Enterprises are sitting on an enormous amount of data that could be fueling their automation journey. Much of the information needed to run a business is existing data. However, much of an organization’s data is inaccessible and unsearchable. This so-called “dark data” is hidden away in paper forms, spreadsheets, emails and literally billions of PDF documents. This dark data is locked away and mostly unusable by the systems and AI required to power the autonomous business of the future. Enterprises are trying to achieve 21st-century outcomes while being shackled to 20th-century data practices.
I recently read a report from NewVantage Partners on a survey they did concerning how big data and AI were driving digital transformation. While 92% of the survey respondents said they were increasing investment in big data and AI, there were some interesting findings that suggest organizations are not realizing the ROI they had hoped. For instance:
- Only 31% have a data-driven organization.
- Only 28% stated they have a data culture.
- 53% are not treating data as a business asset.
- 38% have not realized any measurable outcome from their investment.
Now, there’s a whole list of reasons and hurdles that are preventing organizations from realizing better value and outcomes from their efforts to become data-driven. But, could the underlying approach to becoming data-driven be flawed? Could organizations be struggling because they continue to apply a legacy mindset and approach to data acquisition and storage? Could these challenges be a contributing factor in 70% of digital transformation projects failing?
Just take a look at the growing number of stories, market reports and surveys on digital transformation and you’ll see a graveyard of failed projects, lack of success and high turnover rates. The current approach to digital transformation is not working.
In our push to become data-driven, we capture data and attempt to transform it into computable bits and then stuff the data into rigid structures. This legacy approach to capturing and representing information outside the human brain results in a proliferation of single-use data that doesn’t scale with the business. We always lose some of the meaning, relationships and context. Despite all the advancements in technology, we’re still trying to model the information based on how we’ve historically stored data. We’re still producing flat data.
In the autonomous enterprise of the future, and the digital workers that will run them will need rich, contextual data. The true power and value of digital transformation will only be unlocked by the adoption of a semantic data-driven approach. The future of work will be built on digital workers powered by machine learning, artificial intelligence and knowledge graphs. It’s not enough for an organization to be data-driven anymore–they need to become contextually-driven. We need to start capturing the meaning to achieve a better understanding of the data.
“I liked the idea that a piece of information is really defined only by what it’s related to, and how it’s related. There really is little else to meaning,” said Sir Tim Berners-Lee, the inventor of the World Wide Web.
Context is all about the meaning. Context is more than just a piece of data. Context is the background in which an event, task or process takes place. It is the deep, multi-dimensional understanding of data and the relationships between the entities. Understanding the context can lead to accelerated insight through AI and machine learning. To be competitive, enterprises need more than data; they need context to enhance productivity and efficiency. But our current approaches to capturing and storing data have historically failed to capture this valuable context. As the amount of data grows exponentially, organizations will realize semantic data is key to survival.