We have talked before about the roles of the ETL developer and ETL Plus* teams and how transforming internal data and external warehoused data into useful business intelligence seems to be an insurmountable challenge on some days. The monster that we encounter certainly has been described many times. The analysts spend up to 80% of their time, energy and talent on getting the data into a usable shape and on a secure platform. And they are really not very good doing this. So productivity takes a dive. And you’re locked into the costs.
Extraction is the only first step. That’s obvious. But transformation is the tipping point to usability.
Data transformation is the process of changing the format of data so that it can be used by different applications. This may mean a change from the format the data is stored in into the format needed by the application that will use the data. This process also includes mapping instructions so that applications are told how to get the data they need to process.
The process of data transformation is made far more complex because of the staggering growth in the amount of unstructured data. A healthcare application such as a patient relationship management process has specific requirements for how data should be stored. The data is likely to be structured in the organized rows and columns of a relational database. Data is semi-structured or unstructured if it does not follow rigid format requirements.
The increase in volume of data is one of the most significant trends in healthcare. Analysts at the McKinsey Global Institute predict that the average hospital will be closing in on having a petabyte of patient data by 2015 and most of this data will be unstructured, such as radiology and imaging scans. This massive volume of data, coupled with the challenges of storing and sharing unstructured data, will likely lead to the implementation of patient data warehoused at most hospitals.
If you choose to engage a specialist partner, like our teams at Management Information Analysis, you, quickly accelerate and elevate your decision-making advantages by using a proven resources without adding to your hard or operational costs and overhead.
By providing a competent, affordable solution to these data challenges, you can then start to use data warehouses to reduce the number of unnecessary or repeated tests and treatments.
Personalized medicine is growing trend. As individual patient data becomes more accessible and the means to analyze it become easier, treatment protocols will move from a one-size-fits-all model to treatments based on each patient’s unique medical history and current medical issues. Analysis of genetic markers also will increase, allowing physicians to step in earlier to prevent disease or reduce its impact on patients. They also will be able to more precisely target treatment for diseases that are expensive to treat.
Prevention is another trend that is on the rise. By using big data, physicians can develop a better insight on patterns of factors, both genetic and behavioral, that increase patients’ risk of disease. Using this information, physicians can then recommend medications or guide patients to make lifestyle changes to reduce their overall risk of disease. Disease prevention represents a huge potential cost savings of $70 to $100 billion, according to the McKinsey Global Institute.
Villanova University recently affirmed in a blog article that data can play a key role in managing more than patient treatment. Hospitals are also looking to big data in order to manage logistics such as patient throughput, improve patient flow in triage and make better predictions based on facility population level.
Using big data for these types of analyses, hospitals would know optimal patient discharge times to make best use of bed space without sacrificing patient outcome. Physicians could more accurately prioritize and treat patients in emergency and trauma cases and generally improve patient outcomes while reducing costs by providing the right treatment at the right time.
You can takes steps now to get your data into the shape of intelligence. And we can facilitate that process. You may not know that we can unbundle the ETL Plus* process to access your data and manage the transformation process precisely to your specs and targeted predictive outcomes.
Our custom application development expertise allows our analysts to mix and match data sets as needed. We can blend internal with external data sources to establish historical trends and project need and demand, which are the backbone of any feasibility study. Tame the monster! Talk to us.