The overworked analyst – 3 suggestions from the trenches

By Matt Beckett, Managing Director, CSL UK

Go to the source for requirements

Do your best to avoid working on analysis where requirements have come through second-hand. Go to the person that requires the data and find out why they want it and how they want to use it. This will enable you to deliver more value and be more efficient with your time. There are always nuances to any analysis, however basic the data, and going to the source means you can tease these out.

You’ll often be able to anticipate the next request; again, making the analysis more efficient (especially as the answer will sometimes be “the data can’t tell you that”). 

The ‘Important not urgent’ quandary 

If we feel like we are constantly battling to meet “important and urgent” requests, it is often because we have not been spending enough time on the “important, non urgent” tasks in the past. You need to break the cycle, and the rocks, pebbles, sand analogy is a useful way to demonstrate the point. If you are not familiar with the analogy – here’s a brief summary.  

The main take away is that distractions like requests from colleagues, emails, Teams calls, meetings (the Sand) will always fill your week (the Bucket) if you let them. Plan chunks of time in each week for the important, non-urgent tasks (your Rocks) in quantities that you can defend to ensure they get done. Then plan the project work and important / urgent tasks in (your Pebbles). The sand will get done around those, or at least, you will find you prioritise the right things more naturally.  

Data availability – are you digging your own grave?

Our job as analysts is to provide the wider team with the data and insights they need, right?  Probably not. Our job as analysts is to ensure the wider team has access to the data and insights they need. We should not have to be an active part of that process in every case, and we need to avoid unwittingly becoming an information bottleneck.  

Here are some suggestions that could improve things without breaking the bank: 

• Data understanding – a lack of this in the business causes frustration at best, and poor decision making at worst.  If you have a monopoly on the knowledge of the data’s strengths and weaknesses, you are an unwitting gatekeeper. 
• • Run a session for the business on a dataset, telling all who will listen what it can and can’t do. 
  • Alternatively, use the video communication software we’ve all grown accustomed to in lockdown to record a 3-minute summary and send people the link. 
     
• Data quality – you may feel you have to manipulate the data for others before they can use it.  This can be the case when the data quality is low, when you need to add several data sources together to get an answer, or when complex business rules need to be applied before the data is useful. 
• • Perfect is the enemy of the good – ask if they need an exact or rough number for now – if the latter is good enough, it could be some of the imperfections can be overlooked in this case, and just communicated.
  • Address the core problem – poor data quality is like dragging a boulder along while we work – make a case for time and resource to be focused on improving it. 
     
• Sell-in your reports – don’t assume your users know everything they can learn from the reports you send them; many will only look at a subset of what is already available to them, and rely on you to provide them the rest. 
• • Run regular sessions to talk users through the report suit and what they can do with it.  Drop in clinics can work well in the current environment as they only take up time when people “drop in”.
  • When you release a new report, let the users know how they can use it, perhaps with a short video with some examples.