Master Data Management

March 1, 2022

We are living in an incredible time where computers and technology have allowed us the ability to order groceries to our front doors, attend meetings by video with colleagues on other continents, communicate with space stations outside of our atmosphere, send money to our friends from our cellular devices, upload our resumes to hundreds of job applications with the click of a button, and yes, even have a food delivery service bring us dessert at 11:30 p.m. This is all made possible by data systems that receive, organize, and store our credit card information, addresses, names, and preferences.

 

The conveniences we have available to us are unprecedented, and there are no signs these resources are going to become less available and accessible as life after a pandemic is forever changed.

 

With advancement and progress, comes the potential for setbacks and error. Sadly, each year we see some well-known entity suffer from a security breach leaving millions of innocent consumers vulnerable. On a much smaller and more benign level, we’ve probably all received marketing materials for products we already own or would be completely inappropriate for us. Car insurance for a toddler, might be a little too soon! From banks to grocery stores, the methods companies use to manage their data can have significant impact on whether they are successful and stand the test of time or if they struggle and waste resources unnecessarily.

 

Master data management is the key to ensuring your data is organized, accessible, and safe when it needs to be.  

What is the importance of master data management?


Healthcare is one of the last industries where you hear the term MDM (Master Data Management) mentioned. Most IT industry analysts, software firms and consulting organizations are geared towards your typical company that sells products to people or businesses.

MDM examples are always showing a master list of products or cleansing your way to a consistent list of customers, which is not exactly the mindset of healthcare organizations. But lack of MDM is precisely what is adding untold costs on healthcare organizations (and ultimately on all of us) and inhibiting these organizations from improving the quality of health care services at an affordable cost.


Let’s divide the healthcare industry (simplistically) into insurers and providers (we will position pharmaceuticals, biotech and medical device companies as life sciences). Many of the large insurers have invested in data warehousing and data integration, but smaller insurers, i.e., regionally based HMOs (healthcare maintenance organizations) and healthcare providers, such as hospitals and physician groups, have fledgling efforts or have been bogged down in many of the issues below.


Healthcare organizations have significant data consistency issues regarding the following data subjects:


  • Patients
  • Physicians
  • Procedures
  • Diagnosis codes
  • Service Rates
  • Pay for Performance
  • Compliance


Each insurer and each healthcare provider track this data differently. The problems are magnified because healthcare is regulated on a state-by-state basis along with federal and industry regulations. Throw in privacy and security concerns to worsen what healthcare groups need to deal with.


Most healthcare organizations, even large ones, are an affiliation of generally small physician groups. These groups may be your local doctor’s office, i.e., primary care physicians (PCP), specialists or emergency room (ER) providers. Often these groups do not have a lot of IT resources at their disposal.


Data flow is often flat file transfers between insurers and healthcare provider organizations, as well as from the individual physician groups and the larger provider organization. These flat files are generally not standardized and change each year when contracts are renegotiated between insurers and providers. This is an industry where you typically are not in control of your source data. It is thrown at you and you have to deal with it.


The need for an MDM is significant at healthcare organizations. The benefits from MDM are:

  • Data consistency
  • Productivity
  • Enabling more cost effect patient care

 

It is remarkable when one looks at the number of resources devoted to manually dealing with inconsistent master data throughout health care. People in this industry do an amazing job of dealing with it, but it is often a time-consuming manual effort with much reconciliation. Having an MDM program would improve overall productivity and enable organizations to process and react more quickly to patients, insurers, employers and physicians.


A hidden jewel of an MDM effort though is enabling health care organizations to provide more proactive care. Healthcare providers can develop data solutions oriented to specific populations of patients who have diseases, chronic conditions or are at specific risks. These solutions may be for diabetes or asthma, for instance.


By bringing in historical clinical or demographic data related to patients tied together through consistent master data and taking advantage of predictive analysis, health care providers can proactively help their patients rather than waiting for the next episode when the patient’s health has worsened. Many insurers are linking capitation and pay for performance programs with these kinds of efforts because a healthier patient is a great goal by itself, but better health also means lower health care costs.


The MDM silver bullet product has not been invented for healthcare industry, but these organizations should not despair. There are concrete steps these organizations need to take.

 

1.  First, healthcare organizations need to examine where they are spending their resources on handling inconsistent master data          and focus their efforts on those areas.

2. Second, the efforts need to be in collaboration with business operations, physicians, insurers and IT, and need to involve defining      master data and performance metrics.

3. Finally, such organizations need to leverage their data warehousing and data integration efforts.


What is a master data management strategy?

 

Wondering if your organization can benefit from a master data management review? At Kona Kai Corp, we have a team of BI/Analytics experts analyzing, architecting, designing and maintaining data to ensure your systems are successful We will begin by making a map your data flow. Once we have that information, we will evaluate the structure you are currently utilizing. From there we will Identify and define the source of truth. Once we know what the data is and what the shortfalls are with your current system, we will collaborate with you to optimize performance. We do not just stop there, but we will also collaborate with you to gain insight as to how your data may be used in the future. Finally, we will end by setting up your data owners, and we will help you set up a strong data governance committee and processes for data use and growth.

 

Click here to begin your evolution. Call us at 602-228-8230 or email us at info@konakaicorp.com to revolutionize the management of your data, one byte at a time.

 

INSIGHTS

February 16, 2026
As organizations head into 2026, the conversation around artificial intelligence (AI) is changing. The early years of AI adoption were dominated by experimentation. Proofs of concept multiplied. Vendors promised transformation. Internal teams explored use cases in pockets across the organization. Yet for many enterprises, the results have been uneven at best. In 2026, AI success will no longer be determined by access to advanced models or cutting-edge tools. It will be determined by something far less exciting, but far more powerful: execution. Organizations that struggle with AI rarely lack ambition but instead lack structure and organizational readiness. Here’s what you can expect to see in 2026. Agentic AI Becomes Operational, Not Experimental Agentic AI is often described as the next frontier—AI systems that can reason, plan, and take action autonomously. In theory, this represents a major leap forward. In practice, 2026 will expose a hard truth: autonomy without discipline or readiness creates risk faster than value. The most effective organizations will not deploy agentic AI broadly or indiscriminately. Instead, they will apply it deliberately within clearly defined operational boundaries. Agentic AI will increasingly be used to coordinate workflows, surface decision options, and manage repetitive execution across systems, while humans retain ownership over judgment and accountability. What matters most is not how “intelligent” the agent is, but how well it is embedded into existing processes and platforms. When agentic AI operates outside of governed systems of record, organizations lose visibility, auditability, and trust. When it is designed as part of an integrated operating model, it becomes a force multiplier. In practice, we are already seeing this distinction play out. One organization attempted to deploy autonomous agents across customer operations without clear escalation paths or system boundaries, quickly creating confusion and rework. Another embedded agentic AI narrowly within its CRM workflows to triage cases, surface next-best actions, and route work—reducing cycle time while preserving human accountability. The difference was not the intelligence of the agent, but the discipline of its deployment and readiness of the company. In 2026, agentic AI will succeed quietly inside workflows, under guardrails, and in service of execution rather than experimentation. The Shift from Models to Systems By 2026, the advantage of having access to the most advanced AI model will be minimal. Models will improve, but they will also become more interchangeable. The differentiator will be the system surrounding them. Organizations that see real returns from AI will focus on how data moves, how decisions are made, and how outcomes are measured. AI does not operate in isolation. It inherits the strengths and weaknesses of the environment in which it is deployed. At KKC, we often see AI initiatives stall because foundational questions were never addressed. Data may exist, but not be trusted. Platforms may be implemented, but not integrated. Processes may be documented, but not followed. AI simply exposes these gaps faster. We frequently see organizations using the same AI tools achieve radically different outcomes. In one case, two teams implemented similar predictive capabilities. One struggled due to inconsistent data definitions and disconnected platforms. The other succeeded by first aligning data ownership, integrating systems of record, and defining how insights would be acted upon. The technology was identical. The system was not. In 2026, the most successful AI programs will be built on strong systems thinking. They will prioritize reliability over novelty and consistency over speed. These organizations may appear slower at first, but they will compound value over time while others reset their strategy yet again. Governance and Accountability Take Center Stage AI governance is no longer a future concern. In 2026, it becomes a practical requirement. As AI moves deeper into decision-making, organizations will face growing pressure to explain how outcomes are generated, who is responsible for them, and how risks are managed. This pressure will come not only from regulators, but from customers, boards, and internal teams who expect clarity and control. Effective governance doesn’t limit innovation; it enables it to scale safely. Organizations that invest in clear ownership models, defined approval paths, and ongoing monitoring will move faster because they eliminate uncertainty and rework. In regulated and complex environments, governance determines speed. Organizations without clear ownership stall decisions while debating risk. Those with defined approval models, monitoring, and escalation paths move faster because teams know exactly how to proceed. Governance removes friction while not slowing AI down. In 2026, governance will be recognized as infrastructure instead of overhead. AI Readiness Is No Longer Just Technical One of the most underestimated shifts heading into 2026 is the recognition that AI readiness is as much about people as it is about technology. Many organizations underestimate the cultural impact of AI. Teams may distrust outputs they do not understand. Leaders may struggle to explain how AI fits into decision-making. Employees may fear replacement rather than augmentation. When these concerns are not addressed, adoption stalls, even when the technology works. In several organizations we’ve observed, AI tools technically performed as designed but were quietly ignored. Teams lacked confidence in outputs, managers hesitated to rely on recommendations, and adoption plateaued. Where leaders invested in education, role clarity, and communication, usage increased without changing the underlying technology. Organizations that succeed in 2026 will invest intentionally in education, communication, and change management. They will articulate not just what AI does, but why it exists and how it supports human decision-making. They will prepare leaders to lead differently and teams to work differently. AI is not a software rollout. It is an operating model shift. From AI Theater to Real Outcomes By 2026, patience for AI initiatives without measurable impact will be gone. Executives will expect clear business cases, defined success metrics, and visible progress. AI strategies will increasingly resemble other enterprise transformation efforts grounded in financial outcomes, operational efficiency, and long-term scalability. At KKC, we help organizations move beyond AI theater by focusing on where AI creates tangible value and where it does not. Not every process should be automated. Not every decision should involve AI. Disciplined prioritization will be a competitive advantage. We see many organizations measure AI progress by the number of pilots launched. The more successful ones measure it by decisions improved, hours saved, or revenue protected. In 2026, output metrics will replace activity metrics, and many AI programs will not survive that transition. The organizations that thrive will be those that stop chasing AI for its own sake and start using it as a tool to strengthen execution. What 2026 Will Really Reward AI will continue to evolve rapidly. The organizations that benefit most from it will not always be the most aggressive adopters, but the most prepared. In 2026, advantage will belong to organizations that: Build systems, not experiments Treat governance as an enabler Invest in readiness, not just tools Focus on execution over ambition AI is no longer about proving what is possible. 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