On February 23, 2021, The Knowlton Group was fortunate enough to present with our great client partner, Logix Federal Credit Union, at the Callahan & Associates hosted Credit Union Tableau User Group.

Nicole Lopez, the Manager of Business Intelligence at Logix, highlighted some of the fantastic ways that her team has leveraged the VeriCU Data Platform to deploy actionable analytics-driven output to critical lines of business and executives.  The video above is the replay of the webinar if you weren’t able to join us live.  I’d highly recommend that any credit union (those with and without existing analytics teams) watch this video to learn about some great practical analytics use cases.

Creating a clear path to design your data analytics initiatives around the Credit Union’s business goals. 
Summary:
As organizations look to accelerate their data analytics competency, one of the big strategy questions is “what is the best approach to getting there?”. The key to unlocking your credit union’s analytics potential includes:

·        A thoroughly articulated data strategy and business plan

·        Quick implementation of a scalable/adaptable data warehouse

·        Robust visualization tools

·        Expertise of a data scientist and/or chief data officer

Today, analytics needs to dive deeper into the behavior and decision-making processes of members through analysis of critical, real-time data.  In our new digital and data analytics age, credit unions are relying more on data than even just a year ago, pre-pandemic, to serve members and thrive in a highly competitive market.  If using data to its full potential is something your credit union struggles with, you are in luck.  This quick read shares four actionable strategies you can put in place to get the most value and ROI from data analysis.

A Strategic Road Map for Success

Like most successful ventures, you need to start with a plan – a roadmap – that drives accountability, goal-setting and metrics for success.

We encounter countless organizations that are ready to transform their data into knowledge, growth and profits, but they are still hazy on that first step – Defining the desired business outcome(s) of data analytics.

Teams will need to clearly understand and outline the long-term strategic goals of the credit union (i.e. growth, branching, new products and services, etc.) to identify and select technology solutions that fit the requirements both today and, more importantly, where you want to be in the future. Otherwise, you run the risk of investing in technology, resources, vendors, and staff that might not fit the credit union’s vision; and the cost of pivoting later can be significant.

Analytics efforts should empower data-driven decisions and fuel growth.  As part of this strategy assessment, it’s essential to understand the specific, measurable actions your credit union wants to undertake.  In our experience, we’ve seen tremendous success with past CUs by driving digital branch growth through a focus on identifying member segments and delivering targeted marketing actions.

Too often, executive management teams have an unclear strategic direction with regards to their technology investments and how they align with their corporate objectives. Take the time to clarify the overall strategic direction and outlook. Without this direction as a guide, the credit union’s technology decisions end up driving the overall strategic direction by accident.

Maximize the Value of a Data Warehouse

Are your data strategy and business objectives clarified?  Are you driving growth through measured, systematic, recurring action lists from analytics? If not, now it is time to ramp up the data analytics potential by using a data warehouse. Data warehouses can yield a tremendous ROI for organizations that leverage them to their greatest potential.

Rapidly evolving and improving data warehouse technologies have greatly benefited the financial services industry.  They allow organizations to easily retrieve and store valuable data about members, products, services and more. Data Warehousing solves the ongoing problem of analyzing data from disparate systems and transforming it into actionable information you can use.  It provides a plethora of benefits to a credit union, such as ensuring data consistency, providing a single source of truth to store and cleanse data, improving speed and accuracy on insights, and generating greater efficiency and time-saving solutions.

Developing and maintaining sophisticated data warehouse systems is often too expensive for individual organizations, so many have partnered with service vendors and their cloud-based platforms. To successfully build and deploy your data warehouse, start with a strong plan and foundation. Be sure you have executive sponsorship, adequate architecture and documentation, an implementation team, a sophisticated data integration plan and scalable and adaptable data warehouse.

Waiting to get a data warehouse solution implemented will lead to lost opportunities and insights that could have been gained through an analytics solution. The Knowlton Group’s guide: Buying vs. Building a Data Warehouse drills deep into the advantages, and some of the hiccups, of both scenarios.  Fortunately, our 72 hour Data Warehouse, VeriCU, is designed with a strong pre-built foundation and tons of customizable components. It blends the cost and speed of implementation of a pre-built solution with the flexibility and customization of a custom-built data warehouse.

Extracting Needed Business Insights

With a robust data warehouse in place, you can now get the insights that lead to innovation, superior member services, greater efficiency and so much more. The faster you can extract key insights and analyze data through sets of queries, the further your credit union can achieve business goals.  Visualization tools are paramount to make this happen.

Sophisticated visualization tools allow you to dial into the most important aspects of your member data, such as the profitability of member, branch, region, household, or product.  Visualization tools allow you to segment members based on any segmentation you define, so you can further improve member engagement and marketing messages. You can run reports that share defined information on member attrition, including which members are most at risk of leaving, with specific action plans to boost retainment. Imagine assigning an engagement score to each member based on their overall activity and tracking progress as targeted action helps drive members to the segment-specific ideal-product-bundle.

 Putting Data Science in Practice

Sure, most credit unions don’t need an army of data scientists to put their data to action. But, they need experience, expertise, and real analysis to solve key challenges and unlock what’s working and what can be improved within the organization.

One of the big impediments to implementing a data and analytics program that delivers business value is a misalignment between the business organization and the data organization.  The guidance and experience of a data expert, such as a Data Scientist, Chief Data Officer, or Data Strategy Officer, will help overcome this challenge.

A 2021 survey1 reveals that 65% of organizations have appointed a CDO, up from just 12% in 2012.  Why?  A CDO keeps 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,  you will 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. Striking the right balance and partnership with the business side of the organization and the data analytics initiatives through a CDO is a winning formula.

While many success stories confirm data can add enormous value, most organizations still struggle to build data analytics into their business strategies, and to align their data efforts to the needs of the business.

With proper integration, data can accelerate many business strategies by improving the processes and empowering the people needed to execute them. Propelling your data, unlocking its full potential, and solving key business issues with data does not happen overnight.  Let’s help you lay the groundwork today.  For guidance, contact The Knowlton Group http://knowlton-group.com/

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Sources

  1. CXO Today Survey Report

https://www.cxotoday.com/big-data/companies-continue-to-struggle-with-big-data-ai-investments-finds-study/

 

5 Business Value Drivers to Optimize Your Data Warehouse Investment

Data warehouses can yield a tremendous ROI for organizations that leverage them properly. However, many financial institutions that invest in a data warehouse are not maximizing the potential business outcomes that will drive a positive return on investment.

The amount of stored data and exceeding interest in data analytics has resulted in the growing popularity of data warehousing solutions and analytics platforms. Over the years we have seen that as data management and analysis needs have skyrocketed, the market has introduced data warehouse solutions to address the evolving data centralization and storage dilemma. Well-built data warehouses offer the ability to store vast amounts of data and deliver fast results that a traditional database could not compete against.

As credit unions move away from the traditional database to a data warehouse, whether it is built, purchased, or in the cloud, it’s essential to use the data warehouse strategically to optimize all the potential value it promises.

If your credit union has invested in or is considering purchasing a data warehouse solution, are you leveraging its full value?

Here’s a quick Q&A on value drivers to get you started:

Value Driver #1: Are you gaining faster insights and better decision making?

The goal of a data warehouse is to help answer business questions faster.

In what once took days, can now take seconds. This provides management the ability to make clearer and more accurate decisions regarding the credit union’s members, products, services, branches and so much more. A data warehouse allows the credit union to analyze their data effectively as well as trust it! It creates accuracy, dependably and democratizing data so that more than just the purely technical employees can access it— your team can use and benefit from the insights gleaned from running data queries. This avoids bottlenecks and allows management to make decisions based on pure, clean, data—not on gut instincts.

The data warehouse provides a way to mine, refine, and harvest actionable insights faster – increasing the amount of time available to realize the benefits from those insights. The best data warehouses allow you to gather and analyze various types of data from diverse sources and not only collect and convert that data, but turn it into action plans that drive business decisions for the organization.

Value Driver #2: Are your business units/departments aligned and embracing the benefits of the data warehouse?

Each line of business in your organization (e.g. sales, marketing, lending, product management), and third-party tools should have a centralized data warehouse.  Your data warehouse should not limit data access or produce silos of data. Data remains centrally stored and can be accessed through any warehouse, as long as the user has the necessary permissions.

Most credit unions operate hundreds of disparate application systems.  Individual departments in an organization often focus on their own narrow system and information needs and don’t see the corporate value of integrating data.  A data warehouse helps breakdown those silos of disparate data so your data doesn’t get out of sync.  Best-in-class data warehousing technology will enable better department alignment, a single version of truth that ensures clean and accurate data management that everyone can rally around.  All this leads to more informed managers, making data driven, objective decisions – for which you’ll see the results to your credit union’s business outcomes.

Value Driver #3:  Is the data warehouse automating data collection and scaling with your credit union?

The best data warehouse solution should provide automated end-to-end data management—from initial data collection to analysis and reporting.

Your organization’s data assets will continue to increase, therefore, a data warehouse is one of the best available tools for managing data growth by enabling archival, aggregation and analysis of data from many different data sources. Whether an in-house or cloud-based solution, data warehouses can be highly scalable solutions. Having scalability to meet business needs is an important value driver to take advantage of as it will allow your organization to automatically scale to support any increase in data, workload, and concurrent users and applications without the need for data movement or expensive upgrades down the road.

Value Driver #4:  Are you utilizing powerful visualization tools?

A data warehouse makes it easier to work with popular visualizations tools like Power BI or Tableau. This enables non-technical users to quickly build insightful visualizations and dashboards to highlight critical information in an appealing fashion.  These tools can be used for advanced analytics output as well as traditional operational reports.

Value Driver #5:  Are you easily integrating key non-core data sources within your data warehouse?

With the increasing volume of information collected through a variety of channels, credit unions need a single interface where data is collected and stored – integrating data from common non-core applications such as loan origination systems, third-party servicers, credit and debit card processors, GL systems, and much more.

The right data warehousing solutions allow you to easily combine data from multiple sources to create a unified, single view of the data.  The ultimate goal is to provide users with consistent access and delivery of data across the entire credit union.  Data integration benefits everything from business intelligence and member segmentation to data governance and real time information delivery. Data warehouses can help bring disparate datasets together to further increase the value of your information.

Are you ready to drive greater business value? Getting the most out of your data warehouse means making smarter, quicker business decisions.

If your credit union is embarking on a business intelligence journey, or already down the path and looking to increase ROI of your data analytics investments with a powerful data warehouse, learn more about VeriCU data warehouse platform created by the Knowlton Group.  You could be up and running in 72 hours driving value data insights right away.

 

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Transforming data into a strategic asset means empowering your teams to have fast access to accurate data that they can put to action.  Data and analytics are the key accelerant of an organization’s digitization and transformation efforts. Yet today, fewer than 50% of documented corporate strategies mention data and analytics as fundamental components for delivering enterprise value. 1

Organizations who want to compete in the emerging digital economy need to be strategic on how they go about analyzing and managing data. Companies know their data is a strategic asset and they want to utilize it to make smarter decisions; but the problem is it takes dedication, a cultural shift, empowered people, tight processes, and robust technology.  Those who follow through however, will be the success stories of tomorrow.

Let us uncomplicate the process.  These 5 steps will get you on a well-chartered course to turn your data into a strategic asset.

 

Step 1: Build a Data Strategy & Roadmap

Strategy without execution will fail just as execution without strategy will also fail. Moving towards a more strategic approach to capitalize on data is certainly attainable, and it starts with a Data Strategy. A Data Strategy addresses more than the data; it is a roadmap that defines PeopleProcess, and Technology. Creating your strategy begins by addressing some critical questions and filling gaps quickly.

  • What are your objectives and use cases that can turn data into an asset? 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.
  • How can the credit union empower employees to use the data? If you empower employees through a high-performing culture, a vision of the future, open communication and access to data and analytics assets, you can accelerate delivery, improve quality, and drive user adoption and future success with data.
  • Do you have the right processes and tools that ensure data is accessible and of high quality? Having a robust process and high-quality control disciplines that governs the overall management, usage, storage, monitoring, and protection of the credit union’s data is a critical step in the data strategy.
  • Do you have the right technology that will enable the storage, sharing and analysis of data? As data becomes an even greater asset for the credit union, the ability to store large amounts of complex data in a unified, central database, known as a data warehouse


Step 2:  Create Reliable, High-Quality Data Sets for Rapid Analysis

A vital step when transforming data into an asset is having a proper data collection system. The credit union can amass enormous volumes of data from several sources in a short period of time, but not all of that data is relevant for analysis. Start by defining the types of data that are important and which use cases will drive the greatest return. Meticulous data organization is pertinent for analysis, and it will enable you to remain in control of data quality while improving the efficiency of analysis.  Data cleansing is imperative and will help to ensure data analysis is centered around the highest quality, most current, complete, and relevant data.

If your data is clean, well-organized, and free of silos, the next step is to segment your data for a more detailed and focused analysis. Going back to step number one, define your business objectives and use cases. Consider and plan for how the data will help you achieve this goal and segment the data accordingly. You can sort data into relevant groupings to analyze trends within the various data subsets.

Step 3: Use Information as a Competitive Edge to Drive Growth


In his book, Infonomics, Doug Laney explores countless examples of how organizations can assert economic significance from data. He dives into how organizations should measure, manage, and monetize information as a real asset.   These 3 steps include:

Monetizing information starts with generating economic benefits from available assets to drive measurable business value.

Managing information is to apply asset management principles and practices to information.

Measuring Information is to practice  gauging and improving information’s economic characteristics.

As Doug puts it, “most organizations have a better inventory of their office furniture than their information assets!”  Clearly, the future of data is all about moving from volume, velocity and variety to monetize, manage and measure.  2

 

Step 4: Hire The Right Talent

Data is your organization’s second most valuable asset. The first is your people. Ensuring you have hired data savvy talent that understands how to mobilize data across the entire business ecosystem to serve customers and create valuable data products is the cornerstone of transforming data. As many data analysts and CIO roles evolve to focus less on system uptime and more on using data to drive the credit union forward, having the right data-savvy talent is key.

Data expertise must remain at the heart of a data-centric approach. Investment in core data skills is required to get maximum value out of data and technology, and to ensure that the right processes are in place to translate insight into financial gains.

 

Step 5: Build A Data-Oriented Culture


If your company’s culture does not inspire excitement about leveraging data in new ways to propel the credit union forward, then you will have a hard time achieving your business goals. Building a team who is dedicated to the organization’s success and focusing on new and innovative ways to use the data strategically will make all the difference.  To build a data-centric organization, ensure you are cultivating an organizational culture around data. Treat data as an asset and give employees tools and empowerment to make the transformation.  A few other ways to inspire a data-driven culture include:

Ensure data analysis is a key part of the leadership decision-making. Your management and leadership team will set an example that will trickle down throughout each tier of management and among employees, leading to lasting transformation.

Remove silos and make the data readily available throughout the credit union. Provide every employee access to the data, so they can perform their duties more effectively. Also be sure to educate your teams on the importance of data security, privacy and governance with proper protocols that all must follow.

View data as a key focal point of every decision and strategy. Encourage employees to routinely analyze data and then develop questions and observations from it—making it a rewarding part of their role.

Promote data literacy across the organization.  When employees have the ability to read, understand, and communicate through the use of data they are more engaged in the process and vision.

Share data successes. Celebrating the individuals and teams behind successful outcomes is essential to promoting a healthy data culture.

If you can encourage and drive this cultural shift, invest in the right people, processes and technology, there is every chance that your data will be treated as the asset it truly is—and you and your organization will be well-positioned to reap the rewards that investing and nurturing your data can bring.

What about your organization? Do you have systems in place to effectively and consistently transform data into an asset that can dramatically benefit your organization and members? If not, The Knowlton Group would like to help. Learn More, www.theknowltongroup.com

Resources:
1. https://www.gartner.com/smarterwithgartner/why-data-and-analytics-are-key-to-digital-transformation/

2. Infonomics, https://www.gartner.com/en/publications/infonomics

When used properly, data can be the fuel that propels organizations towards growth and success.  Most credit unions understand that the data that has been collected over the years can be one of their most valuable assets to improve member experiences, boost efficiency and revenue, and make faster, more accurate business decisions.

The problem we all face is having too much data that may not be properly compiled, cleansed, and organized. In other words, it isn’t information — it’s just data and raw numbers.  So, how do you remove the data clutter and get better at data analytics that transforms unorganized data into insightful, actionable information?

These tips will help you and your team transform the mountains of data into steps to improve your data analytics strategy:

Clearly Define Your Key Performance Indicators (KPIs)

Having clearly defined organizational KPIs gives your analytics efforts a guiding direction.  There are thousands of different analytic opportunities your team could explore.  But, if they don’t align with the organization’s strategic objectives (which the KPIs should directly measure), then is that effort really worth it?

If a KPI is to lower the average cost of funds, then your analytics initiatives shouldn’t focus on how to target more CD deposits but gaining greater engagement in core deposits.  This strategic-to-operational alignment needs to exist in your analytics team’s efforts to maintain clarity and focus.

Define Business Objectives

The first step in the process of turning data into information is asking questions and defining your business objectives. Data for the sake of data is meaningless. Start by outlining some clearly defined business goals, use-cases and objectives around your data analytics and analysis. Figure out the questions you would like the data to answer.

For example, how many members have obtained an auto loan or mortgage in the last 24 months but do not have an active checking account? Why are so many loan applications falling off before being approved?

To get the best results out of the data, a question (or series of questions) needs to guide your starting point.

Avoid Confirmation Bias

Many organizations use data today to add support to a belief they already hold.  When viewed from a biased perspective, it is easy to manipulate an interpretation to match a previously held belief.

Successfully embedding analytics in the decision-making process requires you to eliminate confirmation bias at the outset.  Treat each analytics use case with the scientific method approach – establish a baseline hypothesis and objectively measure whether that hypothesis is true or false.  Much organizational learning will come from this rigorous and honest approach to data.

Don’t Let Perfect Get in the Way of Good

Many organizations that are more immature in the analytics journey fixate on superficial data quality issues that arise.  Things like bad social security numbers or inconsistent addresses drive most executives crazy.  But if that data is only 90% or 95% accurate, will your decision change substantially with 100% accurate data?  The answer is almost always, no.

Strike the right balance between data quality and data integrity without chasing some ideal perfection that will never be attained.  Sometimes good enough is good enough.

Successful Analytics Programs are not Grassroots Efforts

Executive support is one of the leading reasons why some analytics succeed, and others fail.  Support for using data as decision-making tool needs to spread from the top of the org chart down.  Without this clear top-down buy-in, mid-level managers will not be required to provide data-driven justification for their recommendations.  This then proliferates further into the organization and minimizes the importance of using data.

Successful analytics initiatives requires executives to hold their teams and each other accountable for bringing data-driven discussions to the table instead of instinct-driven ideas.

 

Many credit unions are starting to see returns on their data and analytics initiatives while improving the decision-making process across the organization. But despite some standout success stories, I still witness far too many failing data initiatives. Begging the question…why do so many data analytics initiatives fail?

Consider these roadblocks to success, and how your credit union can avoid them!
1. Too big of an initial scope.
Very large goals have a downside. They can set us up for failure if we set too lofty of an initial scope to achieve, or if we don’t break the focus down and work towards each goal systematically. Start with a clear idea of exactly what you want to do with the data from a business perspective that will drive value and growth. Some common initial use cases include: attract new members, improve member onboarding, improve member engagement, reduce member attrition, core deposit growth, risk analytics and process improvement.

Analytics initiatives can quickly grow out of control since discovering value from data prompts wanting more data. Pick an initial goal and scale your efforts and focus around a refined list of clear objectives.

2. Heavy reliance on the software solution:
Industry data warehouse products and other off-the shelf solutions shouldn’t be viewed as the silver bullet that will magically solve all data issues.

For successful initiatives, data analytics needs to become a business-driven, not an IT-driven, journey. In many credit unions, the teams reviewing the technology and its capabilities may not be the same team using and deploying the technology. This leads to implementation, training, and process challenges later in the deployment process.
Technology is not the solution to business problems. Process, ownership, accountability and a defined strategy are the solution. Decisions around your analytics initiative should revolve around business goals and objectives rather than a technology or software solution.

3. Lack of internal talent:
Credit Union leaders know that if you want strategic execution, you need the right people and teams. To avoid this fixable problem, create a roadmap that gradually builds the skills, talent, and responsibilities that the credit union will need now and in the future. But before staffing up, you need to take a step back and look at what you want to do with your data, and then assign key roles and responsibilities for data collection, management, and analytics.

As the credit union grows and looks to remain competitive, there’s an obvious need to hire the right data talent who are skilled in analytics, who can interpret data, and make recommendations that offer tangible business value. In a previous article I broke down the responsibilities of data talent and key roles including: Chief Data Officer, Data Scientist, Data Analyst, ETL Developer more. Create a team that strikes the perfect balance between business and technology with the right blend of strategic thought and a tactical mindset.

4. Absence of executive support.
While data analytics has gone mainstream, the C-suite and senior leadership needs to drive the cultural changes that will empower utilization of analytics. Successful analytics initiatives require a shift in how the executive management embraces data. There are many steps you can take to foster a sustainable data-driven culture– one credit union employees will adopt and self-reinforce along the way. Becoming data-driven means that leadership should place data at the heart of the credit union.

Executive management should consistently foster the idea that the insights and opportunities born from the data can improve everything from operations and marketing to risk exposure and member loyalty. What gets measured gets done, and this can only be reinforced from the top down.

If your credit union has hit some obstacles along your analytics journey, it’s best to partner with experts who can develop a strategy and a roadmap. The Knowlton Group specializes in helping credit unions navigate through the analytics journey.

Let’s transform your organization into a data-driven credit union. If you are ready to become data-driven, send us an email or give us a call at 860-593-7842.

Data Analytics is becoming the main driver of innovation in the financial services industry. A recent report shows that data analytics investments in the banking sector totaled $20.8 billion dollars in 2017 and will certainly continue to rise as credit union executives leverage the wealth of potential that utilizing consumer data and developing successful, sustainable data strategies enables.

While more and more credit unions are realizing the value and future potential of data analytics, they are still grappling with some barriers, including: lack of in-house talent, how to appropriate their analytics budget, and formulating a data analytics strategy that sticks.

Plotting the Best Course

In my years of consulting credit unions (and work with organizations in the healthcare, retail, distribution, and government sectors) to help maximize the value of their data, one common thread I see among many is the lack of a strategy and a road map that plots the organization’s best course.  What is a data analytics strategy?

The strategy allows your organization to establish goals from the starting point to provide direction, alignment and a clear path to success. I’ve reviewed several of the key benefits of creating a strategy in a past post. Simply put, your strategy will help you create a solid plan, determine your objectives, allow you to bridge the talent gap, communicate the data analytics objectives and mission to your team, set and measure benchmarks, assess technology capabilities and data quality, and so much more.

Additionally, having a data strategy ensures that data is managed and used as an asset and not simply as a byproduct of your organization’s processes. By establishing common methods, practices, and processes to manage, use and share data across the credit union in a consistent way, a data strategy ensures that the goals and objectives to use data effectively and efficiently are aligned.

Making a Data Strategist

Often, I see credit unions attempt to formulate a data strategy internally using two approaches.  The first approach is to assign the task of establishing the data strategy to a line-of-business head. Perhaps, a Chief Lending Officer or Chief Marketing Officer – someone in a strategic position that sees the big picture for the credit union.  The second approach is to pass it off to IT or another technologist.  This individual tends to build the strategy from the ground-up – focusing on the tactical and technical challenges without necessarily knowing the full strategic picture.

The problem with the first approach – using a line-of-business strategic leader – is that these individuals rarely understand the tactical and technical aspects of day-to-day operations that must be factored into any data strategy.  They might be quite adept in their LOB (i.e. marketing, lending, retail, etc.), but they will struggle to adequately understand how the daily use of data and processes must be addressed by a data strategy.  The outcome from these individuals tends to be a very strategic and conceptual deliverable for what data analytics could do for the credit union – rarely does it address the how.

The flaws of the second approach – using a technologist or IT resource to formulate the data strategy – is that these individuals do not have strategic insight of the credit union nor the enterprise-wide perspective that is necessary.  They may know certain systems or processes inside and out – valuable, no doubt – but lack the big picture for how integration prioritization, deployment and change management planning, and other more strategic decisions need to be factored into the establishment of the strategy.  These individuals tend to place too much focus and emphasis on the technology decisions and not enough focus on other critical aspects that a data strategy entails.

What’s The Best Resource/Team to Create a Data Strategy ?

The best individual or team to establish your credit union’s data strategy is one that balances the strategic business needs of the organization with the technical and tactical requirements that must be addressed.  The best team/individual understands that the technology choices you make are no more critical than how you handle cultural change management throughout the analytics solution implementation.  Striking the perfect balance between business and technology with the right blend of strategic thought and a tactical mindset are key.

The catch? I rarely see this work using only the credit union’s internal resources.  Every vendor has a “analytics solution” now and every company that does anything in consulting or IT has a “data strategy service”.  It’s difficult for individuals within an organization who lack the deep understanding of the analytics solution and service marketplace to formulate the right data strategy that isn’t heavily dependent on sales material alone.

Unfortunately, many data strategy service offerings are led by either line-of-business strategic leaders (approach one from above) or by pure technologists (approach two).  Just like this doesn’t work within a credit union, it certainly doesn’t work with your consultants.

Find a firm that has the right resources from both the technology/tactical realm as well as those with deep strategic line-of-business expertise.  Only with that blend of skills will you yield a data strategy that can guide your credit union to both short-term and long-term analytics success.

Looking for help with developing your data strategy and plan for analytics?  The Knowlton Group is staffed by resources with both extensive technical analytics skills and decades of a line-of-business strategic leadership knowledge.  We’ve worked with many credit unions in the past by helping them understand the full impact of data analytics to their business model, define a compelling data analytics strategy and ultimately provide results. From strategy, to conceptualization to full implementation, we are ready to make your credit union a data-driven organization. Contact us today.

Digital innovation is sweeping across the financial services industry and creating opportunities for banks and credit unions to leverage data as a source of competitive advantage.

Until recently, most credit unions were delegating data management and analytics to the IT department, which in turn created data silos that inhibited the enterprise use of data.

Has your credit union made the business case for creating an analytics team to spearhead important data initiatives? If so, you now need to hire or train the right talent that can turn data into value and deliver on your organization’s data strategy

Chances are you have numerous questions whirling around about how to define the key data roles and responsibilities.  When venturing outside the credit union to evaluate data leadership, this list of tips breaks down key roles and how they should align with your needs.

Chief Data Officer

The CDO is a senior executive who bears responsibility for the credit union’s enterprise data and analytics strategy, data governance, data management, and data utilization.  The CDO’s role will combine accountability and responsibility for information protection and privacy, information governance, data quality and data life cycle management, along with using member data to create business value.

This last point is arguably the most crucial.  If your analytics team is not delivering business value then you’re not achieving the team’s full potential.  The CDO should focus on measurable outcomes for specific use cases to provide the necessary cultural and change management sparks to garner enterprise-wide buy-in.

Data Scientist

A data scientist masters a whole range of skills and tasks from being able to handle the raw data and analyzing that data with the help of statistical techniques, to delivering actionable recommendations based on the underlying data.

The title “Data Scientist” has become a bit of a buzzword as of late.  If your “Data Scientist” can query a database, but the extent of the statistical knowledge is mean, median, and mode…they aren’t a data scientist.

Real” data scientists have deep knowledge of statistical and probabilistic models and know how to leverage those models for specific analytic applications.

The Data Analyst

The data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions.  The insights that data analysts bring to the credit union can be valuable in identifying and even helping to predict the needs of the credit union’s members.  They help develop effective ways to collect the data and compile key findings into reports to share with other teams within the credit union.

Think of the data analyst as the individual who translates between the technical world and the business world.  This individual needs to have basic competencies from a technical perspective, but, most importantly, they need to be able to interpret technical knowledge into practical business terms and vice-a-versa.

A good data analyst doesn’t just produce charts, graphs, and other fancy visualizations.  They produce clearly articulated meaning to describe what the visualizations mean to the business.

ETL Developer/Data Engineer

The ETL Developer/Data Engineer is a critical member of the data analytics team as they are dedicated to the fundamental process of capturing, storing and processing your data.  If your CU leverages a data warehouse as your analytics platform, then the “ETL Developer” most aptly describes the job title.  If your organization is leveraging a data lake or hybrid platform, “Data Engineer” is a more appropriate title.

In the end, this role boils down to ingesting new data sources into the platform.  This may come from non-core third-party applications (i.e. consumer LOS, real estate LOS, online banking, etc.) to external data sources (i.e. demographic data, economic indicators, social media interactions, etc.).

Report/Visualization Developer

To effectively deploy self-service reporting and analytics through your BI portal (i.e. Tableau, Power BI, Information Builders, etc.), someone must be tasked with creating these reports and dashboards. This is the critical role of the Report/Visualization Developer.

If your credit union embraces a more decentralized approach to data analytics, then these resources may reside in the business areas instead of centrally managed.  Regardless of where they reside within the organization, this is an essential function for providing a front-end to your analytics platform.

The Right Role for Your Credit Union?

As credit unions grow and look to remain competitive, there’s an obvious need to hire the right data talent who are highly skilled in analytics, who can interpret data, and insight and tangible business value. Demand for data expertise is growing every day. Be sure to understand which roles are specifically needed by your organization.  Most credit unions don’t have the necessary budget to hire each of the resources discussed.  Determine where the greatest internal need exists and identify strategic partners who can assist with the rest of the functions.

The bottom-line, all organizations have the power to become data-driven by accessing data skills – and on almost any budget.  Ready to formulate a winning data analytics strategy?  Contact The Knowlton Group to get started.

Sources:

  1. Gartner Chief Data Officer Survey