It is well known that a practice should be run based on the best possible data. Indeed, most of our clients have projects either running or planned with the aim of improving data integration, quality and timeliness.

But we see that these data improvement projects are notoriously tricky and sometimes flounder, or don’t even get off the ground. This month we explore three of the main reasons why this is the case and consider how to address them.

The Special Professional

The first problem isn’t a technology problem at all, but rather, a human one: When it comes to achieving excellence in data, the way that a lot of professionals prefer to work is contrary to what a firm needs. How so?

Uniform data makes reporting and analytics easier because data is in the right form and right location for meaningful comparisons to be made. This means that jobs of the same type – like an audit or conveyance – need to follow the same workflow. Similarly, time recording needs to use common codes, and be consistently captured against the correct client and activity.

Achieving this is simplest when processes are standardised but professionals tend to think that their own approach is best, and imbue this into their teams and offices. The idea of adopting universal “best practice” for the good of the firm is met with approval at partner meetings but rarely fully implemented.

Timely and accurate data also leaves partners and staff nowhere to hide. An air of mystery and discretion around winning clients, recording time, billing and service delivery is often the preserve of a professional, and clear and transparent data challenges this.

So, whilst corporately there is a desire for better data, individually there may not be. But it is only at an individual level that adoption can occur.

Before tackling the technology challenges of data, you need to assess and address the human challenges. You will need to help your partners believe in and adopt a “one firm, one process” approach and the transparency of data that goes with this.

Lack of common language

Once you’ve got your partners thinking the right way, you may find it very hard to articulate your data requirements using vocabulary that can be understood both by them and the technology professionals you employ or contract.

So how can this vocabulary gap be bridged?

Firstly, appoint or bring in on a project basis, a dedicated data analyst, whose skills cover both understanding data needs and defining these as technical requirements. You can’t expect them to have your reporting ideas for you, but you can expect them to interpret and then translate your wishes into a detailed specification, and manage their delivery into something you can use without technical support.

Secondly, AI is excellent at taking plain English (or any other language) requests and parsing these into succinct and comprehendible output, regardless of the structure or arrangement of the underlying data. The more data you can expose to an AI – within, of course, the boundaries of data security and privacy – the better and more useful the insights it can provide to you, in non-technical parlance.

Overload

Sometimes there is an urgent need for data, and a request is sent to the IT or Finance department to produce an ad-hoc report. Once produced, it turns out there was already a similar report that could have been used.

Over time a plethora of urgently requested but seldom used reports tends to accumulate and it becomes hard to maintain clarity as to what reports even exist, and why.

To counter this, there needs to be rigorous governance and stewardship of reports. Ad-hoc requests should be treated with caution, and edge-cases for reporting should be accommodated by publishing data as a source for individuals to query using desktop tools such as Excel.

Reports should also focus on exceptions rather than expect the user to wade through pages of data to try to spot something of importance. In fact, our preference when advising clients is to replace the report wherever possible with a task, using the important data items in a report to create actions for users to execute.

Summary

This month’s insight is only skimming the surface of data. A good place to start with data is to influence your firm on the need for uniformity and standardisation. This is not at the expense of individual ability, style or flair: but they are essential underpinnings for future data success.

Jeremy Hyman Associates is a leading independent technology advisory practice acting for PrimeGlobal and several member firms. They are pleased to continue offering a one-hour complimentary consultation session with Jeremy to managing partners or IT partners, as a gesture of our ongoing partnership with them.