AI could repair the damage done by data overload

Hear from the CIO, CTO and other C-level and senior executives on data and AI strategies at the Future of Work Summit on January 12, 2022. Learn more


This article was contributed by Mark Wontobel, co-founder and CEO of Starmind

People who work in large organizations spend a lot of time looking for answers. It may come as a surprise when 90% of the world’s data has been created in the last 2 years alone. Altogether, 2.5 quintile bytes of data are generated every day, the number of which is constantly increasing. However, while the amount of data we produce is rapidly increasing, we have no understanding of how to manage it.

The extent of the damage has caused workers and businesses to come to attention. Employees are overwhelmed with data and unable to find the knowledge they need. This means that business productivity, employee collaboration, project completion efficiency and innovation all suffer. Businesses must face data overload before the gap between data production and management becomes too wide. We need to increase the speed and accuracy of problem solving to increase access to knowledge and increase engagement and productivity at work. Here’s how we get started.

Identify redundant or outdated data with AI

As the data pool grows, it becomes harder to find what we need. Useful and trivial, or related and irrelevant, all live together. Manually running all of this information wastes an employee’s valuable time in unnecessary searches, leading to low productivity and frustrating workplaces.

Consider how most businesses treat their data. You end up with a piece of work, whether it’s a sales target spreadsheet or a project status update, and you save it across different databases. What happens next? In general, nothing. It is easily stored and over time it becomes redundant. When a coworker stumbles upon this information at a later date, it lacks context, which makes it difficult to understand whether it is useful or not.

But we are not just talking about some documents here. To put things in perspective, more visible data is generated annually than stars in the visible universe. On this scale, it is not surprising that when we fail to manage our data, the space between us and the relevant information we seek seems inaccessible. IDC’s survey reinforces this – enterprises are struggling to deal with the complexities of their own data.

In short, old data not only hurts individual workers but also the productivity of businesses as a whole – despite the potential for change. How businesses use data and enable access to valuable knowledge today will have a ‘make or break’ effect on the organization. So, how can we get better at this?

To enhance AI, not to change human knowledge

The data overload challenge is also a collaborative challenge. People get overwhelmed when they can’t easily find what they want. Improving access to knowledge by better connecting experts across the organization helps to combat this.

This is where we look at the advantages of referencing data and the advantages of using AI for referencing data. All that information that is overwhelming when disorganized can in fact be the key to unlocking knowledge.

In order to enable the collaboration of true knowledge and to connect the employees with the information they need, we should start using the data that is available in the organizations to draw conclusions. In doing so, we can associate people with questions with a true coworker (s) with the answer (s).

Artificial intelligence has two additional important qualities that help businesses achieve this and eliminate the problems of today’s legacy knowledge management.

First, AI can be taught to forget. This means that AI can not only recognize who knows about a topic, but it can refer to that information and identify when the information becomes outdated and redundant, i.e. it ‘forgets’ the useless data as needed. ‘May. Second, AI is capable of viewing through silos, using non-sensitive information taken from existing tools. It can use all kinds of information to draw conclusions on a scale, create a live map in an integrated platform or a ‘knowledge network’ of who knows what in the organization.

In short, using data, AI can build a network of knowledge and skills in real time. While searching for answers, everyone can access the most accurate, up-to-date information or the best expert at a particular time to help immediately.

Before zetabytes of data escalate to yotabytes, it is time to acknowledge the role of AI in tackling data overload. With AI, we can begin to leverage data as businesses and employees demand it: to strengthen connections, solve problems, collaborate and find the answers we need.

Mark Wontobel is the co-founder and CEO of Starmind

DataDecisionMakers

Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including tech people working on data, can share data-related insights and innovations.

If you would like to read about the latest ideas and latest information, best practices and the future of data and data tech, join us at DataDecisionMakers.

You might even consider contributing to your own article!

Read more from DataDecisionMakers

Similar Posts

Leave a Reply

Your email address will not be published. Required fields are marked *