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Redundant workplace data has reached a tipping point

Initially published by TechRadar Pro. Our CEO, Marc Vontobel, looks closer at the impact growing workplace data has on organizations and their people. Making data work for us with a solution powered by people and AI. Giving organizations the ability to recycle existing knowlege, remove redundant or outdated data, see-through data silos, and increase workplace efficiencies.

Every day over 300 billion emails are sent and received with the number continuing to grow. We are creating quantities of data that no human will ever be able to manage. We’re no longer producing gigabytes or even terabytes, but zettabytes. A single zettabyte contains one trillion gigabytes. In 2018 we produced 33 zettabytes of data over the year and by 2025, that figure is expected to reach 175.

Yet while we are producing more data than ever before, we haven’t become any better at managing and making sense of it in the workplace. Instead, teams are overloaded by data and unable to find the knowledge they need, meaning business productivity, internal employee collaboration, project completion efficiency and innovation all suffer.

Enterprises urgently need to be able to connect knowledge with business challenges, but it’s only getting harder as data grows exponentially. Inaccessible knowledge means problem solving takes longer, productivity falls, and workers become disengaged. Here’s how we start tackling data overload and begin boosting access to the knowledge that makes businesses stronger.

Recognizing redundant or outdated data

As we add more and more to our data pools, the ratio of valuable information to outdated, irrelevant data begins to shift. Put simply, the more data we create, the harder it becomes to find what we need in real-time. And as a result we lose hours to unnecessary searches.

When we’re at work, the information we save tends to become static. We add it to a cloud drive, drop it into a message on a collaboration app or share it as an email attachment. It then sits there, becoming redundant. When someone stumbles across that information later, it lacks context, making it impossible for someone to separate the useful knowledge it might contain from outdated or trivial parts.

To break this cycle, we need to start treating data like our recycling. Like packaging for our food or clothes, the majority of data is created for a single-use. We don’t hoard every milk carton when we no longer need it. Our homes and streets would be filled with garbage if we did, and we’d struggle to find what we need.

When we’re finished with packaging, we look at what we have and sort it into recycling, so that the useful parts can be used again. We need to apply this mindset to our data at work. Recognizing what’s redundant, outdated, or trivial and extracting the valuable essence and context, for example, who knows what about what within an organization.

Learning to recycle our data is key if we are to stop the data overload, and start enabling access to useful knowledge. And we need to act fast, because the larger the volume of data that businesses are sitting on the bigger the opportunity they are missing to convert it into knowledge that helps the organization and its employees.

Breaking out of data silos

We’re creating more bytes of data each year than there are stars in the visible universe. This is data creation at an incomprehensible level . Especially when we make the mistake of undertaking our journeys of knowledge discovery alone.

We often think that our data pools are one of our greatest workplace resources. But in reality, this information is useless without the people we work with. People know what’s relevant. People provide context, and only people can answer difficult questions quickly.

If you’re part of a small organization with only a couple of team members, finding the right person and asking questions is natural.

But on the opposite side, in a large organization, it’s almost impossible to know who the right person to ask for help is. Asking your teammates isn’t a great solution, because you don’t know whether someone in another team or location will have a better answer. And, asking the wrong person only ends up wasting more time for everyone.

Ensuring a team’s purpose and responsibilities are clearly communicated to the whole business is one step toward breaking down silos. However, individual expertise isn’t always reflected in a job title or department name, each of which can easily become outdated.

This is where we need to enlist artificial intelligence to help. AI can do two crucial things that, in our world of growing pools of data and complex organizations, people and traditional workplace tools struggle with.

Firstly, AI can be taught to forget. That means it can not only recognize where information resides, but it can recognize once that information has become outdated, and then forget it. Secondly, using only non-sensitive information, artificial intelligence today is able to learn who knows what in an organization beyond a job title or department name, because it isn’t tied to any one department it can see through silos in a way that individuals are unable to.

By using AI it’s therefore possible to build a real-time network of knowledge and expertise that can provide everyone with access to the most accurate, up-to-date information. In practice, that means that as soon as a question is asked, AI can begin connecting it with an answer wherever that answer is or whoever can best provide it. Rather than spending hours searching for answers or asking people who aren’t well placed to support, AI can make this process seamless by identifying the right person to help almost instantly.

Leveraging data through AI to quickly answer questions, access knowledge and connect people is a crucial element of both overcoming data accumulation and building truly competitive businesses today.

AI can make data work for us

By connecting us with pinpoint accuracy to people in the know, AI can help everyone to share knowledge, and find answers, in real-time. This is the key to more productive businesses, with less time lost on inefficient searches or mistakes due to outdated information, as well as more satisfying workplaces, where everyone can contribute based on their expertise.

As we find ourselves drowning in more data than ever and unable to access the knowledge we need, AI will offer us a life raft, and a map. It’ll help us make sense of relevant information and leverage it to solve business problems.

Ultimately, enterprises are powered by people and AI is only set to augment their existing ability to contribute with their knowledge. As the zettabytes continue to grow, AI can take that data and use it to empower people to connect, problem-solve, and find the answers they need.

What is a corporate wiki?

A corporate wiki, also known as an enterprise wiki, is a knowledge management system that provides a central location where your company can collect, capture, and update organizational knowledge.

As an internet user, the chances are high that you’ve used Wikipedia to find and acquire information and knowledge. Corporate wikis are comparable because they use similar technology and processes for people to collaborate and share their knowledge. The main difference is that corporate wikis are confined to the people within your organization.

Wikis have been a favored solution for knowledge management because every employee has the ability to read, edit, and contribute new content and knowledge. Plus, they're relatively easy to use. If employees can create a word doc, they’ll have no issue contributing to a wiki. But, if you’ve already used a corporate wiki before, you’re probably familiar with the challenges they can bring and are ready for a more effective way to manage knowledge.

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