Digital transformation is one of the buzzwords in every industry. What is described is nothing more than a significant change that changes companies from the ground up. No organization can escape the digital transformation if it wants to continue to succeed in the market over the next few years. But what does it all include? On the one hand, processes and procedures in the company have to be optimized.
On the other hand, the type of work must also be optimized. This is the only way to develop new business models and make organizations fit for the future. Part of these new models and agile processes is implementing artificial intelligence (AI) and machine learning.
But AI can only provide as good support as the data from which the systems draw their knowledge. This is where the crux lies: Companies store a flood of information that is often unstructured or even filed multiple times – and lose track of it. This is already questionable for security reasons in the age of the new EU General Data Protection Regulation. In addition, employees do not receive a complete picture because they usually only access the data that can be found quickly.
The possible solution for this is an AI system in combination with Master Data Management (MDM). MDM provides a single, comprehensive view of the data. Employees receive a holistic picture of all existing master data. The AI system analyzes this information, draws appropriate knowledge from this, and gives teams initial recommendations for action. Managers can make informed, strategic decisions and develop new business models. Informatica calls this new age “Data 3.0”.
But what exactly does this mean for companies? Most organizations predict that AI will grow enormously and invest in related projects – even if they often do not know precisely what goal they are pursuing. What many companies fail to realize is that the findings made available by the AI systems are only as good as the data that they analyze. A data pool that is incomplete or contains outdated information does not support companies.
To remain competitive, companies must always keep their databases up to date and at the same time be able to analyze them based on a large number of parameters. Here, data management and AI applications work together seamlessly and allow organizations to make the best possible use of the information. While traditional approaches cannot yet be scaled sufficiently to meet current (and future) needs, end-to-end data management platforms can. Data, metadata and machine learning, and AI solutions work together and provide users with the information they need. In this way, they improve productivity and efficiency across the company.
Strong data management is, therefore, the backbone of a company and enables more informed data-based decisions. Thanks to automation and robust AI-based processes – from data discovery to AI and machine learning technologies – data peaks can also be foreseen. A large number of operational challenges can be identified, analyzed, prioritized, and solved.
Artificial intelligence can also take on tasks that employees can only do manually with a great deal of time. This includes, for example, viewing and identifying large amounts of data – and recognizing patterns or anomalies within this information.
AI can ensure predictive operation by analyzing all available data and developing appropriate solutions. This can affect the future maintenance of machines but also the optimization of a supply chain. The basis for this is, in turn, a powerful data management system that provides current information. Data is a real treasure for companies, but they are often not sure how to get it. The combination of data management and artificial intelligence creates a powerful solution. At the same time, the interaction of data management and AI ensures more well-founded decision-making for companies – and thus competitive advantages and future business success.
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