Overcoming Data Challenges to Support HR Initiatives
Earlier this year, we asked over 200 leading HR professionals to tell us about the most pressing challenges faced by them in 2021. Our analysis uncovered four personas across which these challenges varied; while many HR professionals surveyed shared views on top challenges, not all respondents perceived the same challenges equally.
The four personas we identified were as follows:
| Persona: Challenged on all fronts
Proportion of Respondents: 48%Description: People Analytics challenges this group – this includes understanding employee sentiment, internal networks in the new working environment, retaining Top Talent and understanding issues that lead to flight risk. They struggle to track company progress against a workforce plan and internal D&I initiatives.
Most interesting, what underpins all their struggles is the inability to provide timely People Analytics insights internally, and the inability to identify where and how to pinpoint and action areas of high and low performance.
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| Persona: Challenged, but confident in delivering timely people analytics to stakeholders
Proportion of Respondents: 23%
Description: While this group feels challenged with several areas, they are far more confident than the above group when in it comes to providing timely people analytics reporting to internal stakeholders. They still struggle with a large number of other topics highlighted above.
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| Persona: Challenged, but confident in ability to manage talent acquisitions
Proportion of Respondents: 15%
Description: This group feels on top of managing and retaining talent within the company. However, they struggle with delivery of timely insight to internal stakeholders as well as the other challenges highlighted above.
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| Persona: Data-savvy HR – on top of all challenges
Proportion of Respondents: 14%
Description: This group was confident dealing with a range of HR challenges. However, there were still a few topics that were challenging for this group – they include understanding workforce composition, employee sentiment, employee networks and tracking progress towards D&I goals.
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Download our overcoming top HR challenges through data to learn more about the four HR personas.
So why does it remain challenging to leverage People Data to get on top of the most pressing challenges faced by the HR world today?
From our experience, there are five characteristics about people data that make analysis and insight generation challenging:
| No. | Characteristic | Description |
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| 1 | Sourcing | People data is not like data generated by sensors on machines. It is generated by people. The process is often time-consuming and incomplete, inevitably leaving behind data blind spots. | | 2 | Sensitivity | The data is about people, so it’s sensitive with all the associated security and governance implications including stringent GDPR rules. | | 3 | Siloed | The data that is available about People is often protected, hidden and isolated. The data is therefore not openly shared and thereby slowing down data analysis. | | 4 | Subjective | Another important point about People data is that it’s “noisy”, with quality issues, requiring more nuanced analytics methods and approaches for proper analysis. | | 5 | Stability | People are constantly changing and so is the data about them. Hence, measuring and quantifying people data is not trivial – especially with all the underlying volatility and complexity of people. |
Despite the challenges outlined above, People Analytics is gaining momentum – with organisations increasingly performing sophisticated analytics across numerous areas of their HR operations (e.g. using machine learning to screen CVs or predict attrition). However, preparing and managing the data continues to be governed on a project by project basis – it’s time consuming and expensive – not to mention that it slows down the analysis time frame.
Register for our ‘Making Your People Count with Alteryx’ webinar As such, we believe that for an organisation to achieve maturity with their People Analytics, they first need to ‘harmonise their People Data’. This means bringing together people data from diverse data sources, of varying file formats, naming conventions, and columns, and transforming it into one easy-to-understand and cohesive data asset; not only is the current view of the data important, but also how it has changed over time.
From a simplistic technical stand-point this involves extracting and transforming data from diverse data sources and loading it into a common data repository. The data then needs to be cleaned, processed and modelled to make it fit for business analysis. Even though the technology required to support this has existed for decades, unlike Finance and Marketing functions, very few HR functions have been able to harmonise people data. Tools such as Alteryx have built in and third party connectors to access data from most major corporate systems, coupled with the analytic and data science capability to perform advanced analysis on your data. Those who have succeeded in harmonising their data, have done so by generating a single People / HR Data Model.
The following three steps (and they are not technology dependent) are crucial to develop a fit-for-purpose HR Data Model:
| No. | Step | Description |
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| 1 | Define Analytics Needs | Every organisation is unique and will have its own perspective when it comes to its People and their performance. An organisation has to go on a journey to define their analytics needs around workforce planning and the employee journey before harmonizing people data. Otherwise, data will be brought together just for the sake of collecting and storing it in one place. The aftermath will be a data junkyard that can’t be used for business analysis. | | 2 | Map Data Sources | An important exercise is to map data sources to an organisations analytics requirements. Apart from helping to maintain an audit trail of all data transformations and manipulations, this exercise also helps an organisation establish what are the data or analytics gaps within the organisation. This exercise will help develop strategies to deal with any data blind spots (either due to lack of data or due to inaccessibility of the data). | | 3 | Model the Data | The last step is to use the insight from the above steps to develop a data model which will act as a central business analysis repository for people data. This data model should be easy to use and understand for analysts and serve as a trusted data source. |
It is only once the data is modelled that you can perform repeatable sophisticated analysis on it. By using the power of Alteryx, you can quickly harmonise your data and gain valuable insights on your workforce. TrueCue have used Alteryx to successfully overcome a number of HR’s biggest challenges – most notably to predict not only which employees are at risk of churning but what are the most effective individually tailored retention strategies; whether there is unconscious bias in your workforce structure; and whether the organisation is on track to meet its corporate diversity and inclusion targets.
Please join TrueCue’s webinar on “Making Your People Count with Alteryx” and learn more about TrueCue’s approach of developing an HR Analytic Schema and how it unlocks the analytics potential of people data.
TrueCue are proud to be a longstanding Alteryx partner, and were awarded Alteryx EMEA Partner of the Year in 2019. A recognised “Challenger” in Gartner’s 2021 Magic Quadrant for Data Science and Machine Learning , Alteryx is a platform that can discover, prep, and analyse all your data, then deploy and share analytics at scale for deeper insights.
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