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Introduction to Data Standardization

Hadoop, Data Science, Statistics & others

In the earlier days, Data were captured manually by operators or it can be downloaded/uploaded from external systems and there was no need to cleanse or standardize them. But today the data flows into the system from various sources viz., Machines, sensors, Social media (Facebook, Linked-in, Twitter) and Human beings generate data whenever they do online banking/shopping or browse websites.

Since the data flows from multiple heterogeneous sources, the data will have different formats and inconsistencies. It needs to be brought to a standard format for comparison, analysis, and inference. Data Standardization facilitates consolidating the multiple sourced data into a single and consistent format for further processing. In this article let us study the features of Data Standardization.

What is Data Standardization?

There are 13 types of data viz., Big data, Structured/Unstructured/Semi-structured data, Time-stamped data, Machine data, Spatiotemporal data, Open data, Dark data, Real-time data, Genomics data, Operational data, High dimensional data, Trans-analytic data, Unverified outdated data. The size and nature of the application have grown to such a level that it should have the capability to process any type of data from any source and provide useful insights to the end-users.

All the data emanating from various sources should be brought to a common level so that they can be consolidated, compared, and inferred. If the common level cannot be obtained the comparison may lead to wrong inference and the data will become irrelevant. Apple can only be compared with apple and that only will make sense.

Data Standardization addresses the above issue precisely. The design of any application should be in such way that data collected from any sub-system gets synchronized seamlessly with the data from other sub-systems as well as with data in the existing database and it should be a topmost important factor to be considered in the system design even before the data is collected, cleansed and analyzed.

Hence standardization can make meaningful data for analytics purposes.

Use Cases of Data Standardization

3 Major use cases are there in the data standardization viz.,

Mapping the external data into the internal standards and using the in-house analytics tools.

Mapping the internal as well as external data into a common data format and using a third-party analytics tool to get data.

Creating complex business logic to consolidate the data at a common level and derive.

a. Mapping external data to internal format.

Having a data extraction tool to convert the data while extracting the data from the external system into the format of the internal system and use the existing analytics tool for getting insights into the data. This offers a limited standardization facility wherever the external data can be mapped into an internal format for analysis. Organizations with legacy data and well-established analytics can opt for this model.

b. Common Data Model (CDM).

Data collected from external systems as well as the internal data are transformed into a common format and consumed by analytics tools available in the market for deriving insights. The Healthcare industry adopts this model for consolidating the data from multifarious external systems viz., clinical research, patient history, insurance claims/settlement, providing medical care facilities to patients, diagnosis of diseases using AI tools, calculating variable insurance premium based on the lifestyle of the policyholders.

Each Data collected by these applications have different use and hence they have their own definitions, formats, coding standards, logics, intricacies, and relationships. The common data model provided by Observational medical outcome partnership (OMOP) helps the transformation of varieties of data into a common format, coding, language, and definitions. It also offers abundant analytical functionalities that can be performed on the common transformed data.

Steps of Data Standardization

There are various steps involved in standardizing the data. Before getting into that let’s explore the various ways and means to standardize the data at the source and extraction stage.

Source the data in a common format – Wherever possible try to collect the data in a common format

e.g dates, currency, decimals in numeric columns, and usage of special characters. This is possible in surveys, census, and mass collection of data.

Use standards – If there is a data standard or pre-determined way of storing the data in the local system the data collection can be designed

Data Transformation into a common – During the data cleansing stage or extraction stage all the data can be converted to a uniform format.

Common datum – Convert all the data to a common scale and make it a unit of measure agnostic. All the data can be expressed in mean and standard deviation from the mean. This can be executed during the data cleansing stage or analysis stage.

Steps

Finalize the data standards

Locate the data sources and measure the frequency

Design a good survey to elucidate the requirements and get the data

Validate, cleanse the data before using it and use the current

Benefits

Accuracy of the data improves leading to the rich quality.

Usage of data increases.

Rich insights of data can be derived by using all the data available from external

With available data, we can design new business

Enrich data helps in inventing new technologies in

It enables us to move from a decision support system to a decision making

Latest updates or developments in research can be interfaced with any application and it can be fully

Conclusion

Data Standardization facilitates the supply of inputs to data-hungry applications and makes them scale up their performance and utility level.