Congratulations on taking the first steps toward organizing and reining in your data analytics. The funny thing about data collection is that managing and understanding it is a neverending process. But so rewarding! Once you’ve established these best data practices, you’ll notice what a boon data can be to your operations.

Understanding Your Data

Before we dive into some data tips, let’s break everything down. To get on the same page, let’s define the concepts we’re going to be talking about with data integrity, quality, and insight. (Wow, it just so happens that this is one of our tips for later in maintaining these very same concepts!)

Data is the collection of information and statistics that tracks a business’s performance and other factors. Companies use analytics to cultivate, organize, and understand their data.

For your data to be adequate to use in making business decisions, it must have integrity, quality, and insight. 

Data integrity is a process that ensures your data is high quality, accurate, contextual, easy to access and interpret, and secure. Data quality means that the information is useable for your specific purpose. And finally, data insight is the ability to find understanding in the information and make valuable discoveries.

Data integrity and quality both lead to deeper data insights.

6 Tips for Reaching Better Data Results

With data being as vital as it is in driving business, organizations should prioritize making the most of their data results. But, of course, a decision is only as good as the data it’s based on, so let’s go over some of the most important things your company can do to ensure better data integrity, quality, and insight.

Back-Up Data Frequently

Unfortunately, accidents and sabotage both happen. It’s better to be safe than sorry, so back up your data on a regular basis. That way, it will be easier to recover some data if your system crashes or you lose information to a cyber attack or a bug.

It may not prevent you from losing some things, but it can prevent you from losing absolutely everything. You can avoid total loss if you recover everything you’ve backed up.

This is important because it will mean your business can still function; losing all your data could possibly bring your business to a standstill. But also very important is that it can save your company’s relationship with its customers. As long as their sensitive information has not been compromised, recovering their data may just help them keep their trust in your business.

Always Validate Your Data

Before you begin your data collection, decide what you are looking for. You don’t need, or want, to grab everything you can just for the sake of having more information; if it isn’t useable, it only muddies the waters. Set firm parameters ahead of time.

Always ascertain that your process and your source haven’t been corrupted. For example, don’t let information into your data warehouse if it hasn’t been vetted first. To admit data into your system, it must be useable and accurate. Can’t use it or verify its authenticity? Toss it!

If you employ manual entry for your data collection, consider setting a restriction to control data values as they are entered. This can prevent a major goof, making your data more secure and ensuring its quality.

Track Data Sources with an  Audit Trail

If there’s ever a problem with your data, you need to be able to backtrack and see where things went wrong, preventing the same error in the future.

This can be very helpful in a breach situation. An audit trail can zero in on what was taken and how it was able to happen. It might even be able to point to where the breach occurred.

Your company should be able to track every event related to its data. It should show if there’s any change, when it was, and who enacted it, forming a complete record of your data. This should be an automated and electronic process, so it’s tamper-resistant. Then be sure to test it regularly to assess its validity.

Keeping track of your data sources can test how strong your data integrity is. It can check the completeness, thoroughness, and consistency of your data, finding any errors.

Educate Your Team

Every employee who comes into contact with company data needs to respect it and be taught how to ensure its integrity. One very important part of that training should include why it is so important. When we understand the why’s of something, we are more likely to take it seriously.

Make proper data interactions a part of company culture, stressing the importance of careful handling. Help your employees embrace data-driven decisions and their impact on the organization. This way, everybody shares responsibility for it.

Your employees don’t need to be data experts, but they need to grasp how to use and protect data.

Improve Data Security

In this new age of remote work, securing your data is imperative. Having employees scattered and checking in remotely can leave your company vulnerable to a breach or malicious attack. Remote employees need to be taught best data practices.

Only approved employees should have access to your data servers and centers. Classify sensitive data and label it so the proper controls are applied. Doing both of these things cuts down on how many employees are accessing your data, reducing the chance of an inadvertent error. And then encrypt it, making it impossible for people without the key to understand your data.

Check your security system regularly to be sure it’s still effective. This includes updating your software as soon as it’s available, a critical part of security.

Standardize Your Data Across the Board 

Create a shared understanding regarding your company’s data. Define what is considered valid and useable. Creating a standard definition will help your employees better understand what the data is used for.

Then settle on a single format and single way your data gets applied. Your data should always look the same and use the same metrics for all parts. If it isn’t all formatted uniformly, your data won’t be understood relative to the other pieces of data. Standardized formatting will give your employees better insights when it’s all understood the same.

Provide clarity. Identifying what you’re looking to measure will ensure higher data quality; it eliminates the distractions of extraneous data.

Conclusion

These are just a few basic steps a business can take to improve its relationship with its data. Data is a vast and complicated issue, of course, with many varying aspects. However, these tips are the first step any business should take when working with their data; without them, data can become chaos.

There are many ways for an organization to compile and implement data. Is your business gathering data in the most efficient and effective way possible? How about how it maintains data? Each business has unique needs, but there are some approaches to information collection and maintenance that are evergreen. That starts with managed analytics.

What is Managed Analytics?

Many smaller businesses have a dedicated in-house data team that gathers and analyzes their data. However, some don’t even have a dedicated team, only employees whose job includes compiling data. This, of course, can lead to many sorts of data issues. (More on that in a bit.)

However, many companies opt to automate the compiling of company data and how it’s put to use. That’s managed analytics, often achieved through outsourcing. Think of it as a filter for your data, pointing people who need it to where to access it and how to plug it in and gain insight from it.

The goal of data analytics is to make your business data accessible and clear. Analytics also removes data silos, so it’s all available in one location. When your data is siloed off, your employees often don’t even know what other data is available to the company but hidden from their department. 

Managed analytics puts your organization’s data into a visual model and streamlines integration processes. As a result, employees have a clearer picture of what all the data means and don’t need to sort through multiple sources to find the information they require.

What are the Benefits of Managed Analytics?

There are many upsides to outsourcing your data needs. However, one of the most surprising benefits is that it saves your company money. Hiring a team of analytics experts is less expensive than paying an in-house team dedicated to your data, a team that is more prone to human error.

Managed analytics moves efficiently and gets quick results, even more so than the manual process. This is partly because the process is automated and knows precisely what it’s looking for. As a result, managed analytics doesn’t waste time with extraneous or duplicated data.

Duplicate or unnecessary data only muddy your data waters. You can’t get a clear picture of what your data is trying to convey when it isn’t well-organized. By presenting only the needed information, employees can easily make sense of the data.

One of the highlights of managed analytics is that it organizes your data and streamlines how you access it. Your data is structured for you; you receive regular reports and updates, as well as a dashboard that’s simple to understand. A dashboard is a central spot where you can see your full data picture. The analytics team just plugs in your numbers, and you get a well-organized visual representation of your information.

Managed analytics often brings about increased productivity for an organization, too. This is because employees don’t waste time searching for data and trying to make sense of it when they find it. They spend less time organizing and more time putting it to use. Because the figures they need are right at their fingertips, employees are empowered to incorporate cold, hard data into their operations. This solidly enhances your business’s decision-making game.

Most Common Threats to Data Integrity

When an organization’s data integrity is compromised, its decision-making process is endangered, often resulting in bad decisions and poor performance results. Let’s take a look at some of the most common enemies of data integrity.

Human Error

Oops! People make mistakes; it’s only natural. This is one of the largest issues in data collection. An automated system is much less likely to fail the data process. 

An automated process isn’t likely to accidentally hit the wrong key on the computer or inadvertently delete data. In addition, an analytics program will immediately spot and prevent duplicate data from entering the system. And finally, unlike humans, you can rely on a program to always follow procedure.

Integration Errors

Moving data between sources has its inherent risks and limitations. For example, one data set may not smoothly translate from one system to another if it needs to be reformatted. In addition, the data may become corrupted or improperly reformatted.

Not all systems record data the same way, so it could be challenging to recognize that you are dealing with duplicate data that’s only written differently.

Cloud Vulnerability

According to the 2020 Thales Data Threat Report – Global Edition, nearly half of businesses store sensitive data in the cloud. However, just because the data isn’t in the form of a hard copy doesn’t mean it’s secure. Your cloud data is subject to all of the same dangers as your other data. Therefore, it is critical that your business implements security measures for all your information equally.

Outdated Software

Software that hasn’t been updated properly can lead to a breakdown in security. For example, Reuters reported that a 2017 ransomware attack known as WannaCry had 67% of its victims as Windows users who had neglected to install the latest updates to their software.

Of course, your hardware could be another potential risk. Be sure that you also have up-to-date hardware, so it doesn’t also corrupt your data.

Cyberattacks, Viruses, & Insider Threats

Information is a commodity, and bad actors want it. So they’ll go to great lengths to get it from you, including planting a virus. That can be especially sneaky, altering or deleting information without you noticing until it’s too late.

Thales tells us that in 2020, 49% of companies had a data breach. That not only hurts your bottom line but also can damage your reputation with your customers.

These threats aren’t always from outsiders, either. Mckinsey found that 50% of data breaches result from an inside bad actor. This strengthens the argument that only people who need to work with your data should have access to it.

Conclusion

Data is precious to your organization, but you don’t need to drive your team crazy with pulling and analyzing it. Many best practices include automating your data with managed analytics. It takes cultivation and organization off your plate and adds a bonus layer of integrity to your data.

In the world of data analytics, you’ll hear a lot of terms that sound awfully similar to each other. For example, data integrity and data quality are terms that, on the surface, could mean the same thing. In fact, many people use these terms equally, but that is inaccurate.

Both data integrity and data quality, in their proper definitions, are equally important. Your business can’t thrive if your data has one but not the other.

What is Data Quality?

Data quality has to do with the state of your data.

If you have data quality, your data is suitable for your needs. It’s reliable and meets your specific criteria, so it gets the thumbs up from your company. Data quality means that your data is full of practical and valuable information for your business.

Data without data quality won’t serve the purposes that you have in mind for it.

Your company may have fantastic, out-of-this-world data, but if it isn’t useful to your business, it isn’t quality. For example, if you own a thermometer company and come into some primo data on legwarmers, will that be beneficial to you? Nope. Keep moving along, please.

There are six widely accepted components that are considered part of data quality. It must be complete, unique, timely, accurate, valid, and consistent. 

Why Data Quality is Important

Your business has a higher chance of making more impactful and beneficial decisions if it has reached data quality. On the other hand, when your data is substandard, you’re at risk of making decisions that lead to a negative financial impact. As per Gartner, non-quality data can cost a business $9.7 million annually.

Making ill-informed decisions are as dangerous as making blind decisions. And it doesn’t only affect your bottom line. These poor decisions based on flawed data can trickle down to impact your employee’s productivity. This is because they may be incorrectly basing operations on the wrong data, leading them down the wrong path.

This can all result in missed sales opportunities or essential information and goods going to the wrong place. None of that is a positive thing for any company.

With data quality, your business can:

• Make better decisions 
• Zero in on the right audience
• Improve its marketing
• Offer better customer relations
• Incorporate good data more easily
• Compete better in its field
• Enjoy a rise in profits

What is Data Integrity?

Can you trust your data? That’s one of the biggest questions of data integrity.

Data integrity includes data quality but also so much more. It goes further to include how consistent your data remains as it’s integrated and updated. Data integrity means your information remains uncorrupted and unchanged across its lifecycle.

There’s even more to it, though. Data integrity also incorporates data security. It is imperative to protect your company from security breaches and keep it in accordance with regulatory compliance.

Data quality is only one pillar of data integrity. The other is data integration; this is the process of taking business information from multiple sources. Location intelligence and data enrichment give context to internal data by supplementing it with external data, offering a well-rounded data experience. 

Why Data Integrity is Important

Data integrity is a process that makes sure your information is useable so that you can maximize its use. With good data, you’ll be able to plug it into the proper systems because you’ll know exactly where it belongs and what aspect of your operations it speaks to.

Your employees will also have an easier time searching for the data they need. Data integrity ensures that your information is optimally stored, searchable, and traceable. If you have ever pulled a data set that you’ve questioned and then been unable to verify it, that is an excellent example of a lack of data integrity—a frustrating experience for all employees.

Data integrity is also useful for helping your company form better and more personal customer relations. You can target your communications to a specific subset of your customers and have better information at your disposal to pinpoint their needs. 

Of course, data security also plays into this area because if you suffer a data breach and customers’ sensitive data is compromised, they’ll lose trust in you. It doesn’t matter if you’ve done everything perfectly for them up to that point; keeping private data safe is a tremendous responsibility.

You can also free up valuable data storage space through data integrity. Since you would be cutting out all redundant data, you aren’t storing as much. That space means you can collect more data and search your existing data more efficiently.

The Differences Between Integrity and Quality

A business needs both data integrity and data quality if it’s going to flourish. So it’s essential to recognize the differences between the two in order to ensure you have both.

The two concepts are so interrelated that it almost isn’t fair to compare them. The heart of the matter is that you can’t have data integrity without data quality, although data quality without data integrity is possible.

Data quality is where your data process needs to start. If it isn’t quality, it isn’t worth your time. It is the first hurdle your data needs to leap for it to be acceptable for your company to use. Data integrity is the process that makes your data usable.

Data integrity and data quality aren’t an either/or situation. Instead, it’s an and situation. So you need to go further than mere quality.

Conclusion

Your data is meant to work for you, not the other way around. So before you even begin the process of cultivating your data, set your parameters in place; define what type of data you need and how you’ll use it. Right there, you’ll be setting yourself up for data quality. When you’re only targeting the information you need, you streamline your process and save the later work of weeding out unqualified data. Then set up your strategy for data integrity. How will you process and store your data? That will set you well on the road to data success.

Information is becoming more and more critical in our data-driven world. However, when your currency is data, you need to know that it’s got your back. So how can you ensure you follow the right path with your data? Two words: data integrity.

Gartner’s Data Quality Market Survey found that in 2017, insufficient data cost businesses around 15 million dollars. So if poor information can do that to a business, just think what high-quality, good data can do.

Let’s Talk Data Integrity: What is it?

When we talk about a person with integrity, we speak about honesty, virtue, and purity. They have a reliable reputation, one that is consistent. Your data can, and should, have similar characteristics. We aren’t only talking about a state of being and an entire process involving how you maintain and manage your database.

When we look at a business’s data integrity, we’re looking at the big picture. Most people assume that we mean data security, and while the topic does involve protection, it’s about more than that. Data integrity looks at how complete and accurate your data is. You need to know that your information is reliable, consistent, and secure.

Business data must always be consistent. Making decisions in business has inherent risk; if you’re relying on data to make those decisions, you need to know it’s reliable. Unfortunately, nearly 50% of respondents to an IDC survey confessed that they didn’t feel secure in their data quality; that makes every decision a roll of the die.

There are many ways data integrity takes shape, and it comes in many types. There are six main types: entity integrity, domain integrity, referential integrity, and user-defined integrity are a few. However, physical integrity and logical integrity are the two most critical forms.

Physical integrity refers to how your data is stored and accessed, keeping it safe from a data breach. Logical integrity is how you protect your data from human errors.

A final area of data integrity regards compliance. Your business must meet regulatory standards, such as GDPR (General Data Protection Regulation). Data integrity is vital for meeting these standards, which helps your company avoid liability issues.

The Importance of Data Integrity

A company that makes data-driven decisions needs to know the information it’s working from is accurate. Your business decisions are sounder and more likely to pay off when you avoid bad or outdated data.

Data integrity ensures that your data continues to perform for you. Your employees need that flow of information for their work to remain steady; the data has to be available to them when needed. Without a consistent input of data, your data stream may become erratic. That could disrupt your business’s day-to-day operations.

A lack of proper security protocols is dangerous for your company. If your data and data analytics becomes compromised, your business suffers. You must keep your sensitive information safe, not just your company’s but also your customer’s information.

Losing your sensitive data is terrible, of course, but if you lose your customers’ private information, that’s just as bad, if not worse. Your business will see its reputation damaged, and customers will have difficulty trusting you.

Another vital piece of data integrity is ensuring it maintains its original cohesion and original state; that’s how your data stays valuable. It doesn’t just get pulled and remain stagnant in one spot; as you transfer, store, and access it, you run the risk that it becomes altered. It’s essential that doesn’t happen.

Data integrity is critical at every stage of data. It allows for easier access, tracing, and searching. Data transparency makes every step along the way more manageable and more accurate. In addition, it ensures that your data is stable. Sadly, only 35% of global senior executives highly trusted their company’s use of data in a survey done by KPMG International.

How to Get and Maintain Data Integrity

You can take matters into your own hands and get started today to ensure your data integrity. You’ll be on your way by implementing a few specific procedures and routines.

Begin with High-Quality Data

Streamline your collection method before you even begin to gather your data.

Only collect the data you require, right from the beginning. Don’t waste your time and energy sorting through the information you’ll never even use.

Before gathering your data, ensure it’s reliable, accurate, and fully there. Why bother pulling what you don’t need only to spend time later weeding it out?

Evaluate your data before collecting it, and revisit your methods and parameters often.

Remove Redundant Data

Clean out your duplicate data frequently. It’s natural that you will get redundant information, but don’t let it sit there, taking up space and causing confusion.

Simply make it a part of your routine to process and clean out your repetitive numbers.

Perform Frequent Updates

As your data ages, it becomes less accurate. Therefore, old info is outdated info.

Fresh data brings more reliability with it since it reflects any recent changes. Therefore, refresh your data regularly for the most accurate, up-to-date information.

Assess for Mistakes

To err is human, of course, but your data isn’t forgiving.

Regularly check over your collection to spot any human errors that may have occurred. And don’t just rely on one person to get it done; have multiple eyes look it over so that nothing gets missed. Implement a system of checks and balances with a schedule for reassessing your data.

Trust your data, but verify.

Educate Your Employees on Data Integrity

Every employee who comes into contact with your company’s data needs to understand and appreciate the importance of data integrity. They need to understand the why and the how, too, not just to be told: “just do it.”

Educate your employees who work with data on what your plan is for implementing and maintaining data integrity. Then, when everybody is on the same page, looking out for your data, it will be better protected.

Conclusion

Data is at the heart of all your business decisions, so your business needs to have the best information possible. Without data integrity, you’re entering a guessing game; it may or may not end well. Data can be the road to fantastic business opportunities, but it can also be the road to failure if it’s unreliable. Culling your company’s data is only the beginning of the data process; the real work comes with ensuring integrity.