A friend of mine recently found himself in exactly this position. He owns a small marketing agency and introduced a time tracker to generate the data he needed to help inform hourly rates, timescales and resource planning. It should have been straightforward: everyone logs their time under their jobs, my friend does a bit of analysis and everyone is rewarded with good, solid data that allows them to price jobs realistically and avoid resource issues. But after a few months it became clear that this wasn’t working. The output was virtually meaningless and so couldn’t inform the decisions it was intended for.
So what went wrong? Well, my friend does like to do things his own way. If he’d asked me for some tips before setting up the tracker, I could’ve shared what I’ve learnt about data quality and helped him out a bit.
The problem was actually very simple: garbage in = garbage out. This time tracker exercise was always doomed as it had no ground rules, everyone was completing it depending on their own interpretation of how it should be used. The data being uploaded was inconsistent and, at times, unintentionally inaccurate. Infinite monkeys and infinite typewriters may eventually lead to Shakespeare but the same cannot be said of agency staff and shaky time tracker protocol.
Quality data, and the flow of that data to and from the software systems used can have a hugely positive impact.
Created and used correctly, data is incredibly powerful. But it doesn’t happen by chance. The outcome will always depend directly on what you put in and the instructions you give. Clarity and a decent understanding of your material is a good starting point in creating a meaningful framework for actionable and impactful answers. My friend has learned that lesson and will hopefully see much more constructive output in the coming months.
When you’re working with data there can only be one version of the truth. There’s no room for interpretation or nuance. Quite simply, it’s binary. Anyone who works with different technology software solutions that connect to each other via two-way integrations will have experienced this for themselves. One truth means consistency and clarity, no variations or re-keying and no scope for human error. The data flowing back and forth is pure, it is true and can make a vast difference to your business.
As an industry we’re not short of challenges and many firms are currently trying to navigate their way through those posed by the Product Intervention and Product Governance Sourcebook (PROD). Suitability and segmentation are high on the agenda; everyone knows that product design and recommendations must now take into account the clients’ needs, characteristics and objectives.
Segmenting clients is more straightforward, knowing the data has a level of integrity that is likely to satisfy the regulator.
Where a business has invested in a software solution that is supported by high quality data these challenges become less onerous. Segmenting clients based on the required criteria is more straightforward and there is the added comfort of knowing that the data has a level of integrity that is likely to satisfy the regulator when it comes to documenting and evidencing suitability. Quality data, and the flow of that data to and from the software systems advisers use each and every day can have a hugely positive impact. It can help you meet regulatory requirements, help you segment your customer base in order to communicate and engage more meaningfully and develop differentiated services depending on customer’s specific needs.
Data is amazing, but it isn’t random. In order to achieve your desired results, it needs rules and structure. Whether you want a charging model to help grow your business or a suitability framework to inform your recommendations, it’s worth making sure you’re putting the best in to get the best out.
This blog first appeared as an article in Professional Adviser 8/10/18.
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