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 different conversion rates for the same quarter in a meeting. The chances are that their data isn’t reliable and therefore can’t be used to make lead generation projections for the next quarter.
Business leaders need reliable data to make data-driven 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, including what it is, how to control it, and why you should invest in it.
What Is Data Reliability?
Data reliability means that data is accurate and complete, and it’s a vital foundation for fostering data trust within an organization. Ensuring data reliability is one of the primary goals of data integrity, which is also key to maintaining data health, data quality, data security, and regulatory compliance.
Reliable data provides business executives with trusted analytics and insights that eliminate the guesswork of the decision-making process. As such, it’s one of the vital things you should get right when it comes to enhancing the overall health of your organization’s critical data points.
You should measure the reliability of your data throughout its entire lifecycle after it has been transformed and transferred from one base to another. This way, you’ll be better able to determine the quality of the data you collect and the value it adds to your organization.
How to Control Data Reliability
Controlling data reliability involves establishing measures to ensure data meets defined reliability criteria. Most of the time, a data reliability assessment is performed to evaluate the volume of unreliable data.
Data reliability assessment can help you discover problem areas in your data. It can involve the review of existing information about the data by conducting interviews with knowledgeable officials, performing tests on the data, and other measures such as tracing the data to and from the source document.
By running a data reliability assessment, you’ll be able to track three data quality metrics:
- Validity: This is the determination of whether data is properly formatted and stored properly.
- Uniqueness: This is the confirmation that the data is free from dummy entries and duplicates.
- Completeness: This is the determination of whether the dataset includes all the requisite values that your system needs.
Data reliability assessment can also factor in other aspects of data quality, including checking the number of times a dataset has been relied upon, its origin, and how it has been transformed over time. Getting such a deeper understanding is particularly important for datasets related to confidential information, where complete accuracy is imperative.
Assessing the reliability of data enables you to uncover issues within a dataset and the focus areas you need to improve. It may either show you the exact place to fix in a dataset that’s unreliable or reveal hidden problems within a dataset that you believe to be reliable. Suppose the assessment reveals bad data; various measures can be taken to remedy it, depending on what the issues identified were. For example, if invalid data is discovered, it’s likely to be undertaken through a data preparation process.
How to Maintain Data Reliability
Maintaining data reliability is an ever-growing process. But like every IT process, it can be automated and doesn’t have to monopolize all your IT resources. Here are some of the best practices to follow to maintain data reliability:
Collect Data Using a Reliable Method
Data can be collected using a variety of ways, including web forms filled by customers, or via manual entry, which could lead to errors. Data collected using electronic methods tend to be more accurate, given that they are collected automatically. For such digital data collection efforts, utilizing tools like ZenRows web scraping API could prove incredibly valuable.
Ensure that you favor primary sources of data (data obtained directly from the customer) instead of secondary sources (purchasing information from a third-party company). This is because primary data is more reliable since it’s usually collected directly from the original source. It also provides up-to-date information about various aspects compared to secondary data.
Optimize the Data Collection Method
Collecting accurate information is vital to ensuring data reliability. To achieve this, you need to optimize your data collection methods. This involves using the right technology to collect and offer value and convenience to individuals and entities from whom you collect your data. You can also optimize your data collection method by ensuring that the sources from where you obtain your data have valid contact details. For instance, you should refuse phone numbers with letters and emails that don’t have “@”.
Avoid Multiple Entries
Having duplicate entries in your datasets will increase the likelihood of them being inaccurate. Unless you are doing double entry as a method to immediately verify data (data is entered once by one person and then re-entered by a different person so a computer can compare the two entries), data should never be entered more than once.
Know Where Your Data Is Coming From
What is the source of your data? Does the source of the data have a high-reliability rate? To check the reliability of your data source, you should trace it back to where it originated. It is vital that you ensure that the source of your data is trustworthy and that the data is reliable and can be used to make sound business decisions. Keep in mind that using data from unreliable sources can be detrimental to your business.
Clean Up Your Databases
Data should be cleaned up regularly. First, for legal reasons—there are some countries that require that you suppress data after a given duration without contact with the customer. However, it is also beneficial for your business because it allows you to have reliable data.
You can, for example:
- Identify duplicate data: You can let your software clean the data for you or check the duplicate data found.
- Suppress data that has been found to be unreliable (for example, hard-bounced contact in email marketing).
Other ways of maintaining data reliability include keeping an update of logs made to your database, integrating data from multiple departments, ensuring that data is normalized, establishing data quality standards, and creating a plan for data correction and data is secured at your business’ colocation data center or servers.
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:
1. 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 and data governance 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.
2. To Gain Time
You’ve invested in a snazzy business intelligence platform, but what’s the point if you can’t trust the results it dishes out? It’s like having a powerful car with an unreliable engine – you won’t get far.
With dependable data, you can automate the tedious tasks that used to eat up your team’s time. That means no more late nights recalculating figures or second-guessing the reports. Your data works for you, not the other way around.
In the business game, timing is everything, and a solid data pipeline ensures that you receive accurate information when you need it. When your data pipeline is in good shape, you can make decisions on the fly. You’re not stuck in endless meetings trying to validate numbers or agonizing over whether your reports are accurate. Instead, you’re nimble, agile, and quick to seize opportunities as they arise.
But there’s more to it than just efficiency. Reliable data is the key to staying competitive. In a world where every advantage matters, you can’t afford to be in the dark. Reliable data illuminates your path, helping you spot trends, identify growth opportunities, and navigate the twists and turns of your industry.
So, the next time you wonder about the value of reliable data and a well-maintained data pipeline you can trust, remember this: it’s not just data; it’s your ticket to streamlined operations, timely decisions, and a lasting edge in the business world. Trustworthy data and a reliable data pipeline are the backbone of a smarter, more competitive business – and that’s a game-changer.
3. 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.
Outsmart Competition With Reliable Data
Data reliability is essential in helping organizations make informed decisions. As such, organizations with reliable data tend to have a competitive advantage.
Ensuring the reliability of data isn’t a once-and-done activity. Just like with other data health practices, improvements should be made to data reliability consistently. Establishing preventive measures as part of your larger data integrity initiatives to assess how reliable new data is and fix any issues before they propagate across your system can minimize the likelihood of your data reliability deteriorating.
In the end, organizations need to figure out what type of information is accurate and which is not, invest in the right tools, and follow best practices to ensure the integrity and reliability of their data.
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