It’s a statistic you’ve likely heard before: about 80 percent of enterprise data is unstructured, meaning it exists in formats that cannot readily be utilized for making key business decisions, meeting compliance requirements, and other important business needs. To achieve these critical objectives, data classification has become a business imperative. But have you ever stopped to consider just how large a task manually classifying all that unstructured data might be?
The Data Explosion
It’s estimated that we are generating 2.5 quintillion bytes of data each day – and that number continues to grow. By next year, it’s expected the rate of data generation will reach more than 146 GB per day per person on Earth.* For individual organizations, the level of data generation is staggering. From banking and insurance sectors to energy and life sciences, the data generated from millions of customers and research initiatives is resulting in similar explosions.
This surge means the task of data classification vastly exceeds human capabilities. Knowledge workers spend a bulk of their time simply discovering and preparing data, but the fact that the majority of enterprise data remains inaccessible highlights how business data classification needs cannot be met via human means alone.
Here are four key ways document classification using machine learning can help your organization keep up with growing data classification needs.
1. Automated data classification can improve accessibility
Within the volumes of content that enterprises create every day, there exists heaps of valuable business information – and lots of data that, at best, is taking up space, and at worst, introduces errors and skews insights. At least a third of enterprise data is useless due to the fact that it is redundant, obsolete or trivial (ROT)** – but because ROT tends to be embedded within all other organizational data, it would be virtually impossible to perform the level of detail-oriented cross-checking that would be required to identify and eliminate it manually.
For computers, however, searching within countless data sets to find and filter ROT is an automatable task that can be performed to a high degree of accuracy. As part of the progressive data classification process, machines are able to identify and weed out ROT and improve the accessibility of high-quality data.
2. Automated data classification fuels productivity and ROI
As previously stated, knowledge workers can spend most of their time discovering and preparing data – and even then, most organizations still can’t make a dent in their volumes of unstructured information. And considering how fast new volumes of data are being generated, organizations would have to shoulder the salaries and related onboarding costs to hire more employees to manually keep up with the demand.
Adding to the financial toll of manual data classification, opportunity costs can arise from an inability to quickly and efficiently leverage new data in making business decisions. Businesses can also incur compliance penalties and other tolls from having unidentified (and thus unsecured) personally identifiable information (PII) in their fileshares. The accuracy of automated document classification over manual methods can significantly reduce these costs and risks, instead fueling productivity and increasing the ROI of data-based tasks.
3. Faster, better identification of risky data
Whether due to data breaches or compliance regulations, unidentified PII is a ticking time bomb for any organization – and the faster enterprises can identify PII within their data stores, the faster they can take steps to secure this sensitive information and reduce any associated risks. From legacy paperwork to emails, unstructured data is often ridden with PII, thus making better data classification a business imperative.
The rapid pace of automated document classification means enterprises have a means for efficiently crawling data and identifying PII, reducing their risk even as new data is generated. By crawling all existing document repositories, and converting unstructured documents into searchable files, businesses can readily determine where PII is located within their data stores. The classification of such data further improves organizations’ ability to assess and address sources of PII, deleting documents of no value that contain sensitive information, redacting PII in instances where there is no business use for said content, and securing PII that must be retained.
4. Accelerate business decisions
Whether it’s processing insurance claims or using live customer data to identify new business growth opportunities, document classification using machine learning can help to accelerate better business decisions. This is due to the difference in pure processing speed – while it can take humans hours and hours to comb through data for needed information, automated data classification increases an organization’s ability to rapidly and efficiently access accurate, high-quality data – meaning they can use that data that much faster.
Enterprises today require rapid data classification capacity to enable access to critical business information. But if you’re relying on manual processes to classify your unstructured data stores, then you likely aren’t making a dent – and may be missing opportunities to glean valuable insights and reduce compliance risks in the meantime.
Schedule a live demo to see first-hand how Adlib can help you achieve intelligent information governance and content clarity with document classification using machine learning.