Big Data may sound like a misnomer that pre-existing data might have been small, but it is not size alone that defines Big Data.
Big Data is a large set of data that are computationally analyzed to reveal patterns and trends, especially in relevance to human behaviour. It starts with collecting data, follows with analyzing it, and finally comes up with actionable insights that any business can use for different benefits.
In this blog, we will be discussing the characteristics of Big Data and its trustworthiness.
Big Data and Its Characteristics
Big Data is among the most commonly used terms in the tech sector these days. It has become a prime requirement for any business industry to imbibe Big Data analysis for growth and enhancement. With Big Data, organizations can use large amounts of data effectively, which would be pretty challenging with conventional data-processing solutions.
- You can now identify trends in the market.
- Follow patterns interconnecting the data
- Make predictions through analysis
Understanding the characteristics of Big Data is vital to making effective use of it for your business. There are primarily seven characteristics of Big Data. Let us find each one of them.
Processing data in a time-bound manner is the prime requirement for any business. Big Data analytics comes up with a character of high velocity.
Velocity refers to the speed at which data is collected from the sources and stored and its associated retrieval rate.
Hence, in terms of Big Data, you can say velocity is the speed at which your data moves across different systems.
Here volume refers to the size of data that is managed and analyzed. Organizations deal with massive amounts of data to better understand your business, your customers, and the marketplace. It analyzes almost all of the data, or at least more than half of it, and identifies the data that’s specifically useful to your business.
Variety deals with the diversity and range of different data types collected in Big Data. Big Data can be structured, unstructured, and semi-structured depending upon the type of data source,
Structured: It is stored in tabular form in the relational database management system
Unstructured: Such data is raw and includes unstructured files, log files, audio files, and image files.
Semi-structured: Here, data is stored in relations, i.e., tables, but the schema is not appropriately defined, e.g., JSON, XML, CSV etc.
The characteristic defines how valuable the data collected is. It deals with the value of the data that is collected, stored and processed. The value of data comes from insight discovery and pattern recognition that lead to more effective database operations. It helps to build stronger customer relationships, among other quantifiable business benefits.
Veracity means how much data is reliable. The accuracy of the data or information datasets often determines executive-level confidence. Veracity refers to bias, noise and anomaly of data.
It is essential to make use of relevant database sets to make the right choices. Volatility defines how long the knowledge gathered is valid (up-to-date) to use. It determines how long we can hold on to the data collected (use that data).
It determines if the collected information/ data is correct for the intended use. If yes, it is valid; otherwise, it is not.
Trustworthiness Of Big Data
When we talk about the accuracy of the data, it is not limited to the quality/ quantity of data. It must also include the trustworthiness of your data source and data processes.
Undoubtedly, poor data quality leads to targeting the wrong customers and communications. Hence, poor data governance needs to be fixed.
The veracity of Big Data deals with the complete check of the data that determines its trustworthiness. It determines the accuracy of data(truth) that are extracted and processed to derive insights from it.
Moreover, It deals with
Data veracity deals with filtering useful data from large data mines. It also includes the translation of the data. With the help of this character, incomplete datasets or those with errors are realised and further proceeded converted into consistent, consolidated, and united sources of information.
Things To Be Taken Care Of: Big Data and its trustworthiness
Data veracity defines the degree to which data is accurate and precise. The Higher the level of accuracy, the higher the data will be trustworthy. The following things must be taken of:
- Biasedness: Biasedness is the error when some data elements are given false weightage over others. Organizations make decisions based on such inaccurate values and are mistaken.
- Noise: Any non-valuable data in the data set is known as noise and needs to be cleaned. A dataset with less noise leads to better insights.
- Bugs: Software bugs lead to the miscalculation of the data.
Abnormality in data: Anomaly/Abnormality in the data is the data point that is not usual and may be a fraud. For example, debit card fraud is detected based on the large amount withdrawn (which is not the usual activity of the user).
- Data lineage: If organizations store data from many sources and some sources are inaccurate without historical reference, it gets difficult to track them. You can’t track specific data sources from which inaccurate data is extracted and stored.
To check on the integrity and the security of data, you can explore Big Data and analytics solutions offered by Ksolves. Our expert team uses different Big Data governance tools to manage and process data. Connect us at email@example.com, or you can call us on +91 8130704295 and leverage the benefits of the Big Data analytics services.
Why Is Veracity Important?
Big Data is complex. While AI (Artificial Intelligence) and Machine Learning are widely used for Big Data analysis, statistical methods are still needed to ensure data accuracy. Better accuracy gets you the more practical application of Big Data.
For example, you can download an industry report from the internet for some findings. However, you can’t use it as it is before taking some action. Instead, You need to validate it or do additional research before formulating your findings. Working with Big Data happens in the same way. You need to validate it first.
Different methods (including indexing and cleaning the data) and data management platforms will help you integrate, aggregate, and interpret data with research-grade precision. Organizations need a Big Data source and a suitable processing method that upholds a high level of veracity. It would lead to unlocking the power of audience intelligence and driving a better consumer segmentation strategy.
Complex data is hard to deal with using traditional methods. However, Big Data not only deals with the processing of the same but has the potential to draw up valuable insights from it. Data Engineers use Big Data platforms for business analysis and to make accurate data-driven decisions.