Data is everywhere. In a world driven by data, the ability to interpret and leverage information has become essential in every aspect of life.

Forward-thinking businesses are investing significant resources into data and analytics, recognizing the crucial role they play in decision-making, process optimization, and competitiveness.

Data can answer questions like: Where are we now? How did we get here? Where will we be tomorrow? (If we make or don’t make decision X).

However, having data is not enough. It is essential to process and interpret it correctly and transform it into business-useful insights.

In this article, rather than examining each type of analytical task in detail, I’ll focus on the primary functions of our BI & Analytics department, their responsibilities, and results.

Strategic Function

Analytics plays an important strategic role in a company, supporting informed decisions grounded in data insights. It allows businesses to determine the effectiveness of various initiatives and decide whether to scale or optimize certain projects. With analytics, a company can make timely adjustments to its strategy and adapt to market changes.

Behind the high-level business metrics and financial indicators that management primarily pays attention to lies many factors that contribute to their formation. A careful analysis of these factors, examined from different perspectives and with trend-tracking, allows us to understand current revenue sources and forecast future performance. Building a forecast based on existing data is a key aspect of the strategic function, serving as a foundation for planning, budgeting, and resource management.

Marketing
In our industry, the revenue component largely depends on investments in marketing, which is why we place particular emphasis on our forecasting process. Each acquisitional channel has its own characteristics, including a different cost per acquisition, different lifetime, and performance on the product. Another crucial factor is the impact of brand activity, which can take a significant part of the total marketing costs. Brand initiatives can not only increase organic traffic but also enhance retention and performance within our existing audience.

User Preferences and Segmentation
Changing the structure of user segments at the moment may not affect high-level business performance in the moment. However, analyzing the specifics of each type makes it possible to predict how these changes could influence outcomes in the long run.

New Markets
Forecasting profit from investing in new markets is a crucial responsibility. It provides insights into the payback period and helps determine whether an investment is worthwhile. This forecast is supported by deep research and the collection of internal market information.

The importance of the forecasting process can hardly be overestimated, as its result is a tool for making decisions on budget planning and understanding what these decisions will lead to in the long run. The amount of money we invest now in markets and marketing channels largely determines the level of income the company will receive in the next one or two years.

Operational Function

Every team within a company aims to drive revenue. Although analytics itself does not generate revenue directly, it enhances the efficiency of other business units. Requests to the analytics team can include a full range of tasks: AB testing, user segmentation, drop-off analysis, performance analysis, the implementation of monitoring or alerting, anomaly detection, campaign effectiveness evaluation, etc.

Collaboration
While top management usually already has a request for analytics, line teams may still need to be convinced that they need us. 🙂

Ideally, analysts should be integrated into the functional teams, fully understanding their tasks and working closely with them to maximize efficiency. Implementing effective cross-team collaboration can be challenging, especially if a company has just embarked on the “data-oriented” path.

Building these cross-team processes generally involves the following stages:

– Who are you?
At this stage, teams are uncertain about how analysts can contribute and may not see the benefit of involving them in internal processes, especially if they already rely on existing reports and metrics. Here, analysts need to put a lot of effort into proving their ability to be helpful.

– Analyze everything for me
After the analyst shows their ability to improve performance (or at least identify areas for improvement with insights visuals ).

At this point, the analyst is usually flooded with requests to analyze nearly every aspect of team operations. This stage is followed by many reports and an infinite number of ad-hocs, which leads to a 250+% load of analytical capacities.
At this stage, the analyst must demonstrate considerable resilience and determination (these stakeholders can be quite challenging 😉 ) to learn how to filter out valid requests and be able to prioritize them.

On the positive side, this stage is accompanied by a huge number of hypotheses that the team wants to test, knowing that now their efforts can be correctly evaluated.

– Let’s be partners
This is what healthy relationships between team X and the analyst should strive for. At this stage, common processes become established, and the analyst is aware of what is happening in the specific part of the product. They act as a consultant, help in planning team activities, evaluate the potential value of new implementations and approaches, and help with their prioritization.

– I can do it myself
Actually, this stage is an extension of the previous stage, where the analyst is no longer involved in every issue that needs to be analyzed. By understanding the team’s specific responsibility and areas of influence, the analyst develops approaches, creates essential tools and teaches stakeholders how to use them to perform self-service analysis.

The team not only knows which reports and metrics to monitor for tracking changes, but they can also find the causes of deviations using the provided tools and approaches. Meanwhile, the analyst is focused on more complex analyses, data research, and involvement in strategic planning.

Identifying Performance Indicators for Different Departments

A fundamental step in the collaboration between the analytics team and business units is to define performance indicators. Beyond the primary KPIs, additional performance indicators may not be immediately visible but should clearly reflect the health of a specific business area. These indicators usually need to be “tested” to identify their sensitivity to the impact of direct changes in the operation of a particular area of the product.

We use established metrics for monitoring or alerting, allowing each stakeholder to stay informed and respond to changes as quickly as possible. Monitoring often only signals the presence of anomalies without identifying their cause, but the data usually holds an answer, a common analytical task.

Effective collaboration between business units and the analytics team builds on well-established cross-team processes and clear communication.It may not be easy and fast, but it is definitely worth the effort.

Technical Function

Data. From a click on a product to a stunning graph.

This function is not really a separate component but rather an integral part of the previous points and all analytical tasks in general. The technical part of our department’s work is usually out of sight for end users of analytical products, but it is exactly where the basis for powerful analytics is built.

From simple product clicks to comprehensive reporting visuals, data goes through several important stages managed by data engineers and analysts. And this is where the “data magic” happens.

Data collection
Any user interaction with the platform ( along with operational process occurring without the direct user participation ) is recorded in the system. This stage focuses on gathering data.
Data cleaning
Primary data is not received in a very “clean” form, so in order to work with it, it needs to be cleaned, including removing duplicates and errors.
Data aggregation
In most cases, the final data for reports is aggregated, as there is no need to work with a maximum level of detail. This helps to reduce the final amount of data by tens or hundreds of times.
Data enrichment
The final data sources on which the report is based are ordinary “flat” tables, but the data in it is collected from multiple original sources. In order to enable the final report not only to view the indicators but also to analyze them, this data must be supplemented with attributes stored in other sources, such as: acquisition channel; user segment; type of first user activity, etc.
Data quality maintenance
A stage that is often overlooked but is quite important in a “healthy” life cycle of data. The analytical department is responsible for the accuracy of the numbers on which it helps the business make decisions. Therefore, it is extremely important to build a process to control the integrity and quality of data.
Data visualization
And only now, when the analyst implements the final data set with all the necessary attributes, you can start the creative part — visualization. (Although, of course, at all the previous stages, the analyst and data engineer also have room for creativity 🙂).
The final part of the data cycle is a very important stage, as its result is a tool that will be used to understand the health of a particular product area and help in making operational decisions. Data visualization itself is a rather extensive topic that deserves a separate book, so I won’t go into it too much.

Afterwords
In today’s world, analytics is no longer just a tool for collecting data; it has become the foundation of effective business decision-making. Investing in analytical capabilities allows companies to understand the market better, use resources more efficiently, and adapt to changes faster. Those who can transform data into strategy lead their industries.

Enjoy Analytics. And Let’s Grow Together!