At this point, you may be a bit overwhelmed by the quantity of technical skills involved in a people analytics career. But, you don’t have to take on all of these skills without support. The wonderful thing about our data-driven world is that a plethora of tools and technology exist to work with data. When chosen and used appropriately, technology can help you apply your skills with less effort than going it alone. Technology today can empower even the most analytically hesitant person to conduct statistical analyses for people analytics. It can completely remove the need to learn coding languages (if you want to avoid them), and can help individuals who may not have extensive design experience create beautiful visualizations. It can also enhance efficiency, compliance, and consistency in our approaches to people analytics. Leveraging technology is one of the best ways to enhance and amplify your people analytics skills. So, what tools and tech are best for your people analytics career? 

I’ve said it before and I’ll say it again, the best place to start is right where you are and to leverage your strengths. Exploring technology options is no different. It is true that technology can help you shore up weaknesses and fill in skill gaps. However, I like to recommend focusing first on tools that complement your strengths. If you’re strong in quantitative analysis, explore platforms with robust statistical analysis capabilities. If you love diving into the words and thoughts of employees in text responses, look for natural language processing abilities. If you are inspired to create visuals that tell a story, pick up some data visualization technology.

There are a few reasons why I believe it’s best to start with a strengths based approach to technology adoption. First, if you are already adept and interested in a topic, you’ll be able to unleash more unique and advanced functionality available in technology products in ways others can’t. This will put you in a unique position to be the absolute best at what it is that you are best at – letting you capitalize on the power of specialization. You’ll become the “go to” for that special thing. Second, there are so many things to learn in the people analytics space, but learning and integrating new technology does get easier each time you do it. And finally, you’ll probably just have more fun learning the tools on a topic you are strong in. By starting with technology you enjoy on a topic you are good at, you’ll actually be inadvertently building skills for learning and adopting technology in general that you can apply when you branch out to new and different types of technology in the future. It will be less of a struggle since you won’t be simultaneously learning how to adopt new people analytics technologies at the same time as you are also learning a topic or approach you are less familiar with.

Let’s explore some technology examples you might want to consider for amplifying your strengths in people analytics. We’ll start with general-purpose data tools before diving deeper into people analytics specific options.

General Data Analysis, Visualization, and Management Tools and Technology:

Spreadsheet software:

• Examples: Excel, Google Sheets, Numbers, and more.
• Great for: Basic analysis and visualization of small to medium datasets.
• Benefits: Usually freely available within most organizations, well known and easy to share data and outputs with others. Offers basic formulas and functions for calculations and data manipulation.
• Limitations: Limited in advanced capabilities like statistical analysis and data cleaning. Difficult to manage and collaborate on complex analyses with large datasets. Prone to errors due to manual data entry and manipulation.

User-friendly, Guided User Interface (GUI) platforms:

• Examples: Power BI, Tableau, SPSS, and more.
• Great for: People who know what they want to analyze but don’t want to learn the code or math behind the techniques. Creating visually compelling dashboards and reports to communicate insights effectively.
• Benefits: Many tools offer drag-and-drop features, pre-built dashboards, and intuitive workflows, and templates for easy data analysis or visualization which minimizes the need for coding skills and reduces required learning time. Most include robust documentation and user help guides. Most are designed to allow for interactive exploration of data.
• Limitations: May require a subscription fee. Requires training to learn the software’s functionalities effectively, especially for advanced functionality. Depending on the setup, can be difficult to share with others who are unfamiliar navigating that specific tool. Creating truly impactful visualizations still requires design skills and principles.

Coding-based options:

• Examples: R, Python with libraries like Pandas, Statsmodels for advanced analysis, and more.
• Great for: In-depth statistical analysis, model building, and customization.
• Benefits: Highly flexible and powerful for complex analyses. Open-source options (R, Python) are free to use. Vast online communities and resources for learning and troubleshooting. More AI tools are becoming available to decrease the relatively greater learning curve required for using these tools.
• Limitations: Requires programming knowledge, creating a steeper learning curve. Debugging code and managing errors can be time-consuming. Not ideal for non-technical users. Effective and personalized visualizations that match company level aesthetic expectations can be effortful to create.

Data storytelling tools:

• Examples: Chartic, Flourish, and more.
• Great for: Creating engaging data narratives with animation and interaction.
• Benefits: Can start with templates and features specifically designed for data storytelling. Enhances audience engagement with data visualizations.
• Limitations: May have limited customization options compared to general-purpose data visualization tools.

Design software:

• Examples: Canva, Adobe Illustrator (basic skills), and more.
• Great for: Enhancing the aesthetics and visual design of data visualizations (if you have some design skills).
• Benefits: Offers a wide range of design tools and features for creating polished visuals.
• Limitations: Requires design knowledge and skills for effective use. Is not a substitute for other data visualization tools.

Qualitative Data Analysis:

• Examples: Text analytics platforms like Rapidminer, Lexalytics, Qualtrics, and more.
• Great for: Qualitative data analysis, coding themes and patterns from interviews, surveys, open-ended responses.
• Benefits: Facilitates systematic coding and categorization of qualitative data. Greatly reduces time to analyze large volumes of text. Can enhance consistency of analysis.
• Limitations: Can be expensive, especially for enterprise-level licenses. Requires training to learn the software’s functionalities effectively. Requires a people analytics expert to ensure important individual points of view are not ‘lost in the crowd.’ While exceptional at pattern recognition and quantifying qualitative data, current technology options are still problematically inferior to the analysis capabilities of humans with regard to assessing human experiences, context, nuance, and cultural differences.

Survey platforms:

• Examples: Qualtrics, SurveyMonkey, and more.
• Great for: Data collection through surveys, questionnaires, and polls. Offers features for basic data analysis and reporting.
• Benefits: User-friendly interfaces for creating and deploying surveys. Offers various question types and survey logic features. Can automate data collection and analysis to some extent.
• Limitations: Limited in advanced analytic capabilities. May require a subscription fee for advanced features and larger survey volumes. Can for some create a false sense of quality in survey design, impeding reliability and validity outcomes. Requires knowledge of data privacy and security to ensure appropriate usage.

Data management platforms:

• Examples: Tableau Prep, Alteryx, and more.
• Great for: Streamlining data cleaning and transformation even without extensive coding knowledge.
• Benefits: Offers visual interfaces for data cleaning and manipulation tasks. Reduces errors associated with manual data manipulation. Integrates easily with popular data visualization tools.
• Limitations: May require a subscription fee. Less customizable compared to coding-based options.

Cloud-based data warehouses:

• Examples: Snowflake, Amazon Redshift, Microsoft Azure Synapse Analytics, and more.
• Great for: Securely storing, organizing, and accessing large datasets from various sources.
• Benefits: Scalable storage solutions for managing big data. Facilitates collaboration and data sharing across teams. Enables efficient data exploration and analysis.
• Limitations: Can be expensive depending on storage requirements and data processing needs. May require technical expertise for administration and maintenance.

Database platforms:

• Examples: SQL Server, MySQL, and more.
• Great for: Storing and managing structured data in relational databases.
• Benefits: Robust platforms for managing large datasets with complex relationships. Allows for efficient data retrieval and manipulation using SQL queries.
• Limitations: Requires technical expertise for database administration and query writing. Not ideal for unstructured data sources.

People Analytics Specific Tools and Technology:

The examples in the prior section provided  solutions meant to support data activities in general. However, you can also consider platforms that are designed specifically for people analytics. These people analytics platforms come in a variety of configurations, offering different levels of focus and comprehensiveness. Let’s explore them, starting with the most topic-specific:

Talent-process & people analysis specific tools

Starting at the most specific are the platforms that will be best for those focusing on just one particular type of people data (e.g., performance, recruiting, or learning analytics) or looking to conduct a specific type of analysis (e.g., organizational network analysis). For this, there are a plethora of talent management systems that include people analytics functionalities into their offerings. These tools are beneficial because they usually include more topic specific and relevant analytics capabilities. For example, a learning management system (LMS) like Cornerstone or Pluralsight will likely come pre-built with data capture, analysis, and reporting functionality that is specific to the nuances of employee learning activities and effectiveness, whereas a performance management system like Performica will come with different functionality. You can also leverage technology that has been built to provide a specific type of analysis applied directly to people analytics use cases, for example, Organization Network Analysis (ONA) is a more general type of graph based analysis, but technology platforms like Polinode or Trustsphere have built their technology systems explicitly for use within organizations, simplifying the process for organizations who only want to use this type of analysis for people analytics purposes, allowing them to leverage the depth of their specific knowledge on the analysis technique along with a specific application of that technique.

People analytics platforms

A slightly broader set of tools that is still very specific to people analytics are platforms designed to cover the full range of people analytics processes. Platforms like Visier, OneModel, or Worklytics are designed specifically for the challenges and outputs needed when working with people data and people processes in organizations but allow for assessment of the majority of potential talent processes in one system. They can be especially helpful to a single people analytics practitioner or small team of people analytics professionals looking to cover a wide array of possible people analytics applications. They can also reduce the amount of training needed since multiple types of data and analyses can all be accessed in one common system. They do require the user to have a knowledge of people analytics principles and it requires effort and creativity for the user to determine the features and functionality that are most useful for a given situation and for interpreting the outputs appropriately.

Information systems

Finally, the most broad category are the tools typically referred to as information systems. Most people analytics data will be housed in a Human Resource Information System (HRIS), Human Capital Management (HCM) System, or other variations though some people analytics data may also sit in other operational information systems such as Enterprise Resource Planning (ERP) or Finance systems. These systems vary in scope, with some covering everything related to employee data (payroll, time management, performance, learning, compliance, etc.), while others focus on specific aspects of the employee lifecycle where data is generated, stored, accessed, managed, or shared. Examples include companies like Oracle HCM, Workday, SAP, and many others. An HR information system of some kind is a business necessity today. This presents a benefit for people analytics professionals already within an organization since they can leverage the existing system’s functionalities to get started immediately; requiring less initial investment compared to specialized solutions. When paired with the more general data tools presented in the previous section, this can be the fastest and most economical option for many. Many HRIS platforms even offer talent management or people analytics-specific add-ons to cater to more specific needs. A downside to these platforms is that because of the breadth needed to meet required operational processes for the organization, they generally are focused more on core functionalities and can be limited in people analytics specific functionality and customization is often limited to pre-defined user configuration options.