Although AI will evolve even more in the future, we should not be blind to the effects that AI already has on our day-to-day lives. AI is being used across a wide range of industries, from healthcare and finance to transportation and retail.
Whether completely (and intelligently) automated or done by humans with computer level support, decision-making drives business value. Therefore AI and IA are often the last steps in the data value chain where high quality and well governed data is leveraged to make intelligent decisions.
Your data is an opportunity to get a competitive edge, to mitigate risks and to grow your business in a sustainable way by making the right decisions. But too often data remains unavailable, unknown or not combined and opportunities are missed. Are you seeing the untapped potential? Are you looking for the next step to take?
Don’t forget to also start treating your data as an asset! Check out our view on Data Governance
Working with Datashift has proven to be an excellent experience. Their data expertise provided us with valuable insights and data-driven solutions which have helped us make informed decisions and improve our business outcome. They collaborated with different stakeholders to better understand processes, dare to challenge in order to clearly define problem statements, before performing the required data analysis. Thanks to Datashift's high-quality data profiles, we were able to turn data into real business value.
Wouter Duerinck
Head of Client Data Analytics, Euroclear
"We shouldn't try to solve everything with AI. In fact it should probably be avoided when possible. However some challenges cannot be solved with traditional computer programs. Coincidentally, these are the challenges that are going to determine our future."
Microsoft introduced Fabric this week. Possibly and probably the game-changing solution that can transform data analytics and integration. We dived into what Microsoft has unveiled with Microsoft Fabric and how this can impact Data Engineers, Analysts and Scientists. But we also have some open questions that we will answer once we are finished testing this exciting technology.
How can you successfully generate business value from AI? For sure, it is no easy task to get started with AI and machine learning. So check our practical examples of how AI technology and machine learning algorithms can be applied to 8 business domains.
This blogpost will look into the evolution that AI has made in order to become what it is today. Next to that we will look forward, how AI can likely impact our future. Lastly, we will try to formulate a plan of attack that companies need to consider to be best prepared for the drastic changes that AI will have on our way of working.
Artificial Intelligence & Machine Learning technologies have been on the rise throughout several industries. But what about AI & ML in data governance? What developments can strengthen data governance practices, and how will they most likely do that? And what is possible today?
As AI has a growing impact on our society, new initiatives and organizations focusing on AI safety and ethical AI continue to spring up.
We should use AI to make our organization smarter! Chances are you’ve recently heard this statement and see organizations acting to it. Leading companies are using AI across departments to increase productivity. Customer care organizations are using chatbots and speech recognition in their customer contacts. Marketing departments are predicting churn and segmenting their customer base.
Venturing into the field of AI may seem daunting at first. While AI has been hyped immensely in the last decade, it is a deeply technical field. Gartner even made a prediction that 80% of AI will remain alchemy run by wizards whose talents won’t scale in the organization. Your organization probably doesn’t have the scale and technological know-how of Google, Apple or Facebook. This might lead you to conclude that developing AI models isn’t for you. You are right, partially.
