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Introduction to Data Reliability

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

According to a recent survey, less than 50% of business executives rate their organization's data as “good data”. Every business needs reliable data for decision-making. So, where does that leave the executives who have data that isn't in a reliable state? They are either using unreliable data or worse. They are making wrong decisions. So to make the right decisions, we need reliable data. Although people see it as another part of the process, in reality, 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 data reliable to avoid those losses and make the right decision. In this blog, we’ll begin with the definition of data 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 much data is complete and accurate, ultimately building data trust across the organization. Organizations can make the right decisions based on analytics with reliable data, which removes the guesswork. It is the tool that gives us accurate analysis and insights. It is the most crucial aspect of improving the quality of data. Data is the fuel for every business and organization, but it has also become the must-have part of every business. We all are investing in it, even if we are not working directly on it. If an organization acknowledges the importance of it and invests in it, then it would be way more profitable as compared to businesses who are not investing in it and ultimately, they have to pay the cost when they have data downtime and even wrong prediction results due to unreliable data.

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

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How to assess it?

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 data reliability as it is not a concept based on one particular tool or architecture. Assessing it gives the idea about the state of data and how much it is in a reliable state.

  • Validation: parameter that defines data is stored and formatted in the right way, which is basically a check for data 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 that data. As checked, the data 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.
  • Data Security: We assess data security to check if data is modified in the process or not. It might happen by mistake or intentionally. Having robust data security leads to achieving reliable data.
  • Data Lineage: Making data lineage gives the whole idea about the transformation and the changes that have been made to the data flowing from source to destination. Which is an essential aspect of assessing and achieving it.
  • Updated data: In some scenarios, it is highly recommended to update the data and even update the schema according to the new requirements. It can be assessed by how much your data is updated.

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

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Difference between Data Reliability and Data Validity?

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

For example, we have valid team member data, but some emails are missing. This means data 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 data reliable.

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

  • Data validation tools: There are tools and open source libraries that 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 data.
  • Data Lineage tools: Making data lineage to the data transformation gives us an idea about the operations performed on data and what changes were made. Which is very helpful in improving data 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 a griffin, Deequ, and Atlas collectively help us make data 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 changes in data by mistake or maybe intentionally.
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How to make Data Reliable?

Various tools and technology can achieve it. Making Stored data reliable is ten times more costlier than keeping track when ingesting data. To make reliable data, We should see data as a code. Like a code in any programing language, it doesn’t get compiled if there are errors. When we start seeing every small error in such a manner, We will achieve data quality and hence data 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, and data lineage 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 or data lake, 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 data 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 data was reliable in the first place or not. So we have to keep our data up to date and find out data health. Rome wasn't built in a day, So is it. To achieve it, we have to 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. Data quality gives the idea about the data quality parameters, which helps analyse and achieve it. Data quality metrics can be achieved at a lineage's source, transformation, and destination level.

Schema Mapping

Schema mapping techniques help make reliable data, as data 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?

The benefits of Data Reliability 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 data 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 data reliable.

No data downtime

Data downtime is erroneous data, 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 on data is built. As per the customer’s perspective, the organisation becomes trustworthy as it always gives the right results with no data downtime.

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What is the future scope and its trends?

Most organizations have acknowledged the importance of reliable data, and many are working on it. This data era is evolving each day, and we are developing tools like deequ, griffin, lineage tools and many other tools that help achieve data quality. It depends on a particular case scenario, but still, 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 the must-have requirement for every business. Having data is not enough if it is not reliable. As data helps in making predictive analysis and many other conclusive results, accurate data should be in a reliable state to make those results accurate.

What are the use case?

The use case of Data Reliability is 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 can we apply the solutions. So here, we will try to implement some of the Solutions to make data reliable.

The first step is data collection from the hardware components in our use case. Here, we can use reliable sensors to ensure accurate data. After that, sensors send data to a common component where data from all sensors is collected. Then before sending data 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 on our range like water volume cannot be in a negative value, and it cannot exceed 2000 as it is the tank's maximum capacity.

But even at the last stage, which is database storage, we have to 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, and without it, a business can be at losses. So to avoid any loss or wrong results, we must have data in a reliable state.

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