World of Big Data
In today’s world of Big Data, the ability to collect, store, analyze and move data from one computing environment to another has become an integral part of business operations. Unlimited algorithms and variables exist within the creation of data, further complicated by storing and merging disparate and incongruous elements. Because Business needs to consume and share data it, therefore, depends upon data being normalized into specific, expected, and transportable units of information.
Reliance on quality, accurate, consistent, and secure data is essential to the healthcare sector. Privacy and security during the exchange of health information is not only protected by law, but it’s also critical to providing needed interoperability between multiple healthcare technology platforms.
The Healthjump Platform provides a data warehouse solution that collects and stores patient demographic and social history, in addition to appointment, encounter, allergy, diagnosis, immunization, lab, vitals, and procedure records. Capturing large volumes of structured and unstructured data requires data normalization to be of maximum use.
What is Data Normalization?
Normalizing data prepares data to be loaded into a structured database of predefined tables and columns that are determined by specific business needs. Considering that the database may receive information from numerous systems and interfaces, multiple processes are involved to scrub, reorganize, and reformat the data during the data load.
A major benefit of data normalization is cleanup. Normalization processes are designed to eliminate duplicates, redundancies, inconsistencies, and anomalies while resolving data conflicts and maximizing data integrity.
Data normalization examples include techniques that find exceptions or conflicts that are easily identified for correction such as inconsistencies and missing data.
- Inconsistency - Name: Bill Jones / Name: William R. Jones
- Missing - Name:
In many cases, rules are applied during processing to automatically convert non-conforming data into uniform, complete, expected, and reliable output.
- 1-999-999-999 converts to 1(999)999-999
- Texas converts to TX, Arizona converts to AZ
- 01/01/9999 converts to January 1, 9999
The Healthjump jumpSTART extraction process extracts over 250+ pre-defined fields using a set of built-in established procedures to ensure precise, reliable, quality controlled, secure and accurate data.
The ultimate goal of data normalization is to ensure all data is clean, consistently formatted and the data elements read exactly the same throughout all records in the entire data structure.
Benefits of Normalized Data
A leading benefit is the ability to quickly access, view, query, and analyze consistent data. The power to modify and update data on the fly is enhanced by the presentation of clean data void of duplicates, redundancies, and errors. Providing insights to benchmark data trustworthiness is integral to accurate data usage and reporting.
Normalized data provides an additional major benefit: interoperability, the capability of computer and software systems to exchange and share data from a range of vital sources, including laboratories, clinics, pharmacies, hospitals, and medical practices.
Data consistency is important, but hidden benefits include:
- The addition of easy to understand descriptive and standardized naming conventions.
- Referential integrity (data in one field being dependent upon the value in another) consistently provides validation and control of data accuracy.
- Security is enhanced by the ability to lock-down and control access to sensitive data elements.
- Optimal performance and space utilization is simply inherent in a well-normalized database.
The Healthjump jumpCONNECT data warehouse stores massive amounts of normalized quality-analyzed data in a structured format for ease of lookup. jumpCONNECT uses standard API requests for data retrieval, and Business Intelligence (BI) tools can easily bolt-on to provide SQL queries and drag and drop dashboards for direct access.
What does it mean to normalize data? By now, you should have the understanding that without data normalization, raw data is frequently nothing more than a jumble of unusable and inaccessible elements. It’s the normalization process that brings the order necessary for effective data management.
How do you tell if your data is normally distributed?
There are multiple ways to determine if your data follows the standard norms of distribution. The key takeaway is that the distribution should follow a probability that dictates how the values of a variable are distributed. A normal distribution of data reflects a small range of deviation within the high and low ends of the data range. Database administration controls the management and maintenance of a database including monitoring and troubleshooting deviation of the norms.
Normalizing data using Python or other pre-processing tools to prepare data to rescale the data for normalization and standardization ensures all values are within range.
The importance of Data Normalization in Machine Learning
Big Data, Artificial Intelligence (AI), and Machine Learning are the wave of our future. It’s already part of our everyday life:
- Voice recognition to compose a text message or get directions
- Hear when you ask Alexa to change your music
- Personalize services based on previous buying history
- Identity recognition by touch, iris or face
- Email filtering to automatically mark as trash or spam
- Blocking unwanted telephone calls
- Fraud detection in banking
- Language translation
The power of Machine Learning is that it includes algorithms that further generate algorithms.
Why normalize Data?
Machines learn via computing power, algorithmic innovation, and data availability. They require data availability as in - clean data, accurate data, dependable data, normalized data!