This article explores how commercial analytics can transition from merely managing data to becoming a profit enabler, offering actionable insights to guide business decisions.
Commercial analytics is essential for organizations seeking to extract value from the data they collect. Yet, despite the massive volumes of information available, many companies struggle to translate that data into actionable insights that drive profitability.
Too often, analytics is viewed as a cost center, where time and resources are consumed without directly contributing to business growth. However, with the right approach, analytics can evolve from merely managing data to becoming a profit enabler — one that not only informs decisions but also drives tangible business outcomes.
The Problem: Data Overload, Actionable Insights Lacking #
The heart of the problem lies in today’s business tendency to collect vast amounts of data without a clear strategy for its use. Many businesses fall into the trap of gathering data for its own sake, resulting in data silos, overwhelming volumes of unused and unstructured data.
Analysts often end up spending lots of time (or too much) managing these data systems rather than deriving valuable insights to support better decision-making.
We’ve become data hoarders. We’re collecting everything we can get our hands on, but we’re not asking ourselves the crucial question – why?
This disconnect between data collection and data application is a persistent issue in many organizations, leading to a perception of analytics as merely a cost center rather than a source of business growth.
The Solution: Shifting to Decision-Enabling Analytics #
The key is not to collect more data, but rather to focus on gathering the right data — data that is directly aligned with the organization’s core business objectives.
This shift requires analytics to evolve from a passive function that manages data to an active role in enabling decisions, guiding teams with precise, relevant, and actionable insights.
The goal isn’t to be data-driven. It’s to be insight-driven and decision-enabled.
This approch involves several key strategies:
Start with the Problem, Not the Data #
Instead of collecting data indiscriminately, analysts have to start with specific business problems or objectives. Only then do they determine what data is needed to address these issues.
Prioritize Quality Over Quantity #
In the world of analytics, more isn’t always better. Rather than overwhelming the team with hundreds of metrics, the focus should be on a few key performance indicators (KPIs) that directly impact profitability and drive results.
We used to have a dashboard with over 200 metrics. Now we focus on just 7 core KPIs. The clarity this has brought to our decision-making process is remarkable.
Prioritize Analytics Requests Effectively #
In managing analytics workloads, teams often face an overwhelming volume of requests, many of which are low-value or driven by curiosity rather than business impact. To address this, a structured triage process can help prioritize tasks based on their potential to contribute to the organization’s bottom line—either by generating revenue or reducing costs.
This process begins by asking stakeholders to articulate how the requested data or analysis will drive specific outcomes. For example, teams may need to explain how insights from a report could lead to operational improvements, such as optimizing a lead form to increase conversion rates. Documenting these requests and their eventual outcomes—whether decisions made or measurable impacts—provides a clear framework for evaluating and justifying the team’s efforts.
Over time, this approach can evolve into a prioritization mechanism where resources are allocated to requests with the strongest business case. This not only filters out less impactful tasks but also encourages stakeholders to carefully consider their requests before submitting them. Such a system fosters alignment between analytics teams and their stakeholders, ensuring that efforts are directed toward activities that add significant value.
The long-term benefits of this approach include increased efficiency, better collaboration, and a clear demonstration of the analytics team’s contribution to the organization’s goals. By focusing on meaningful outcomes, analytics teams move beyond simply being busy to actively driving measurable business success.
Provide Actionable Insights #
The true value of analytics lies not in reporting what happened, but in providing insights that guide future actions. This means moving beyond descriptive analytics (what happened) to diagnostic (why did it happen?), predictive (what will happen) and prescriptive (what should we do about it) analytics.
For example, instead of simply reporting a drop in customer retention rates, an effective analytics team might provide insights into the factors driving this trend and recommend specific actions to reverse it.
Thermostat or Thermometer, Which One You Are?:
- A thermostat controls temperature.
- A thermometer measures temperature.
In this analogy (used by Adam Greco), the thermometer and thermostat represent two different approaches that analytics teams may take in their work. A thermometer simply reports the current state or situation, providing information about what is happening right now or what has happened in the past, without influencing or changing the environment. It just “measures” and “reports” facts. In contrast, a thermostat not only senses the temperature but also has the ability to adjust and control it, actively changing the environment to achieve a desired result, whether it’s making things cooler or warmer.
Most analytics teams (about 90%) function like thermometers: they mainly report data without taking action to drive change or impact the business. These teams may provide valuable insights but fail to apply them to improve processes, websites, or apps. On the other hand, thermostat teams use data to actively shape and influence decisions, making an impactful contribution to the company’s growth and success. The challenge posed to teams is to move beyond simply reporting data and strive to engage in more thermostat work, where they leverage data to create tangible changes.
This approach not only drives better business outcomes but also helps overcome the perception of analytics as a cost center.
Overcoming Organizational Barriers #
One of the most significant challenges businesses face when transforming analytics into a profit enabler is organizational resistance. This resistance usually results from silos between departments, a lack of a data-driven culture, and inadequate communication between business units and analysts.
To overcoming these barriers it’s necessary:
Break Down Data Silos #
In many companies, different departments — such as marketing, sales, and operations — collect and analyze data in isolation, without cross-referencing it with other departments, leading to missed opportunities for cohesive, company-wide strategies.
Silos between departments create a fragmented approach to data usage, where valuable insights may be available but are not accessible for teams that need them most.
To overcome this, analytics should not exist in a silo, instead leadership must encourage a culture where data must be treated as a shared asset that informs decisions across all departments.
When we broke down the walls between our marketing and sales data, we discovered patterns that revolutionized our customer acquisition strategy. It was like finding hidden treasure in our own backyard.
Promote a Data-Driven Culture #
Transforming analytics into a profit center requires more than just technical solutions. It demands a cultural shift where data-driven decision making becomes the norm across all levels of the organization.
When we made data literacy a core competency for all our managers, we saw a dramatic shift in how decisions were made. Our quarterly reviews went from gut-feel discussions to evidence-based strategy sessions.
Ask Better Questions #
Central to this transformation is the need for better communication between analysts and business stakeholders. Training business managers to ask the right questions and helping analysts focus on delivering actionable insights will improve the value analytics brings to the organization.
Too often, businesses approach analytics with broad or vague questions like, “How do we improve our strategy?” While these questions are important, they are often too expansive for actionable insights.
To overcome this, more targeted questions are needed — for instance, “Should efforts on certain keywords be increased?”; “Where should budget allocation in paid media be shifted?”; “Which user journeys on our website are underperforming, and how can we improve them?”.
When we started asking more specific questions, our analytics team was able to provide insights that directly impacted our bottom line. It was like turning on a light in a dark room.
The Future of Commercial Analytics: A New Frontier #
As we look to the future, the role of commercial analytics is set to expand even further. Emerging technologies like artificial intelligence and machine learning are opening up new possibilities for turning data into profit.
Imagine a retail chain where AI-powered analytics not only predict customer demand but automatically adjust pricing and inventory in real-time to maximize profitability. Or a manufacturing company where predictive analytics prevent equipment failures before they occur, dramatically reducing downtime and maintenance costs.
These scenarios are rapidly becoming reality for companies at the forefront of analytics innovation.
Conclusion: The Analytics Advantage #
Commercial analytics is at a crossroads. As companies collect more data than ever before, the challenge lies in translating that data into actionable insights that drive profit and improve decision-making.
By focusing on asking better questions, aligning analytics with business strategy, and integrating new technologies like AI, businesses can unlock the full potential of their data. The key is to move from seeing analytics as a cost to viewing it as a critical enabler of business success.
In this new landscape, analysts will play a pivotal role in guiding businesses through the complexities of data, ensuring that the insights they generate lead to real-world value.