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Top Parameters To Measure And Define Quality Data

Data has moved from the shadows into the spotlight, becoming the currency for modern business, and it’s changing lives and organizations everywhere. Data is driving decisions that have been previously made using blind hunches, gut feelings, and assumptions. But what makes data important? The answer to this question is delivered in a series of parameters and standards. The precise combination of these parameters takes data beyond the realm of the simple, creating rich business intelligence, precise analysis, and beautiful reports — defining data as quality.

Parameters To Measure And Define Quality Data

Why Quality Data Matters

Many people believe that data quality is a given in their industry, but the reality is that most businesses deal with incorrect or low-quality data. Inaccurate data costs money and time. It can result in poor decision-making and a lack of trust in the underlying evidence.

IBM discovered that poor data quality costs the US economy $3.1 trillion per year due to poorer productivity, system breakdowns, and increased maintenance expenses, to name a few of the negative consequences of poor data quality.

“Organizations feel that poor data quality is responsible for an average of $15 million in losses each year,” according to a survey conducted by the research firm Gartner. Gartner also discovered that over 60% of those surveyed had no idea how much bad data costs their companies because they don’t measure it in the first place.

Intrinsic Data Quality Parameters

How do you define or measure data quality? You might be tempted to think of it as the opposite of bad data, but that’s not exactly true. What we’re really attempting to do is to ensure the data we use supports the business cases that describe our measurements of success – that is, that our data don’t just stay clean and pristine but actually answer the questions that get asked of them. The following is an overview of the top parameters to measure and define data quality:

Accuracy

The extent to which data accurately reflects the real-world condition of affairs that it depicts is referred to as accuracy. It is the degree of truthfulness, freedom from error or defect, or the exact correspondence of a statement to the facts.

Completeness

This refers to the existence of a complete set of data that is required to achieve a specific outcome. The absence of even one vital piece of information can significantly impact the final results.

Privacy and Security

Privacy refers to keeping customer information safe, secure, and private at all times; this includes data sent or received over a network. Security, meanwhile, is about protecting customer data from any unauthorized access. This includes protecting customer information from hackers by using appropriate encryption techniques. From the customers end, many are using anonymous proxies to hide their identity online and avoid collection of private information by websites or highjacking of sent information.

Consistency

Consistent data means that information is stored in the same format across systems and databases. Inconsistent data can lead to misinterpretation and can cause problems when combining datasets from different sources.

Timeliness

Timeliness refers to the age and availability of the data. This parameter measures the time it takes for data to be produced, as well as how recently it was updated. What good is data if it is out-of-date? It’s important that your data always be up-to-date so you can make accurate decisions and plans based on it.

Accessibility

Data accessibility measures how easy or difficult it is to access the information being queried. Ideally, all relevant data should be easily accessible by authorized users in real-time. However, this parameter can sometimes be limited due to technical constraints like hardware limitations or compatibility issues.

Validity

Everywhere data points appear, they are in the same and correct format. This is also known as data integrity. A high rate of validity indicates that all data follows your set formatting requirements. Validity can be determined by comparing the number of format mistakes for a data item to the total number of times that data appears in your databases.

In the end, evaluating data quality is a joint endeavor that requires the participation of many different individuals, each of whom has a distinct role to play in order for the process to be successful. But when you engage everyone, instead of relying on one or two people, you’ll be more likely to reap the full benefits of your data quality strategy. As your organization faces new hurdles and challenges in the coming years, remember that this commitment should be part of everyone’s day-to-day activities.

Final Thoughts

Fortunately, several vendors offer a number of solutions for managing your data quality. One solution will not fit every company, so you need to start with having a clear definition of the areas that are important to you and which level of investment you want to make to fix them. Then, once you have that understanding, it’s time to explore your options. Rest assured that there is more than one solution out there to help take care of your data quality.

Deepak
Deepakhttps://www.techicy.com
After working as digital marketing consultant for 4 years Deepak decided to leave and start his own Business. To know more about Deepak, find him on Facebook, LinkedIn now.

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