A wealth management firm’s data assets can be the missing ingredient that helps them to reach their business goals. The most well-run companies have been able to develop a competitive edge via data analytics capabilities that enable executives to gain unique insights into trends and customer behavior.
But companies cannot benefit from these capabilities without a strong internal data analytics team. A study by consulting firm Deloitte revealed that half of business leaders surveyed said that the greatest benefit of data analytics teams was better decision-making.
In cooking, good ingredients alone do not make a gourmet meal. The same holds true for data analytics teams. You can recruit the best people on paper, but if they’re not combined and managed correctly, your results will be subpar at best.
This article summarizes comments from Cara Dailey, EVP, Chief Data Officer at LPL Financial that she gave as part of a webinar in our series on data-driven wealth management, delivered in partnership with Xtiva Financial.
The webinar focused on what goes into building a strong data team that brings experienced, cross-functional skill sets to bear. While this is no easy task, using the best ingredients and resources, keeping a careful eye on flavor and balance, and putting the time into presentation result in a great product every time.
Daily used her expertise in building data teams across a range of industries to explain what goes into a strong foundation, how to get your entire company involved in data strategy, and the best guidelines for maintaining a good data product over time.
Take Stock of the Data Pantry
The best way to start your data journey is to take stock of where you are, Dailey advised. When embarking on a data transformation journey or doing more with analytics, it often comes down to the business problems you are trying to solve, she explained. “What’s your ‘why’? Why do you want to do this?” The next step is assessing where you are in your data journey from a maturity standpoint.
Dailey recommended that firms conduct a maturity assessment on their centralized data platforms, data lakes and/or data warehouses, and whether they have tools in place that can easily access the data and report on them. The use of these centralized data structures are increasing across the industry, with research finding that almost 70% of firms are now using them. The structure in use today should shape the type of team needed when moving forward, Dailey advised.
Collect the Best Data Ingredients
While it is cheaper to hire less experienced people and then train them, Dailey instead recommended hiring more seasoned people who are able “to punch above their weight class”. This means that they have worked at similar sized or larger companies and solved the same type of issues. But they should also be open-minded and not just supplying old solutions to new problems.
Dailey recommended selecting people who aren’t too academic, as data science is quite academic already and can lean towards being very theoretical whereas data teams are solving real-world problems. She also suggested leveraging external consultants as a useful way to bring in new and varied talent with different ideas.
Taste As You Go
“When I worked at Nike, we worked to create a culture obsessed with data,” Dailey recalled. She emphasized the importance of always looking for ways to improve your product and never getting comfortable with the status quo.
Since product managers should always be driving towards a better customer experience, pairing them with data scientists is a good way to ensure critical perspectives are considered, Daily advised. “If you love your product too much, you’re not going to think outside the box,” Dailey argued. Data scientists can help push the product boundaries and avoid the team becoming apathetic.
Eat Your Vegetables
Dailey described that one of the hardest issues to solve when building out a data and analytics organization is not allowing the team to become a bottleneck to the rest of the organization. “You don’t want that data team to be the only ones that know how to get the data and understand it,” she warned. The company will be more effective if every worker is armed with the right tools and capabilities to understand and use the data for their own purposes.
Literacy is a major component of rolling out data and analytics teams, but it’s hard to implement and requires a cultural shift. “It’s like getting people to eat their vegetables,” Dailey said. Everybody wants data, but no one wants to take the time to understand it.
As an example, Dailey brought up her experience working with data metrics at Nike, where they struggled with getting alignment on their key performance indicators (KPIs). In order to keep consistency in their global supply chain, they created a set of ten metrics that were defined consistently, and each one had a business leader who owned it and was in charge of engaging the organization to understand the metric.
By making sure everyone knew how the metrics were defined, how they were measured, how quality of the data was ensured and who was in charge of it, it built trust around the system and helped the data system work as an accelerator. If people understand something and trust it, they’re much more likely to make better use of it.
If you want your data to be used, how it’s presented is key. One method Dailey offered to help make data easier to digest is translating the numbers into a story. By creating depth and structure, business and technology come together to animate the meaning behind the data and help others understand it.
Dailey reinforced the importance of creating data products that the organization can use to make decisions and then put a good front end on it so it feels easy to use. “I think people diminish the fact that you have to have a user experience around data. I personally believe this because I think otherwise, it’s just noise to executives and management. They end up saying that it’s too complicated and they don’t understand it.” If the presentation doesn’t make the data easy to turn into actionable insights, the work that went into it will be wasted.
Another of Dailey’s recommendations was to build an internal portal for other members of the company to use, that would display customer insights, dashboards, and a data catalog. The portal should include the quality of the scoring, the methods, and who owns the metrics. Making the data easy to access and interact with greatly increases the chances of people using it.
By putting together a great team based on your company’s current and future needs and then creating structures to keep everyone involved in the data culture, it is possible to revolutionize the power of your company’s data.
Just like the cooking process can transform a collection of raw ingredients into a gourmet meal, building the right analytics team can turn your company’s data assets into powerful ideas and insights that leapfrog you ahead of the competition.