Traits of a Robust Records Management Software

 

Exploring the challenges of records management professionals to determine the traits of a robust records management software

It's fascinating to note how the emergence of big data has made it even more challenging for records managers to cope with the massive volumes of data generated every day, from categorization to distribution to retention. In fact, according to an IDC study, data professionals burn nearly half of their weekly time – 30% looking for, controlling, and processing data, plus 20% duplicating work. However, this is primarily due to the traditional records management approach, which many companies still follow religiously.

Traditional Records Management and Classification Approach

Users used to categorize records as they were generated, making the traditional records management and categorization straightforward and uncomplicated. However, this strategy does not scale with the current daily data volume, and the additional weight hampers users who seek to carry out their real-world duties. As a result, productivity suffers, and the accuracy of these manual classifications is frequently compromised.

Apart from the traditional method of records management, a more recent industrial technique is to handle categorization within a records management platform using a set of hand-coded rules. In this method, the managers construct a clear hierarchical framework, which allows them to categorize incoming documents in the system based on their metadata. However, this method also appears to be failing as firms deal with data from content sources that aren't structured like content management systems, such as email, chats, IoT sensors, and so on. But it's not only the old method that's problematic; the surge of big data has also created a slew of new problems.

Other Records Management Challenges

Most business documents include a profusion of data, including sensitive data, which must be appropriately classified and kept for compliance and regulatory purposes – in most circumstances, determining how to categorize a document requires manual investigation. However, merely identifying the correct category for these documents is insufficient because these content information sources exist in various formats, including spreadsheets, web pages, word documents, coded files, photos, and audio and video files. Furthermore, with the ever-changing regulatory compliance landscape, hand-crafting a set of procedural rules that will parse and accurately categorize such a vast range of documents is tricky.

However, the increased data generation because of the pandemic in recent years has resulted in a demand for more efficient records management software. A robust system that can do the following:

The Requirements

Identify and Index data – Different businesses hold different data types, such as credit card data in financial institutions and insurance policy data held by healthcare businesses. As a result, advanced records management software should be able to recognize most, if not all, kinds of data. Indexing data is just as vital as classification and categorization since it speeds up and simplifies the review, dissemination, flagging, deletion, or remediation process.

De-duplicate data – De-duplication is a method of eliminating all the redundant, outdated, and trivial (ROT) data that file analysis algorithms have identified. This reduces the size of your entire content collection, as well as your PII footprint, making it much easier to manage.

Defensibly Delete PII – While it may seem beneficial to save as much data as possible, PII can pose a security risk if left unchecked. As a result, you must delete PII from customers who have stopped doing business with you, employees who have left the organization more than a year ago, data found on inactive devices or in abandoned accounts, and persons who have asked for their personal data to be removed.

Classify data as Sensitive – Not every piece of information is equally sensitive. Email lists, for example, must be protected, but their level of privacy is far lower than that of customer records, including credit card information. By classifying data according to confidentiality and the impact if their privacy is violated, you may obtain a sense of what your security program requires.

Automate Remediation – The above stages of managing your data footprint will help you achieve compliance in the short term. The best way to ensure a complete records management system is to regularly review and modify your rules and perform file analytics audits daily. File analytics processes can be scheduled to run every night or outside of business hours, reducing operational downtime.