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Data Reliability Benefits and Its Framework | The Ultimate Guide

Chandan Gaur | 20 December 2022

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Introduction to Data Reliability

The day we’ll see data as a code will be when we achieve reliability. And of course, it has become the fuel for every business aspect, but the question is, is that fuel reliable?

According to a recent survey, less than 50% of business executives rate their organization's data as “good data.” Every business needs it for decision-making. So, where does that leave the executives who have it that aren't in a reliable state? They are either using unreliable or worse. They are making wrong decisions. So to make the right decisions, we need reliable ones. Although people see it as another part of the process, it has already become the “Must have” part of every business.

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Bad data can lead to huge losses, so we make it reliable to avoid them and make the right decision. In this blog, we’ll begin with the definition of reliability and then move on to the feature and how we can achieve it.

What is Data Reliability?

It is an aspect of data quality that defines how it is complete and accurate, ultimately building data trust across the organization. Organizations can make the right decisions based on analytics with reliable sources, which removes the guesswork. It is the tool that gives us accurate analysis and insights. It is the most crucial aspect of improving its quality of it. This is the fuel for every business and organization, but it has become the must-have part of every business. We all invest in it, even if we are not working directly on it. If an organization acknowledges its importance of it and invests in it, then it would be way more profitable than businesses that are not investing in it.

It would be really difficult or even impossible to achieve complete it. So rather than making data reliable, we will first assess how much we have.

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How to assess Data Reliability?

It is a process used to find the problem in data, and sometimes we don't even know the existence of these problems. We assess the various parameters to assess the reliability as it is not a concept based on one particular tool or architecture. Assessing it gives the idea about its state of it and how much it is in a reliable state.

  • Validation: parameter that defines data is stored and formatted correctly, which is basically a check for it's quality and ultimately leads to it.
  • Completeness: How much data is complete and missing? Checking this aspect gives how much you can rely on the results taken from it. As checked, it can be missing, leading to compromised results.
  • Duplicate data: There shouldn't be any duplicate data. Duplicacy can be checked to achieve reliable results and also save storage space.
  • Security: We assess data security to check whether it is modified in the process. It might happen by mistake or intentionally. Having robust security leads to achieving reliability.
  • Data Lineage: Making data lineage gives the whole idea about the transformation and the changes that have been made to the flowing from source to destination. Which is an essential aspect of assessing and achieving it.
  • Updated data: In some scenarios, updating the data and even updating the schema according to the new requirements is highly recommended. It can be assessed by how much it is updated.

Along with these factors, when we dig deep into it, we know the other aspects of data quality, like the source,  transformation, and operations, which are essential aspects of sensitive data.

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Difference between Data Reliability and Data Validity?

When we talk about data reliability and validity, there's a misconception that they are the same. Although these two are different in a way, they are still dependent on each other.

Data validity is all about validation, whether it is stored and formatted in the right way or not. At the same time, it talks about data trustworthiness. In short, validation is one of the aspects which is required to achieve it. This means you can have it fully valid, but it can have duplicity, or some of it might be missing, which will ultimately be unreliable.

For example, we have valid team member data, but some emails are missing. This means it is not reliable. In case we want to send mail to all employees for some information, then there would be a failure in some cases like some Employees will not get mail because there was some missing Email address or in other words, data was not complete and reliable.

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What are Data Quality frameworks?

It is a concept that includes many data quality parameters, completeness, uniqueness, and validation. So there’s not any direct tool to achieve it. Rather, we use many tools depending on our databases and use cases to make it reliable.

Below described are the various tools used to achieve different aspects of it.

  • Data validation tools: Tools and open-source libraries can be used to validate our data. For example, AWS Deequ is a library built on top of spark by which we can check the completeness, uniqueness, and other validation parameters of it.
  • Data Lineage tools: Making data lineage to the data transformation gives us an idea about the operations performed on it and what changes were made. Which is very helpful in improving quality. Apache Atlas is one of the open-source tools which can be used to make data lineage.
  • Data quality improvement tools: there cannot be a general tool for data quality, which can be achieved by fulfilling various data quality parameters. Tools like griffin, Deequ, and Atlas collectively help us make it reliable. So it completely depends on your particular case that how you proceed to achieve it.
  • Data Security Tools: Not Everyone in the organization should have access to data. This is to avoid any unexpected data changes by mistake or intentionally.
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How to make Data Reliable?

Various tools and technology can achieve it. Making Stored data reliable is ten times costlier than keeping track when ingesting data. To make reliable data, We should see data as a code. Like a programming language code, it doesn’t get compiled if there are errors. When we start seeing every small error in such a manner, We will achieve quality and hence reliability.

So in the further discussion, we will go through some points which will help us make data reliable from the initial state to the final storage.

Ingest with quality

It is advised to ingest reliable data to save time and money. So when you take data from any source, you should have validation parameters such as rejecting null or invalid data and ingesting only what you need. Because ingesting too much data that is not required can slow down the whole process.

Transform with surveillance

After ingestion, the problem might occur at the data transformation level. So to detect such problems, we make data lineage, which shows us the complete journey of data from source to destination and what changes have been made at what level, which is ultimately necessary to make our data trustworthy and reliable.

Store with validation

As they say, prevention is better than cure. So, before dumping data into our database we should do every possible validation to check it. Because once bad data gets saved into databases, it would be ten times costlier to make that data reliable again. Also, it is essential to make sure that it is in a required schema according to the database we will save data.

Improve data health frequently

Data, once saved, will not be reliable forever, no matter whether the it was reliable in the first place or not. So we must keep up to date and learn about data health. Rome wasn't built in a day, So is it. To achieve it, we must go through the process and frequently put little effort into making that data reliable.

Data Quality Metrics

Data quality metrics quantitatively define the data quality. It gives the idea about the parameters, which helps analyze and achieve it. Its metrics can be achieved at a lineage's source, transformation, and destination levels.

Schema Mapping

Schema mapping techniques help make reliable data, as it is mapped to the required format before saving it to the database. Hence, there will be no conflict of schema mismatch and no data missing due to this reason.

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What are the benefits of Data Reliability?

The benefits o are described below:

Accurate analysis of data

With reliable data, the results would be more accurate than unreliable data. For example, we have temperature measurement data from a sensor stored in a database, and then with some Analysis, we want the average temperature. But if the data we stored wasn't reliable, let's say some points were missing. So in such a scenario, we will have wrong results.

Business Growth

Reliable data is the key to business success. We predict certain trends based on our data, like predicting upcoming traffic on our website, but if the data on which we are applying predictive analytics is filled with duplicity. In such a scenario, We will get the wrong analysis results. So to resolve this problem, we make our it reliable.

No data downtime

Data downtime is erroneous, incomplete, duplicated, or invalid. Data downtime can lead to huge losses in the business in terms of time and economy. Reliable data can help reduce that downtime or no downtime at all.

Brand Value

Reliable data helps make accurate results, and trust in data is built. From the customer’s perspective, the organization becomes trustworthy as it always gives the right results with no data downtime.

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What are the future scope and its trends?

Most organizations have acknowledged the importance of reliable data, and many are working on it. This era is evolving daily, and we are developing tools like deequ, griffin, lineage tools, and many other tools that help achieve quality. It depends on a particular case scenario, but there are parameters (explained above) on whose basis data reliability tools can be developed.

As data has become a crucial aspect of every field, making data reliable will be high on-trend. As of now, many organizations have not even acknowledged its concept, but it is soon going to be a must-have requirement for every business. Having data is not enough if it is not reliable. As it helps in making predictive analysis and many other conclusive results, it should be reliable to make those results accurate.

What are the use cases of Data Reliability?

The use cases are listed below:

Agriculture data gathering for Predictive Analysis

In this case, we are gathering data from IOT sensors and sending it to the database via a data pipeline. And further, on that data, predictive analysis is done, such as: finding wind speed and weather conditions to find out the crop quantity from the farm. In such a scenario, if data is not reliable because of any reason like the IOT sensor goes off and we miss data points or data pipeline restarts, and we get duplicate data. So all these extreme cases will lead to missing out on it and hence not getting the right results.

How to overcome this Problem with it?

Complete Data reliability cannot be achieved in one step or one go; We have to go through the whole process and see where we can apply the solutions. So here, we will try to implement some of the Solutions to make it reliable.

The first step is collection from the hardware components in our use case. Here, we can use reliable sensors to ensure accurate data. After that, sensors send it to a common component where data from all sensors is collected. Then before sending it to the database, we can add a lambda function or another suitable component to do the schema mapping according to our database and requirement. We can add a filter for the accepted values in the data stream pipeline. We can add a filter on the water_volume Column, which contains the volume of water present in the water tank in integer data type. We can add filters to our range, like water volume cannot be in a negative value and cannot exceed 2000 as it is the tank's maximum capacity.

But even at the last stage, database storage, we must keep working on it. In this particular case, or in general, it is recommended to have a data lineage not just on the process level but also on the operational database level.

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Conclusion

Data has become the fuel for every business and organization. It becomes very important to have the right quality fuel. This can be achieved by data reliability. It is not just the need anymore, but instead. It has become a must-have part of every business. As discussed above, having it is very important; without it, a business can be at a loss. So to avoid any loss or wrong results, we must have data in a reliable state.