Evolving Data Fluency: The Journey to Data Mastery

Information is data in context.

This phrase takes me back many years to when I worked at MCI Communications. MCI reshaped the business landscape for long-distance communications by giving consumers more options at lower costs, beginning with the monumental task of breaking up AT&T. At the time, I remember people commenting that MCI thought of itself as more of a law firm with a microwave tower on the roof.

This was followed by a messy divestiture that opened up a competitive long-distance landscape. The only problem was that AT&T was still a well-funded 800-pound gorilla and had the vast majority of the long-distance consumers. So, they were able to win customers back by offering a $50 check and leveraging their scale, position and wealth. There was no way an upstart like MCI could play this game, so we decided to change the game leveraging data, analytics and AI (back then we called it data mining) in a way that had never been conceived—offering greater value to consumers.

These were the early days for predictive models. They were costly to create, and no one had yet managed to leverage them effectively for MCI’s large-scale use cases, which included predicting consumer preferences, such as product offerings and contact channels, as well as behavior, like loyalty and usage. We also had this crazy idea of trying to do this work faster and cheaper, leveraging massively parallel technology based on Unix rather than mainframe, which had yet to be proven on data intensive workloads. At the start of the journey, the strategy was to avoid being on the leading edge. By the end, we joked (nervously) that we had crossed over to the bleeding edge.

Fast forward several years, and we had exceeded our wildest expectations. MCI was not only able to compete, but they also developed novel, viral products and services for consumers that were sticky and focused on people we knew would be loyal, leaving the remaining non-loyal consumers were left to AT&T and other competitors to fight over. In addition, these loyal consumers were immune to marketing gimmicks like the $50 check.

MCI also introduced new long-distance products that had never been considered or attempted. Those who have been around long enough will no doubt remember MCI Friends & Family. This was a huge success and an early foray into large-scale customer relationship management (CRM).

It took AT&T over four years to respond, and during that time, MCI grew its market share and became the leader in long-distance communications. This is an excellent example of a data-driven organization that excelled at understanding and engaging the consumer. The data fluency of the information ecosystem, and how it evolved, made this possible.

During this time, an unassuming technical writer who became quite consequential in our success made an observation about our work that has stuck with me ever since. She observed that we had mastered putting data into context to form information and integrating that information into the business process to drive action, becoming smarter with each iteration. This simple formula has been the basis for every success I have had innovating with data ever since. Our last blog in this series, “Data Fluency or Bust,” referred to this as the virtuous learning cycle.

In time, I have come to appreciate in a very practical way how data fluency contributes to the maturity of the information ecosystem and its ability to accelerate and amplify the virtuous learning cycle—turning potential energy (data) into kinetic energy (information > action). Organizations, like MCI, that achieve mastery lead and thrive in uncertainty and pave the way for transformation in their respective industries. We are now seeing this play out in healthcare.

Data Fluency Evolution

As discussed in “Data Fluency or Bust,” there are four building blocks to data fluency, as illustrated in the image below: data consumers, data producers, data community, and data platform. As each building block matures, so does data fluency and the effectiveness of the information ecosystem. To become a data-driven organization, mastery must be achieved for all building blocks.

It is worth pointing out that focusing on some building blocks and not others doesn’t mean you will get some of the benefits but not all. It is more likely that data will become a strategic liability. For example, focusing on data consumers and data producers will likely mean you will end up with a patchwork of reports and dashboards with significant waste, solutions that aren’t resilient and don’t scale, and no single source of truth. This increases headwinds to data innovation and causes organizations to make bad decisions faster.

The key to success is to grow all four building blocks together—delivering value iteratively and often, as illustrated in the image below. In this illustration, there are four stages of maturity: data aware, data proficient, data savvy, and data mastery. Let’s take a brief look at each.

• Data Aware: The organization recognizes the importance of data but lacks systematic approaches. Data usage is isolated within specific departments (typically IT or business departments or both), with limited data governance and quality control. Most decisions are still made based on intuition rather than data.

• Data Proficient: The organization has established data processes and basic infrastructure. Multiple departments use data regularly, with standardized reporting and dashboards. The organization is starting to become effective at telling stories with data and integrating data products into the workflow. Data governance frameworks exist, and there is increasing reliance on data for operational decisions.

• Data Savvy: Data is integrated with robust data architecture and democratized access across the organization. Cross-functional teams collaborate using data, and data-driven decision-making is part of the culture. Advanced analytics capabilities exist, and data is used proactively to identify opportunities. The organization begins to leverage data to automate business processes and advise clinicians.

• Data Mastery: Data is a core strategic asset with enterprise-wide fluency. The organization has a mature data culture where data informs virtually all significant decisions. Innovation and competitive advantage are actively derived from data, with sophisticated analytics capabilities including AI/ML integration. Data literacy is considered a fundamental skill at all organizational levels.

Each level of fluency builds on the next. Though there may be greater emphasis on some building blocks depending on the level of maturity, I’m sure you can see that all of them need to mature together to advance from one stage to the next.

Getting Started on the Journey

Hopefully, I have made the business case for data fluency and how it needs to evolve in an organization that wants to become data-driven. If so, you may be wondering where to start. There is likely a big gap between where you are today and data mastery. Also, you are likely not starting greenfield. This means you have a legacy environment with technical debt and bad habits that need to be cleaned up as part of this next stage in your journey.

Not to worry. You likely have many assets you can leverage (people, processes, and technology). As for technical debt and bad habits, be opportunistic when you can. Fix them little by little as you deliver business value. It is always best to align and right-size investments (new technology, technical debt clean-up, etc.) with something you are doing to create business value.

To start this next stage, step back, assess your current situation, and create an action plan to frame the journey ahead. The action plan aligns stakeholders on the current state of the environment, vision going forward, and game plan for getting there. The goal is to do just enough planning to get started and evolve the plan over time based on lessons learned. Below are the sections to the plan:

• Business Context: A brief assessment of the current information ecosystem, including data consumers, data producers, data community, and data platform. This will be used to finalize the community charter, operating model and game plan.

• Community Charter: A lightweight charter that establishes a shared understanding of what we are building, who we are, what we stand for and how we will be measured. This is a tool for deciding what will be developed and supported by the community.

• Operating Model: A detailed operating model for how work gets done. This includes finalizing the operating framework and aligning it with existing governance, initial delivery teams based on strategic and operational priorities, executive sponsors, senior leaders, and a community of advisors.

• Game Plan: This provides a high-level overview of the information ecosystem’s future state, the strategy for building it, a six-month roadmap, anticipated headwinds and tailwinds and working assumptions.

The timeline for completing this work is 90 days. Anything more and people begin to lose interest. It is also vital to engage as large an audience as possible. This promotes engagement and shared ownership of the action plan. It is also a great way to get to know the community. Some of the best ideas for the strategy come from these conversations.

Creating the action plan aims to do just enough to get started. To this end, time-box each of the sections. Give yourself 30 days to complete the business context, 30 days to draft the community charter, operating model, and game plan, and 30 days to refine and implement the action plan. Then, get to work demonstrating value.

After about six months, review and revise the plan as appropriate based on lessons learned. Ideally, complete this review every six months for the first two years. Things will settle down at that point, which will be your new steady state on your journey.

Action Plan Execution and Refinement

Once the action plan is created, it’s essential to execute it thoughtfully. Execution isn’t about perfection but progress. The goal is to ensure that value is being delivered as you make strides toward maturing the four key building blocks. As you implement the plan, keep the momentum by consistently delivering small, incremental wins that can be leveraged for bigger achievements.

One critical aspect of execution is the iterative feedback loop. As you move forward, you will uncover new insights, challenges and opportunities. The most successful organizations create feedback mechanisms for continuous learning and refinement. These mechanisms help teams stay aligned, pivot when necessary, and ultimately keep the organization on the path to data fluency maturity.

Overcoming Challenges

While the roadmap to data fluency is theoretically straightforward, the journey can often be rocky in practice. One of the most common obstacles organizations face is resistance to change. Data-driven transformation can disrupt established processes, challenge long-held beliefs and require new skill sets.

Many people find this journey very uncomfortable, and given the evolutionary nature of this journey, change is the new norm. So, success is learning to get comfortable with being uncomfortable. The key to overcoming this is a clear, consistent communication strategy that highlights the benefits of data fluency and showcases early successes—something that will be developed when working with the broader data community.

Also, focusing on willing and invested participants is essential. Too often, leaders sell or mandate their way to participation. This rarely works, and they end up with compliance (at best) and silent resistance (at worst) rather than owners who will lead the change. So, stay focused on willing and invested participants. This way, stakeholders are more likely to buy into the vision and become champions for the change.

Another significant challenge is the fragmentation of data sources. Many organizations have data scattered across different departments, systems and platforms. This data can remain siloed without a unified approach, reducing its effectiveness. Overcoming this requires implementing strong data governance practices and creating an integrated data platform that consolidates and harmonizes data across the enterprise. The goal is to create a single, trusted source of truth that all departments can rely on.

Lastly, data quality is another challenge that cannot be overlooked. No matter how sophisticated your analytics and models become, the results will be flawed if the underlying data is inaccurate or incomplete, especially now with the introduction of intelligent automation. A commitment to data quality is essential, and this involves not only setting up systems for ensuring accuracy but also fostering a culture where data is seen as a valuable asset that must be curated and maintained.

Measuring Success

As your organization moves through the stages of data fluency, it’s crucial to measure progress along the way. The metrics for success will vary depending on your organization’s goals. Some common indicators of success include:

• Analytics Maturity: The level of sophistication in your data and analytics capabilities, such as the ability to leverage advanced analytics and AI. IIA’s DELTA Plus Model is an excellent place to start if you are looking for a cross-industry assessment.

• Satisfaction Survey: This is an early, qualitative indicator of success focused on data consumers and data producers. Its purpose is to determine whether their needs are met, whether they see business value, and to identify opportunities for improvement. This survey can be conducted across the community every 6 to 12 months. Alternatively, it could be used in the early stages of a new data product launch to gauge whether it works before sufficient adoption history is collected. Net Promoter Score is an excellent framework for these surveys.

• Adoption Rate: The percentage of employees using data-driven tools, processes and decisions. Adoption metrics should be closely monitored for every new data product. Mature organizations quickly respond if they don’t see adoption or see it decrease. This means work needs to be done to integrate the data product into the workflow or that the data product needs to be shut off.

• ROI Impact: This is a mix of qualitative and quantitative outcomes, such as reduced costs, increased revenue, enhanced operational efficiency and improved patient experience.

• Data Quality: The accuracy, completeness and timeliness of your data.

As these measures improve, you’ll see how data fluency transforms your organization into a more agile, innovative, and competitive entity. Once you’ve achieved foundational maturity, the next challenge becomes scaling the practices, tools and capabilities to sustain growth and innovation over the long term.

Sustaining Data Fluency

Sustaining data fluency requires a continuous investment in people, processes, and technology. As organizations mature, they must evolve with new tools, methodologies and strategies to keep pace with the ever-changing business environment.

Additionally, organizations must invest in upskilling their workforce to remain at the forefront of innovation. Data literacy must become a core competency across the workforce, not just within specialized teams. Data training should be an ongoing part of employee development, empowering everyone from executives to clinical to operational staff to leverage data in their decision-making processes.

A data-driven organization never stops evolving. As new data sources emerge, new technologies are developed, and new business challenges arise, maintaining data fluency requires a culture of continuous improvement. Regularly reviewing your data strategy, refreshing your technology stack, and re-engaging your community will help ensure you stay ahead of the curve.

Future of Data Fluency

We are witnessing an exciting time for data fluency in healthcare. The rise of electronic health records, IoT devices, telemedicine and artificial intelligence has brought an influx of data that has the potential to revolutionize patient care. As healthcare organizations grow in data fluency, they will be better positioned to predict patient outcomes, personalize treatments, optimize operations, accelerate research and improve overall care quality.

However, the journey in healthcare will require even greater collaboration across disciplines. Data analysts, data engineers, data scientists, clinicians, researchers, administrators and senior leaders will work together to create an information ecosystem where data truly informs every decision. Healthcare organizations that master data fluency will improve care delivery and set the stage for next-generation innovations that we are only beginning to imagine.

Final Thoughts

The journey toward data fluency is a strategic and transformative endeavor that requires sustained effort, collaboration, and a clear vision for leveraging data to drive meaningful change and impact. As MCI Communications has shown, organizations that master data fluency to become data-driven can compete and lead in their industries, even in the face of dominant competitors.

This is the essence of the virtuous learning cycle: turning raw data into valuable insights that fuel smarter decisions and better outcomes. Data fluency isn’t just about technology or analytics; it’s about creating a culture where data becomes a cornerstone of decision-making at every level. This evolution of data fluency, with its four building blocks—data consumers, data producers, data community, and data platform—provides a robust foundation for organizations to thrive in an increasingly data-driven world.

In healthcare, the stakes are even higher, as the integration of data can directly impact patient care, operational efficiency, and overall healthcare outcomes. Organizations that embrace data fluency will be equipped to innovate and adapt, providing higher-quality care while maintaining a competitive edge in a rapidly changing landscape.

As you embark on your data fluency journey, remember that success isn’t defined by perfection but by progress. Focus on incremental improvements and embrace the discomfort of change, as this is where growth happens. Keep your stakeholders engaged, stay focused on creating value, and always view data as the key to unlocking the potential for transformation.

The future of data fluency is exciting. Those who master it will not only shape the future of their organizations but also set the stage for the next wave of innovation, particularly in healthcare, where the impact is profound. The work begins now, and the path ahead is full of opportunities for those willing to commit to the journey.

Next Month

Next month, we will explore one of the most critical and complex building blocks to achieving mastery: the data community. In this community, collaboration, knowledge sharing, and cross-functional partnerships drive meaningful change. It’s not just about the data and technology but also the people who make the ecosystem work.

Building a strong data community requires aligning diverse stakeholders, fostering a culture of continuous learning, and ensuring that everyone involved has a shared sense of purpose. We’ll explore how to create and nurture this community, the challenges it presents, and the transformative impact it can have on achieving data fluency—and unlocking the full potential of your information ecosystem.