Top 5 Data Management Challenges Healthcare Organizations Face

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Micah Horner, Product Marketing Manager at TimeXtender

In the United States, healthcare costs make up more than 17% of GDP.

The data that healthcare organizations generate, collect, and manage is massive, having an ever-growing impact on the industry. RBC Capital Markets, a global investment bank, estimates that approximately 30% of the world’s data volume is generated by the healthcare industry. By 2025, the compound annual growth rate of data for healthcare will reach 36%. That’s 6% faster than manufacturing, 10% faster than financial services, and 11% faster than media & entertainment.

With such a large amount of data being generated by healthcare providers, it’s no surprise that data management challenges are becoming increasingly difficult to manage.

But data is a great asset for healthcare organizations when managed properly. With that, let’s take a look at the five most common data management challenges that healthcare organizations typically face and how they can overcome them.

Challenge # 1: How do you maintain security and compliance in accordance with HIPAA?

HIPAA set data protection standards for healthcare data, ensuring that data is accessible to those who are authorized to see it while also keeping data secure from hackers and unauthorized individuals.

In 2020 alone, healthcare data breaches totaled 599, up 55.1% from 2019, according to a new report from cloud security company Bitglass. These breaches affected more than 26 million people. Data breaches can be financially devastating for healthcare providers and cause significant damage to a person’s reputation. With HIPAA compliance being so important, the stakes are high if something goes wrong.

How to overcome this challenge:

A data management platform that includes data security, data lineage, and data quality monitoring features can help healthcare organizations maintain data privacy, protection, and security.

Look for a platform that automatically manages data access, monitors data activity across the entire data environment, and stops sensitive data from falling into the wrong hands. Being able to automate data product documentation, data lineage and data quality help immensely. Find a platform that allows you to create users, determine access and enable proper permission to ensure data security and quality of your healthcare records.

Challenge # 2: How do you maintain data quality?

Data quality is vital in data management. After all, what good is data if it’s not accurate data?

The data collected and used by healthcare organizations can help data scientists create data models that can predict future patient outcomes. This data is then used to help healthcare providers to create better treatment protocols. It even helps pharmaceutical companies develop new treatments for patients.

With data quality being so important, healthcare data management teams need to be sure they are collecting data that is accurate, which can be difficult with data being spread across so many different data sources.

How to overcome this challenge:

To better manage data quality, healthcare organizations must have complete data lineage. Data lineage is the data management challenge that involves data owners being able to trace data lineage back to its source data, then identify data accuracy and quality issues.

Platforms that contain data lineage and data impact analysis features provide data owners with a full audit trail of data changes, allowing you to quickly detect data anomalies and respond accordingly.

In addition, data quality alerts let data managers know if data is inaccurate or incomplete so they can take action to correct it before making reports, storing it for backup purposes, etc.

To simplify, it’s helpful if all features are available within the product you select to manage your data to deliver easy integration, without requiring extensive customization or a patchwork of third-party tools.

Challenge # 3: How do you ensure data availability?

In modern healthcare organizations, it is becoming increasingly important to be able to make data-driven decisions quickly. However, to make data-driven decisions, you need high-quality data that are easy to access and understand.

If data is hard to access and understand, data analysts and data scientists won’t be able to use data effectively, and healthcare providers won’t be able to provide patients with the best possible care.

How to overcome this challenge:

The need for easy, fast access to data is why data warehouses are so important. A data warehouse is a data management strategy that collects data from other data sources, cleans it up, and stores it in one central location to be used for analytics and reporting purposes.

While data warehouses offer many benefits, they can be complex and time-consuming to set up and manage without proper data management software. With modern data estate technology, you can automate, take advantage of no-code / low code, centralize your data in a single location, and use a drag-and-drop interface to build a data warehouse significantly faster than traditional methods.

Having all of your data stored in a single format and location also lays the foundation for any type of advanced analytics, such as AI and Machine Learning.

Challenge # 4: How do you find qualified staff to use these complex systems?

The data management skills shortage is one of the most pressing challenges in healthcare today. Data management has evolved into a complex profession that requires many years of hands-on experience to master its tools and processes. Healthcare data is growing by the day, and there are not enough data professionals to keep up with the increase.

Few data professionals have the data management skills necessary to take data architecture strategies from concept to completion, whether it’s creating data warehouses or data lakes.

The data also needs to be cleansed, prepared for analysis, and transformed into a format that is compatible with the data warehouse platform – a process that can take several months.

For healthcare organizations that do not have a large IT team or data engineers on staff to prepare data, the data management process can be extremely challenging.

How to overcome this challenge:

As healthcare organizations are expected to embrace data analytics, many are seeking new tools that give data analysts and scientists the data they need quickly and easily, removing the need for a large team of data engineers who are experts in data cleansing and preparation.

Your data management system should be smart enough to do much of the data preparation for you so that data scientists and data engineers can focus on higher-impact activities like analysis, rather than data preparation.

Again, data automation can help. With automation, healthcare organizations can integrate, cleanse, and prepare their data rather easily. Further, getting back to an earlier point, if you look to centralize all of your organizational data into a single platform, you can use semantic data modeling to provide designated access to specific types of data in the warehouse to use for analytics.

This is quite essential as it allows healthcare professionals to quickly access the data they need without having to rely on a large team of data scientists, accelerating data journeys and making data-driven healthcare a reality.

Challenge # 5: How do you decide which healthcare data management tool to use?

The data management market is full of tools that promise to meet the data challenges of the healthcare industry. However, few of these data tools give healthcare data professionals the capabilities they need to succeed.

Even worse, the vast majority of data management platforms on the market are actually just a patchwork of slow, complex, manual tools that data professionals have to cobble together.

Many data management platforms claim to be simple, automated, low-code / no-code solutions for data integration and preparation. However, when you look a little further, you’ll find that the low-code / no-code elements are extremely limited, and data professionals are still stuck manually coding most data integration processes.

Healthcare organizations need a solution that makes data integration and data preparation quick and easy and meets compliance requirements. The tools that data professionals need to succeed are out there, but healthcare data teams must do their homework before choosing a data management tool.

For example, data management tools should provide healthcare organizations with options for both cloud-based and on-premises deployments, giving you the flexibility you need to build data architectures that fit your unique data requirements.

The data management platform that you select should provide a data modeling tool, data integration capabilities, data cleansing and preparation, data governance and security capabilities, and the scalability to grow as your healthcare organization grows.

How to overcome this challenge:

Fortunately, data tools do exist that provide automated data integration and data preparation, reducing the learning curve for healthcare data management professionals and speeding up data journeys, all while giving data scientists access to more data in less time. And your data users will no longer have to wait days or weeks for data to be ready for analysis, allowing them to drive higher value from your healthcare data.

In addition, by selecting a single data management platform you can power all of your data needs, you can reduce data silos, increase data accessibility, and dramatically speed up your healthcare data journey. Once data is integrated into a single platform, healthcare data professionals can use semantic modeling as mentioned earlier. Additionally, the main objective that should be top of mind for healthcare is a platform that accounts for data governance and provides data lineage features to allow you to monitor your data at every step, ensuring compliance with HIPAA regulations and other patient privacy regulations.

About Micah Horner

Micah Horner is the Product Marketing Manager at TimeXtender, a low-code, drag-and-drop, data estate builder that enables organizations to make better business decisions without sacrificing compliance and governance of their data infrastructure. He is passionate about technology, storytelling, and strategic messaging.

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