Navigating the world of people analytics often leads to a critical decision: which tool best serves your needs?

While Tableau has held a strong foothold in the market for its versatility, HR professionals have begun to encounter a multitude of hidden costs that reveal its limitations, especially when compared with specialized tools like eqtble.

The ongoing debate around the use of Tableau in the people analytics space usually centers on notions of flexibility, cost, speed, and the potential fear associated with transitioning to a new tool. But these discussions often overlook the significant, less apparent constraints associated with Tableau.

Does Tableau truly cater to the unique requirements of people analytics? Or is it merely providing a 'good enough' solution while obscuring the potential advantages of a more specialized tool like eqtble?

In this article, we will address these pressing questions and pull back the curtain on the hidden costs of using Tableau for people analytics.

We will detail the ten primary challenges that HR teams face when using Tableau, highlighting its limitations while considering the potential benefits of adopting an HR-focused solution like eqtble.

1. The Skills Gap: A Disconnect Between Data Understanding and Application

The first significant hidden cost of using Tableau in an HR context is the considerable skills gap that often arises.

The challenge here lies in the discrepancy between who understands the data best and who is tasked with developing the dashboards and utilizing the product.

Traditionally, talent management teams, recruiters, and heads of talent are the people who have the most in-depth understanding of HR data. They interact with this data on a daily basis, they know the nuances, and they understand the context behind each metric and trend.

However, these individuals are typically not the ones developing dashboards in Tableau. Instead, this responsibility usually falls to an external BI team or shared services team.

These teams, while they might have expertise in Tableau, may lack a deep understanding of the specific intricacies and requirements of HR data. This gap often leads to a disconnect in the application and effectiveness of insights derived from the dashboards. Also, this skills gap necessitates additional training and resources to bridge, leading to increased costs and time delays.

HR teams need to continuously liaise with the external BI team to explain their requirements, interpret the data, and translate the resulting insights into meaningful action. This ongoing need for collaboration and clarification can prolong the time it takes to derive actionable insights from the data.

2. Building Models: An Arduous and Complex Task

The second hidden cost of Tableau lies in the necessity of building models, setting up data warehousing, and managing ETL processes from scratch.

The complexity and time-consuming nature of these tasks are even more profound when dealing with HR data, given its inherent intricacy and the difficulties associated with integrating diverse data sources into a cohesive, unified ecosystem.

First, let's consider the nature of HR data. It's rich, diverse, and often stored across various platforms and databases. These might include talent management systems, performance management platforms, recruitment tools, payroll systems, and many more.

This data can range from structured formats such as numerical and categorical data to unstructured formats like open-text feedback. Creating a single, coherent, and accurate view of this data is a complex process.

Tableau, as a generalized BI tool, requires users to manually build models that make sense of this data. This often requires specialized skills in data modeling, ETL, and data warehousing, which may necessitate hiring dedicated data engineers and data scientists. This not only inflates the cost but also extends the time it takes to begin deriving valuable insights.

The manual preparation and integration of HR data often leads to errors, inconsistencies, and redundancies. Such issues can compromise the accuracy and effectiveness of the insights derived.

3. A One-Size-Fits-All Delivery Model: A Barrier to Real-Time, Ad Hoc Analysis

The delivery model of Tableau is another factor that significantly impacts its usability, particularly in the context of HR analytics. The tool's one-size-fits-all approach does not effectively cater to the dynamic, real-time, and self-service needs of HR practitioners.

Imagine a typical scenario in which a CPO requests a specific dashboard from the analytics team. The team then builds this dashboard in Tableau and delivers it back to the CPO. However, if the CPO needs additional or ad hoc data during a meeting or presentation, they often cannot obtain the information immediately from the existing dashboard. This requires a return to the data team for further requests, leading to delays and inefficiencies.

Unfortunately, this process is neither truly self-service nor conducive to real-time decision making. As a result, many HR teams find themselves defaulting to traditional tools like Excel or Google Sheets to supplement Tableau. These platforms, while familiar and more flexible, come with their own set of issues, such as data integrity, version control, and scalability problems.

4. Data Security and Privacy Concerns

Data security and privacy are primary concerns for organizations, especially when dealing with sensitive HR data. Tableau's model of data management presents some potential risks in this area.

In a typical Tableau setup, data might be stored on individual workstations or laptops while dashboards are being developed. This exposes organizations to potential data breaches should these devices get lost, stolen, or compromised.

When it comes to data privacy, Tableau demands hands-on setup for permissions. As a result, organizations need to allocate extra time and resources for establishing and handling access controls, potentially leading to lapses in security and compliance.

5. The Hidden Financial Costs

When considering the cost of Tableau, it is important to look beyond the obvious licensing fees.

The hidden costs associated with using Tableau can quickly inflate your budget, especially when you factor in the human resources and infrastructure required to fully utilize the platform.

Firstly, let’s consider the cost of hiring data analysts. You will need skilled analysts who can operate Tableau effectively and develop your dashboards. These professionals are highly sought-after and come with a hefty price tag - anywhere between $150,000 to $200,000 per analyst depending on the level of experience and location. 

In some cases, you may need to hire multiple analysts to support the numerous dashboards and ad hoc requests that your organization needs. This could easily push your human resource investment towards a million dollars.

Secondly, the management of Tableau is manual and requires ongoing attention. You need dedicated staff to maintain the dashboards and ensure they're up-to-date. This results in continued operational expenses that add to the cost.

Thirdly, setting up a data warehouse is not optional with Tableau - it's a necessity. This means hiring a data engineer to establish, manage, and maintain your data warehouse. This again adds a significant chunk to your overall cost.

Lastly, don't forget the infrastructure costs. As you scale up your operations to cover your entire organization, you will inevitably incur costs associated with increased data storage and processing power. Tableau's licensing costs also increase as you add more users, which can add a considerable expense if you're a large organization.

When summed up, these hidden costs make it clear that the total cost of ownership for Tableau goes far beyond the initial licensing fees. 

6. Time - The Stealthy Drain on Resources

When considering a people analytics tool, the time required for setup and deployment is a critical factor. Often underestimated, the time investment can substantially impact your team's productivity and your organization's ability to make data-driven decisions swiftly.

In the case of Tableau, preparing the platform and setting up all the necessary dashboards is not a task that can be completed overnight. In reality, the process can stretch over an extended period - often as long as a year or even more.

This is because Tableau's setup requires several steps such as data preparation, building the data warehouse, creating data models, and finally, designing and developing the dashboards.

Data preparation itself is a time-consuming process due to the complexity of HR data. It needs to be cleaned, integrated, and organized before it can be used for analytics.

Building the data warehouse is another major task. It involves setting up the infrastructure, designing the data schema, and transferring all the data into the warehouse. It also needs continuous maintenance and updating to ensure its stability and reliability.

Creating the data models for Tableau is also an intricate process. This involves defining how different data elements relate to each other and how they should be processed for accurate analytics.

Finally, there's the task of designing and developing the dashboards. This needs a deep understanding of the organization's requirements and the ability to translate these needs into effective visualizations.

All these stages demand a significant time investment, and any mistake or oversight can lead to delays and require further troubleshooting. It's also important to note that this is an ongoing process, with time needed for continuous updates, maintenance, and handling ad hoc requests.

7. Lack of Specialization: Tableau is Not an HR-focused Tool

Tableau's primary strength is its versatility, being a generalized BI tool. It's designed to cater to a wide variety of industries and functions, offering broad-based capabilities for data visualization and analytics.

This is undoubtedly advantageous for many users, as it allows for a large array of applications. However, this generalist nature can also serve as a disadvantage when it comes to addressing specific needs, such as those in the HR space.

People analytics involves a unique set of challenges and requires specialized solutions. There are complex calculations related to attrition rates, employee turnover, employee engagement scores, diversity and inclusion metrics, talent acquisition effectiveness, and more.

Additionally, HR data is multifaceted, involving aspects such as employee demographics, performance, learning and development, recruitment, retention, and others.

Unfortunately, out-of-the-box Tableau does not come with the specific tools and features needed to handle these HR-specific complexities. The tool lacks dedicated people analytics capabilities that can accurately process and analyze the multi-dimensional nature of HR data.

It does not provide specialized features tailored to the needs of HR teams, such as specific visualizations for HR metrics, pre-built models for HR analytics, and HR-centric dashboard templates.

Instead, these functionalities would need to be custom-built within Tableau, demanding additional time, effort, and specialized skills. This makes it harder for HR practitioners to leverage the platform effectively and might lead to delays or inaccuracies in obtaining critical HR insights.

In contrast, a dedicated HR analytics solution like eqtble is designed with these specific needs in mind. It comes with in-built features tailored to people analytics, understanding the intricacies and specificities of HR data.

eqtble can provide a more seamless and efficient user experience for HR teams, enabling them to extract relevant insights more rapidly and accurately. This difference in focus - a generalist tool versus a specialized solution - is an essential consideration in this comparison.

8. Scalability Concerns

Scalability is an essential feature for any business solution, as it directly impacts the tool's ability to accommodate the growth and evolution of an organization.

Regrettably, scalability is a significant challenge when dealing with Tableau, especially for organizations managing extensive data sets and a large number of users.

When it comes to people analytics, the volume of people data that an organization generates and needs to process can increase exponentially. When this happens, Tableau's performance can take a hit. It can struggle to maintain the same level of speed and efficiency in data processing and visualization when handling large data sets.

This can result in slower response times, extended loading times for dashboards, and a general decrease in the platform's overall responsiveness. For organizations that rely heavily on timely and accurate data insights, these performance issues can significantly impede their ability to make data-driven decisions swiftly.

Additionally, scalability in Tableau is closely tied to its licensing model. As your organization grows and you need to add more users to the platform, you need to purchase additional licenses. These costs can add up quickly, especially for large teams or rapidly expanding organizations. It can become a considerable financial burden, making the tool less cost-effective as you scale.

9. Complex Calculations - The Struggle of Implementing Essential Metrics

While Tableau is a powerful tool for data visualization, it falls short when it comes to dealing with complex calculations, especially those specific to the HR space. Key metrics that are critical for HR operations, are not readily available out of the box in Tableau.

For HR teams to effectively leverage Tableau, these complex calculations would need to be manually coded and integrated into the Tableau environment, which can be a daunting task. This would often involve a somewhat 'hacky' workaround, cobbling together a solution within the constraints of Tableau's system. For those not deeply familiar with the intricacies of Tableau, this could be a significant challenge.

This obstacle can slow down HR teams, force them to rely on external resources, and limit the scope and flexibility of their data analysis. It may also lead to errors and inconsistencies, particularly if the calculations aren't properly set up or maintained, which could potentially undermine the reliability and credibility of the data analysis.

On the other hand, platforms like eqtble, which are designed with HR analytics in mind, provide these calculations as in-built features. eqtble is preconfigured to handle these complex HR metrics, making it easier for HR teams to implement and track them accurately.

By streamlining this process, eqtble frees up HR teams to focus more on analysis and strategy rather than the intricacies of data calculation and manipulation.

10. Presentation Readiness - The Disconnect Between Data and Decision-Making

The final challenge Tableau presents lies in its readiness for use in key decision-making scenarios.

Despite its ability to handle vast datasets and create intricate visualizations, Tableau often falls short when it comes to presenting actionable insights in an effective and influential manner.

A typical workflow involves taking screenshots from Tableau and incorporating these into PowerPoint presentations. This may seem like a trivial step, but it represents a considerable drain on productivity. It also highlights a crucial point: nobody presents data directly from dashboards in board meetings or to CEOs. The presentation format of choice remains PowerPoint or PDF documents.

Tableau excels in informing users about the data - it's excellent at painting the picture. But when it comes to making the data actionable - making it a tool for driving change - there's a disconnect.

Data becomes actionable when it transitions from being an abstract number or a graph on a screen to a concrete plan or strategy. This typically involves taking the data, digesting it, writing down observations and action points, and then sharing these insights with people responsible for implementing change.

In contrast, a dashboard is more of a 'nice-to-have' tool. It's informative, yes, but it doesn't typically lead to action. It's rare for someone to look at a dashboard, see a metric such as 'time to hire', and immediately develop a plan for improving it. In essence, a dashboard is often just a collection of information with no clear pathway to actionability.

Coexistence and the Future of People Analytics

While we have outlined a stark comparison between Tableau and eqtble, it's important to recognize that these tools can indeed coexist in a synergistic manner. Both tools offer unique capabilities and advantages, suggesting a world where they work together to cover all bases of people analytics.

eqtble can effectively close the gaps in terms of ad hoc queries and self-service experiences. Its intuitive design and user-friendly features make it an excellent tool for HR teams, removing the dependency on a separate data team for every little query. In essence, eqtble can help HR teams become self-sufficient in their day-to-day analytics needs.

On the other hand, Tableau can continue serving as a powerful tool for creating static dashboards, delivering intricate visualizations and deep insights into HR data. These dashboards can be fixed and presented to the team, providing a broad overview of the data landscape.

Moreover, eqtble's 'data stream' product opens new avenues for collaboration between the two platforms. By funneling data from eqtble into a Tableau instance, you can create a streamlined data ecosystem. This approach partially solves some of the challenges we've discussed, particularly around data modeling.

But it is essential to note that while this synergistic approach makes the costs more manageable and solves some issues, it does not address all the challenges. Complexity, scalability, and hidden costs still pose considerable obstacles, emphasizing the need for a more integrated, HR-focused solution like eqtble.

To fully understand how eqtble can revolutionize your people analytics, we invite you to book a demo.