Today’s businesses integrate data into every aspect of their operations. However, this focus on data comes with challenges. We recently wrote about how to determine when you have too much data, and in this blog, we’ll go over the three main challenges of using the data you decide to keep.
Your company’s data collection practices may result in “big data.” Big data consists of extremely large data sets that can be analyzed (by computers) to discover trends and make predictions about future data. Obviously, there is a huge benefit to being able to predict customer demand or operational efficiency. However, big data also comes with difficulties: the bigger the data set, the harder it is to capture, clean, and store that data.
Capturing Big Data
The challenges that come along with big data begin at the moment of capture. It’s very difficult to get any amount of accurate and timely information—much more so when you’re capturing large amounts of data. In addition, if your software systems are not integrated with each other, data will be stored in silos, not unified into large data sets.
To overcome this challenge, it is essential to use automated systems and software solutions to capture and update data. There is simply no good way to manually capture big data. By using software (such as an ERP or information management system) that connects to your whole business, you can easily gather real-time data from your operations.
Cleaning Big Data
After you’ve captured big data, the next challenge is cleaning that data. As a general rule, the larger and more complicated a data set is, the more errors it likely contains. Customers may enter their names or addresses in different ways on subsequent orders, for example. This messy data can cause many problems for your company: you could send a customer duplicate flyers or send an email to a deactivated inbox, for example.
In order to clean your big data sets, you’ll need some sort of automated system. Just like collecting big data, cleaning big data is nearly impossible to do by hand. Consider using a tool like MFSQL Connector, which allows you to access files in external SQL databases, search the content of those files, and import the correct files into your M-Files information management system.
Storing Big Data
While data storage becomes cheaper and easier every day, there remains the challenge of security. If the security of your big data is breached, vast amounts of sensitive information about your business and your customers may be compromised.
Therefore, it is essential to have strict security measures in place for big data storage. Cloud-based storage can be a great choice; the best cloud-based storage is protected by sophisticated firewalls and security measures. Storing your data off-site also mitigates the possibility that hackers could access that data through a loophole in your on-premises security.
Looking for Help Managing Big Data?
Laminin provides top-of-the-line software solutions to help you manage your big data. Check out M-Files for an AI-enabled information management system, and pair M-Files with MFSQL Connector in order to properly clean your data. With MFSQL Connector, your developers can use SQL to integrate M-Files with your other software systems as well as automatically analyze how users add metadata such as dates, names, and numbers to your database.
Good data management takes time and care, especially when you’re working with big data, but the results will be worth it. If you’re ready to partner with us and get control of your data, contact us today.