Using Dynamic Data Labelling to Drive Business Value
Dynamic Data Labelling with Ixivault
Before deriving any value from data, you need to find and retrieve relevant data. Search allows you to achieve that goal. However, for ‘search’ to work, we need two things: A search term needs to be defined by humans; data must be indexed for the computer to find it with cost and speed efficiency and to keep the user engaged. But search efficiency breaks under the sheer scale of all-available data and the presence of dark data (with no indexes or labels attached), when considering either finance or response time points of view.
Technologies like enterprise search never took off for this exact reason. Without labels, it’s ineffective to ask a system to select results from the data. At the very moment of creating the data the creator knows exactly what a file contains. But as time passes our memories fail, and other people might be tasked with finding and retrieving data long after we’ve moved on. Searching data in enterprise applications often means painstakingly looking up each subject or object recorded. For end-user applications like MS Office, we lack even that possibility. Without good labels search and retrieval options are near impossible. And, while the people who create data know exactly what’s in it, the people who come after, and the programs we create to manage that data, cannot perform the same mental hat trick of pulling meaning from unsorted data.
At SynerScope we offer a solution easily recover data that was either lost over time or vaguely defined from the start. We first lift such ‘unknown’ data into an automated, AI-based, sorting machine. Once sorted, we involve a human data specialist, who can then work with sub-groups of data rather than individual files. Again, unsupervised, our solution presents the user with the discerning words that represent each sub-group in relation to each other. In essence, the AI presents the prime label options for files and content in each subgroup, no matter what the size in number of files, pages, or paragraphs. The human reviewer only has to select and verify a label option, rather than taking on the heavy lifting task of generating labels.
Thus labeled, the data is ready for established processes for enterprise data. Cataloging, access management, analysis, AI, machine learning, and remediation are common end goals for data after Synerscope Ixivault generates metadata and labels.
SynerScope also allows for ongoing, dynamic relabeling of data as new needs appear. That’s important in this age of fast digital growth, with a constant barrage of new questions and digital needs. Ixivault’s analysis and information extraction capabilities can evolve and adapt to future requirements with ease, speed, and accuracy.
How Does Unlabeled Data Come about?
Data is constantly created and collected. When employees capture or create data, they are adding to files and logs. Humans are also very good at mentally categorizing data – we can navigate with ease through most recent data, unsorted and all. Whether that means navigating a stack of papers or nested folders – our associative brain can remember the general idea of what is in each pile of data – so long as that data doesn’t move. But we’re very limited by the scale we can handle. We have mental pictures of scholars and professors working in rooms where data is piled to the ceiling everywhere, but where little cleaning was ever allowed. This paradigm doesn’t hold for digital data in enterprises. Collaboration, analysis, AI needs and regulations always put too much pressure on knowing where data is.
Catalogs and classification solutions can help, but automation levels for filling process are too low. That leads to gaps and arrears in labeling data. The AI for fully automatic labeling isn’t there yet. Cataloging and classifying business documentation is even harder than classifying digital images and video footage.
Digital Twinning and Delivering Value with Data
Before broadband, there was no such thing as a digital twin for people, man-made objects, or natural objects. Only necessary information was stored in application-based data silos. By 2007, the arrival of the iPhone and its revolution in mobile and mobile devices changed that. Everyone and everything were online, all the time, and constantly generating data. The digital twin, a collection of data representing a real person or a natural or man-made object was born.
In most organizations, these digital twins remain mostly in the dark. Most organizations collect vast quantities of data on clients, customer cases, accounts, and projects. It stays in the dark because it’s compiled, stored, and used in silos. When the people who created the data retire or move to another company, its meaning and content fade quickly – because no one else knows what’s there or why. And, without proper labels your systems will have a hard time handling any of it.
GDPR, HIPPA, CCPA etc. forces organizations to understand what data they have regarding real people, and they demand the same for any historic data stored from the days before those regulations existed.
Regulations evolve, technologies evolve, markets evolve, and your business evolves, all driving highly dynamic changes to what you need to know from your data. If you want to keep up, ensuring that you can use that data to drive business value – while avoiding undue risks from business regulations, data privacy and security regulation – you must be able to search your data. Failing this, you could get caught in a chaotic remediation procedure, accompanied by unsorted data that doesn’t reduce the turmoil, but adds to the chaos.
Dynamic Data Labelling with Ixivault
Ixivault helps you to match data to new realities in a flexible, efficient way, with a dynamic, weakly-supervised system for data labeling. The application installs in your own secure Microsoft Azure client-tenant, using the very data stores you set up and control, so all data always remains securely under your governance. Our solution, and its data sorting power, helps your entire workforce – from LOB to IT – to categorize, classify, and label data by content – essentially lifting it out of the dark.
Your data is then accessible for all your digital processes. Ixivault shows situations and objects grouped by similarity of documentation and image recordings and allows you to compare groups for differences in the content. This simplifies and speeds the tasks of assigning labels to the data. Any activity that requires comparison between cases, objects, situations, data, or a check against set standards is made simple. Ixivault also improves the quality of data selection, which helps in a range of applications ranging from Know Your Customer and Customer Due Diligence to analytics and AI based predictions using historical data.
For example, insurance companies can use that data to find comparable cases, match them to risks and premium rates, and thereby identify outliers – allowing the company to act in pricing, underwriting or binding or all of them.
SynerScope’s type of dynamic labelling creates opportunities to match any data, fast and flexible. As perception and the cultural applications of data change over time, you can also match data with the evolving needs for information extraction, change labels as data contexts change, and to continue driving value from the data you have at your disposal.
If you want to know more about Ixivault or its dynamic matching capabilities in your organization, contact us for personalized information.