Managing Data as a Strategic Asset

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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 People, Process, 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

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Data and Analytics Roles at your Credit Union

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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. 1 

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

 

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Why the Lag Part 2: Solutions to Top Data Analytics Challenges

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If you have reviewed the some of the important steps and hurdles to overcome for credit unions to improve their analytics maturity outlined in our first article on Why the Lag, then you are ready for some more steps in the process.

Challenge:

 Maintaining data quality is a hurdle for many credit unions, but it is a critical component to becoming data-driven. To achieve consistent and reliable member data, credit unions must constantly manage data quality at the source so that they can trust and use the data to enable quicker and more knowledgeable decision-making.  As the saying goes, “garbage in, garbage out” so the importance of clean data can’t be understated.

Solution:

Step one is to know what data you’re collecting, why you’re collecting it and where it comes from.  Make sure that every component is coming from a trusted and knowledgeable source. Validate data as it is entered by automatically flagging missing, incorrect, and/or inconsistent information. Whenever possible, eliminate the opportunity for free-text fields and opt for drop-downs instead.

If you discover problems with incoming data, go all the way back to the original source to make corrections.  The data warehouse or analytics platform is not the place to make those data quality corrections.  Otherwise, you will constantly be correcting for inaccuracies.  Use the data warehouse to identify issues, and then make the corrections at the source.

Challenge:

Lack of leadership buy-in is another challenge we see for those credit unions failing to successfully implement a data strategy. For any new initiative to work well, all departments within they credit union need to communicate, work together and see the payoff of becoming data-driven.  Buy-in will require fortitude and integration into the strategic plan, culture and budget.  This is where analytics becomes as much of a change management problem as it is a technical one.

 Solution:

 To gain support and financial approval for your data analytics initiatives, you need to give senior managers a snapshot of how these efforts can pay-off.  Be sure to provide the “why” the credit union should invest in data analytics and the multitude of ways the data will improve efficiency, member engagement, marketing effectiveness and more. Be transparent and encourage team members to want to be a part of this transformation with concrete examples of how it will improve the “whys” for your credit union (time savings, member service increase, cost reduction, etc.)  Show evidence and examples of how the competition is using data to grow and increase market share.

 Challenge:

 A lack of analytics talent is a major obstacle faced by credit unions desiring to be data-driven. Hiring, training and managing highly skilled, knowledgeable, data-savvy personnel is costly. Given the explosive growth on the job posting sites for those with analytics expertise and the intensifying competition to fill more jobs than there are qualified people, it is difficult to attract and retain the right talent.

Solution:

An effective data analytics talent effort should consider not just compensation but also cultural fit. Striking this balance is critical to set both the data scientist/ data analytics hire and the credit union up for long-term success. Also, consider if anyone in house has the foundational skills necessary to build upon.  Your “Excel gurus” could very well be trained to become your organization’s modern analytics expert.

Millennials, particularly, find it appealing to work with organizations with a strong social and community conscience.  Credit unions inherent operating model – from their community focus to their charitable presence – are well-positioned to offer job applicants the right cultural fit.

Still not sure if your internal team has the right skills?  Consider working with outsourced firms that can augment your internal data efforts.

Is your credit union making the most of member data? If not, what is holding you back?  At The Knowlton Group, we believe that every organization – no matter their size – can become data-driven. The best data and analytics program starts with a great strategy and clearly defined roadmap and implementation plan. Our personalized approach to each engagement ensures that the specific needs and goals of your credit union are captured for maximum results. Want to know how you can further improve your members’ experiences? Let’s talk. Contact me today at [email protected] or call 860-593-7842.

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