This post originally appeared on Hakkoda's website.
Last week, data.world had the honor of sponsoring and attending Hakkoda’s From Modernization to Monetization: AI in Financial Services Leadership Summit in Miami, Florida. The event was filled with the leading minds and practitioners across Data and AI business divisions in some of the world’s most dynamic leading financial institutions and innovation organizations.
Our minds all shared a common goal - how do we best prepare and ready our organizations, our systems, our data, our literacy, and the necessary change management for the future that is already in our proverbial “living rooms” – forget about the proverbial “front door.”
Like many other regulated industries, Financial Services touches nearly every human every day. We humans and enterprises large and small require money to live, to transact, interact, provide, survive, and thrive everyday. Financial Services is as omnipresent as the air we breathe; our responsibility to serve fellow humans should never be underestimated nor be set aside as “side of desk” work.
For those of us in Financial Services and adjacent industries, our modus operandi is clear - chart a future that is transparent, simple to understand, engaging, ethical, inclusive, provides value, and embraces the future with intelligence, governance, confidence, and a growth mindset.
To do so requires insatiable curiosity, profound patience and intelligent inquiry because at the core of all that we must accomplish and provide in financial services rests our data. And friends, we are still figuring out our data decades after we started collecting it in large mainframes. Our work is not for the faint of heart, but it is work worth doing – 100%.
The good news - data is not going anywhere. Jobs related to data and AI will continue to rise as we investigate and solve the complexity of managing, accessing, discovering, and democratizing our data as we enable our organizations for an AI-assisted future. Below are just some of my observations & takeaways from our Financial Services Summit.
#1. Data Governance is hot, so hot that maybe we should consider renaming it
The role of data governance is now one of the hottest roles in the AI-obsessed narrative and for good reason. Without well organized, high quality, trusted lineage, cataloged, and semantic enriched data your AI initiatives risk not being confidently enabled and may increase risk to your organization rather than drive ROI.
Chief Data Officers agree that in our data modernization efforts lie the keys to monetization through: risk reduction, productivity gains, continuous proactive compliance, automated governance, pattern discovery and optimization to prevent fraud and other bad actors, and providing customized and bespoke experiences for every customer our organizations touch.
Governance is many times the umbrella placeholder for much of the above. When inclusive of everything needed to provide confidence in our AI pursuits we are actually talking about Data Enablement. Enablement for the future. Enablement for growth. Enablement for next generation use cases we haven’t even conceived of yet. Our shared AI future is through Data Enablement.
#2. Modernization requires both internal and external effort
What Internal Effort Looks Like
Curiosity seekers, innovation champions, evangelists, starting small, inclusion, and change management are the keys to driving internal data modernization and innovation successfully.
Seek to find your champions for data innovation and modernization internally first. Look for teammates in your data organization and business users who challenge the why to how things are done and the why things are not done. Look for those collaborators and tinkerers/experimentors who get excited about doing things better, differently, and who love constraints. Constraints fuel innovation.
Once you find the curiosity seekers who are excited to become a modernization champion, pair them with a SME, an engineer, or executive who can share legacy knowledge (or pain) and help them with the inevitable roadblocks that exist in large enterprises when it comes to challenging the status quo with new ideas for growth.
Insist on starting small, collecting the proof, strengthening the business case, and then expand out into your department or use case “family.” Starting small is the advice Marc Rind, Chief Data Officer at FiServ implored to the audience. Get hyper focused on one small problem that has tangible benefit to a team or business division. Test your hypothesis. Learn with your stakeholders, innovate with them, bring cross-functional teammates along on the journey with you for easier discovery, early buy-in, and evangelism once you get traction.
Get to know your Chief Security Officer and their team - really well. Dispose of the old notion that security and compliance teams are there to say no and to hamper innovation and progress. They want tangible innovation and enterprise value as much as you and your champions do.
Socialize your problem space, invite security and compliance into your data modernization and innovation efforts, allow them to learn with you via customer and stakeholder feedback. The collaboration will strengthen your initiative and you will build your cadre of influencers and change agents.
And perhaps most importantly, remember that it is not the technology that fails our modernization work, it is the lack of change management that fails the organization. Mobilize your change management from the start by employing executive sponsorship, peer allyship, and a cohesive plan for business unit and eventually enterprise change management and adoption. Change management plans should start in the first third of your initiative. In fact many Financial Services leaders shared that change management can never start early enough.
What External Effort Looks Like
Externally, Financial Services organizations face friction around Data and AI when it comes to managing customer expectations. Customers ingest news touting that their financial services company is AI first, ready to serve them what they need, when they need it, and how they want it.
The problem is that the average customer doesn’t see their mortgage rate decreasing or their savings rate increasing, therefore growing doubt and general angst towards the efficacy of “all of this technology and AI talk” that floods the airwaves. Or worse, they are denied a mortgage or the great rate advertised and suddenly AI is the enemy because the perception is that no human was involved and therefore the entire decision lacked all context of the human condition and was never going to be able to make an exception to their personal situation.
Stop adding complexity. Take the relationship that price elastic goods and services (eggs) have with inelastic goods and services versus (mortgages). This changing relationship and therefore consumer behavior is hard enough for finance and economic scholars to predict, let alone convey to we laypersons.
By introducing AI into an already confusing money system we are adding complexity (and in the customers eyes, cost) to a system that is already not well understood and somewhat mysterious. We are introducing customer hesitancy by accident. We are still selling the solution (AI) rather than taking the time to explain the problem we are solving and how it will benefit them, the customer.
“What will it do for me” still wins. Customers will always ask “what does it do for me?” Customers will always win. It is our job to make them want to win with us. Our job requires us to shift our thinking and external communications to illustrate the why behind what we are doing versus the how we are doing it. Communicate customer value and outcomes first, let the technology deliver and delight.
One of the panelists put this beautifully: “Do what’s valuable, not just what’s cool.”
#3. Modernization and automation for the sake of technology may be a disservice to your organization
I heard on a tech podcast recently that many times our tech obsessed culture seeks to modernize and automate every business function and process just because we declare it possible. This blanket approach many times is a disservice to our teams especially when we don’t include them in formulating the business questions when determining where problems and opportunities lie for improvement.
One of the topics discussed at the summit was the importance of humans at the center of our work. Our Data and AI work should not replace humans; it should help humans optimize to focus on what matters most - enabling them to do their best work, efficiently and with confidence.
AI should be considered Augmented Intelligence because it is not about replacing humans; it’s about giving us more, better tools and enhancing productivity to make better decisions, faster; leading to better human outcomes and overall experience.
Taking the next step
We don't have all of the answers, but we do have the drive, the intellect, the tools, and a builder's curiosity to take on data modernization and innovation with a commitment towards excellence, a practice mindset that embodies test and learn quickly, and the humility and collaboration to share both our triumphs and failures along the way.
We must act to innovate and serve our customers’, employees’, and organizations’ pain points and opportunities with tangible and worthy modernization work that yields trusted data enablement. Without a trusted, well-governed and democratized data foundation even the most sophisticated AI initiatives, tools, and applications will stumble.
To help you and your teams get started in laying that foundation, the data.world team has developed an AI readiness survey. This assessment evaluates your current capabilities across data culture, governance and compliance, data management and enablement, quality and semantics, operations and infrastructure, AI strategy and change management, plus advanced analytics and generative AI.
By benchmarking against industry peers, you'll receive actionable insights to accelerate your AI journey and identify key areas for improvement.
Ready to embark on your AI journey? Take the data.world AI readiness survey or speak with one of Hakkoda’s data modernization experts today.
