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4 Best Practices That Impact The Success Of Your MDM Implementation

Making the right clinical and business decisions requires the leadership teams of healthcare providers and life sciences companies to have access to the right information, at the right time, and in the right format. Health data is a strategic asset. It includes health information captured throughout a patient’s healthcare journey – right from the time the individual first entered into the healthcare system until the end of the treatment process. Organizations strategically use this data to improve the quality of patient care by determining the best possible treatment plans. The healthcare users also get direct benefits when data enables the ecosystem’s business operations to be tightened and managed efficiently. In turn, organizations are impacted by the availability and use of the data at the center of this ecosystem in many ways, including:

  • Improving operational efficiency
  • Reducing operations costs
  • Enhancing safety
  • Enabling real-time insights
  • Increasing the speed of delivery of healthcare and medicines to the patient

 

Pharma Regulations and Compliance

With data privacy norms being the foundation of data management in healthcare and life sciences, complete anonymization of individual data and overall privacy is critical. Various industry regulations (Physician Payments Sunshine Act, IDMP, PQ/CMC, etc.) are enabling life sciences companies through data privacy and compliance. In turn, this is helping companies access reliable data and use it with discretion and integrity. To manage the regulators’ demands, organizations are looking at creating data masters of not only patient and product data, but also of other traditional data such as HCP/HCO, affiliations, payer-plan and new domain data such as supplier, location, asset, and employee.

Every country and its regulators demand the product data in different formats and files based on different national data hubs and systems. Creating master data helps companies not just to store and report this vast volume of product data to different regulatory/governing bodies globally, but it also enables them to use this information in improving their commercial operations.

To manage these outcomes, maintaining accuracy of data across various product lines, product types, and SKUs from different geographies and through various healthcare systems is both a challenge and a necessity. Companies, therefore, look to their commercial and business IT teams for robust enterprise data management solutions with business-critical data governance and data privacy being built in at the inception. In turn, IT teams cater to their various business stakeholders by looking at data masters (product or patient) that can have all the answers in one place as a ‘single-version-of-truth.’

A robust Master Data Management (MDM) solution can ingest, integrate, and analyze multiple datasets to provide business insights for better commercial decisions. MDM solutions that have built-in data quality, effectiveness, security, and infrastructure, with each having a direct impact on business performance, can lead to real on-the-floor benefits and provide hands-on controls to business users. That said, the real barrier to MDM adoption is not always technology-led. MDM implementation is cross-functional and enterprise-wide, and its benefits are best derived by organizations that foster collaboration between business and IT.

So, how can organizations ensure the success of their MDM implementation? 

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Here are four practices that every team implementing an MDM program must follow:

1. Change Management:

Organization-wide change management is not just a ‘good to have,’ but a ‘must have’ for the success of any MDM implementation. Teams are often reluctant to implement changes as they are wary of losing out on the familiarity and functionality of the current system. Many implementations fail simply due to the lack of stakeholder involvement and their sign-off on the purpose and need for MDM. Since ‘change’ in the legacy IT and data management system is at the foundation of implementing a robust MDM solution, organizations need to manage expectations and re-engineer processes that will consequently change. These include: 

  • Changes in organization structures
  • Reinforcing communication between business and IT
  • Actual infrastructure change (if at all) because of the new implementation
  • Actual outcomes of the implementation that will impact vendors, analysts, consultants, and business users

Change management ensures that the process disruption is minimal. It also ensures that the stakeholders are aware of the changes and prepared for adoption. 

2. Business Rules Management:

How can you ensure that all relevant stakeholders extract the maximum value out of the master data? The answer lies in documenting and building business rules (both external and internal) into the MDM solution. Building consistent business rules to provide accurate and periodic data to different stakeholders and business users is crucial to decision-making. Establishing clear rules ensures accuracy and quality of data, aiding the leadership in taking the right business decisions. While it is critical to capture existing rules, it is also essential to create new rules and establish processes that can manage the evolving nature of data. Business rules aid the change management process as there is no dependence on an individual or role (or wait for a change manager) to approve every change request. This brings significant efficiency across processes due to the reduced overall cycle time.

3. Choosing the right MDM Solution:

MDM delivers business value straight to an organization’s bottom line by providing a reliable, relevant, and accurate view of business-critical master data. So, choosing an MDM solution that aligns with the business requirements is critical. By not setting the demands and expectations of the solution upfront, organizations often get short-changed on their MDM investment. A successful MDM solution must provide a unified and complete functionality for data integration, data quality, data profiling, data mastering, and data governance.

Checking the boxes in this checklist can aid in selecting the best-fit partner/solution:

  • The MDM partner is from the desired industry vertical and therefore, has the necessary data and domain depth and knowledge.
  • The MDM partner/vendor has the required MDM technical skill set, expertise, and implementation experience.
  • The MDM solution offered is scalable as the organization grows, and the partner can bring in support as per the forecasted growth and business plans.
  • The MDM solution supports flexible deployment options like on-premise and on-cloud implementation.
  • All architectural styles are supported, and a broad set of data integration connectors are offered.
  • The MDM solution is built on a future-proof, next-gen technology

4. Data Governance:

The volume of big data has grown exponentially, thanks to electronic health records (EHRs) and interoperability, making it easier for organizations to access information. The electronic environment has further increased the number of privacy and security risks, prompting organizations to anticipate and proactively mitigate them. Data governance is at the core of every MDM implementation, but even more so in healthcare, as the data can impact a patient’s life, making it critical to ensure its accuracy and privacy. Effective data governance can ensure that the internal decision making is consistent and effective, lower the risk of compliance failures, and of course, improve overall data security. Further, healthcare and life sciences companies procure data from several vendors, and governance becomes an essential element for data on-boarding. The establishment of a Data Governance Council sets-up a systematic approach via a governance framework design, an organizational structure aligned to business priorities, ownership, and roles and responsibilities assigned to suit the MDM strategy.

Conclusion

With the healthcare and life sciences industry focusing on value-driven products and services, companies must constantly leverage business-critical data to deliver better customer experience and improve patients’ quality of life. MDM is the very foundation of this process as it provides a system of records ensuring the master data is clean and available, accurate, and private to those who need it.

MDM is not about better integration. It is about completely shifting the way a business leverages its most crucial information assets. It is about increasing growth opportunities, shortening time-to-market, improving the accuracy of decision-making, and addressing regulatory compliance. Organizations must understand the strategic importance of MDM and must choose the right MDM solution keeping the cost of a lost opportunity in mind.

An MDM program is not a ‘project’ but a ‘commitment’ by the business to leverage information to improve business process outcomes. Its success depends on effective processes and policies, organizational-level changes, and how stakeholders measure the business value of the implementation.

Let us show you how Axtria can enable your cloud MDM initiatives. Contact Us Now!

 

Suggested Reading:

The Axtria 5 Step Guide - "5 Steps To A Single Source Of Truth With Master Data Management!"

 

 

Tags: Master Data Management, MDM, Cloud MDM, Information Management, Cloud Data Management

Axtria Connect

Axtria is a global provider of cloud software and data analytics to the Life Sciences industry. We help Life Sciences companies transform the product commercialization journey to drive sales growth and improve healthcare outcomes for patients. We continue to leapfrog the competition with platforms that deploy Artificial Intelligence and Machine Learning. Our cloud-based platforms - Axtria DataMAx™, Axtria SalesIQ™, and Axtria MarketingIQ™ - enable customers to efficiently manage data, leverage data science to deliver insights for sales and marketing planning, and manage end-to-end commercial operations. We help customers in the complete journey from Data to Insights to Operations.