Navigating The Rough Waves of Data Lakes

“I can’t change the direction of the wind, but I can adjust my sails to always reach my destination.”

― Jimmy Dean, Country Music Singer and Businessman

Data lakes are an increasingly powerful tool that wealth management firms can leverage to pool structured and unstructured data and drive reporting about their clients and business. But if they are not harnessed correctly, they can quickly become swamps.

Like a sailing ship that needs to harness the wind to cross an ocean, a straight and efficient course must be charted from port to port with a knowledgeable crew or risk ending up alone and lost at sea.

The process of linking up data sources into a single location and creating a good flow of data in and out of the lake takes time and effort but the rewards can be many. However, many advice firms have not made the investment, with nearly two-thirds (63%) of data scientists in financial services firms saying their organization is not currently able to combine data and analytics in a single environment.

As part of our work to help our enterprise wealth management clients get the most benefit from their data assets, Ezra Group has partnered with Xtiva Financial Systems to produce a series of webinars. Our goal is to bring leading industry experts in data strategy, data architecture and systems implementation to share their experiences and best practices.

The fifth webinar series is called Data Pollution: Don’t Let Your Data Lake Get Turned Into A Swamp and included as a panelist was Nathan Stevenson, CEO of ForwardLane.

In this article, we summarized Nathan’s explanation of the power and danger of data lakes and how to connect data from different sources so it will bring you to a new destination of insight rather than swallowing you out at sea. 

In case you missed this webinar, you can click here to unlock your access to the full recording.

Drawing a Data Map

If you want to sail from port to port and grow rich as a sea trader, it’s important to have a good map to ensure you get to the right place and know of all the hazards and stops along the way. data lakes vs data warehouses

For large firms with a range of structured and unstructured data from different systems and workflows, data sources must be connected efficiently. By drawing a detailed map between the sources, data lakes can be harnessed as a powerful tool to integrate and activate all the information that is usually spread across different databases.

How much unstructured data is out there? Findings from a wide range of analysts including IBM, Gartner and IDC have come to a ballpark estimate of around 80% of enterprise data being unstructured data

But the message is that unstructured data is important. It’s present, and must be managed. Data that’s unstructured and unanalyzed is a $430 billion opportunity cost in 2020 according to IDC. That’s five times the size of the global AI, RPA and data analytics markets combined.

Firms can quickly find themselves lost at sea when trying to work with data from a range of systems and workflows such as Salesforce, their CRM notes, call transcripts, records in custody across different platforms, and performance market data.Stevenson illustrated how ForwardLane charts a course for navigating through their clients’ data by analyzing data types and matching them up across sources, platforms and marketing campaigns. ForwardLane’s unifying process creates a tailored digital environment to suit a firm’s needs. (See 3 Strategies for Wealth Management Firms to Avoid Falling into a Data Swam)

Navigating to Your Destination

It’s one thing to just float in the middle of the ocean while staring at your map– it’s another thing to get out your sails and travel somewhere. 

By harnessing the power of an organized data lake, information can be connected and activated by using an AI-powered signal engine to provide insights. These insights can support advisors by informing them of what’s happening with their clients, letting them know who to call, what to say and what downstream actions to take, Stevenson noted. Rather than data remaining messy and disconnected, ForwardLane’s AI Insights Engine connects information from different sources and turns it into actionable intelligence that advisors can use to improve their efficiency. 

Though technology adoption is increasing among financial advisors, those centering technology in their practice and making use of advanced systems and artificial intelligence are still very much on the cutting edge. According to the InvestmentNews Research 2020 Adviser Technology Study, just 9% of advisors are currently using innovative tools like artificial intelligence, although advisor satisfaction with technology is almost 10% higher when they do use AI-powered tools. (See Ep. 145: Big Data as a Service is the Next Big Thing in Artificial Intelligence)

We may all be in the same data sea, but we’re in different boats

“We’re finding that large firms might have different ideas as to what a data lake is,” Stevenson noted. “While a lot of firms are moving towards Snowflake, that transition is slow. Some have data lakes and some have disparate databases.” Snowflake currently has 19% market share in data-warehousing software, and their popularity continues to rise with over 9,000 companies as paying lakes vs data warehouses

He explained that for big firms with data in dispersed locations, solutions like Snowflake can be useful. Snowflake is a storage solution where the data doesn’t leave the system– everything stays inside the lake. Connectors pass elements to where they’re needed, when they’re needed, which can save a lot of time and effort that was previously required for importing data, matching it, and accessing it. 

Snowflake was founded in 2012 and is a disruptor in the market for cloud-native analytic databases. Around this time, AWS did a one time license deal to acquire the intellectual property of the ParAccel MPP database, on which it built Amazon Redshift. In the latter part of the decade, Microsoft threw its hat in the ring with SQL DW, which the company evolved into Azure Synapse. There are other players as well, such as IBM.

Perhaps the most prominent Snowflake feature is how the platform decouples its compute and storage capabilities, unlike many other data warehouses in the public cloud. This means that companies using a lot of storage but little CPU, or vice versa, can save a good deal of money. (See 3 Smart Strategies for Corralling Your Data Infrastructure)

Connecting the Ports

The holy grail of financial services data is a 360-degree view of the client. By bringing together information from dozens of sources, they hope to alchemize a detailed picture of a client’s financial life to act as scaffolding around their advice process. To create the broadest picture possible, ForwardLane works to connect across all business areas including sales, data science, and client support. (See 4 Benefits of a 360-Degree Client View)data lakes vs data warehouses

Stevenson described one of the main benefits of unifying all a firm’s data as connecting the dots across an organization. For example, it would be possible to notify an advisor if their client contacted customer service and update them if the issue was delegated to a different team member. ForwardLane is also looking to use their data collection and connection abilities for lead generation, practice growth and retention signals, and collaboration with other types of fintech software, he said. 

A large, independent broker-dealer (IBD) that ForwardLane worked with had interesting data on their clients, but it was all in disparate systems, Stevenson explained, which forced the advisors and staff to constantly go to different places to find the data they needed. Leveraging their data lake technology, ForwardLane was able to bring all the IBD’s data into one place and connect it into their insight engine.

The data was then surfaced into Salesforce where the advisors and sales teams managed their daily workflows. “The outcome of that was improved discovery,” Stevenson noted. “They were able to offer more products and services to clients that they did not even have on their target lists and really surfaced up opportunities that they wouldn’t otherwise have been able to find.”

The Right Data Tools for the Job

While some firms used to build their data lakes in-house, most realize that it’s more efficient to buy from a specialized vendor. ForwardLane has spent over $8 million developing their AI-based platform, according to Stevenson and have gone through a long process to determine what makes an insight valuable. Now, RIAs and broker-dealers don’t have to start from scratch and build their own data science team. “We believe in the ‘fintech as partner’ approach” he noted, “and we see it as a collaboration with our clients.

Having an expert at the helm makes it easier to navigate rough waters and build solutions for complex problems. Stevenson described an issue that ForwardLane ran up against at enterprise firms was sending data to Salesforce and then pulling it back out again. Salesforce did not have a specific capability for the function, which made it inefficient. Like the Panama Canal allows ships to travel back and forth between oceans, ForwardLane developed a custom application (called FlexPort) to move data in and out of different CRMs. (See Ep. 94: Building Invisible Digital Infrastructure with Tricia Rothschild)

Bringing in a Data Captain

If you have a big enough ship, you need to have an expert captain to steer it. Companies of all sizes are amassing more and more data every year, as data storage continues to become cheaper and regulators  require holding more and more of it for compliance reporting. The trend is not expected to slow down anytime soon, and by 2025 the world is projected to be storing 175 zettabytes (a 1 followed by 21 zeros) of data globally. 

With data management becoming more and more cumbersome, Stevenson explained that a large firm managing between $800 billion to $2-3 trillion AUM should have some type of data analyst or more importantly “a data product owner, who has some marketing expertise, some sales expertise, who understands enough about data to be dangerous, and can bring together the big picture.” He emphasized that the central focus should be more about what the client experience looks like and what insights do you want to give? (See 3 Smart Strategies for Corralling Your Data Infrastructure)

Balancing the Ledger

On a trading ship, it’s important to keep track of what cargo you have, where it came from and where it’s going. For enterprise wealth management  firms with substantial data resources, that means having clear naming conventions and a corresponding data structure.

Data catalogs are a critical feature for properly organizing enterprise data. “Think of it like a naming convention,” Stevenson said. “If you have different labels for your investment strategies and different labels between your accounts and how you think about them, then it can get quite complicated when you’re trying to tie together that data.” He noted that a data catalog is also helpful when speaking to vendors to help them understand the work they need to do. A data catalog can end up saving a lot of downstream costs, according to Stevenson, not least in time spent in meetings trying to sort everything out in lieu of an organized system.



The Wealth Tech Today blog is published by Craig Iskowitz, founder and CEO of Ezra Group, a boutique consulting firm that caters to banks, broker-dealers, RIA’s, asset managers and the leading vendors in the surrounding #fintech space. He can be reached at