With the rapid rise of data analytics, there is a high demand for a key player in the workplace — the Chief Data Officer. The CDO is quickly becoming a new hero for financial institutions ushering in digital transformation. From uncovering profitable insights to developing data-backed business strategies and keeping data systems clean and relevant—the CDO is proving to be an essential addition to the org chart.

If you are a financial institution and are looking to fill the CDO position or outsource this expert to oversee a major data initiative, you will want to start by knowing the profile of this key new executive position.

Below are some essential qualities not to overlook as you seek the right CDO for your organization:

A Team Player

Every organization is split into divisions, groups, or departments such as Operations, IT, and Finance. But, when it comes to sharing customer and organizational data, silos can be detrimental to the success of any data and analytics project. Data needs to be shared and processed freely. Therefore, a key quality to seek is someone with a team player mindset—able to help break down silos and ensure all relevant roles in the financial institution have access to data that impacts their decisions. Since the CDO understands the data on so many levels, they will work with the rest of the C-suite to maximize the value data can provide to the organization. Most CDOs are usually tasked with using the information to automate business processes, understand customer behavior, and ultimately utilize data as a business asset. Collaborating with department heads and working with other teams will be critical.

An Industry Expert

The CDO is a role that requires being able to understand all different aspects of data analytics – from data quality and data governance to data integration and data visualization. But for financial institutions to truly reap the benefits of hiring a Chief Data Officer, an ideal candidate should understand the financial services industry. They should have experience working within the credit union or banking space, have a thorough understanding of the stakeholders, the target customer base, and any industry challenges. They will use this knowledge to help define, develop and implement how the FI should leverage data for maximum business outcomes.

A Referee

One of the top motivations to hire a CDO is to help capitalize on data opportunities and drive revenue. The CDO needs to act as a referee to ensure data is used correctly, is clean and can be used for insight and action. Just as important, the CDO will need to drive ways that data can be used for innovation. Basically, your CDO needs to be good at playing offense and defense. The CDO needs to create data management processes, policies and tools to make the data more useful. The CDO can help unlock data for better marketing, and greater customer service opportunities. But, they will also need to help secure and safeguard the data. Being able to play on both sides of the data equation is critical to the success of data-driven projects.

A Data Champion

For many FIs, data analytics is still a relatively new area or concept. Therefore, a CDO will need to champion the data and be the leader for what a strong data strategy can achieve for the FI. By championing the data, the CDO will constantly be working to improve the quality of the data and should encourage users to follow established data governance policies. The CDO needs to have a good working relationship with team members and possess the business maturity to set a data framework that all will use and follow. To continuously champion the data, a good CDO should understand the latest technologies around data analytics and be learning constantly to stay ahead of the curve to ensure a return on BI investments.

A Visionary

An important quality in a CDO is someone who can make meaning out of the data. Your organization will want someone who has a vision for business opportunities over the next 1,2 5, and even 10 years and can use the data in innovative ways in support of driving revenue. A CDO who has a vision for what results, activities and actions are needed to align the data strategy with the FI’s overall business strategies will set the organization up for future success.

Finding the right CDO can make the difference between a financial institution that leads and one that simply is just keeping up or worse – not getting a true ROI from their data investments.

Is your organization in need of a Chief Data Officer? Contact The Knowlton Group to help your organization achieve all your business intelligence and data-driven objectives with our Chief Data Officer expertise. Email us at [email protected] to learn more!

For years, financial Institutions have been using data analytics to seek out opportunities, reduce costs, create efficiencies, make better and faster decisions, and ultimately improve the customer experience. Though many have begun the data analytics journey, most are not getting the most value of their data analytics initiatives. To be fair, it’s not the easiest process. You must build the right infrastructure to capture data. Then, you need the ability to access and extract data. Lastly (and most importantly), you have to convert the data into meaningful insight with the goal to create real business opportunities.

Today, with the increased focus on data management, data protection, and analytical competency, many financial institutions are finding gaps in their data program and are discovering they don’t have the right skillset to manage all facets of their analytics initiatives.

Enter the Chief Data Officer.

The CDO has proven to play a significant role in helping many FIs steward their data, drive operational intelligence with data, and generate real business value by monetizing data assets. Historically, IT departments were responsible for most of the big data and analytics projects. But as the push to deliver insights from the data has become a major driving force behind data and analytics projects, the role of the CDO has become crucial. According to Gartner research, by 2019, 90% of large global companies will have an appointed CDO. (1)

Why a CDO?

Not sure if your organization needs a CDO? If considering adding a CDO to your org-chart, it’s important to be able to make the distinction between your existing data analyst, data scientists and CIO. While data scientists typically have backgrounds as mathematicians or statisticians, CDOs should have a background in the financial services industry, know your market and combine that with a technical understanding of data and its potential for reaching growth goals.

Here are six advantages a CDO can bring to your financial institution:

Develop a Data Strategy

Data analytics is a powerful enabler for fraud detection, business intelligence, marketing insights, product research and so much more. With massive volumes of data flowing through the FI every day, it can be difficult to decide what data to keep and analyze. A CDO makes those key decisions as part of an overarching data strategy. They can help with mapping out goals, monitoring progress, and fine tuning the strategy as needed.

Enhance Data Governance

The CDO bears the responsibility of stewarding the data and ensuring data quality. They help implement proper data management and data governance systems and processes to ensure data is trustworthy, reliable, and available for analysis across the financial institution.

Leverage Data

The CDO will help find ways to use existing data to uncover new business opportunities, solve business challenges, and dive deeper into other insights that produce new sources of revenue tied to data.
Oversee Data Protection: Risk management and compliance with customer data are major issues among regulators, and the CDO can assist to ensure compliance and reduce risk. The CDO helps define the information management strategy to meet compliance demands; and shares information with the chief risk officer. If your FI lacks a dedicated compliance team, then a CDO can become a major contributor towards this function.

Manage Data Operations

A CDO makes key decisions around the storage, handling, and use of a FI’s data, including the type of platforms used, connections to/from production applications, analytics processes, and efficient flow of data.

Enhance Data Communications

Ultimately the CDO is a liaison between other departments. The CDO will work closely with the IT department, yet help with strategy in marketing, operations and many other functions throughout the FI. The CDO will collaborate with other departments, while overseeing goal setting.

Not sure if you should hire a Chief Data Officer yet?. Let The Knowlton Group act as your outsourced Chief Data Officer. Your financial institution will receive a trusted advisor and a strategic partner to enable you to leverage the massive amounts of data flowing through your organization. We will work with you to create long-term strategies for growth, top performance, and carve out a competitive advantage. Our mission is to help transform your organization into a data-driven organization. Contact The Knowlton Group today by email at [email protected] to learn more!



1. Gartner Estimates That 90 Percent of Large Organizations Will Have a Chief Data Officer by 2019, January 2016. Gartner Press Release

Why do some data and analytics projects fail, while others go on to produce significant business outcomes? Today, many financial institutions are actively using data analytics to turn their data into actionable and profitable insights. However, the reality is most analytics projects do not always translate into easy success and big wins.  Too often, these projects will hit a plateau, unable to deliver the pot of gold or even a positive return on investment. So why the sub-optimal results?

Since deploying a data and analytics program is a complex process, there are many pitfalls and mistakes that you can make. While this list is by no means exhaustive, below are some of the most common mistakes we’ve seen as financial institutions embark on the data analytics journey.

Running before walking; walking before crawling:

Like most major projects, realistic goals and timelines need to be adhered to for the greatest chance of success.  I call this the crawl, walk, run phase of the data analytics project. The “crawl” phase is where the organization stops living and dying by their Excel VLOOKUPs and starts to use relational databases and common reporting tools.  During the “walk” phase of analytics is where the real analytics can begin. Your staff does not need to go to ten different places to get data for a report.  Near real-time analytics can be developed.  The “run” phase of analytics is where data science and statistical models become fully realized and leveraged.  During this time the data is working in every way imaginable and the organization has the structure, the culture, and the skills to make full use of the data’s potential.

Ignoring KPIs and success measures:

When you’re just getting started, it can be tempting to focus on small wins. However, it’s important to establish metrics like new business opportunities, customer satisfaction, onboarding, marketing, etc. Raw data must be turned into actionable information for it to have any real meaning.  That is why we emphasize the importance of establishing well-defined Key Performance Indicators (KPIs). KPIs are the quantifiable measures that a financial institution uses to gauge its strategic progress. The key is to keep the success measures simple, practical and relevant to the organization.  This is what will help you turn raw data into actionable, useful pieces of information so you can continually refine your KPIs for ongoing success.

Putting technology before strategy: 

When embarking on a data analytics project, one piece of advice is to not let IT drive the program. Many financial technology firms are offering best-of-breed software solutions that have features and functionality that are typically under-utilized or forgotten about in the urgency of deployment. In most cases 60-90% of the product features are often unused despite paying for 100% of the product. Clearly understand and outline the long-term strategic goals of the financial institution to identify and select technology solutions that fit your data analytics goals today and tomorrow. Otherwise, you run the risk of investing in software that doesn’t fit the financial institution’s vision, and the cost of converting later can be significant.

Overlooking data quality:

Data scientists know the importance of accurate and complete data. After all, if the data itself is unreliable, you’ll wind up making invalid conclusions based on your analysis. To avoid that pitfall, it is incumbent to spend the time and effort to diligently prepare and clean the data coming from all sources. This includes a broad range of cleansing such as: incorrect values, typos, aliases, inconsistencies, duplicate entries, outdated consumer information.  Your data need not be (nor will it ever be!) 100% clean to get started with your project.  It is, however, important to establish policies and procedures to identify and clean bad data on an ongoing basis.

Lacking data governance:

Think of data governance as a set of rules for inputting and maintaining data. It is a continuous quality control discipline that governs the overall management, usage, storage, monitoring, and protection of the financial institution’s data. Without dedicated governance processes, overtime, poor data quality will lead to inferior service delivery, reduced employee productivity, missed opportunities, and increased costs.  There are several subtopics in data governance that we have shared previously.  You can tackle data governance in a variety of different ways – just be sure not to overlook it in your analytics initiative.

Underutilizing talent:

Enlist the help of an experienced Chief Data Officer to keep everyone accountable and aligned with the overall strategic business objectives associated with your data and analytics projects.  Whether hiring a full-time executive or an outsourced expert like The Knowlton Group (who can resist a shameless self-plug!), view them as a trusted advisor and strategic partner.  Consultants and data scientists will work with your data analytics teams to pour through the data and train your staff to uncover and act on new opportunities that lead to your desired goals. These experts can advise decision-makers, monitor progress, track success and establish best practices that will position your data analytics project for success.

Underestimating the journey:

A fully-developed analytics program can take 18-to-36 months to deploy in its entirety. Deploying an advanced analytics initiative and building a data culture takes time, and many executives don’t plan for the long-term commitment.  Turning the data generated by consumers into actionable, comprehensive insights is a big task. The initial steps should include establishing short-term and long-term business goals. Once the strategy and business goals are in place, the technology infrastructure needs to have an effective and rigorous process to collect, cleanse, compute and consume the data. Staff needs to be assigned—and with the right skill-set to then use this complex data for discovery of insights and interpret results that lead to business opportunities.

What mistakes has your organization experienced while deploying your data and analytics program?

Next Steps

This list of pitfalls provides just a glimpse of the many mistakes financial institutions make when embarking on their data and analytics journey.   Before implementing any analytics solution, spend the necessary time determining how you would like data to strategically support the financial institution. Focus first on your KPIs and success measures and then explore your technological, operational, and deployment needs. With this discovery completed, you will have the foundation of what will be a working roadmap for your data and analytics journey.

Need expert help planning, designing, and implementing your data and analytics strategies and solutions? Contact The Knowlton Group today by email at [email protected] to learn more!

If you have reviewed the important first six steps outlined in our first article on Building an Analytics Roadmap, then you are ready for the final steps to complete the journey.

To recap, we firmly believe that analytics in the bank and credit union industry will become necessary for survival.  Those who adopt early will see significant competitive advantages.  However, those who adopt without careful planning, strategy, execution and commitment will see their time and money wasted due to poor data governance, inadequate technology, lack of data talent, and uncommitted or misaligned leadership.   These final steps in building a solid roadmap will get you on your way to reaching your BI goals.

Step 6: Assess & upgrade technology capabilities:

Nothing can derail your data analytics project than a lack of the right hardware, software and data analytics technology. Create an assessment of your technology inventory as part of the roadmap and indicate the need for further technology capabilities that align with your overall strategy.  Assess if complex infrastructure and security is already in place or if the analytics systems require significant infrastructure updates including robust security, abundant data storage, and other components of a solid infrastructure.

Step 7: Ensuring data quality:

Data quality can vary significantly depending on how it was collected, stored, cleansed, and processed. Establishing data quality checks ensures that data is complete, timely, and accurate. Implementing a comprehensive data profiling effort and data quality initiative can help identify data quality issues earlier in the implementation timeline.  Working with subject matter experts from the various business units can help identify and clean up any issues that arise.

Step 8:  Identify analytics success measures:

Many organizations are excited to get started building out a data warehouse and establishing an analytics program.  However, most fail, at the outset, to establish measures that define success (or failure) for the analytics initiative.  These can be ROI-related or measures around adoption and utilization of analytics throughout the organization.

Establishing these success measures at the start of process allows management to gauge whether or not the program is meeting expectations.  If success is not being met, management and the BI team can take corrective actions to re-align the analytics program with expectations.

Step 9: Create an analytics workplace culture:

Having an analytics-ready culture is a huge indicator of future success. FIs that have taken the time to create a workplace culture that understands, visualizes, and believes in the capabilities of the data will see results. Establishing a culture in which everyone — including your leadership — understands and appreciates how data brings value to business initiatives will ensure that your processes and culture are aligned around producing profitable insights and positive business outcomes.  Getting the best value from your data means you must first trust it yourself and then give employees the opportunity to learn and embrace data-driven decision making. (2)

 Step 10: Create a data dictionary:

A data dictionary provides detailed information about each data element within your analytics environment.  Users need to know what the data fields and metrics mean. When you have a clear list of metrics and their definitions, it helps to eliminate assumptions, hours of guesswork, errors, and confusion.  Take the time to generate the data dictionary with clear, unambiguous and agreed-upon definitions. This requires collaboration of all the key players within the data analytics project.

Step 11:  Provide ongoing training:

You would never implement a new LOS without training your staff on how to use it, right?  Similarly, an organization won’t have success with analytics without providing proper and consistent training.  Business users need to be properly trained on the data elements included in the data model, how to access information, and the various other aspects of the analytics program.

Step 12:  Prioritize implementation phases:

A successful data analytics roadmap should divide the total implementation into logical phases to help reduce the risk of failure.  Start with the highest priority initiative(s) and refine the initial scope to mitigate the downside risks. Set goals and milestones by breaking up the entire project into manageable phases. This approach not only helps manage development of the solution, but it also gives the analytics team time to train business users on how to leverage each subsequent phase of the implementation.  Failing to do this is one of the most common reasons that BI projects fail.

Are you ready to start building your FI’s data analytics roadmap?  The twelve steps covered in the past two articles may seem daunting.  The Knowlton Group can help.   Our expertise, years of deep experience working with FIs on assessing and implementing proven data analytics strategies and solution can work for you.   Contact us today to learn how we can help assist your BI strategy or assess your current goals to make sure your capabilities are aligned.

2. 4 Strategies to Create a Data-Driven Company Culture  

Here is a surprising statistic I recently read: more than 90% of strategic plans are not successfully accomplished with 67% of failed plans attributed to a breakdown in execution. (1)

I’m a firm believer that strategy without execution will fail and that execution without strategy will also fail. But few organizations have figured out the antidote to close the gap—especially when formulating plans to become data-driven. For those financial institutions ready to implement analytics initiatives, success requires a top-down approach. You should focus first strategically, from a higher level, before you start focusing only on the operational, the technology and some of the tactical components that analytics requires.

Sounds simple, right? If only.

The antidote requires masterful integration and alignment and deployment of high-level goals down to more tactical objectives. Key strategic leaders need to make a top-down commitment to the implementation of data and analytics through communication with the gatekeepers of key operational processes, constant training, reinforcing a culture of accountability, and doing all these things in a manner consistent with a shared long-term plan.

So, how does this correlate to credit unions and community banks working to implement a successful data analytics program? Start with the end in mind by crafting a roadmap to guide you along the journey. These key steps will help navigate your course:

Step 1: Determine your objectives:

The first step in crafting your data analytics roadmap is to clearly understand your financial institution’s strategic business objectives and outline how data analytics will help achieve and/or measure progress towards those objectives. Knowing your strategic business goals now (i.e. deposit growth, opening new branches, new products, digital channel alignment, etc.) and in the coming years will help your organization devise a living, breathing roadmap that aligns analytics with key business objectives.

Establishing your goals from the starting point will provide direction, motivation and a clear path to success.

Step 2: Create a long-term budget:

In budgeting for your BI initiatives, remember that this is not a one-time purchase. A successful data analytics program requires continuous investment as the data needs of your FI grow. In most BI/data strategy projects, plan for a minimum of 18-36 month roadmap. Planning and developing the implementation this way ensures greater success from a development perspective but also allows time for cultural shifts in the organization to take place.

Step 3: Build awareness:

Once you have clearly outlined the long-term strategic goals, during the planning and goal-setting phase, make certain objectives for the data analytics program are documented and communicated with all personnel who will be involved in the initiative. All too often, I’ve seen business and IT leaders develop their own priorities and silos which is a large reason why so many data analytics projects fail.

One of the first orders of business is to ensure the entire organization understands and is aware of why you are building an analytic-driven organization and how it will support the overall business and growth goals.

Step 4: Appoint a committee

Once you have documented and agreed upon the strategic direction, identify the capable individuals within the FI who, given the time and resources, can select an appropriate technology vendor, software upgrade or technology investment for your data analytics program. This effort should not be driven from a technology perspective — instead it must be a business-led effort based upon the strategic priorities of the FI. Senior managers and employees that represent each of the major business functions can bring broad knowledge of the business, operations and existing technologies to the table.

Step 5: Bridge the talent gap

Too often analytics projects fail due to lack of resources or the right analytics talent. Answer critical questions such as: “Does our organization have the right technical team and data savvy talent to achieve the goals we’ve established?”

Analytics projects often require different skill sets especially with some of the new tools and technologies that are available. Drill down and make sure you have the right people in your organization to transform data analytics into profitable insights and actionable information.

As your organization begins to fully leverage data and analytics for decision-making, key staff such as a chief data officer, data stewards, and a data governance team will become increasingly important.

Additionally, when you start combining business, IT, data, and corporate strategy issues all on the same project, you need clear and experienced leadership. Recommended the organization hire experienced outside consultants and third-party partners that can help assess your staff, technology capabilities, and readiness before you launch any data analytics program.

Stay tuned as we provide the final critical steps in deploying your data analytics roadmap.

Does it sound complex so far? Yes, data analytics can be complex especially if you don’t have a roadmap in place to guide your strategy. But, if executed well, analytics systems can have an enormously positive impact for your organization.

Still skeptical? The Knowlton Group can help. Our expertise, years of working with FIs on assessing and implementing a proven data analytics strategy, can work for you.

Contact us today to learn how!


(1) The Balanced Scorecard, authors David Norton and Robert Kaplan

By now, you’ve undoubtedly heard the terms “artificial intelligence” and “machine learning”.  If you haven’t already, take a quick read of our previous article where we explain the basics of machine learning.

Today, banks and credit unions are learning how to use the power of artificial intelligence (AI) to boost customer engagement, decrease costs, improve revenue, and pin-point fraud. AI is poised to truly revolutionize the way financial institutions gather information, harness data and interact with customers and members.

So, what exactly is AI?

AI all started out as science fiction: computers that can talk and think like humans.  Industry experts use the term artificial intelligence as an umbrella term that includes multiple technologies, such as machine learning, deep learning, and computer vision.  AI is the general field that covers everything that has anything to do with programming machines with “intelligence,” with the goal of emulating a human’s unique reasoning ability.  Think of AI as developing computer systems to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, translation between languages, and much more.

Uses Cases and The Power of AI

From Google’s development of the driverless cars to Skype’s launch of real-time voice translation, AI is now becoming an everyday reality that is changing aspects of our lives. To give you a better idea of how AI is becoming more prevalent and how it’s evolved, here’s a short list of popular AI use cases and some applications FI’s are widely embracing today:

Voice enabled assistants. Did you know the first tool enabled to perform digital speech recognition was the IBM Shoebox, presented at the 1962 World’s Fair? Today, everyone is familiar with voice assistants and other smart device voice technologies. As more and more people gain familiarity with voice assistance to quickly gather information, we will be seeing an increase in acceptance of and a rise in the demand for other applications that rely on voice enabled technology.  From healthcare to driving directions to workspace operations, this form of AI can make a significant impact on how businesses operate.

Financial services are a high-profile industry for voice assistance. In December 2017, Jack Henry’s Symitar® division introduced voice-enabled financial transactions to Amazon® Alexa®  through its Financial Innovations Voice Experience (FIVE) solution. Consumers can simply speak to Alexa to conduct a wide variety of transactions such as: check account balances, transfer funds, make payments, get loan payoff amounts, cancel payment cards, and more.

Smart Assistants: Smart assistants and home robots like Aido have come into the domestic scene. From assisted healthcare to automated customer service, consumers are experiencing the power of smart machines all the time. Even the Drone technology has been re-designed to accomplish tasks for you autonomously by a command on your smart phone.

The capabilities and usage of smart assistants is expanding rapidly, with new products entering the market. An online poll found the most widely used in the US were Apple’s Siri (34%), Google Assistant (19%), Amazon Alexa (6%), and Microsoft Cortana (4%).

Marketing Automation: Retailers and big brands are investing in the power of AI to further personalize and customize marketing emails based on customer preferences and behavior to engage them more and to prompt consumers to make a purchase. AI tools and software allow companies to send customized email newsletters based on previous interactions recipients have had with content to create a richer, more engaging brand image.

Risk Management: Fraud detection and risk management is an imperative focus for banks and credit unions. That’s why AI is being applied to fraud mitigation technology at a rapid pace. Through AI and algorithms, financial institutions are more effectively mining data to uncover suspicious activity and meaningful patterns, which then translates to information used to detect, spot, and mitigate fraud. Using AI to identify accounts, customers or transactions, for instance, that have unusual characteristics can expedite warning signs of abnormalities and verify suspicious activity that fraud is taking place.

Analytic Tools: Financial institutions realize they have a head start with the application of AI, since they have large data sets and experience with analytical tools.  Improving the customer experience is one of the greatest use cases for banks and credit unions since AI and advanced data analytics provides the opportunity for improved and faster decision making by deriving deep and actionable insights (e.g. customer behavior patterns). Some of these interactions will be with new voice or chatbot technology, while other applications will be behind the scenes, supporting marketing communication.

The use cases of AI are limitless—especially for financial institutions. AI helps us open our minds to how machines can help perform task more efficient and more accurate, while delivering greater overall results.

By partnering with right data analytic professionals, the power of AI and the insights it leads to can be realized faster, ultimately determining the financial institutions’ competitive differentiation in the future. If you have questions regarding AI or machine learning, contact The Knowlton Group today.


Financial Brand:  How FI are Turing AI into ROI. Sept. 2017

USA Today: June 5, 2017: Apple Unveils $349 HomePod to bring voice to home audio

Dataversity: AI overview May 2017
Internet of Things: Tech Target


Explaining the Basics of Machine Learning

Financial institutions are no strangers to Machine Learning. Many institutions are investing heavily in this technology to improve cyber security, customer segmentation, and marketing campaign management.

To simplify the discussion, think of machine learning as self-driving cars, practical speech recognition, and effective web searches. Machine learning is the science of getting computers to act and learn from data without being explicitly programmed. Machine learning works with data and processes it to discover patterns that can be later used to analyze new data. Industry experts refer to machine learning as “training” that requires sending large amounts of data to the algorithm (a process or set of rules to be followed in calculations or other problem-solving operations) and allowing the algorithm to adjust itself and improve.

There is no way a human can look at large volumes of data and make sense out of it. Even if it is possible, the data would be peppered with errors. Precisely why machine learning is considered a significant technological development that is so widely used in everyday situations that you probably experience without even realizing it. For example, have you visited an online store and looked at a product but didn’t buy it — and then saw digital ads across the web for that exact product for days afterward? Has your credit card been declined while you were traveling or on vacation? You may have been on the receiving end of machine learning.

Machine Learning Use Cases

Machine learning’s capabilities are proving to be particularly useful in identifying patterns across large volumes of customer and user data and helping drive better outcomes. Here are a few use case examples of its impact on certain industries.

Fraud Detection: Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering. PayPal uses tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.(1)

Automotive Industry: Self-driving vehicles could lead to a safer, cleaner, more efficient future for transportation. Software developers use machine learning algorithms to power computer vision that allows the vehicle to make decisions in ways that are similar to human decision making.(2)

Health Care: In computer-aided diagnosis (CAD), machine learning techniques have been widely applied to learn a hypothesis from diagnosed samples to assist medical experts in making a diagnosis. Machine learning has recently made headlines by helping to identify cancerous tumors on mammograms and to identify skin cancer. In a trade medical report, the results of a deep machine-learning algorithm helped diagnose diabetic retinopathy in retinal images. (3) Additionally, machine learning can be used to understand risk factors for disease and assist physicians by more effectively diagnosing and treating patients.

Machine Learning Then and Now

Machine learning continues to evolve and prove its worth in endless applications. Ten to five years ago, companies were limited in their ability to analyzing data sets. The technology to learn from massive data simply didn’t exist. Today, with fast computers and swaths of data sets, companies can analyze and learn things that are much more complex. Plus, the renewed interest in machine learning in recent years has exploded even more due to the amount of data companies collect, consume and compute has grown exponentially.

Beyond just a new technology buzzword, machine learning is reshaping many industries in a wide range of applications. Leveraging the insights, predictions and data-backed decision-making poses a tremendous opportunity for industries—especially for financial institutions. Through machine learning and the data insights produced, financial institutions can significantly improve business decisions and business outcomes—and machine learning will continue to refine and improve these outcomes over time.

Look for our next articles that explain artificial intelligence as we explore ways in which financial institutions can harness the power of AI and machine learning.

If you are interested in learning more about how your financial institution can best utilize machine learning applications, contact The Knowlton Group today. We are experts in data analytics and offer several services to enable your organization to become data-driven.

1. Forbes, September 2016: Top 10 AI and Machine Learning Uses Cases Everyone Should Know About
2. IoT for All: November 2017
3. The JAMA Network Journal: Dec. 2016

Data is rising at an incredible pace, covering all aspects of a consumer’s life. In the past two years, more data has been created than in the entire previous history of the human race. (1)

2017 has certainly been the year that data and analytics has redefined the financial services industry. For those financial institutions leading the way in data analytics initiatives, a survey reported that 48% of organizations are achieving measurable results from their data analytics investments – the first time the survey has found a near majority since it began in 2012. (2)

As we look back on data analytics maturity in 2017, here are a few highlights of use cases shared by financial institutions that are experiencing real value—and a substantial return on investment from their analytics initiatives.

Effective Marketing and Segmentation:
Suncoast National Bank, based in Stuart, FL realized that it needed to find a better way to target customers and promote to them more effectively to reduce customer acquisition costs. Combining data analytics and marketing automation software, the end goal was to gain insights into their customers’ future financial needs and behavioral trends. Using customer data analytics, their marketing staff was able to run dozens of targeted marketing campaigns that, in some cases, generated returns on investment in excess of 100 percent. (3)

Improved Efficiency:
Ohio Health Care Federal Credit Union has capitalized on using data analytics. Deploying big data initiatives, the credit union has transformed their member data into actionable goals that allow Ohio Health to remain competitive and relevant, discover trends in member behavior, and enrich member relationships. With the proper data in place, Ohio Healthcare was able to automate the on-boarding process for both direct and indirect loans. (4)

Prior to relying on data analytics, the process of on-boarding was entirely manual. After organizing the data and automating the process of sending emails and letters, the processes were evaluated on a daily basis and executed automatically to the appropriate audiences. They were able to set up a process to automatically identify and target members with upcoming home equity line of credits expiring, and send them a timely informational letter about renewal.

Enhanced Member Experience:
Affinity Plus Federal Credit Union tapped into its large data warehouse from a CUSO, OnApproach M360, to identify members who could benefit from refinancing their mortgages. The goal: to enhance service through the use of data analytics. The St. Paul-based credit union used information from their data warehouse to identify 1,400 members who would benefit from refinancing into short-term mortgages. To date, members who refinanced will save more than $2.6 million and the credit union has written $28 million in new loans. Affinity Plus recognized an opportunity to look at the data in a way that they could proactively reach out to members and save them money. The refinancing campaign was the first outreach initiative to come out of the change. (5)

Addressing Regulatory Compliance Constraints:
Regulatory compliance is a major concern for most financial institution executives. Many of the Dodd-Frank and Consumer Protection Act rules took effect in the last few years—adding even more regulatory burden to financial institutions. Data analytics is helping both banks and credit unions gather, organize and analyze data, compile reports and comply with requirements. (6)

In 2017, more and more banks and credit unions relied on data initiatives to make their compliance management operations easier, more efficient, and less costly. The vast expanse of information now available can help financial institutions remain in compliance by providing information about lenders and customers. Examples include using predictive analytics to detect suspicious activity before it happens and leveraging personal information that helps with the “Know Your Customer” regulations.

Data is the Foundation for the Future of Banking

As we’ve seen in these examples, data and analytics has and will continue to be a tremendous asset for financial institutions to remain competitive, profitable, and operate efficiently in 2018 and beyond. Your insights are only as good as your data, so be sure you have the right people and tools in place to collect and analyze your data assets in the coming year. The results can be transformative not only for your financial institution but, most importantly, your members and customers.


1. Source: McKinsey & Company Report: Straight Talk About Big Data http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/straight-talk-about-big-data 1
2. Big Data Executive Survey

3. Small Banks Using Data https://www.bankdirector.com/index.php/magazine/archives/fintech-issue/small-banks-using-big-data

4.Big Data and Small Credit Union

5. Affinity Plus Enhances Service through Data

6. Big Data Addresses Credit Union Compliance Mandates

Today’s A:360 discusses why it is critical to boil analytics down to well-defined questions. A question is the fundamental building block of analytics. Well-defined questions can shape and simplify the delivery of analytics to an organization. For those business users who aren’t quite sure what data they are looking for, helping them shape a question can be an excellent starting point.

Watch and Listen

Click to Watch on YouTube.

Listen to the Podcast

Click to Listen on SoundCloud
Click to Listen on iTunes

Read the Transcribed Audio

Hey everyone. Welcome to today’s A:360. My name is Brewster Knowlton, and today we’re going to be talking about why success with analytics needs to start with a well-defined question.

I’ve seen a lot of instances where individuals go up to their analytics staff and they’ll ask some very general questions like, “I want to see more data” or “show me some analytics”. That’s like going up to someone and saying, “I want dinner”. Well, what do you want? There’s a lot of choices. The same thing goes for analytics. There has to be that specificity.

The best way to define that specificity for analytics and define what you’re really going for is to frame every analytics idea or every analytics objective in the form of a question.

Asking, what are you trying to accomplish?

If you can get the business users who are requesting information and analytics from you to ask a question, that helps change the context of conversation that you have with them. It’s not about simply producing a report, it becomes about helping them answer a question. This changes the parameters and context in which you gather information and present it. But, in order to do that, you must start with a question.

Often times, people will ask for data and/or reports just so they can try to figure out what they’re looking for. They have an idea, but they don’t quite know how to articulate what they are looking for in the form of a question. This is where a strong analytics team can really show its strength. It’s not so much in the technology, it’s in helping the business translate what they’re trying to figure out into a well-defined question, and then figuring out can we go about answering that question.

This may sound overly simplified, but this really is the fundamental starting point for analytics. There’s an article that I wrote that’s called, “When Life Gives You Data, Make Information”. It talks about the distinct difference between data and information. At its core, it really comes down to asking a question.

The difference between data and information is that data is just raw numbers. Information is the actionable intelligence built off of that underlying data. Let’s look at an example from business users in lending. suppose one of your business users comes to you and says that they need a report of all loan applications in a pending status. They’re really trying to ask a couple of things. For instance, they may be trying to figure out how to improve their close rate. Or, answer the question “why are so many loan applications falling off before being approved?”. They may be trying to figure out how to increase throughput or productivity. My point is, they haven’t really defined a question, and as a result, they’re grasping at straws. They’re looking at all of this data and trying to make sense of it. Helping these users to frame a question at the very beginning, not at the middle or the end of the analytics gathering process, can help them target exactly what they’re looking for and may allow you as the analytics individual within your organization to be able to better provide what they really need or what they’re really looking for.

Again, this seems oversimplified. It’s funny because I’m sitting here doing over a four-minute podcast about why it’s important to ask a question with analytics. But it really is something that falls by the wayside, especially as we get inundated with requests. People will just say, “I want data. I want data. I want data”. My suggestion is to take a momentary step back, and. as simple, insignificant and superficial as it may seem, just ask: “What question are you trying to answer?” That question alone will help spark a conversation that I can assure you will improve the process by which you can deliver analytics to your organization.

That’s it for today. Thanks again for listening to today’s A:360.

Subscribe to have new content sent directly to your email!


Photo Credit

More and more financial institutions are investing in developing their own analytics teams. Data warehousing and other modern analytics platforms are becoming the norm and not the exception. As these organizations start to develop their data strategy and implementation roadmap, some of them find that their data is being held hostage.

What do I mean by that?

Let’s assume that you are running a CRM system, for example, that is on-premise. More likely than not, the data for that CRM system is being held in a SQL database. Getting data out of a SQL database is easy in the world of data warehousing and analytics.

Now, let’s assume you are running a loan origination system (LOS) that is a hosted, third-party application. Except for a handful of exceptions, you will not be able to access a SQL database housing this data directly. However, your analytics team needs to get this data out of the hosted environment. You likely will call up the vendor, and they will give you a quote for how much they will charge you to provide you with your data.

Let me repeat that. They will give you a quote for how much they will charge you for YOUR data.

Avoiding Data Hostage Situations

Access to data is typically an afterthought in the product evaluation process for new software acquisition. As more organizations take steps towards becoming data-driven, the need to have easy access to their data will become even more critical than it already is.

Data access, then, should become part of the software evaluation process – a forethought instead of an afterthought.

Most vendors have the means to provide the data to you a number of different ways. By waiting until after implementation, however, data access becomes a product and/or service increase as opposed to an existing feature of the software acquisition. This is typically where you receive a quote for how much it will cost to have data delivered to your organization.

Some of you may be reading this saying “but I can access all the data I need from a web portal they’ve provided to me.” In that situation, reports must be manually opened and downloaded if you want to do anything with that data. Your analytics team will need data automatically downloaded or transferred to a specific location on a regular (usually, nightly) basis. Access to a reporting portal that requires manually downloading of reports and data is insufficient for a data-driven organization.

Key Point: Negotiate access to raw data at the beginning of the software acquisition process not after it has been implemented.

Ways Data Can Be Delivered

Most vendors have several ways of automatically delivering data to your organization:

  1. SFTP (Secure File Transfer Protocol) – a secure way to send files to and from vendors. For those vendors that cannot allow direct access to a SQL database, this is usually the most common delivery method.
  2. SQL Replication – some applications (shoutout to MortgageBot) will set up a replicated SQL instance on your network. Put simply, they are putting a copy of the production database on your system for reporting purposes. This a dream come true for analytics teams that need access to raw data.
  3. Physical Copies of Database Backups – some vendors are able to send you a physical copy (i.e. encrypted external hard drive) that contains a copy of a SQL database up to a certain point in time. Then, they can SFTP over backups and/or log files that update the database. A hybrid of the first two options, there is a bit more work in this solution, but it is still a viable option
  4. API – as credit unions and community banks start to build their own development teams, APIs are becoming more commonplace. Think of this as the language through which a development team and an application could communicate. Depending on how open the API is, this may be a sufficient option to gather the raw data required by your analytics team.

There are few other delivery methods, but, for the most part, they are derivations of the methods already mentioned.

As you are negotiating or re-negotiating with your vendors, make the conversation about data access and delivery a priority. Some of the most successful financial institutions are achieving their success through their increased use of data analytics.

Avoid having your data held hostage and make data access a priority in all software evaluation processes.

Subscribe to have new content sent directly to your email!


Photo Credit