by Konstantin Tskhay​

August 28th

Building a Strong People Analytics Foundation for Early-Stage Startups

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Hey Friends,

Today, we will discuss data infrastructure for HR Leaders at Start-Ups and Scale-Ups.

Why?

If you get this right from the start, you can effectively drive your People Analytics forward for a long time.

But before we do, here are some ANNOUNCEMENTS!

Did you know I will speak on October 28th at the Tech Talent North conference about the importance of ROI in People Analytics with Daneal Charney​? If you're interested in checking it out, the Early Bird is on, and it looks even better with my 25% off: use code ktskhay25 for a discount.

Now, that's not all! I also joined the DataDriven council with Lydia Wu,Dawn Burgess, Jennifer Hanniman, and Adriana Bokel Herde, and we talked about—you guessed it—data quality once again!

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This is precisely the meeting where we highlighted a trend or challenge many large and small HR teams face: data quality.

So, what is the trend?

Well, if there is one thing that keeps people analytics professionals awake at night, aside from an excessive amount of caffeine, it's messy data:

You might remember me discussing this data a few months after our people analytics meetup here. But what is more concerning is that they are actually true.

Messy data is:

• Not having accurate data that reflects agreements
• Having duplicate data due to issues with infrastructure
• Creation of too many fields with information that is not needed
• Manual entry that is incredibly prone to error
• Data that lives in 7-8 different systems
• Not having a primary key for any data

And the list goes on and on. But you get the point.

All this reveals 5 guiding principles when structuring your HR data.

1. Ensure Data Clarity, Relevance, and Accuracy

• What It Is: Data clarity, relevance, and accuracy mean that the information collected is precise, up-to-date, and aligned with the organization’s needs. Clear data is easy to understand; relevant data directly supports decision-making, and accurate data correctly reflects the organization's current state.
• Why It’s Important: Without clarity, relevance, and accuracy, your data can lead to incorrect conclusions and misguided decisions. Clear and accurate data helps understand employee trends, identify problems, and make informed decisions that align with organizational goals. Relevance ensures that the data collected is actually useful and does not just add noise.
• How to Achieve It:
  • Don't Collect Data You Don't Need: You can collect a lot of data, but do you really need to know everyone's hobby? Focus only on the things that matter.
  • Regular Data Audits: Conduct regular reviews to check for data accuracy and relevance. Remove outdated or incorrect information. You might be surprised by what you find.
  • Standardize Data Entry: Implement standardized processes and templates for data entry to reduce errors and maintain consistency.
  • Train Your Team: Ensure that everyone involved in data management understands the importance of accuracy and follows best practices.

2. Keep the Data Simple

• What It Is: Keeping data simple involves collecting only the most essential information directly contributing to your organization’s needs. It means avoiding overly complex data sets with unnecessary fields that do not add value.
• Why It’s Important: Simple data is easier to manage, analyze, and draw insights from. Overcomplicating data collection can lead to inefficiencies, wasted resources, and difficulty extracting meaningful insights. It also reduces the likelihood of errors and helps maintain focus on what truly matters.
• How to Achieve It:
  • Identify Core Metrics: Focus on a few key metrics that are most important to your organization, such as turnover rates, employee engagement scores, and recruitment efficiency. What data do you need to draw insights from?
  • Limit Data Fields: Avoid creating too many data fields. Only collect what you need to answer specific questions or measure key performance indicators.
  • Limit Options: Use standardized scales in data collection to ensure standard definitions and shared understanding. Adding too many unnecessary options creates clutter.
  • Streamline Processes: Simplify your data collection and analysis processes using automated tools and precise, straightforward methods. Avoid human error at all costs.

3. Establish the Source of Truth

• What It Is: Establishing a source of truth means consolidating data into a single, centralized system where the most accurate and up-to-date information is stored. This is the definitive source for all organizational data, preventing inconsistencies and confusion.
• Why It’s Important: A single source of truth ensures that all decision-making is based on consistent and accurate data. It eliminates the risk of conflicting information from multiple sources and fosters trust in the data used across the organization.
• How to Achieve It:
  • Centralize Data Systems: Use an HRIS or a centralized database to collect and store all employee data. Ensure all departments use this system as the primary source.
  • Integrate Systems: Ensure that all data systems are integrated so that updates in one system automatically reflect in all others.
  • Maintain Data Consistency: Regularly synchronize and clean data to ensure all systems align with the centralized source of truth.
  • Never transform the data directly in the system: Use the system as a database and always conduct analysis on a copy of the data, including any data transformations and manipulations.

4. Think Sustainability and Growth

• What It Is: Thinking sustainability and growth means building scalable data processes and systems that can adapt to future needs. It involves planning for future organizational changes and ensuring the data infrastructure can grow alongside the company.
• Why It’s Important: As your startup grows, your data needs will become more complex. A sustainable and scalable data infrastructure allows smooth transitions and avoids costly overhauls. It supports long-term planning and ensures data processes remain effective as the organization evolves.
• How to Achieve It:
  • Choose Scalable Tools: Invest in systems and tools that can grow with your organization. Avoid solutions that may become obsolete or require a complete overhaul as the company scales.
  • Plan for the Future: Regularly assess your data needs and update your strategy to accommodate growth. Anticipate changes in data volume, diversity, and complexity.
  • Build Flexible Processes: Develop data processes that can quickly adapt to new data types, more extensive data volumes, and more sophisticated analysis requirements.

5. Start Early

• What It Is: Starting early means setting up robust data practices. It involves thinking about People Analytics and data management as soon as possible, even if the company is small and just starting.
• Why It’s Important: Early investment in good data practices saves time and resources in the long run. It prevents the buildup of bad habits and messy data that can become difficult and costly to fix later. Early adoption ensures you are prepared to scale efficiently as the company grows, including transferring data between systems, creating integrations, and achieving scalable insights.
• How to Achieve It:
  • Establish Data Practices Now: Even if your organization is small, set up basic data management practices and policies early on: What data will you collect, how will you collect it, where will you store it, and how will it help you grow?
  • Educate Your Team: Instill a data-driven mindset by educating your team on the importance of data accuracy and best practices.
  • Document Procedures: Record clearly your data processes from the beginning to ensure consistency and provide a framework for future growth.

If you are a start-up, invest time and effort in building your data infrastructure properly. This means primary keys, data storage rules, audits, and accuracy.

Yes, it's a lot. I know.

But you will thank me later when you need to scale your enterprise to new heights.

Cheers,

K

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Whenever you’re ready, there are 2 ways I can help you:

#1

If you’re still looking to get started in People Analytics, I recommend starting with my affordable course:

​Practical People Analytics: Build data-driven HR programs to 10x your professional effectiveness, business impact, and career. This comprehensive course will teach you everything from building an HR dashboard for business results to driving growth through more advanced analytics (i.e., regression). Join your peers today! ​
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#2

If you are looking for support in your human capital programs, such as engagement, retention, and compensation & benefits, and want to take a more data-driven approach, contact me at Tskhay & Associates for consulting services. Or simply reply to this email!