Fusion Theme Blog | AI & Machine Learning

Data executives empowered: The importance of the data community and elevating the CDO

Jul 22, 2021

Almost 20 years ago, Capital One recognized the need for one person to oversee their data security, quality, and privacy, and the role of the Chief Data Officer was born. Now reports show that 68% of organizations have a CDO (Harvard Business Review, 2020). And while the role has become more common and has significantly evolved, many data executives are still struggling to get a seat at the table or bring data to the forefront of their organization. In fact, in a recent survey, only 28% of respondents agreed that the role was successful and established. Company leaders agree that there needs to be a single point of accountability to manage the various dimensions of data inside and outside of the enterprise, including the quality and availability of that data. But now we are at a crossroads — what is the best way to align the work that the CDO does with the strategy of the business as a whole? The reality is that CDOs often struggle to find the internal and external support and resources needed to educate others to align with the organization’s goals. Implementing enterprise data governance, data architecture, data asset development, data science, and advanced analytics capabilities — such as machine learning and video analytics — at scale, is not an easy task. To be successful, data executives need support, resources, and communities focused on the elevation of data. We are proud to continue to help these communities come to life for the benefit of our colleagues and clients, establishing local resources here in the Midwest with global scale, reach, and impact. Read on as Mark Johnson, our Executive Leader for Data Management and Analytics, provides insight on the current state of data and the CDO, and provides details on multiple opportunities for data leaders of different levels to get more involved in the data community. Q: How has the role of data changed/evolved for organizations? The reality is that information is everything. This global pandemic proved that to many organizations. For some, it showed that their digital network was ready, and they were aptly prepared to take on COVID. For others, it has forced them to recognize their own immaturity with data and analytics. On its own, managing data is not exciting — the information just sort of exists. To give data value, you have to put it to use. And so, I think we are going to see the Chief Data Officer and/or Chief Data Analytics Officer really find their own in the coming years. It’s time for their seat at the table. The C-suite is now asking questions that can only be answered with data, and now they truly understand both the value and consequences of the data game. Q: What do you think are the biggest challenges facing CDOs/data leaders today? I think that the biggest challenge for data executives today is the acquisition of talent that is seasoned and experienced where you need them to be for your organization. Higher education hasn’t necessarily kept up with the data world, and often times it takes additional training to reach the right levels. The reality is that right now the talent is manufactured in the real world. Data executives have to be connected and equipped to mentor, train, and keep the right people. Q: You’ve mentioned that data leaders need to connect with each other. What value can people expect from these data communities? I think there is tremendous value. As we are seeing the power of data evolve in organizations, and the role of data leaders evolve as well, I think coming together to collaborate and share elevates the leader, the organization, and the view of data as a whole. In these communities, it gives people a safe space to talk about how they are doing, what they are doing, what their biggest challenges are, and what solutions are working for them. These communities have truly become both a learning laboratory and an accelerator for data. Q: As a big proponent of connecting data leaders, you have been involved in creating different opportunities for people to get together. What groups/events would you recommend, and how can people get involved? I personally have been involved with the MIT Chief Data Officer and Information Quality Symposium (MIT CDOIQ), which is such a great opportunity to start with for connection. It has developed into additional opportunities for data leaders at all levels to get involved and create the kind of community we need to truly elevate the value of data. Organizations like the CDO Magazine, the creation of CDO roundtables across the nation, and the International Society of Chief Data Officers (isCDO) all evolved from connecting data leaders and identifying common challenges. MIT CDOIQ: The International MIT Chief Data Officer and Information Quality Symposium (MIT CDOIQ) is one of the key events for sharing and exchanging cutting-edge ideas and creating a space for discussion between data executives across industries. While resolving data issues at the Department of Defense, the symposium founder, Dr. Wang, recognized the need to bring data people together. Now in its 15th year, MIT CDOIQ is a premier event designed to advance knowledge, accelerate the adoption of the role of the Chief Data Officer, and change how data is leveraged in organizations across industries and geographies. Fusion has been a sponsor of this symposium for seven years now, and we are so excited to see how the event has grown. Designed for the CDO or top data executive in your organization, this is a space to really connect with other top industry leaders. CDO Roundtables Fusion has always been focused on building community and connecting people. And when one of our clients, a Fortune 500 retailer, mentioned wanting to talk with other data leaders from similar corporations, we realized that there was a big gap here — there was no space that existed where data leaders could informally come together, without sales pitches and vendor influence, and simply talk. That’s how the CDO roundtables were born — a place that allows data leaders to get to know each other, collaborate, accelerate knowledge growth, and problem solve. We just started two years ago in Cincinnati, but now, now we’ve expanded to multiple markets including Indianapolis, Columbus, Cleveland, Chicago, and Miami. These groups are designed for your CDO/CDAO and truly create an environment for unfiltered peer-to-peer discussion that helps solves data leadership challenges across industries. If you’re interested in joining one of these roundtables or starting one in your market, email me or message me on LinkedIn. I’m here and ready to get these roundtables started with executives in as many communities as I can. The more communities we have, the more data leaders and organizations we can serve. International Society of Chief Data Officers (isCDO) Launched out of the MIT CDOIQ symposium, the isCDO is a vendor-neutral organization designed to promote data leadership. I am excited to be a founding member of this organization, along with our Vice President of Strategy, David Levine. Our ultimate goal is to create a space that serves as a peer-advisory resource and enables enterprises to truly realize the value of data-driven decision making. With multiple membership options available, isCDO is the perfect opportunity for data leaders looking to connect with their peers and gain a competitive advantage by focusing on high-quality data and analytics. CDO Magazine I am really proud to be a founder of the CDO magazine, as it really is a resource for all business leaders, not just the CDO. We designed the magazine to be a resource for C-suite leaders — to educate and inform on the value proposition, strategies, and best practices that optimize long-term business value from investments in enterprise data management and analytics capabilities. Check out the publication here. And if you’re interested in contributing content or being interviewed, let me know at mjohnson@fusionalliance.com. Closing: The role of the CDO is integral to organizations, but it’s still evolving. Now more than ever, it is important that data leaders come together to collaborate and problem-solve. Fusion is excited to be a part of each of these initiatives, and we are committed to being an agent of change in the communities we serve and beyond. By connecting global thought leaders we believe that organizations will realize the value of data to power their digital transformation. If you’re interested in joining any of these data communities or just have questions, feel free to reach out to Mark via email or on LinkedIn.

4 ways banks can leverage the power of machine learning

Jan 19, 2021

The financial industry has faced waves of changes over the last two centuries. Emerging nations, the American gold rush, the power of the stock market, and even the Great Depression have all shaped how banking works and what consumers expect from their banks. Notably, from 2015 onward, bankers began to list technology risk among their top five concerns1. While these changes have increased banking access and options for the average consumer, they also brought in more tech-savvy competition and greater regulatory scrutiny as heaps of data have become digitally accessible. Ironically, the very technological disruption that has so upended the financial industry will also be what brings new opportunities for growth and increased wallet share. This is especially true with advanced data tools such as artificial intelligence (AI) and machine learning (ML); according to one source2, 83% of early AI adopters have already achieved substantial (30%) or moderate (53%) economic benefits. In light of the benefits machine learning can bring, we’ve compiled four major areas where we’ve seen ML used to reduce costs, increase revenue, and mitigate risk for banks. 1. Acquire new customers Gone are the days where marketing was limited to just a few channels; now banks must maintain an omnichannel presence in order to reach younger consumers who may not listen to the radio or watch TV. Acquiring new customers means reaching them where they are with messaging that’s highly targeted and relevant. Yet as margins get slimmer and budgets are squeezed, reaching these consumers with targeted messaging without breaking your budget can be a challenge if you’re not careful. How machine learning can help Making the most of your marketing involves making the most of your data. Machine learning can help you identify trends in consumer behavior and interests, which can help you deliver the right marketing messages in the right channels at the right time. ML opportunities Identify which existing bank customers will buy another bank product Score your commercial leads based on risk, profitability, and probability to close ON-DEMAND WEBINAR: Learn how to turn data into insights that drive cross-sell revenue 2. Deepen relationship with customers Digital transformation has affected every business in profound ways, especially in the area of reaching customers and managing the customer relationship. Today’s users want a more seamless experience, more targeted messaging, and on-demand access to information, and they’ll move to the bank that can meet their digital demands. How machine learning can help AI and ML allow you to combine your leadership’s decades of experience with customer engagement data. So not only will you have a gut check of what customers want, you’ll have quantifiable data to back it up. Which means your sales and customer relationship initiatives will ultimately be more effective at targeting customers ready to upsell and at cross-selling more of your products to hungry buyers. ML opportunities Identify high-value customers early and engage with them differently Predict the likelihood of a customer taking their deposits elsewhere Identify which disputed purchases are legitimate Project a customer’s lifetime value for those with a limited history with the bank 3. Reduce Financial Risk Consumers are becoming both more credit averse and less credit worthy, which extra pressure on banks and credit unions of all sizes. On top of that, banks face increased risk caused by data breaches, fraudulent activity, and increased costs brought on by regulatory compliance3 . These challenges make maintaining adequate cash reserves more difficult than ever before at a time of increasing market volatility. How machine learning can help Machine learning can give you the insights needed to reduce your overall financial risk by helping you identify fraud and financial liabilities early – so you make and keep more of your profits. ML opportunities Clarify the liabilities on your balance sheet and determine which are the greatest risks Detect fraud and misuse of the company’s finances Project cash reserves to reduce excess bank cash GET THE USE-CASE WORKBOOK: The ultimate guide to machine learning use cases for banks 4. Optimize investment offerings The investment management arm of today’s banks continues to change rapidly as industry challenges increase. Today’s investment managers deal with increased market volatility, capped organic growth, and increasing fees. Because of these challenges, they struggle to keep up with shifting expectations of clients who demand a better investment turnover. How machine learning can help Machine learning can be used to detect patterns hidden in a bank’s historical investment data combined with external financial data. These patterns produce actionable insights that can increase the accuracy of key investment decisions. ML opportunities Match securities to investors based on trade history and market conditions Dynamically price securities based on competitive offerings, market saturation, and risk profile See how one institution used ML to predict their deposit customers' likely deposits on a daily basis, freeing $40,000,000 in excess cash reserves The up-and-coming (and existing) opportunities for financial institutions to win with ML are staggering. One source estimates that advanced data initiatives like AI and ML are predicted to boost overall business profitability by 38% and generate $14 trillion of additional revenue by 20354. And while it’s true that digitally savvy industry newcomers may take advantage of these trends faster than their legacy peers, legacy banks and credit unions hold something the younger competition doesn’t: mountains of historic data that, when mined for insights using AI and ML, can give them a leg up in retaining customers, increasing their wallet share, and reducing their overall financial risk. To adequately leverage these four opportunities, banks will need to embrace the very technology disrupting the industry. Banks that view their data as one of their most important assets and embrace AI and ML to create new insights will likely see growth, whereas those who don’t will struggle to keep up. 1) 2015 Banking Banana Skins Report 2) 2017 Deloitte 3) 2017 Financial News 4) n.d., Accenture

Top 3 reasons to invest in machine learning for mobile

Aug 24, 2020

Artificial intelligence (AI) and machine learning (ML) have completely transformed mobile development. Mobile app users today are often looking for an easy and relevant user experience — one that has been customized for them. The best way to get there? Machine learning. Machine learning identifies anomalies and patterns that ultimately optimize the user experience. If your technology conversations have stalled at the brainstorming or ideation phase, consider why. If you don’t have a clear answer, you’re not alone there either. “Strategic decision makers across all industries are now grappling with the question of how to effectively proceed with their AI journey,” says Marianne D’Aquila, research manager, IDC Customer Insights & Analysis. Despite questions about how to proceed, organizations know they need to invest in ML for mobile before current competitors, and those waiting in the wings, figure out how to profit from it first. Considering the speed at which machine learning is being adopted and spreading, and its potential to quickly help companies on multiple fronts, the time for execution and implementation is now. Here are the top three reasons that make machine learning development for mobile important right now: 1. Machine learning for mobile increases app security “Facial recognition” ($4.7 billion, 6.0%) and “fraud detection and finance” ($3.1 billion, 3.9%) were among the top five categories of AI global investment in 2019, according to the AI Index 2019 Annual Report (an independent initiative at Stanford University’s Human-Centered Artificial Intelligence Institute). It’s not surprising. From TikTok’s recent security flaws to Target’s $18.5 million settlement, app vulnerabilities and potential data breaches are breaking news, and there are few signs of a slowdown. While the short-term financial impact can hurt, the long-term cost of losing the trust of customers and partners can be even more painful. Companies that receive users’ personal information (e.g., passwords, billing addresses, answers to security questions) for processes such as app authentication or making purchases must continually optimize how the data is used. Through machine learning and automating parts of the process, you can identify anomalies faster, allowing you to see patterns and manage potential weaknesses more quickly. Operationally, ML can detect and staunch security issues related to data inside your company, such as logistics or pricing anomalies, that could be a drain on resources. For example, if one of your products is selling faster than usual via a shopping app, it could be related to a pricing error. Do you really want that $450 device on sale for $4.50? The mobile application landscape is comprised of a wide variety of operation system versions, devices, and software systems. This creates a much greater number of attack surfaces that attackers can target. (A first step to optimizing security is risk evaluation and awareness. Contact Fusion to hear more.) 2. Machine learning leads to increased mobile privacy It could be argued that the recent news cycle around privacy indicates a real desire for clarity, if not outright skepticism. In more than 3,600 global news articles on ethics and AI from mid-2018 to mid-2019, the dominant topics were “framework and guidelines on the ethical use of AI, data privacy, the use of face recognition, algorithm bias, and the role of big tech.” You’ve heard about Russia’s role in the 2016 election and the use of personal information for ad targeting. These sorts of debacles haven’t led consumers to give up on digital. Instead, they are demanding more privacy oversight and are being more cautious about the apps they use. Privacy concerns are complementary to security issues. While security comprises keeping personal data from hackers, trolls, or criminals, privacy is more related to keeping personal data in a person’s own hands, away from any individuals or organizations that don’t need to be privy to it. For example, if you use an activity tracking app to record runs, you might appreciate a note when you hit a milestone: “You had a personal record today!” Machine learning makes it possible for the mobile app to directly detect this activity and send a congratulatory message without any human intervention. There’s no need for a stranger to know you clocked a fast 10K. Machine learning on the edge further increases privacy by eliminating the need for data to be sent to the cloud. When ML on the edge is in place, individualized data never leaves the device, keeping the user’s personal information in their own hands at all times. Amazon, Alexa, and Google Home employ ML on the edge, as some functions are offloaded to a device while others have to go to the cloud. In addition to supporting privacy, the reduced travel time for data makes these apps and devices faster. 3. Machine learning for mobile helps create personalized customer experiences Consumers expect their demographic, behavioral, and other personal data to be secure and private, while they also want increasing levels of personalization. Delivering on these demands can be a delicate, real-time balancing act for companies, but machine learning helps make it possible to juggle data acquisition with protection and those prickly questions around how to use the data to everyone’s advantage. But is there a clear business case to pursue personalization? According to a 2019 Salesforce report, the answer is yes, as 75% of 8,000 consumers and business buyers surveyed expect companies to use new technologies to create better experiences. Machine learning for mobile enables you to make user-experience headway on several fronts. First, it can help you build a baseline of customer app usage. Once you have that baseline, you can see patterns in user behavior. Next, particular behaviors or deviations from the baseline can trigger delivery of a relevant coupon, suggested product to explore, or a reminder to revisit an abandoned shopping cart. Even more sophisticated, ML can serve up colors, screen layouts, and language that appeal most to a particular user. And with machine learning, the reactions are in real-time. The more your user engages with your mobile app, the more refined and personalized the experience becomes. Through machine learning, your brand becomes more closely aligned with the customer experience that your customer desires. Getting started can feel uncomfortable at first, but at Fusion, we’ve found that organizations often have low-hanging fruit ripe to benefit from machine learning for mobile. You just need to be able to see and then act on those opportunities. Working alongside you on this journey should be people who understand data science and machine learning, and who can uncover weaknesses to target. Now is the time to move forward on machine learning for mobile initiatives. Current market conditions indicate a shortage of professionals in machine learning and data science. Fusion fills this gap. If you’re interested in hearing more about machine learning for mobile, let us connect you with one of our experts.

Articles about AI & Machine Learning

Data executives empowered: The importance of the data community and elevating the CDO

4 ways banks can leverage the power of machine learning

Top 3 reasons to invest in machine learning for mobile

Connect With Us