The day we'll see data as a code will be when 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?
How do you determine the reliability of data?According to a recent survey, less than 50% of business executives rate their organization's data as "good data."Know more about : Data Reliability Benefits and Its Framework | The Ultimate Guide
Why Data Reliability?
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. Nowadays, people see data reliability as just another part of the process, but in reality, it has already become the "Must have" part of every business.
Bad data can lead to huge losses, so to avoid those losses and make the right decision, we make data reliable. in this blog, we'll begin with the definition of data reliability and then move on to the feature and how we can achieve data reliability.
What is Data Reliability?
Data reliability is an aspect of data quality that defines how much data is complete and accurate, ultimately leading to building data trust across the organization. With reliable data, organizations can make the right decisions based on analytics, removing the guesswork.
Data reliability is the tool that gives us accurate analysis and insights. Data reliability is the most crucial aspect of improving the quality of data. Data is the fuel for every business and organization, but data reliability has become the must-have part of every business. We all invest in data reliability even if we are not working directly on data reliability. If an organization acknowledges the importance of data reliability and invests in it, it would be way more profitable than businesses not investing in data reliability. 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 difficult or even impossible to achieve complete data reliability in one go. So rather than making data reliable, we will first assess how much data we have is reliable.
How do you determine the reliability of data?
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 data reliability is not a concept based on one particular tool or architecture. Assessing data reliability gives the idea about the state of data an how much it is in a reliable state.
- Validation: This parameter defines whether data is stored and formatted correctly, which checks for data quality and ultimately leads to data reliability.
- Completeness: How much data is complete and missing? Checking this aspect shows how much you can rely on the results taken from that data. The data can be missing if not checked, 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. Data Lineage is an essential aspect of assessing and achieving data reliability.
- Updated data: In some scenarios, it is highly recommended to keep updating the data and even update the schema according to the new requirements. Data reliability can be assessed by how much your data is updated.
Difference between data reliability and data validity
There's a misconception about data reliability and validity that they are the same. Although these two are different in a way, still they both are dependent on each other.
Data validity is all about data validation, whether the data is stored and formatted in the right way or not. In comparison, data reliability talks about data trustworthiness.
In short, data validation is one of the aspects which is required to achieve data reliability. 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 employee data, but some emails are missing. This means data is not reliable. If 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.
What are the frameworks of data reliability?
Data reliability is a concept that includes many data quality parameters completeness, uniqueness, and validation. So there's no direct tool to achieve data reliability; instead, we use many tools depending on our databases and use-cases to make data reliable.
Below are the various tools used to achieve different aspects of data reliability.
- 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 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 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 data reliable. So it entirely depends on how you proceed to achieve the data reliability in your particular case.
- 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 intentionally.
How to make data reliable?
Various tools and technology can achieve data reliability. 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 programming language code, it doesn't get compiled if there are errors. When we start seeing every minor 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 lead to 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 or data lake, we should do every possible validation to check data reliability. 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 ensure that data is in a required schema according to the database we will save.
Improve data health frequently
Data once saved will not be reliable forever, whether the data was reliable in the first place. So we must keep our data up to date and check data health over time. Just like Rome wasn't built in a day, so is data reliability. To achieve data reliability, 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. Data quality gives the idea about the data quality parameters, which helps analyze and achieve data reliability. Data quality metrics can be achieved at a lineage's source, transformation, and destination level.
Schema mapping techniques help make reliable data, as data is mapped to the required format before saving it to the database, and hence there will be no conflict of schema mismatch and no data missing due to this reason.
What are the Benefits of Data reliability?
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 that is 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.
Reliable data is the key to business success. There are specific trends we predict 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, which can be incomplete, duplicated, or invalid. Data downtime can lead to considerable losses in the business in terms of time and economy. Reliable data can help reduce that downtime or no downtime at all.
Reliable data helps in making accurate results, and hence trust in data is built. From the customer's point of view, the organization becomes trustworthy as it always gives the right results with no data downtime.
What are the future scope of data reliability?
Most organizations have acknowledged the importance of reliable data, and many are working on it. This data era is evolving daily, and we are developing tools like deequ, griffin, lineage tools, and many other tools to help achieve data quality. Data reliability 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 is going to be high on-trend. Many organizations have not even acknowledged the Data Reliability concept, but very soon, it will 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, data should be in a reliable state to make those results accurate.
Use case - Agriculture Data Gathering for Predictive Analysis
What is was the problem?
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 data reliability and hence not getting the right results.
The Solution - Data reliability
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 data reliable.
In our use case, The first step is data collection from the hardware components. Here we can use reliable sensors to ensure the data it gives us is accurate. After that, sensors send data to a common component where data from all sensors is collected. Before sending data to the database, we can add a lambda function or another suitable component to map the schema according to our database and requirement. We can add a filter for the accepted values in the data stream pipeline. In our case, 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, database storage, we must keep working on data reliability. In this case, or in general, it is recommended to have a data lineage not just on the process level but on the operational database level.
As data has become the fuel for every business and organization, it becomes imperative to have that fuel of the right quality, which can be achieved by data reliability. Data reliability is not just a need anymore but has become a must-have part of every business. As discussed above, data reliability is critical; 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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