Maximizing Impact: 3 Essential Data Project Types for Leaders
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Chapter 1: Understanding Data Projects
Data analytics teams play a pivotal role in supporting various foundational elements within an organization. However, as the head of data analytics, determining which project to pursue next can be quite challenging, especially when faced with continuous requests from management.
This article aims to guide you in selecting impactful projects by categorizing them into three main types: analytics, automation, and data products. We will explore what each type entails and the considerations to keep in mind when embarking on these projects.
Section 1.1: Analytics Projects
Analytics initiatives can often yield quick wins by addressing fundamental questions from the business and translating them into actionable insights. But how do you identify a promising analytics project?
What Do Analytics Projects Entail?
Data analytics projects can manifest in various formats such as ad-hoc reports, polished Jupyter Notebooks, dashboards, or even PowerPoint presentations. Each format serves to narrate the current state of the business to stakeholders.
Typically, an initial phase of ad-hoc analysis helps ascertain whether further exploration of the data is warranted. Once you conclude this analysis, the next step is to present your findings effectively.
It's crucial to construct a narrative with your data, ensuring that your analysis highlights 1–2 key takeaways for management to focus on. Your data visualizations should support this concise message.
How to Select Your Analytics Projects
Presenting your project effectively is vital, but equally important is ensuring that your efforts align with projects that deliver significant business value. To identify high-priority projects, engage with stakeholders to understand their most pressing questions and the potential impact of addressing them.
Consider asking directors and business owners in key departments the following questions:
- What Key Performance Indicators (KPIs) do you rely on for your operations?
- Where do you access these KPIs?
- What metrics would you like to have readily available?
- What actions do you take based on these metrics?
- How do you assess their impact on the business? Is it critical?
As Ethan Aaron, CEO of Portable and former Head of BI, wisely noted, the objective of analytics is not merely to present data but to enable actions that significantly influence the business.
Once you grasp what questions are most critical to your business leaders, you can compile a list of valuable projects that align with management's objectives.
Section 1.2: Automation Projects
Automation offers several key benefits:
- It enhances process scalability.
- It reduces operational costs.
- It minimizes human error.
However, much of the automation work is conducted behind the scenes, which can lead to a lack of appreciation from management unless it significantly affects the bottom line.
To maintain effective data-driven operations, robust automated systems are essential. This includes automated data pipelines, data quality assurance, and model deployment, among others.
What Should You Automate?
Identifying suitable automation projects can be challenging. While automation can save time, it also introduces technical debt, even when utilizing low-code solutions.
When evaluating what to automate, focus on processes that either consume considerable time or are expected to grow in complexity. If a task is infrequent, automation may not be worth the investment.
Although the C-suite might overlook automation projects, analysts and project managers often have repetitive processes ripe for automation. The question remains: should they be automated?
Chapter 2: Data Product Projects
Once your team has honed its skills in analytics and automation, you can contemplate the development of a data product. However, you must first have a solid understanding of your analysis processes and a foundational automated data infrastructure to build upon.
Data products can take various forms, including:
- Processed datasets that categorize inputs using natural language processing.
- Dashboards that provide insights.
- API endpoints for managing dynamic pricing.
While some companies develop these products internally, many turn to external solutions due to constraints in time, budget, or expertise. Data products can significantly boost revenue; in fact, some organizations focus exclusively on creating them.
To construct a data product, the process typically involves ingesting data, mapping it for consistency, and applying business logic to deliver outputs in the form of dashboards, models, or APIs.
Data products are particularly rewarding to develop, as they often demonstrate a clear return on investment.
The first video titled "Get NOTICED in Data Science!!! (3 types of GREAT projects)" offers insights into how to stand out in the data science field through impactful projects.
The second video, "5 Projects for a Data Analyst Job | All Materials Included," provides a comprehensive overview of projects that can enhance your portfolio as a data analyst.
Where to Begin?
Choosing the right projects as the head of data analytics is critical for achieving success in your role. It’s easy to become sidetracked by technical questions like tool selection or programming languages. However, C-suite executives primarily seek results.
While it’s important to establish standards, if you find yourself months into a project without delivering any tangible outcomes, you may encounter frustration from leadership.
To prevent this, focus on building a foundational data stack, addressing key questions, and continually refining your processes.