Big data is arguably the buzzword of the decade, but its real power lies in how it’s analysed and applied. As a case study, we use the latest market data from the largest database of KMP remuneration in Australia, to show how deep data that is also narrow can produce valid and reliable answers.

GRG Remuneration Insight 125

by Denis Godfrey & James Bourchier
30 June 2020

Big data is arguably the buzzword of the decade, but the power of data doesn’t come from volume alone, it comes from how you are able to analyse and apply it. In this Insight we use the latest market data from the largest database of KMP remuneration in Australia, delving deeper into an analysis of Human Resources roles as a case study, to look at how data that is very deep if narrow, can be used to give a valid and reliable answer where previously there was no data to work with. As a bonus, we give you the benchmark data from the recently completed 2020 GRG All Industries KMP Remuneration Guide, for Human Resources roles, across almost the whole of the ASX (down to a market cap of $25m).

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There is Always a Critical Role that Suffers from Gaps in Disclosure

The executive roles required to be disclosed in the Remuneration Reports of listed companies (as part of the Annual Report) is always a subject of debate. The requirement is to disclose Key Management Personnel, however, the definition is left to the accounting standards which were never designed to be relied upon by the Corporations Act, and are remarkably vague about the definition; “those persons having authority and responsibility for planning, directing and controlling the activities of the entity, directly or indirectly, including any director (whether executive or otherwise) of that entity”. Some would argue that this includes all members of the executive leadership team who collectively make decisions about the activities of the Company, while others would argue that this only applies to the CEO, and possibly the COO and/or CFO. The result of this lack of clarity is that many roles that are absolutely critical to certain types of businesses suffer from poor disclosure; it is surprising that in an age where the success of most businesses relies heavily on its people and technology strategies, HR and CTO roles are infrequently disclosed. If you have an interest in risk roles, governance roles, scientific or research heads, let alone product owners or stakeholder managers, you will likely struggle to find transparent benchmarks backed by reliable and visible data. In fact, most businesses have a specialist and critical role that is a senior executive if not KMP that requires benchmarking, but which is hard to sample. Over the last 10 years GRG has been developing multiple strategies to provide valid and reliable benchmarks for these roles. Below, we take a look at the role that is asked for most often, and is among the least disclosed: HR Roles.

GRG 2020 All Industries Remuneration Guide Data for HR Roles Example

The following table outlines a summary of standard benchmarking results for HR roles (limited to Fixed Pay) based on the 2020 GRG All Industries KMP Remuneration Guide (the Guide), showing all standard market capitalisation ranges (shown in $000’s). It should be noted that while the ASX market has experienced a high degree of volatility in recent months, this volatility is not reflected in Guide data, which is based on market capitalisation relationships as at 1 July 2019. In order to use the Guide data, our example Company would need to consider its market capitalisation at that time in selecting reference points, which was say $1.25b. For may companies, while the market capitalisations have fallen since the end of January 2020, their market capitalisation relativities with peers have not changed materially – this is easy to check by comparing end January market data with current market data. For some this will be an important step for benchmarking at this time, in order to ensure that short term volatility does not produce an outcome that is likely to be inappropriate as the volatility settles.

The foregoing data is presented graphically in the following chart:

It should be noted that GRG does not calculate quartiles (P25 and P75) for samples of less than 5. Some key observations about the nature of the data available for HR includes:

  1. The largest sample size is 8 (shown in green on the preceding table). This is for companies in the $100m to $250m market capitalisation range, where there are around 250 companies on the ASX that fall in this range, and at least 160 companies that are valid for data collection purposes (i.e. after excluding foreign entities, overseas operators, LICs and REITs etc.). That is at best 5% of those companies that view HR as KMP, despite the critical impact the role has across the whole of organisations.
  2. Potential sample sizes for larger market capitalisations are much smaller due to the tapering off of the number of listed companies with values over $1 billion, yet the sample sizes for HR roles don’t drop off to the same extent indicating that larger companies are more likely to recognise the critical, organisation wide contributions that HR roles make.
  3. There are 3 ranges where it is not possible to show even a P50 result, one each at the bottom, middle and top of the market, shown in salmon. In these parts of the market, only tailored comparator groups can produce a result unless we rely on new deep data methods.
  4. Because of the small samples sizes in most slices, the P50 trend jumps up and down through the middle of the market – this kind of volatility is undesirable in a data set and indicates that a few outliers in each range are having an overweight impact on the P50. It is indicated however that executive HR Fixed Pay appears to bottom out around $200,000 with little change across the first few tiers of the market, that in the middle of the market it is around $400,000 and the most experienced HR executives top out at nearly $1m.

Deep Data Approach

Drawing on the deep data available to us, we can look at all of these data points in a different way; while Fixed Pay covers a large range from $200,000 to $1m, a variation of 5x across the market, we can instead look at the relationship between CEO Fixed Pay and the Fixed Pay of the roles reporting to them. Obviously the maximum possible outcome would be 100%, and the minimum theoretically 0%, however, in reality, we see the relationship for HR tends to range between 70% at the small market cap end of the market and around 40% at the large market cap end of the market; a similar trend to what we see for all roles in fact, indicating that it is a relatively stable, valid and reliable relationship which varies to a much smaller degree at only around 2x rather than 5x (compared to looking at Fixed Pay alone).

By drawing a line of best fit through this relationship, we can produce a formula with a single input, market capitalisation, that predicts the relationship of:

HR Fixed Pay with CEO Fixed Pay (CEO Pay %)

– which we call role relationship regression. It is an easy method to get tailored, large, valid and reliable samples of CEO data, which we can then use to derive an outcome for our missing data points. In this case the formula is

Fixed Pay % = -0.0000000000007x Target Market Capitalisation + 0.427

If we then take the midpoint of the $750m to 1.5b market capitalisation range (i.e. $1.125b market cap), for which we have no data shown in the table above, we can deduce that the HR role typically has a CEO Pay % of 42.62% for a company with a market capitalisation just over $1 billion dollars. By applying this percentage to a robust CEO benchmark we can deduce an appropriate HR Fixed Pay outcome. If we know that a reliable CEO benchmark in this case was $850,000, then we know the HR role should receive $360,000. Looking at the trend across the data tables shown above, this outcome appears appropriate for a typical HR executive at this point in the market. Because we examined the relationship with CEO remuneration and applied it to a tailored CEO benchmark, it can produce a much more tailored outcome than a standard market data sample that is scraping the bottom of the barrel, particularly in this case, where there was no data available. The analysis can be tailored further by only looking at the HR/CEO relationship within sub-sectors of the ASX. This can be done even for extremely rare roles such as scientific officers, where there may be only a handful across the whole of the ASX. However, as with any benchmark, this approach doesn’t capture the subtleties of role designs that vary between organisations, only giving an outcome for a “typical” role.

Further Tailoring by Digging Deep and Applying Informed Adjustments

Having established a typical role relationship between the HR role and the CEO role, the outcome is external looking – no account has been made of the particular HR role design and how it fits within our example organisation’s particular design. By examining the relationship between each executive role and the CEO role, it is possible to build a map of typical remuneration relativities, for example:

Let us assume that the HR role in this case is in the context of a financial consulting firm where its people are the most valuable asset. The role is Head of People, Performance, Culture and Reward and the Company’s success relies on the HR role being unusually strategic about recruitment, retention, learning and development, risk culture and leadership. In this case a typical HR role would not be the ideal match, and some adjustment needs to be made. Through consultation it becomes clear that the role is slightly less senior than a mid-sized business unit in the organisation, more senior than a theoretical Chief Risk Officer role, and likely a peer to a Corporate Development and Strategy role. Therefore, an adjustment to the benchmark for the role can me made to 51% of the CEO role, based on the available information, producing an outcome of $434,000.

This approach can be applied to all roles in the organisation, even for roles where there is no data available in the market which can often arise for specialist roles like Chief Biochemist. By examining the role design and impact relative to other roles in the organisation, and tying roles that have market matches to typical role relationships, a customised map of Fixed Pay relativities can be developed and applied.

Individual Differences

This approach, indeed no benchmarking approach, can take account of individual differences such as experience, qualifications, time in the role and calibre of the individual. These are typically accounted for via a subjective assessment of an individual and positioning within a policy range around a benchmark – usually +/- 15 to 20 percent.


While in volatile times such as these, organisations may be likely to retreat to basic, low cost remuneration sense-checking solutions such as the Guide, new technologies and methodologies are becoming available that address not only volatility but also leverage deep data to provide robust, tailored answers that were not previously available. With the 2020 AGM season looking like a high risk one for Remuneration Reports and strike risks for Boards, some companies, particularly those with rare, or even uncommonly disclosed roles that are at risk of loss if improperly managed, will need to ensure they are armed with appropriate data, advice and the right rationale to communicate to shareholders.

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