THE PATH TO BECOMING A DATA-DRIVEN COMPANY

Roman Pinkovskyi
Dec 3, 2025
7 min read

What do business managers typically think when they hear the term "Analytics Implementation"?

"Are we setting up a new BI system? Or adding a slew of reports?"
"Will this mean attractive dashboards displayed in our open-space office?"

How is it often executed? Companies frequently overwhelm the process by introducing an excessive number of metrics, branding it as "360-Degree Analytics". This approach is often celebrated as a project success by management.

A year will pass. And here is a life hack from experienced analysts: stop sending any report for at least a day and observe the reaction. Will anyone notice its absence? Will anyone reach out to request it?

A kind of corporate spam: reports that add little value but persist without an "Unsubscribe" button.

What is this article really about?

After years of working across multiple sectors (retail, FMCG, and IT) and at the intersection of business, analytics, and technology, I’ve seen firsthand how companies attempt to “implement analytics” — and why a lot of these efforts deliver only minimal impact.

This article isn't about selecting the right BI system or designing dashboards - those are mere tools. The real question is how to effectively utilise those tools and identify your target audience. Most importantly, what is your ultimate goal? Drawing from my experience as both a former analyst and a top manager, I’ll share key insights for both those executing analytics initiatives and the stakeholders who rely on them.

Here’s some practical wisdom I've gathered from over 15 years of experience in building analytics and guiding companies toward data-driven management.

What to Watch Out for on the Path?

1. Data quality

You may have seen this “Analytics pyramid”:

As you can see, there's still a long way to go before reaching the pinnacle of the pyramid. The first step is transforming raw data into high-quality information.

If you're a medium-sized business, what would you prioritise:

Receiving analytics with a 2-hour delay and 95-97% data accuracy?
Or waiting several days for analytics with 99.9% accuracy?

While there will always be a margin of error, in the fast-paced retail or FMCG market, even a few days’ delay could be vital.

In some companies, I’ve implemented a data validation rule where the system wouldn’t send automated reports if the data quality fell below a certain threshold. Management understood this rule - if a new report appeared in their inbox in the morning, they could trust the data.

The specifics of quality control logic vary by company, but it's crucial to incorporate logical checks. For example, flags should be raised if you see a gross margin of 239% or an EBITDA of 105%.

Recommendation:

If you want your analytics to be relied upon and effectively utilised, ensure it is trustworthy.

2. Data gathering

Log every business action in your ERP or CRM systems. Based on my experience, this data will prove its value sooner or later.

For example, in one of the B2B companies I worked with, we decided to record every customer request for a commercial quote — even when it was clear that a large share of them would never turn into a deal. The conversion rate was around 25%, meaning only a quarter of all requests resulted in an actual sale.

Over time, this data became extremely valuable because it helped us avoid the classic survivorship bias. In most companies, only successful cases — the ones that “survive” and generate revenue — are analyzed, while the massive pool of lost opportunities remains invisible.

By analyzing the accumulated “unsuccessful” quotes, we were able to identify patterns, recurring reasons for rejection, and the specific stages where customers were losing interest. This allowed us to adjust the commercial model and significantly improve conversion in B2B sales.

Recommendation:

Collect all available data, even if it doesn't seem immediately useful — you'll likely find it valuable in the future.

3. Target audience

It's crucial to have a clear understanding of your target audience, including their specific needs and concerns.

Recommendation:

Build a clear structure for your target audience. For instance, you might segment them as follows:

Owners and Supervisory Board
Top Management
Sales Department
...

This structured approach is crucial even for accurately naming and organising fields in your report tables.

4. "Sell" your analytics

As Blaise Pascal famously said, “I would have written a shorter letter, but I did not have the time.”

Unfortunately, in the field of analytics, it often happens that there isn't time to avoid or correct the following mistakes:

Mistake	Solution
Too much textual information	Graphics are generally more effective than text in conveying information. Create clear and readable visualisation
Technical or system names for fields and metrics.	Сhoose descriptive labels that enhance clarity.
Unreadable number formatting.	For instance, present £12,386,192.23 as £12.4 million. Detailed pennies are rarely necessary for decision-making.
Too many redundant elements.	Remove unnecessary fields, charts, and tables, retaining only the most relevant data for making decisions.

Car Dashboard Analogy

I often use the association with a car dashboard display. Consider the car dashboard analogy when designing your analytics. Imagine you are a driver commuting from point A to point B. Drivers usually have a few indicators or gauges on their dashboard:

Speedometer
Petrol level
Tachometer
Oil temperature

Interestingly, the last three gauges are only relevant when they deviate from the norm. For example, an oil temperature of 90 degrees is expected all the time and does not impact your driving efficiency. Since this temperature is normal 99.9% of the time, it rarely provides actionable insight. In fact, some car manufacturers, like Volvo, have removed certain constant gauges, such as the oil temperature, from their displays because they offer little practical value to the driver.

Just as with a car dashboard, avoid displaying redundant metrics in your analytics dashboards.

If you include too many trivial indicators - like showing the oil temperature constantly - your analytics might be rich in content but fail to engage users due to cluttered presentation.

This is similar to how well-tasted but poorly presented products in the FMCG or retail markets can deter consumers.

Recommendation:

Simplify. Conduct A/B testing to find the most effective way to present knowledge and insights.

5. What problem are we solving?

Understanding your target audience is only half the battle; the other half involves comprehending their specific problems. This insight allows you to develop bespoke analytical products tailored to address these issues effectively.

Based on my experience, the needs and possible solutions might include:

Target audience	Needs	Solution
CEO or top-management	Daily helicopter view of basic KPIs, execution of sales plans. Remember, the time of CEOs and top managers is one of the most valuable resources in the company.	Dashboards with traffic light logic.
Sales management	Sales plan execution, analytics in details	Graphs and tables with the required level of details
Sales managers and sales representatives	Achieving personal targets, your progress vs team progress	Flat reports, mobile reports. Gamification elements perfectly complement such types of reports
Development teams	Search for patterns and opportunities, cause-and-effect relationships	Everything that is based on the “what-if” principle: from pivot-table or OLAP to multi-factor analysis of big data

Thus can visualise analytics tools based on complexity and business values:

Recommendation:

Вибирайте та впроваджуйте різні аналітичні інструменти, пристосовані до конкретних потреб кожної цільової аудиторії.

6. Be one step ahead

Who is more valuable to the company:

A manager who excels at analysis?
Or an analyst with a deep understanding of business and sales?

In my experience, both profiles significantly enhance your expertise and contribute to a more data-driven organization.

A manager who excels at analysis

A manager proficient in data analysis can significantly reduce your workload by leveraging effective tools, allowing the business to take full advantage of the insights provided. This creates synergy, rather than simply “passing the buck.”

For example, sales directors who actively use OLAP tables in their spare time to identify patterns and trends will have an intimate understanding of the sales process.

During interviews for managerial positions, I often ask, “Are you familiar with pivot tables?” Although this question might seem unusual and wasn’t listed in the job description, it serves as an indicator of the candidate’s ability to comprehend data aggregation and decomposition. Such candidates are more likely to grasp the underlying nature of processes and understand the intricate details “under the hood.”

However, it’s important to note that fostering this analytical mindset requires cultivating a new culture of information consumption. This involves educating and demonstrating the advantages of a data-driven approach to ensure that all team members fully embrace and benefit from it.

An analyst who understands business

An analyst with a strong grasp of business trends transforms from a mere data processor into a strategic asset who can correlate business activities with data, uncover patterns, and generate actionable insights.

These analysts often come from a business background and are seeking to leverage their experience in a new analytical role. In fact, this combination “business experience + analytical skills” is highly valuable.

Such analysts can identify growth opportunities and business development areas that may be challenging for traditional managers to detect within the sales system.

Recommendation:

Alongside implementing an analytics system, focus on building a strong analytical culture within your company. Cultivating this culture will support your transition to a data-driven organization and enhance overall decision-making and performance.

In conclusion:

Ultimately, having a TV with dashboards in your open-space office is a superb idea. As far back as 2021, we at AGROMAT installed such a monitor, which displayed a real-time sales dashboard in the open office where the trade marketing, purchasing, and logistics departments collaborated.

Within weeks, these dashboards became a focal point for daily discussions. Team members engaged in conversations about plan execution, observed trends, and even made friendly bets on monthly performance outcomes.

This approach not only kept everyone informed but also fostered a sense of unity through analytics.