Is it possible to have too much of a good thing?
When it comes to data, the answer is a conditional no. In business, knowing one’s client is key to success, however, poor practices can quickly combat any positives offered by using data to drive growth or strategy. Stagnant and isolated data can lead to a costly mess with little value, and poorly stored data could lead to ruinous legal and regulatory consequences.
In few places is this more true than in the financial industry, said Adrian Johnstone, co-founder and president of Practifi, during his presentation at this year’s T3 conference.
“Today’s wealth management industry is awash with data but starved of insight,” he said. “We have so much information but lack clarity on how to manage it and what to do with it.”
Financial firms potentially have tens of trillions of pieces of data available to them today, much of it portfolio and demographic data about their clients—but there’s also user behavior data. Such as software and client interaction data linked to websites, system security data that reports who is accessing which records, marketing campaign data and more.
That data is often isolated in legacy platforms bereft of openness and integration, said Johnstone. Poor data management practices lead to stagnant and dirty data. Many firms lack the resolve to enforce good data practices and need to build a better culture around collecting, processing and maintaining data.
“Data without context is almost entirely useless and data without purpose is worse,” said Johnstone. “Context is critical. Unstructured data like thousands of words amassed over years in nondescript ‘notes’ or ‘comments’ fields is of much lower use than well-structured and organized data clearly captured in defined fields that signify the context we talked about.”
Johnstone proposed firms adopt a seven-step cycle of data management: Capture, Validate, Process, Analyze, Store, Maintain and Archive
Data capture is where creating a strong culture around data begins, said Johnstone, as it is “the foundation of good data.” Firms should try to capture the data needed to create the kind of client experience they aspire to.
Firms should not get hung up on worrying whether their team or clients will provide the required information, said Johnstone. In a prospective client’s case, “if they believe in the value you can deliver them, they’ll take the extra few seconds to provide the information you ask for upfront.”
Many wealth management systems fail to validate data, but it should be a part of their process starting at the time of capture, said Johnstone. Simple validations like checking addresses and preventing duplicates can save time and frustration. More sophisticated validation can help mitigate commercial and regulatory risks. Data validation should be an ongoing process.
Before data can be used, it has to be processed—structured and organized—so it can be leveraged in automation and passed through integrations. Processing routines prepare every new piece of data that enters a system after it is captured and propagated to allow it to flow to the next step in the data cycle.
“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,” shared Johnstone. “Those doing this manually miss out on the value of processing amendments. Instead, they become simple annotations lost to time.”
Analyzation is the stage in the data lifecycle that trips most firms up because it becomes the sum-total of their thinking—how are they going to use data to draw insights to drive growth and better experiences.
“We hear about the power of Artificial Intelligence or AI and about the future that could be unleashed if only you knew how,” said Johnstone. “The draw here can be addictive. It’s this stage that fuels the seemingly insatiable desire for more data. The more you have, the more insights you can gain. Right?”
Data analysis is only useful if data isolation and data stagnation are mitigated, and it takes the other steps in the process to make sure data is collected, shared, stored and maintained correctly. Amassing more data is useless if firms cannot take action on it.
Poor data storage practices can have serious consequences for firms, said Johnstone, and firms should understand how data is secured, accessed, recovered and backed up.
“I’m regularly surprised at how few clients realize the vulnerabilities in their data storage. There are also those who still cling to the belief that the physical server in their office provides the best security,” he explained. “Worse, few have any idea of how their data is currently backed-up and where those back-ups are stored.”
Firms need to make sure their data is in a secure cloud storage environment, said Johnstone, and that it is regularly backed up and secured by layers of protection.
“Good data hygiene is data that is updated because, in the financial industry, data is dynamic,” stated Johnstone. Clients move, change jobs, change cell phones, marry, start families, divorce and retire. Dirty and poor-quality data slow system upgrades and migrations, limit integration, and taint the client experience.
“If you don’t maintain your data it quickly becomes stagnant,” he elaborated. “As I said at the beginning, stagnation is as big an evil as isolation. Maintenance is a fundamental flaw in most firms’ data handling.”
Data maintenance should involve the cooperation of a firm’s internal team and its technology partner. Third-party partners can proactively notify firms of records that have become stale or that have missing pieces of data. Team members can also give a record a once-over every time it is accessed to make sure it is complete and up to date.
Johnstone’s last step in the data management lifecycle is also one of the most important when maintaining a manageable and coherent set of client data. At some point, legacy data should be sunset and moved away from a firm’s active systems. Data retention is still important, so the legacy data should be moved to an archive location from which it can be retrieved when needed.
To summarize, data needs to have both context and purpose otherwise, the data firms are amassing quickly turns into a problem by being difficult to manage and subsequently, losing its value. Data can strengthen your firm only when used with purpose and strategy by adopting the seven-step cycle of data management.