Data Analytics for Professional Services

Move beyond the technical – and get out in front

Move beyond the technical – and get out in front

Data science is becoming increasingly important to professional-service businesses who want to carve out a competitive advantage. Anyone working in the industry will know how easy it is for customers to shift around suppliers on a whim, and data analytics could hold the key to keeping them. Whether you’re operating a recruitment or law firm, giving financial advice, or providing technical consulting advice, the path to more growth and better retention is offering clients more value and improved service – all of which is powered, in part, by data.

So how do you seize those opportunities? For a start, it takes much more than a grasp of the technical side – Leo Dreyer, GM Leverage Technologies, MYOB business partner, and data analytics specialist, explains. 

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Start with business outcomes

Data analytics are too often relegated to IT and forgotten about – the thinking is that with the right technical people and software, the golden insights will flow. But, Leo explains, gleaning business insight from data is not just a question of technical skills. It’s a strategic task. Before they even begin thinking about technology solutions, business leaders must first ask themselves what business problems they’re trying to solve – and what information they’ll need to do that. 

"You have to start with the end in mind – focus on what the business is trying to achieve with the data. You can’t just set a system up and hope you’ll get something out of it"

Give data to the people

While ‘big data’ and ‘data analytics’ are buzzwords that send non-technical people running, the reality is that real value is unlocked when non-technical people have more access to the data. Leo explains that’s the reason why his company focusses on enablement.

“When people can write their own reports, or tweak their dashboards to suit, that’s when the data becomes most useful.”

The new systems available these days, like Power BI for example, have made that process even easier. The visualisation of the data, says Leo, is key:

“Not everyone can read a report, but put that table into a graph or couple this with predictive analytics and suddenly it becomes clear – people can see the trends.”

This also allows for much-needed domain knowledge to be used when interpreting trends – especially when the results are a little unexpected. While some systems let you input predictable external trends or big events, like seasonality or a market downturn, often there’s far more nuanced understanding required. It means that investing in a system people can use themselves is important.

Without it, Leo says people are “sitting on top of a gold mine without being able to access it.” 

Create opportunities for collection

Another issue, says Leo, is that businesses set systems that measure only what they’re currently doing – rather than designing new processes, customer interactions, or other data points to collect the information they really need.

Some BAU data will need to be collected, of course – like web hits, spend behaviour or sales figures, but for anything deeper and more specific, you may need to redesign parts of your operation. That, of course is a business issue, so again, leaving analytics in the hands of people with little power to affect structural change – like your IT department – means you’re unavoidably limiting the value of any data analytics project.

"Another issue, says Leo, is that businesses set systems that measure only what they’re currently doing – rather than designing new processes, customer interactions, or other data points to collect the information they really need."

More data isn’t better

It’s tempting to skip the ‘thinking’ stage, and go straight to collection – and to be safe, you’ll probably just collect every bit of data you can. But, Leo warns, too much information and reporting can quickly become counterproductive

For new systems, he recommends that businesses work backwards – asking themselves what they need to know, and then collecting only those data points. For existing systems, a simple way to test whether the reports being generated are actually used and useful is to stop, and see if anyone notices.

Get the experts in early

The self-service nature of modern systems needs careful handling – if they’re not set up correctly, all the effort can be wasted. Leo points to one client who was creating their own reports, but kept getting inconsistent results.

A few small errors – in the source of the data, or the way in which they were arranging the information – were the culprits. The big thing, says Leo, is to ensure the raw data outputs are set up by implementation experts initially.

“There are API integrations, you can just hook them up and pull the data, and then visualise it. And maybe it will show a trend – but it might not be correct, for a variety of reasons. It needs some human intervention early on to make sure it will always deliver reliable insights.” 

Your people can ask questions – but will they?

Equipping your people with the ability to interrogate useful business data is one thing – getting them to do that, and base their actions on it is quite another.

Tom O’Toole, a senior fellow and clinical professor of marketing at the Kellogg School, suggests in Kellogg Insight that alongside a new analytics push, companies need to also shift their mindset.

He explains: “It’s about encouraging, expecting, and enabling people to say, ‘Hmm, I wonder how we could use data to predict or improve or optimize that?’.”

Kellogg goes on to recommend that companies push for a culture of questioning, which means welcoming questions from everyone, in every department, even ones that haven’t traditionally used data to inform their roles, like admin, for example.

Leo agrees. “Information is key,” he explains. “That’s how you can get more insight, and make decisions based on fact, not assumption and emotion.”

He takes it one step further – saying that while you need a questioning mindset to make use of an analytics system, a good system can itself help build that culture - especially when it uses features like embedded BI and predictive analysis.

“When users can easily slice and dice the data to suit their day to day - or better still, when the system is smart enough to suggest next steps - that begins to train people into going to data as a first step, rather than relying on their gut”

"That’s how you can get more insight, and make decisions based on fact, not assumption and emotion."

Empower your people to take action

Data analytics is there to help you get ahead – so speed is the name of that game. Once you have an insight that could improve customer experience, efficiency, profit margins, or speed to delivery, act on it now – or your competitor will, says Leo. Sometimes that’s just a matter of having information accessible to inform people’s everyday decisions.

“People need real-time data so they can make a decision today about things that have just happened – not from a static report post month-end, that might be over 15 days old,” says Leo.

For larger changes or initiatives, he suggests creating a framework to empower your staff, so they can look at the data, make decisions and take action without unnecessary bureaucratic oversight.

“Bigger businesses get unstuck with their rules and protocols,” says Leo. “They should be using technology to enforce business process and getting management to take action how they see fit within those parameters.”

"That’s how you can get more insight, and make decisions based on fact, not assumption and emotion.“People need real-time data so they can make a decision today about things that have just happened – not from a static report post month-end, that might be over 15 days old."

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AI – waiting in the wings

Those businesses already established with data analytics – and the culture to suit – will be best positioned to make the most of the next big thing: artificial intelligence. As AI gets more and more sophisticated, the future for data analysis is exciting. With an AI engine, you won’t just collect and analyse data in milliseconds – you’ll also be delivered with meaningful recommendations, that are ever more refined.

But, says Leo, you’ll still need the basics to underpin this revolution – a comprehensive integrated business management software, and a business that is fully engaged in the value of data.