Data analytics for professional services – Move beyond the technical to 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 elements.
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, 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. And that’s the reason why I focus on enablement at Leverage Technologies.
When people can write their own reports, or tweak their dashboards to suit, that’s when the data becomes most useful.
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, people are sitting on top of a gold mine without being able to access it.
Create opportunities for collection
Another issue 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.
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, 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.
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 optimise that?’”
O’Toole 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.
I agree – information is key.
That’s how you can get more insight, and make decisions based on fact, not assumption and emotion.
And 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.
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.
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.
For larger changes or initiatives, create 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. They should be using technology to enforce business process and getting management to take action how they see fit within those parameters.