Group meeting discussing their findings on the laptop.
HealthMay 14, 2015

How to get executive buy-in on your data normalization solution

We recently outlined the key steps to take your data normalization project to the next level or get started at a high level. This post focuses on the first step toward implementation: obtaining executive buy-in.

Robin’s challenge: manual data management

While consulting on a data normalization project for a large health plan, I worked with Robin, an application implementation/support analyst in the Information Systems department. Robin was responsible for configuring applications across multiple departments and lines of service. For the data normalization project, she was assigned the role of integrating a terminology server with the health plan’s data warehouse. This was a critical step, as the terminology server was already receiving and normalizing data for claims determinations, but the results could not yet be put to use. Unfortunately, nearly all of Robin’s time was already committed to updating claims systems to reflect the latest coverage policies – a high-value task in day-to-day operations. It looked like the data normalization project would have to limp along indefinitely while Robin relied on whatever scraps of time she could eke out of each week.

This was escalated to upper management. The VP of Technology explained the rationale for using data normalization to centralize processing of claims across the organization. Without data normalization, the health plan would be stuck with its redundant and incompatible departmental claims processing systems, with all their well-known inconsistencies and inefficiencies when it comes to managing data. The centralized system would allow the health plan to generate claim responses that were more consistent, timely, and accurate, improving provider satisfaction.

With an executive supporting the strategic importance of the project, the team made the tough call to allow Robin to finish off the data normalization project while deferring and reallocating some of her other daily tasks. This was the turning point in the project as it communicated how important this project was for the entire company.  Ultimately this resulted in a successful on-time rollout of the project, fulfilling its value proposition.

The need for executive support in data normalization

The dilemma Robin faced is typical. Like other strategic investments, data management projects will be challenged to compete with other immediate business needs for funding and staff. Organizations face never-ending cycles of configuration, maintenance, and upgrades of information technologies such as EHRs, claims processing systems, clinical information systems, and enterprise management systems. There is an inexhaustible need to configure reports to respond to business needs such as accreditation, meaningful use, ICD-10-CM conversion, and advanced payment models.

In Robin’s case, the health plan’s data normalization project avoided being derailed because senior management endorsed its clear value proposition. Given the inevitable challenges these projects will face, it is critical to develop a strong foundation of executive support to ensure a commitment to seeing the project through. In short, data normalization efforts must be organizational projects, not IT projects. The table below presents potential value propositions for key provider and payer stakeholders.

Potential value propositions for key stakeholders

Executive stakeholder
Potential value propositions for data normalization
Data Warehouse Manager
Produce more credible, actionable data by enhancing the quality and spectrum of data normalization in the ETL (extract-transform-load) process
Chief Technology Officer Streamline systems to reduce the overhead of maintaining a multitude of custom applications
Chief Financial Officer Improve revenue cycle by enhancing the validity and precision of billing codes
Chief Medical Officer Integrate disparate clinical and claims data to improve:
  • Population health/care management
  • Risk adjustment
  • Quality metrics
  • Management of and compliance with benefits policies

Once executive support is solid, the project leader is ready to engage mid-level stakeholders across the organization, including the business owners of the systems that are sources of the data to be normalized and the downstream users of the outputs of those systems. 

It is important to establish clear expectations of time commitment. As a rule of thumb, a typical pilot project will require about three months of work. In addition to the efforts of the project manager, key team members should be expected to devote 10 to 20 percent of their time to the project during this period. If it’s not possible to get such a commitment, perhaps you should reconsider the timing of the project. If a data normalization project is under-resourced, it will not have a viable end-date and may never demonstrate a return on investment. On the other hand, a properly resourced project can sustain the energy and enthusiasm of the team members for the duration of the project, in spite of potential challenges.

Investment in data normalization pays off

When resources are tight, it is always tempting to stick with ad-hoc solutions that seem sufficient. In the realm of data normalization, the cheap solution is usually to shuffle spreadsheets from person to person by email until they’re ready to be uploaded using a legacy process that must never be reconfigured lest the entire process be inadvertently broken. In the process:

  • Managers can’t track workflow reliably
  • It is very difficult to reconcile the changes across different versions being passed around
  • Errors are introduced due to keystroke errors (a single mistyped digit results in an inexplicable code) and juxtaposition errors (data being entered one row or column off).

These ad hoc solutions to data normalization are a false economy. Data quality suffers. Reports aren’t credible. As credibility is undermined, they jeopardize institutional investments in data warehouses and health data analytics systems. 

In contrast, a robust data normalization solution is a cornerstone for reliable data governance. Investment in data normalization can quickly pay dividends for healthcare organizations, preparing you to take advantage of things like natural language processing (NLP). The key is to develop a well-scoped project that is likely to produce a quick win for its executive champions.

This is just the first step in ensuring a successful data normalization project. Next, take a look at constraints that may impact your data normalization plan.

As you move forward in your data normalization project, it can help to leverage the expertise of professionals who work on solving these challenges every day. The Health Language team can lend expertise and best practices from over 25 years of working with the top providers, payers, and health IT vendors on healthcare terminology management. Contact us to learn more about how Health Language team and solutions can help your organization.

Speak To An Expert


Brian Laberge
Solution Engineer, Health Language
Brian supports the company’s Health Language solutions by ensuring that solutions help customers with their challenges, as well as works with the Sales Team and clients to understand their needs.