40% of the project's time and budget should be spent on improving data quality: identifying and removing duplicates, standardizing data formats, defining clear input rules, and automating validations and checks. When the foundation is in order, analytics produces reliable results. Well-prepared data accelerates and enhances analytics more than any subsequent patching.

Challenge 4: Buying a ready-made solution

The challenge arises when an organization chooses the market's most well-known tool assuming it will solve all problems. In this case, it's forgotten that the tool is just a means, not a solution in itself. The right approach is to first define business objectives, identify key metrics and KPIs, and map current data and integrations. Only after this should a tool be selected that truly fits the organization's needs. The most expensive solution isn't necessarily the best. The best solution is the one your company actually uses and from which value is generated.

Challenge 5: Implementation without change management

Resistance arises in the organization if new tools and processes come as a surprise. Change is feared, benefits aren't understood, or there's concern about one's own role. Therefore, it's important to invest in change management. The reasons and benefits of the change must be communicated clearly, key people should be involved early on and given the opportunity to influence solutions. Additionally, personnel must be properly trained; learning cannot be left to chance.

Getting Started Guide: The First 90 Days

If you identified that your company is ready, here's a concrete plan for the first three months:

Weeks 1-2: Clarifying objectives

Goal: Determine what problem you're seeking to solve.

Actions: Start by organizing a 2–3 hour workshop with management, where you list the 3–5 most critical business questions. Prioritize the questions based on what would produce the most value fastest. After this, define success as concretely as possible. Vague descriptions like improved decision-making aren't sufficient. For example, sales forecast accuracy improves by 15% and we save 20 hours per week in reporting is a much better goal. Also identify metrics that indicate change. What KPIs will you follow and how will you assess return on investment.

Result: A one-page document with a clear goal and metrics.

Weeks 3-4: Current state mapping

Goal: Determine what data exists and what the data quality is like.

Actions: Start by determining what data your company has, which systems produce relevant information, in what format the data is, and how often it's updated. Next, assess data quality by taking a random sample and checking for errors, gaps, duplicates, and the adequacy of historical data. Generally, at least a year's worth of data is necessary. Finally, map integrations: determine whether systems support API connections, whether intermediary steps like CSV exports are used, and who manages access rights. This overall picture reveals how ready your data is to be utilized.

Result: An Excel table assessing data sources (location, quality, integrability).

Weeks 5-8: Pilot solution planning

Goal: Design a minimal viable solution (MVP).

Actions: First, it's worth choosing a clear pilot area. A suitable one is a limited business-relevant process or unit where results are visible in about 4–8 weeks. Once the pilot area is selected, decide on the technology solution. Even an existing tool may suffice for the pilot, as long as it's flexible and doesn't require long license agreements. Project success requires a team with an owner, 2–3 key users, and technical support either internally or from a partner. Finally, create a project plan that includes milestones, clear responsibilities and schedules, as well as risk analysis and pre-considered contingency plans.

Result: Project plan and decided pilot area.

Weeks 9-12: Implement and learn

Goal: Build the first working solution and gather feedback.

Actions: Start implementing the pilot. Integrate and clean the necessary data, build a simple dashboard or report, and test it with a limited user group. Gather feedback through, for example, weekly retrospectives and determine what works, what doesn't, and whether value is starting to show in practice. Measure results by comparing them to original goals, document learnings, and calculate preliminary ROI. If the pilot is successful, assess how to expand it and what to invest in next. If the pilot is unsuccessful, consider why. Modify the pilot if necessary.

Result: A working pilot and a decision on next steps.

Starting Data-Driven Leadership: Don't Wait for the Perfect Moment

Many companies postpone starting data-driven leadership because data quality isn't perceived to be good enough, a suitable person responsible can't be found, or a new system implementation is in progress. But the truth is that the perfect moment will never come.

If you found 3–5 of these signs in your company, the readiness to start data-driven decision-making is already in place. Start small, learn quickly, and expand as successes come.

Remember:

• Data doesn't improve by itself → it improves when it starts being used
• The organization doesn't change all at once → change happens step by step
• ROI doesn't materialize by waiting → it's generated through action

Your competitors aren't waiting. And neither should you.

Explore Empirica's Integration services.

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Empirica Finland specializes in AI solutions for B2B organizations and has helped companies across industries leverage autonomous agents to enhance their operations.