What is Data Reliability?
Everyone wants to be a data driver. But to be data-driven, you first need to have reliable data. Having data is quite different from having reliable data, which is also different from being data-driven. To paint a clear picture, let’s take a scenario where two marketing executives present other sales figures for the same quarter in a meeting. The chances are that their data isn’t reliable and therefore can’t be used to make sales projections for the next quarter. Business leaders need reliable data to make informed decisions. Of course, obtaining and maintaining reliable data is easier said than done, especially for businesses that are just starting in data analytics. In this post, we’ll walk you through everything you need to know about data reliability.
What is Data Reliability?
Data reliability means that data is complete and accurate, and it is a crucial foundation for building data trust across the organization. Ensuring data reliability is one of the main objectives of data integrity initiatives, which are also used to maintain data security, data quality, and regulatory compliance.
With reliable data, business leaders can eliminate the guesswork when it comes to making informed decisions. It is the fuel that delivers trusted analytics and insights. And it’s one of the most important things to get right when it comes to improving the overall health of an organization’s data.
It can be tempting to jump headfirst into implementing processes and policies that you hope will improve data reliability, but the kinds of issues that cause poor data reliability are numerous, and each cause must be treated differently. The first step is to find out which data is reliable and which is not, and this can be determined by a process called data reliability assessment.
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 a check for its 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.
What are the benefits?
Accurate analysis of data
With data reliability, 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.
Data reliability 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.
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.
3 Reasons You Should Invest in Data Reliability
If an organization has high-quality data, business leaders will be better positioned to make informed decisions. This means that they will have a greater chance of becoming successful. Deciding to invest in data reliability means you are investing in your organization’s future. Here’s a look at other reasons why organizations need to invest in data reliability:
To Diffuse a Data-Driven Culture in the Organization
In today’s data-driven business landscape, data plays a significant role. Even so, determining valuable data from the large volumes of data that businesses generate can be challenging. Investing in data reliability can help foster a culture where employees use data in their decision-making. Needless to say, it’s easier to motivate employees to use data when the outcome is correct. If they can’t trust the data, they won’t use it.
To Gain Time
What is the point of having a BI platform if you can’t trust the result and have to recalculate everything? When your data is reliable, you can automate data analysis and dashboard reports. This means that you can make timely decisions since you trust your data.
To Reassure Customers
Your customers want to know that their data is being handled correctly. The collection, processing, analysis, and handling of customer data can be overwhelming – with a lot to consider to ensure that you remain compliant with various standards. As a means of reassuring your customers that their data is safe and is being used for the right purposes, you need to invest in data reliability.
How can observability help improve Data Reliability?
Data observability is about understanding the health and state of data in your system. It includes a variety of activities that go beyond just describing a problem. Data observability can help identify, troubleshoot, and resolve data issues in near real time.
Importantly, data observability is essential to getting ahead of bad data issues, which sit at the heart of data reliability. Looking deeper, data observability encompasses activities like monitoring, alerting, tracking, comparisons, analyses, logging, and SLA tracking, all of which work together to understand end-to-end data quality – including data reliability.
When done well, data observability can help improve data reliability by making it possible to identify issues early on to respond faster, understand the extent of the impact, and restore reliability faster as a result of this insight.
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.