How to Ensure the Validity, Veracity, and Volatility of Big Data

High volume, high variety, and high velocity are the essential characteristics of big data. But other characteristics of big data are equally important, especially when you apply big data to operational processes. This second set of “V” characteristics that are key to operationalizing big data includes

  • Validity: Is the data correct and accurate for the intended usage?

  • Veracity: Are the results meaningful for the given problem space?

  • Volatility: How long do you need to store this data?

Big data validity

You want accurate results. But in the initial stages of analyzing petabytes of data, it is likely that you won’t be worrying about how valid each data element is. That initial stream of big data might actually be quite dirty. In the initial stages, it is more important to see whether any relationships exist between elements within this massive data source than to ensure that all elements are valid.

However, after an organization determines that parts of that initial data analysis are important, this subset of big data needs to be validated because it will now be applied to an operational condition. When the data moves from exploratory to actionable, data must be validated. The validity of big data sources and subsequent analysis must be accurate if you are to use the results for decision making.

Valid input data followed by correct processing of the data should yield accurate results. With big data, you must be extra vigilant with regard to validity. For example, in healthcare, you may have data from a clinical trial that could be related to a patient’s disease symptoms. But a physician treating that person cannot simply take the clinical trial results as without validating them.

Imagine that the weather satellite indicates that a storm is beginning in one part of the world. How is that storm impacting individuals? With about half a billion users, it is possible to analyze Twitter streams to determine the impact of a storm on local populations. Therefore, using Twitter in combination with data from a weather satellite could help researchers understand the veracity of a weather prediction.

Big data volatility

If you have valid data and can prove the veracity of the results, how long does the data need to “live” to satisfy your needs? In a standard data setting, you can keep data for decades because you have, over time, built an understanding of what data is important for what you do with it. You have established rules for data currency and availability that map to your work processes.

For example, some organizations might only keep the most recent year of their customer data and transactions in their business systems. This will ensure rapid retrieval of this information when required. If they need to look at a prior year, the IT team may need to restore data from offline storage to honor the request. With big data, this problem is magnified.

If storage is limited, look at the big data sources to determine what you need to gather and how long you need to keep it. With some big data sources, you might just need to gather data for a quick analysis.

You could then store the information locally for further processing. If you do not have enough storage for all this data, you could process the data “on the fly” and only keep relevant pieces of information locally. How long you keep big data available depends on a few factors:

  • How much data is kept at the source?

  • Do you need to process the data repeatedly?

  • Do you need to process the data, gather additional data, and do more processing?

  • Do you have rules or regulations requiring data storage?

  • Do your customers depend on your data for their work?

  • Does the data still have value or is it no longer relevant?

Due to the volume, variety, and velocity of big data, you need to understand volatility. For some sources, the data will always be there; for others, this is not the case. Understanding what data is out there and for how long can help you to define retention requirements and policies for big data.

As a consumer, big data will help to define a better profile for how and when you purchase goods and services. As a patient, big data will help to define a more customized approach to treatments and health maintenance. As a professional, big data will help you to identify better ways to design and deliver your products and services.

This will only happen when big data is integrated into the operating processes of companies and organizations.

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