For example, regarding data, the trend of promoting it as the fundamental raw material to guide business decisions triggered a collecting frenzy among many companies. However, gathering data just for the sake of it—without a clear strategy to leverage it and extract real business value—can be frustrating and unprofitable.
With advancements in big data and the possibilities opened by sensor technology, smart devices, and the Internet of Things (IoT) framework—combined with business intelligence solutions—it is now possible to capture data of widely varying types and from highly diverse sources. Data collection techniques for both structured and unstructured data have advanced remarkably. Yet, gathering massive volumes of data without a clear purpose makes little sense, as companies ultimately end up not knowing what to use it for, why they have it, or how to process it.
Instead, the baseline requirement is to focus on collecting specific, high-quality data that can subsequently be leveraged through data analytics initiatives powered and refined by machine learning. Achieving this requires a clear vision of which customer data points truly matter, aligned with the specific outcomes and business value the company aims to attain. Otherwise, determining which data points to collect becomes nearly impossible.
An international survey conducted among leading global companies revealed that 97% are investing in data initiatives. However, the report notes that "achieving data-driven leadership remains an elusive aspiration for most organizations." Furthermore, only 40% reported that they were managing data as a core business asset within their companies.
Quality Over Quantity
Even Andrew Ng, a prominent figure in the artificial intelligence field, began advocating for "good data" rather than relying strictly on the concept of "big data." The reality in this space is that while quantity remains relevant, quality is paramount—as is understanding precisely what type of data is necessary to hold.
The primary objective is to help focus the business strategy, which today must be driven by data and specialized operational capabilities. Regarding data itself, organizations must identify which initiatives it should feed (for instance, understanding customer behaviors, anticipating market moves, etc.) and clarify the specific experience they aim to deliver to the end user or client.
Phased Progress
A robust data strategy must factor in the specific business outcomes an organization aims to achieve. Additionally, companies must recognize that each stage of their data evolution will yield different results: initially, they will identify who their customers are; next, they will understand their behavioral patterns; and ultimately, they will determine whether the customer lifecycle itself is effective. All of these insights subsequently enable more targeted, relevant communications.
However, understanding internal data does not mean an organization should ignore its external environment—these represent distinct strategic dimensions. This brings the focus back to strategy and prioritization: it is impossible to monitor everything all the time. Organizations must define where to place their bets and which approach will deliver the highest initial value. That decision depends directly on the available data types, the company's core operations, its target outcomes, and its internal capacity to execute on those challenges.
At Baufest, we bring the expertise required to partner on these initiatives, helping organizations make faster, smarter business decisions powered by their data.