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Data Quality in The Age of Big Data

December 23, 2020Category : Blog
Data Quality in The Age of Big Data

What is the first word that conjures up in your mind when you hear the word data quality? It’s difficult to really define it in real objective terms. Why do we need it but? Just because of the sheer amount of data that is available.

The ‘size’ of data is no longer tin TB’s but the PB (1PB = 210TB), EB (1EB = 210PB), and ZB (1ZB = 210EB). According to IDC’s “Digital Universe” forecasts, 40 ZB of data was already been generated by 2020. But quality is really where it is at.

This translates really well when it comes to data quality. Good data, as we have mentioned, is really not that simple to describe. Data quality is the ability of your data to serve its intended purpose defined by several characteristics.

A quick online search will give you multiple definitions. As long as you can use that data to aid your business decisions, it is of good quality. Bad quality data add to your workload instead of aiding it. Imagine you’ve made certain marketing decisions based on secondary research conducted two years ago, what good even is that?

Data Quality Dimensions

Intuitively you might say that real-time data is the best data. Not entirely true. While data is only as good as ‘fresh’ (because are we moving at warp speed or what), there are other determining factors to access data quality, that we cannot ignore.

The interspersed characteristic of data quality dimensions is important to provide a better understanding of data quality as data quality dimensions do not work in silos.  Some of them such as accuracy, reliability, timeliness, completeness, and consistency dimensions can be classified into internal and external views. Each of these classifications can be further divided into data-related and system-related dimensions. Or, data quality dimensions can be classified into four categories; intrinsic, contextual,  representational, and accessibility.

A). Data Accuracy

This dimension has been plugged into semantic accuracy and syntactic accuracy. The latter refers to the proximity of the value towards the element of the concerned definition domain, whereas, semantic accuracy refers to the proximity of the value towards the actual world value.

B). Data Availability

Democratizing data is a double-edged sword. But what good is data if it is not accessible to everybody who needs to crunch it?

C). Completeness

Data cleansing tools search each field for missing values, They fill those to give you a comprehensive data feed. However, data should also represent null values. Null values should also be assigned equal weightage as long as we can identify the cause of the null value in the data set.

D). Data Consistency

Consistent data reflects a state in which the same data represent the same value throughout the system. All denominators should be on equal footing as long they denote the same value. Data is usually being integrated from varied sources to gather Information and unveil insight. But, different sources have different schema and naming conventions, inconsistency after the integration is expected. Keeping in mind the sheer volume and variety of data being integrated, consistency issues should be managed in the early stage of the integration by defining data standards and data policies within the company.

E). Timeliness

Data timeliness is defined as the variable of datedness. The datedness attribute includes age and volatility as a  measure. This should, however, not be considered without the context of the application. Naturally, the most current data has more potential to be considered as high data quality, but it does not precede the relevancy.

Data quality dimensions such as accuracy, completeness, consistency, and existence are related to a  classification of integrity attributes. It can be described as the innate ability of data to map to the data user interest. As compared to representational consistency, the lack of inconsistency in integrity attribute has been defined from the data value perspective and not just the format or representation of the data itself.

Web Scraping as The Most Viable Solution to Monitor Data Quality

Web scraping uses crawling tools to scour the web for the required information. It can be integrated with an automated quality assurance system to ensure data quality for all dimensions.

How Do You Structure Such A System?

At a broader level, the system is trying to gauge the integrity of your data along with the umbrella of the data you have crawled.

A). Reliability

a). Make sure that the data fields crawledhave been taken from the correct page elements.

b). Collecting is not enough. Formatting is just as important. Ensure that the data scraped has been processed post collection and presented in the format asked during the collection phase.

B). Area Covered

a). Every available item has to be scraped, that is the very essence of web scraping.

b). Every data field against every item has to be covered too.

C). Different Approaches to Structure the System

Project Specific Test Framework

As the name suggests, every automated test framework for every web scraping project you work on will be absolutely customized. Such an approach is desired if the requirements are layered and your spider functionality is highly rules-based, with field interdependencies.

Generic Test Framework

The other option is to create a generic framework to suit all your requirements. This works if web scraping is at the core of all business decisions and customized pieces will be not feasible. This framework also allows to quickly add a quality assurance layer to any project.

Solution

Web scraping services are the best bet to manage data integrity. It comes with both manual and automatic layers. It also gets rid of all HTML tags to procure ‘clean’ data. Enterprise web scraping service like PromptCloud maintains the data quality of data for hundreds of clients across the globes and the zettabytes of data they procure. We also handholds you through the process and our customer support team is always one call away.

Still not convinced that data quality is essential? Here’s a 3.1 trillion dollar reason for you. The annual cost of poor quality data, in the US of A alone, was a whopping $3.1 trillion in 2016.

If you liked reading this as much as we enjoyed writing this, please share the love. We think you might enjoy reading this just as much.

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