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The data paradox — why more is not always better

Data is supposedly the new oil but is it possible to have too much of a good thing? Just as having oil spilling everywhere can be disastrous, the same is true of poorly structured and unmanaged data.

Back in 2006, British mathematician Clive Humby coined the phrase ‘Data is the new oil’ and ever since then, people have become accustomed to seeing data as a very good thing. In fact, the appetite for data has become so voracious that Glassdoor rated Data Scientist as the second best job in America last year. 

So, to the question, is it possible to have too much data? 

What is less shared about Mr. Humby‘s quote is arguably the most important part. “Data is the new oil. Like oil, data is valuable, but if unrefined, it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so, data must be broken down and analysed for it to have value.”

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Just as having oil spilling everywhere isn’t good, the same is true of poorly structured and unmanaged data. 

This is the data paradox. It’s not how big your data supply is, but what you do with it that matters. 

Talk of oil and data can be hard to contextualise. So in human terms, every stream of data in a business is like an individual, a voice to listen to – clients, employees and other business partners.

Imagine an adviser meeting with an important client in a highly detailed investment performance discussion, and unannounced, another client walks into the office and immediately begins sharing important information on their future plans. While trying to contain these two sensitive conversations and ensure critical details aren’t missed, a senior member of the team enters the room to explain a compliance issue has arisen requiring urgent attention, then, another team member enters with a question on policy and then another and another.

This is a flood of information — all important, but jumbled or unrefined. Each of these ‘voices’ is representative of a flow of data streaming through a business today. Without investment to capture, structure, analyse and consume the voices, they can easily overwhelm a team and detract from the business's potential.

Today’s wealth management industry is awash with data but starved of insight. Data is often isolated in outmoded legacy platforms managed by vendors who don’t value the benefits of openness and integration. 

Firms are amassing more data but not maturing their approach to dealing with it near fast enough. For a lot of firms, the reason for wanting more isn’t even clear. Adding more data without a strategy is like pouring fuel on an inferno. 

Data without context is almost entirely useless, and data without purpose is even worse. 

At Practifi, we see this every day. Our team migrates many clients a month from outmoded industry CRMs and badly customised major platforms, regularly addressing the issue of unstructured and poorly organised data. Eventually, these issues must be confronted. 

What’s the solution?

The very first action should be to gather and assess the data available. Is it structured, secure, and accessible? Does it have context and is it current? There is a seven-step data lifecycle that can dramatically improve outcomes.

1. Capture is the foundation of good data. When determining data capture requirements for prospects or clients, it is important to consider how the information will be used later in personalisation, marketing and sales. It’s not enough to get a contact number or email address and then move on. Capture needs to consider what is next in the process and whether data can be enhanced or eliminated.

2.Validating data at the time of capture is surprisingly uncommon in wealth management systems, particularly older ones. Simple validations like checking addresses, preventing duplicates, or verifying that the date of birth qualifies prospects to use a company’s services all save time and frustration for everyone involved. Data in wealth management, unlike more transactional industries, is dynamic. As such, data validation must be an ongoing process, not a one-off.

3. Processing the newly captured and validated data is where data gets structured and organised in the background. Every time a new piece of data is added to a system, processing routines run again. For those yet to invest in systems or those who haven’t updated them in over a decade, much of this processing occurs with paper files and Excel spreadsheets.

4. Analysing data leverages its potential but if a business is not capturing, validating and processing data well, pouring investment into analysis isn’t going to pay dividends. This is where the data paradox bites. More is definitely not better. Driving actionable insights from data is the ultimate purpose. Amassing more, even good quality data is of little use unless a business can take action on it.

5. Where data is stored is something many firms pay little attention to; yet, there are considerable consequences to getting it wrong. There are also those who still cling to the belief that the physical server in their office provides the best security. Worse, few have any idea of how their data is currently backed up and where those backups are stored. 

Data must be stored in a secure cloud storage environment that is regularly backed up with layers of protection in place.

6. Data is dynamic and must be maintained. In even the most basic of situations clients change jobs, move house, get married, start families and go through divorces. If data is not maintained, it quickly becomes stagnant. Messy, outdated and poor-quality data slows system upgrades and migrations, limits integration potential but worst of all, taints the client experience.

7. And finally, businesses need to know when to archive data. Borrowing from the oil analogy, old data leaking all over the place is not a good thing. There are good regulatory and commercial reasons for data retention but without care, legacy data becomes an anchor to growth. It slows systems, confuses reporting, impedes change, and contributes little. It should be moved into a secure archive location from which it can be retrieved should it ever be required. There are many solutions to this depending on the data source. 

Applying all this effort needs to be for a purpose. For those who get data management right by deploying the seven-step model, the payoffs can be huge and result in actionable insights that have the power to touch every aspect of a business, from client experiences and staff retention through to deploying capital. If a business is growing through acquisition, the return on investment in data is amplified through every transaction. 

So, back to the opening question - is it possible to have too much data? So long as the lifecycle is in place and the data has both context and purpose, then no, it’s impossible to have too much. Get either of these things wrong however, and more data quickly turns into a problem to manage and loses value.

Adrian Johnstone, president and co-founder, Practifi

The data paradox — why more is not always better
Adrian Johnstone
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Neil Griffiths

Neil Griffiths

Neil is the Deputy Editor of the wealth titles, including ifa and InvestorDaily.

Neil is also the host of the ifa show podcast.

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