Careful planning and good data are required for systems to become intelligent. For once, the hype about technology is justified. Artificial intelligence (AI) has the potential to change everything: the way we work ( AI companies ), how we live, how we make decisions, and even how we understand human nature. AI technologies have existed in various forms for several decades. Thanks to the sheer explosion of data – the raw material that powers AI – these technologies have advanced at breakneck speed over the past few years.
Table of Contents
AI In Business: A Growing Market
The Internet of Things (IoT) is expected to grow to an estimated 150 billion networked sensors within the next ten years. These are built into home appliances, vehicles, and industrial robots that make the products people consume. People also wear sensors on their bodies – in the form of fitness trackers or medical devices. This multitude of sensors continuously generates new data and feeds the AI systems. These are programmed accordingly to create better services, products, and customer experiences based on this information.
Organizations know that they have to master these new challenges. Those who make the best use of the flood of data will be able to make faster, smarter decisions and thus make more relevant and compelling offers. You can significantly change the way your company works in a modern digital world.
But there is a catch: AI alone is not a silver bullet. Similar to humans, AI relies on points of reference and experience to develop its intelligence. In addition, the supplied data also plays an important role so that the advantages of AI can be realized. If the data used to train and make decisions about artificial intelligence are incomplete or of poor quality, the AI investments will not generate a corresponding return on investment. There is hardly a company that is not planning an AI initiative or is already busy implementing it.
AI Is Only As Good As The Underlying Data
Unlike flash drives and mobile apps, AI is not a stand-alone plug-and-play technology. It takes careful planning and sound data for the systems to become intelligent. An AI algorithm can only be as good and accurate as the data used to train it. In other words: a one-sidedly distorted data pool ultimately leads to biased decisions. Incorrect data records also lead to incorrect decisions.
If companies are serious about being guided by objective knowledge, they need to create a suitably robust database. The quality of data management, like research & development or corporate culture, is an essential basis for successful AI initiatives.
Arguments For Good Data Management
AI can better analyze data and improve human decision-making. However, several companies still do not manage the complexity and volatility of the data and thus cannot fully realize the benefits of AI. The result: AI implementations are prevented from being truly intelligent.
IT executives recognize that new technologies like AI require hard work. Forward-looking companies have figured out how to use people and technology together to make their AI successful – and unlock its potential. This requires new, data-oriented roles and processes. It’s not about breaking free from old architecture. Instead, it is necessary to redesign operating models to cope with the new wave of technology.
AI Company: The Data Is The Essential Foundation
From a technological point of view, it is clear that a solid data management strategy is an essential prerequisite for effective AI. For AI, it is not the often exciting front-end (like a virtual assistant) but the robust data back-end. Data is the basis for every change because business-critical insights are only created based on good data. Artificial intelligence can help ensure that the entire data path can be tracked, recorded, and visible across the entire company – and can thus be fed into every transformation activity.
This places several demands on the data management teams. They need to simplify data access from various established and emerging data sources and refine their data cleansing techniques to clean invalid or redundant data from the most critical data sets. The aim is to answer all questions regarding the origin and quality of the data required for decision-making. They need a data management platform that can implicitly use artificial intelligence at the data engineering level to achieve this.
Three Kinds Of Data Experts Are Needed To Leverage Insights
Organizations need to redefine today’s data and analytics roles to make the best possible use of insights gained from AI. Before developing or selecting your AI solution, you need to understand the possibilities, limits, distortions, and gaps within your data pool. This very human process requires data teams to work with in-house experts to understand how the business works.
This realization requires a team of data scientists and data engineers who have extensive know-how in this area. Ideally, it will be led by a chief data officer. However, such specialists are in great demand and at the same time hardly available on the market.
AI will also redefine and develop customer relationships. It also offers the opportunity to deepen knowledge about customers and, on this basis, to develop products and services that are tailored directly to their requirements. This new customer contract is part of the generation change and is also called Data 4.0. It defines the next age of trustworthy companies – and is at the same time a more important differentiator than, for example, the product price.