Being responsible for the financial running of a recruitment firm means that you are heavily dependent on data to give you the trusted information you need to ensure company financial success. However, this isn’t always possible due to data challenges that are easy to spot, but hard to pin-down and resolve. Here are 3 common data issues encountered by Recruitment FD’s, and how to resolve them.
The inefficiency of using spreadsheets to build reports
Spreadsheets are commonplace within Finance teams everywhere, and rightly so as a trusted operational tool. However, often they become the answer regardless of the question, which is especially true for reporting. Just because spreadsheets are good at holding data does not mean that they are ideally placed to handle your reporting requirements, for the following reasons:
· Reports within spreadsheets are often dependent on data from different sources that either has to be imported and then manipulated, or manually keyed in.
· These processes are often dependent on a small number of individuals who exclusively possess the knowledge
· The whole process must be repeated each week or month, for the latest version of the report
· Spreadsheet-based reports produced by multiple teams but from the same data sources can often lead to differences and unintentional discrepancies between these reports. This results in the need for reconciliation and explanations, underpinned by a general lack of trust
· Spreadsheets may only hold data for a given month or year, making historic year-on-year trend analysis very difficult
· Reports within spreadsheets are by their nature two-dimensional – rows and columns. This makes reports quite inflexible in that there are only two choices for how you want to report your metrics (with one of these commonly being time). It is difficult to ‘pivot’ this data easily without a lot of prior preparation
Whilst there is no single quick-fix to addressing all of these challenges, there are various steps you can take to significantly reduce the amount of manual reporting intervention needed:
· Consider automation tools
There are lots of tools that can automate a manual technical activity, performing it instantaneously and as frequently or repeatedly as required. This frees up time for other activities and eliminates any likelihood of human error. Automation tools include Zapier, Make (formerly Integromat), Power Automate and other RPA (Robotic Process Automation) tools like UI Path.
· Data-based extracts, not report-based extracts
If you’re running data extracts from your source applications to build a specific report, instead consider building data extracts based on subject areas (client, candidates, placements) – and use these as the single basis for multiple different reports. This reduces overall effort, provides more reporting flexibility and ensures that multiple reports consistently use the same underlying data.
· Spreadsheet-equivalent tools
Rather than assuming you have to use spreadsheets, there are other similar tools that can be used that offer additional tools and functionality not easily found in spreadsheets. Online database platforms like Airtable are a great way to build tables of inter-connected data, which you can also supplement with alerts, triggers, online forms and email automations. Also, powerful reporting tools such as Microsoft Power BI or Tableau can be used to provide a powerful dedicated reporting capability separate to your spreadsheets
Talking about the data they want, rather than the insight they need
Linked to the prior issue is the common mistake where you hear people asking or talking about the data that they think they want, rather than the actionable insight they need to support the business outcomes that they’re responsible for.
If you hear a business conversation where someone is asking about the mechanics of how they get data (for example, “have you got this month’s sales data”), then there is a fundamental problem.
In an ideal world, the delivery of timely, accurate and actionable data should be given, and would prevent such requests. The types of question you should be hearing is “Who are our best clients that give us profitable repeat business?”. Sounds easy, but how do you deliver this? There are 4 simple steps:
· You start at the end with these ideal questions as they are referring to the business outcomes that you’re trying to achieve. Any report or data extract that isn’t ultimately supporting a business outcome is a waste of time and effort
· For each desired outcome, state what is the actionable insight that you would need to produce this (using the example above: the ability to interrogate a list of all clients, by consultant, including placement details, activities, revenues, and associated costs for the last 5 years). Sketch out the story of what screens or reports you’d ideally need, and how you’d need to move and flow between them (such as “drilling down” into more detail of a selected client)
· Break this down into the individually related information domains that you need to query (client, placement, staff etc.)
· Finally, build data extracts with all the detailed columns of data needed, that support each of these specific information domains – so that you can query, join and manipulate the data together
A wider view beyond ‘just’ financial-based data
Another common issue is the unnecessary limitation of financially based reporting that does not include non-financial (operational) data. Many finance reports come directly (and only) from your financial application platform (e.g. Xero, Sage etc.). There is a goldmine of additional, useful data that exists in other application data systems such as your CRM or candidate management system. Including data from these other sources would help unlock a deeper understanding of your financial performance:
· Staff performance. Staff / consultant details (HR data including grade, experience / time within the business, L&D activities, recent performance/review scores and so on)
· Placement complexity to fulfil. Additional placement details (CMS data either based on the type of placement, the seniority or niche specialism of the role, or salary expectations and working arrangements)
· Client cost to serve. Additional client details such as how much effort does it take to work with theme, do we have payment challenges with them, and what’s the likelihood of additional work?
· Candidate quality, supply and demand. What types of viable candidates are you able to source for these placements? What type of candidates are more likely to apply for and succeed in which types of placements?
From this type of analysis, you may find that only certain types of senior contract niche placements are profitable in the long-term, or that staff who haven’t taken a particular training course often take longer to convert roles. These types of conclusions are only possible by combining both financial and operational data together to provide the actionable insight needed.
Use the recommendations from these three topics to identify the data and actionable insight that you actually need, irrespective of where the data comes from, and automate the underlying processes as much as possible. The result is higher quality actionable insight that adds business value, which is efficiently and automatically produced on a repetitive basis.
Dave Sheppard is the founder of POB Enterprises and specialises in providing Data Strategy services and solutions, including a recruitment-specific Data Audit offering