5 Artificial Intelligence and Data Predictions
The Artificial Intelligence environment and the Data that powers it, look very different from a year ago. The predictions below broadly consider the public landscape over the coming year, some of the challenges to consider and how to keep things moving in the right direction.
Prediction #1: Insight Data will Replace Big Data
The term ‘Big Data’ was hot last year, but no one could agree upon a good definition of what ‘big’ really meant. The size of data (from any source) does not magically impart importance. The value in data comes from the insights that can found there.
There will be a new dialog around data that asks what is at the core of any given situation, process or business environment and how can better information be advantageous. The (data) tail should stop wagging the dog.
A better understanding is dependent on a defined viewpoint thanks to the knowledge in a particular field, situation or set of experiences. People with these skills, who can ‘see the wood for the trees’, will become more valued.
This will be the year that thoughtful insights into data will become more in demand, as industry leaders realize the power held there. Engineering can only solve part of the problem; different viewpoints are also needed to provide better solutions to our most pressing issues.
Prediction #2: Data in a Post-Truth World
A large section of the general public is increasingly distrustful of official sources of data or information. The trend towards a post-truth view of the world will grow stronger and impact more domains both public and private.
Scientific Fact, an Expert or Industry view can no longer be used as a singular point when presenting to the public. Any fact needs to be backed up with a fuller discussion to better inform the subject.
The use of clear English that is both jargon-free and non-condescending will be crucial. There will be a greater need to explain the reasoning behind any data presented including the background and shortfalls of any approach.
Any data or fact needs to be presented with the audience in mind. The use of storytelling to illustrate in context will help to get across ideas. This need for education should be seen as an opportunity to communicate with an audience who has not been successfully reached so far.
Failure to engage (all of) the public will lead to a widening split that will impact all sections of society, from politics to education, health to security. A continuing lack of outreach will severely hamper technological and scientific progress.
Data is the fuel that makes AI operate. Without data AI becomes inert. We need the public to understand the importance of data, how it is used and how it can ultimately help them.
Prediction #3: The Public Demand For Information
In direct contrast to the prediction above, the general public will continue to demand more data to help make informed decisions. This can most obviously be seen in the medical field when patients increasingly research their own conditions online using search engines and available information.
The want and desire for more information to aid decision-making will expand to include all areas of life. As access to data becomes more freely available, all business, organizations, and Governments should accept and adapt to a changed relationship with their consumer, audience, or citizen. This challenge should be met head on and be part of strategic planning.
There should be less reliance on advertising, PR or ‘spin’ surrounding public communication, to be replaced with a more direct and depth message. An informed audience can be the very best representative possible for spreading information.
A public that is increasingly better equipped to make informed decisions, can be a powerful force. Business, organizations, and Governments should encourage this behavior, be an information source, and part of the dialog. Now, more than ever information can be viewed as power, as the way to shape the world.
Prediction #4: Data Will Become Connected
Siloed data will become connected as organizations realize that their data has no value if left in storage. It is only when data is considered, examined and tested that it becomes useful.
Furthermore, information in context is much more valuable than information in isolation, for this reason, we will see many more data partnerships. This will happen across domains, where a different data set gives context, and in the same domain where different viewpoints on a wider data set are valuable.
Approaches like that of Google BigQuery will expand from website analytics into more fields with increasing more diverse subject matter.
Information is not a zero-sum game, where the winner takes all. Instead, it is increasingly collaborative in nature, where each player can bring a unique perspective and ask a new question.
Prediction #5: Companies Not Using AI Will Get Left Behind
While there will not be a single year in which AI takes over, but rather instances where the power of AI will become visible to the general public with force. The obvious example will be when self-driving cars arrive on mass on our streets. At that point the car companies who have not embedded the technology will be left behind, relegated to the old way of doing things.
Every company should be thinking of how they can potentially use AI now in their business. Data should be collected and processes examined. The early adopters are being rewarded with very strong results. As AI improves core capabilities its influence will become more powerful. Anyone late to the party will have a very steep learning curve, not just with the technology, but also with the new approaches that should be taken to data.
There will be a split between those companies who are forward thinking, embrace AI and the new possibilities, and those companies who cannot adapt. The speed and impact will be bigger than any previous technology disruption we have seen.