Tidy Data helps companies by monitoring the data veracity in the ETL pipeline. Monitoring data is an important part of capturing and storing reliable information.
What Does Data Veracity Mean?
Data veracity is the measure of how credible the data is. This is measured by determining the trustworthiness of the source, type, and processing of the data. The data can also be compared to historical data and expected data. Ensuring data veracity means that the customer can be sure of where the data came from and that their reports are accurate.
How to Measure Data Veracity?
Data veracity is a measure of precision. It’s great to have a lot of data, but it’s even better to have good data. But how do we determine what good data is? Let’s break big data down into examples of data veracity.
Data biases come about from statistical biases. This is when data is altered or approximated to favor the company or other thoughts from the user entering the data.
Data comes from many different sources. Sometimes an inaccurate source may be discovered and if there is no previous reference or historical data related to the source it would be difficult to verify the accuracy of the data. A real-world example would be having a lot of negative comments on a company’s products and having the bot-generated comments being lumped with comments from actual users.
Bugs in programming can lead to data being calculated or transformed incorrectly when it is saved or transferred.
These are usually sensor related and include things such as a visual sensor trying to determine which moving object is a person or interference if sensors are placed too close to one another. In addition, badly written code can cause memory faults or other issues that can corrupt or give null data.
Data security is a big part because a malicious user, or outside threat, could intentionally alter data to give bad results. One example may be someone incorrectly recording the company’s net worth to try to alter stock prices. All these elements of big data play a role in determining data veracity and must be dealt with accordingly to get the complete, and correct, picture. Here at TidyData we understand how important data veracity is to your company’s well being and peace of mind.
What is Big Data?
Big data is, well...a lot of data. The idea behind big data is that data comes from many different sources creating rows upon millions of rows of data to create an overall picture of some organization. Often the data is compiled into smaller chunks or displayed in many different types of reports for different types of workers in the company.
Big data is growing at an astonishing rate. Market revenues for big data software and services are expected to reach $103 billion by 2027 but current trends may push that number even higher. Data is expected to continue to grow and it currently shows no trend of slowing down, in fact, it is increasing at an appalling rate.
Big data is important because it gives companies that competitive edge. And yes a lot of software and service vendors say the same thing but recent studies by Accenture show that a lot of executives agree that investing in big data has either helped their company gain an edge over the competition or they think that investing in big data would improve their company’s output.
How Does Big Data Help?
Big data helps companies because it allows them to quickly and efficiently analyze millions upon millions of data points. That sounds pretty cool, but what does that actually mean? It means that a company can find things like trends among customer product reviews, what customers from one region prefer over another, or even how long between vacation times do employees start to slow down on their production levels. Being able to easily see these types of things allows a company’s management or sales teams to quickly make decisions that would otherwise take lots of surveys spread out over several months. Having the data able to be accessed live makes a huge difference in time when large organizations start throwing the “change” word out there.
What Types of Big Data Are There?
Without getting down into the bits and bytes and database fields, there are three main types of data. It is important to understand these classifications to understand the true scope of big data. By understanding these types of data, we can better understand how different types may be useful to us. Here are the three main classifications listed below.
Structured data is big data that is already neatly placed into rows and columns that our databases understand. Astonishingly, this only accounts for about 20% of the data collected by big data. What that means is that while this data is nice to come across, the majority of the data we find needs to be transformed to fit within our structured databases. The two main sources of structured data are machine-generated and Human input.
This data is exactly as it sounds, data that has no structure to it. These can be images, social media activity, and other types of information. Unstructured data is also classified further into Human-generated and machine-generated. Machine-generated data often includes imagery such as surveillance or satellite images or GPS data. User-generated data often comes from social media and includes things such as posts, likes, text, and other activities.
The line between structured data and unstructured data is sometimes not very clear. The easiest way to look at it is data that doesn’t fit the conventional column-row organization of a database but still does have some sort of organization to it. This could be data in the form of XML or some other file type that wasn’t initially meant for database storage.
What Are The 4 V’s of Big Data?
Big data is broken down into four dimensions called the 4 V’s of big data. You can find many different lists of these “V”s including lists of 5, 6, or even 7. The four listed below are industry across most industries and give an overall big picture of what we’re looking at with big data.
Volume includes just the sheer size of the data we are dealing with today. It is estimated that we, worldwide, are creating 2.5 quintillion bytes of data every day. Furthermore, 90% of all data has been created in just the last two years.
Data velocity is the speed at which we are uploading data. A recent IBM study found that every minute there are approximately 72 hours of streaming video uploaded, 216,000 Instagram posts, and 204 million emails sent.
Veracity is the certainty of the data which we have talked about above. Poor data costs US companies about 3.1 trillion dollars a year.
Data comes in all different types from text to videos to images and captured data. Media and documents make up the majority of data by volume accounting for about 80% of all data. These types of data are also the most difficult to organize and manage. Here at TidyData we understand that and will help you implement a data management solution that will fit whatever needs you have.
What is Data Quality?
Data quality is based on data completeness, reliability, and relevance. Data veracity goes a long way in improving the quality of data. If the data can’t be trusted, we might as well not even have it to begin with. The term relevance looks more at the date of the data. If someone went through a trend watching horror films that ended 5 years ago, an ad service probably wouldn’t be too efficient at targeting that person for horror films now especially if they’ve found a new interest. This is just one instance of where this can be applied, there are many more useful applications beyond just ad services.
What Are The Benefits of Data Cleanliness?
As mentioned above, bad data accounts for approximately $3.1 trillion dollars lost among US companies per year. A shockingly low percentage of companies are only using .5% or less of their available big data. By leveraging the data they have available, a company can increase the efficiency of their targeted advertisements to increase their revenue. The ROI on Big Data has been proven to be worth it many times over.
How Automation Corrects Information Values
Automation processes in the big data world mean much more efficient processing of big data. By utilizing technologies such as AI or machine learning, these automatic processes can clean and correct data on the fly instead of enlisting Humans in the painstaking process of combing through gigabytes of data.
Additionally, data can be corrected as it is being inputted if you have data corrections on your fields. These small scripts can save lots of time later correcting issues due to bad human input. For example, you may have a phone field and with 10 digits in each phone number, the chances are pretty high that at least 1 out of 100 entries will have an error. By using data correction, you can account for incorrectly formatted numbers (missing a digit or not enough) and even check against phone numbers already in the database.
What is Data Monitoring?
Data monitoring is the act of reviewing and analyzing your data before it gets to the data warehouse. It also allows you to track and measure your data as it is being transferred. By having automated procedures in place, a company can track the quality and usefulness of its data to ensure their reports and analyses are accurate.
How is Monitoring Used to Reduce Errors?
Data monitoring is used to reduce errors by determining where the errors are coming from. It’s all well and good to find an error and correct it but if we can prevent it from its source, we can eliminate that process altogether. If we find the trends where bad data is coming from or what types of data come across as incorrect, we can find out how to fix the issue so it won’t happen again.
How TidyData Helps Its Clients
TidyData helps its clients by offering an easy to use Software as a Service data pipeline management tool. This allows the client easy access to data monitoring tools which will check for data veracity, cleanliness, errors, and lag time in the data transfer process. At TidyData we are dedicated to the customer and want to provide you with the best experience. By continuous data pipeline management we can offer real-time solutions to improve your big data processes.
Contact us Today
Get in touch with us today and we’ll determine what solution will work best for your big data problems and implement a data pipeline management so your team can work on the bigger things your company needs to focus on. Let us take the task of managing the flow of your data so your developers don’t have to.