Big Data can either be a headache or a potential cure-all for the healthcare industry, depending on how it’s managed. On the one hand, all of the information being captured from electronic health records (EHR), digitized insurance claims, physician notes, patient monitoring devices and more can aid in developing smarter data-driven insights and lead to improved patient care and treatment. Yet before being able to harness the power of all this data, healthcare organizations must first address several challenges that, if left unchecked, can negatively impact the health of the business:

• Access to and preparation of structured and unstructured data from multiple sources
• Addiction to and limitations of legacy technologies
• Deficiencies and gaps in the skills and manpower required to unify all of the necessary data for analysis at scale

For healthcare organizations looking to overcome the challenges in data scale and complexity, the prognosis looks good for those opting to incorporate data preparation solutions into their daily regimen. Studies done on the implementation of self-service data prep solutions in particular have revealed positive results with IT teams, data analysts, data scientists, and business analysts, ultimately allowing users to build and manage data products and transformation scripts more effectively and on demand.
Consider the benefits a data preparation solution can offer your healthcare organization, as outlined in a recent Blue Hill Research report:

• Empowered exploratory analytics. Simplifying data preparation for exploratory analytics makes data useful more quickly and easily than traditional solutions. By removing legacy inefficiencies and technical requirements of analytic data prep, data preparation solutions provide a superior approach for business teams for data discovery and analytic transformation without relying on IT.
• Increased productivity. The most efficient data preparation systems make existing data resources more productive across the organization. These solutions present an avenue to bring volumes of unstructured text-based and inconsistent data into a cohesive format for further analysis. By reducing the time to integrate, cleanse, and prepare data, analysts can utilize more data in important analytic tasks, such as feature construction, model construction and validation, and exploratory visualization and content augmentation.
• Raw source data on demand. Data analysts, scientists, and business users (non-programmers) can pull data from various formats and sources directly into downstream visualization and analytics tools. As healthcare service providers demand increasing visibility into internal operations, external findings, and patient monitoring device data feeds, there is an opportunity to defray demands on IT by placing this data directly into the hands of those who will need to perform the analysis.
• Improved organizational data usage. Data preparation solutions accelerate the time to clean and manipulate data and couple automated routines for anomaly/irregularity detection with visual summaries so end users can identify and fix data quality anomalies more quickly. This enables a consolidation of silos of data across health information systems and external feeds to present decision-makers with a consistent and real-time view of operations. Bringing relevant information to light in the context of the broader organization not only stands to improve operational efficiency, but patient outcomes as well.