According to a recent article by Ilona Hetsevich in Towards Data Science, successful data analytics projects follow a structured process designed to ensure meaningful outcomes. From understanding the problem to making insights actionable, each step plays a vital role in creating value from data.
In this post, we explore key takeaways from Hetsevich’s article while adding deeper insights into how organizations can elevate their analytics initiatives to meet complex, real-world challenges.
1. Understand the Problem Before Diving In
No amount of sophisticated analysis can make up for a poorly defined problem. Hetsevich highlights the importance of starting every project by clarifying objectives and aligning on expectations. For example, vague requests like “Why are sales down?” often mask more nuanced challenges—whether it’s understanding customer behavior, identifying operational inefficiencies, or responding to external market pressures.
Taking time to understand the broader business context ensures your analysis focuses on metrics that matter. This doesn’t just save time; it enables analysts to ask more targeted questions and deliver solutions that align with organizational priorities.
For instance, when diagnosing declining sales, it’s not enough to focus on traffic metrics. Dig deeper to ask: Are we targeting the right audience? Are recent pricing changes impacting conversions? How do seasonal patterns or competitor activity fit into the picture? These questions guide a more meaningful exploration of the data, leading to insights that are actionable and impactful.
2. Set and Manage Expectations
Setting clear expectations is critical to maintaining trust and alignment with stakeholders. As Hetsevich points out, many people underestimate the time, effort, and complexity required to prepare, analyze, and interpret data. Managing these expectations upfront ensures that resources are focused on high-impact tasks rather than quick fixes or low-priority requests.
One effective approach is to establish a formal intake process for analytics requests. This not only standardizes how tasks are evaluated but also encourages stakeholders to articulate their needs more clearly. For example, requiring stakeholders to outline their objectives, timeline, and intended use of insights forces a shift from reactive requests to strategic collaboration.
Additionally, regular communication throughout the project lifecycle—whether through status updates, milestone reviews, or progress dashboards—ensures transparency and keeps stakeholders engaged in the process. This way, everyone stays aligned, and the analytics team can focus on delivering quality work rather than managing last-minute surprises.
3. Preparing the Data: The Often-Overlooked Foundation
Data preparation is where the real work of analytics begins, and it’s often the most time-consuming step. Cleaning and organizing data ensures that the analysis is based on reliable, high-quality inputs. Hetsevich highlights common challenges, including missing values, inconsistencies, and duplicate records, all of which can derail an otherwise promising project.
Organizations can go beyond the basics by investing in automation and advanced tools to streamline the data preparation process. Automated anomaly detection, for example, can flag irregularities like outliers or missing data points, enabling analysts to focus on more strategic tasks. Similarly, developing robust data documentation and governance frameworks ensures that datasets are easily accessible, well-maintained, and aligned with business objectives.
Aligning metrics with organizational goals is equally important. If the goal is to increase customer retention, then focusing on lifetime value, churn rate, and engagement metrics is more meaningful than looking at top-of-the-funnel traffic. This alignment transforms raw data into a powerful decision-making tool that drives strategic outcomes.
4. Exploration: Where Insights Take Shape
Exploratory Data Analysis (EDA) is where the magic happens. This phase allows analysts to uncover trends, refine hypotheses, and develop a deeper understanding of the data. It’s not just about looking for patterns—it’s about asking better questions and challenging assumptions.
Organizations can amplify the value of EDA by fostering collaboration between analysts and domain experts. For example, a marketing analyst exploring campaign data might uncover unusual patterns that a product manager could explain based on recent feature releases or updates. These cross-functional insights ensure that EDA not only reveals what’s happening but also uncovers why.
Innovation during this phase can lead to breakthroughs. Feature engineering, for instance, can transform raw data into more meaningful variables, capturing patterns that would otherwise go unnoticed. This process requires creativity and domain knowledge but can significantly enhance the predictive power of analytics models.
5. Crafting the Narrative: Turning Data into Action
As Hetsevich explains, insights are only valuable if they resonate with stakeholders and drive action. This requires a balance between technical accuracy and effective storytelling. Data analysts should tailor their narratives to their audience, focusing on what matters most to decision-makers.
For example, when presenting to executives, highlight high-level takeaways and ROI implications. For technical teams, provide the granular details they need to implement changes. Visualizations should be clear and uncluttered, emphasizing key insights without overwhelming the audience.
Organizations should also take an active role in following up on recommendations. Establishing timelines, success metrics, and regular progress reviews ensures that insights are implemented and deliver the intended impact. This not only validates the value of analytics but also reinforces its role as a strategic driver of business success.
From Insights to Impact
Data analytics is as much an art as it is a science. It requires not only technical expertise but also a deep understanding of business needs, collaborative processes, and the ability to translate insights into action.
To learn more about Ilona Hetsevich’s approach to data analytics, read the full article here.
At Bronson Consulting, we help organizations navigate the complexities of data analytics, from problem definition to implementation. Our expertise in data management, governance, and strategic alignment ensures that our clients maximize the value of their data while staying ahead in an increasingly data-driven world.
