Everyone in the business world aims to be data-driven, but achieving this takes effort and strategy. Behind the scenes, data strategists work diligently to make this goal a reality. As a senior data strategist at Amplitude, my daily tasks involve analyzing user behavior, integrating new data, and solving data issues. Despite these efforts, data strategists face several challenges, such as communicating with stakeholders and managing data cleanliness.
Overcoming these hurdles allows us to deliver better insights and enable informed decision-making across the organization. In this article, I’ll discuss the common challenges faced by data strategists and share practical solutions for each.
Key Takeaways:
– Data strategists are essential in transforming data into actionable insights but face significant challenges.
– Poor data quality can hinder analysis, highlighting the importance of good data governance.
– Managing a heavy workload requires transparent communication and prioritizing tasks.
– Effective storytelling with data involves using multiple sources and clear documentation.
1. Managing Poor Data Quality
Poor data quality, which includes incomplete or unstructured data, can create significant obstacles. Without high data hygiene standards, data strategists spend most of their time cleaning data instead of analyzing it.
Foster Strong Data Governance
Gaining leadership support for data governance is crucial. Educate stakeholders about the importance of data hygiene to ensure sound business decisions. Quality decisions require quality data, and personalization needs timely data updates. Creating a data governance framework with educational resources, such as a data dictionary, helps everyone understand and use data more effectively.
Utilize Tools for Data Hygiene
As products evolve, so does the data underlying them. Tools like Amplitude assist in managing data by merging events, renaming properties, and transforming historical data into useful insights. Derived properties in Amplitude can turn raw data into meaningful information, allowing for easier analysis.
2. Prioritizing Workload and Time
Data strategists often face numerous urgent requests, leading to stress and overwhelming workloads.
Collaborate on Analysis Prioritization
Engage stakeholders in discussions about the impact and effort of their requests. Set clear expectations and realistic timelines from the beginning. Maintain a visible backlog of tasks to ensure transparency and alignment with business goals.
Break Down Analysis Tasks
Clarify vague requests by asking specific questions, and break down larger tasks into smaller, manageable ones. Regularly check in with stakeholders to refine the scope and receive feedback, ensuring the analysis meets their needs.
3. Communicating with Stakeholders
Data often tells only one side of the story. It’s essential to communicate comprehensive analyses, even if they contradict stakeholders’ expectations.
Expand Your Data Perspective
Use multiple data sources, including historical data, behavioral data, surveys, and session replays, to get a holistic view. Interrogate your findings with targeted questions to understand customer behaviors and the impact of different factors.
Tell a Relatable and Actionable Story
Effective data storytelling combines analysis with clear documentation and relatable narratives. Use tools like Amplitude Notebooks to enhance storytelling and share insights with colleagues, encouraging collaboration and feedback.
Path to Better Data
Addressing challenges like data quality, workload prioritization, and stakeholder communication can unlock the full potential of your data. Focus on asking the right questions and trusting your answers to provide high-quality insights quickly. With the right tools, achieving cleaner, more accessible, and manageable data is within reach.
If you aspire to a world where data is simpler to manage, try Amplitude for free or explore our guide on behavioral data and event tracking.
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Jennifer Rudin
Senior Data Strategist, Amplitude
More from Jennifer: As a Senior Data Strategist at Amplitude, Jennifer aims to break down data silos, increase data literacy, and build confidence in internal data infrastructures.