September 19

How Can Managed Analytics Help Maintain Data Integrity?

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.

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