Unlock the other 99% of your data – now ready for AI

For decades, companies realized of all sizes that the data available to them have a great value, to improve user and customer experiences and develop strategic plans based on experimental evidence.

Since artificial intelligence has become increasingly available and practical for business applications in the real world, the potential value of the available data has grown significantly. Adoption of artificial intelligence successfully requires a great effort in collecting data, arrangement, and prior processing. Moreover, important aspects such as data governance, privacy, identity, organizational compliance and security must be processed from the beginning.

In a conversation with Henrique Lemes, the leader of the America’s Data Data platform, we explored the challenges facing institutions in implementing practical artificial intelligence in a set of cases of use. We started examining the nature of the same data, its various types, and its role in enabling the active applications that work on behalf.

Henrik highlighted that the reference to all the institution’s information is simply as “data” that reduces its complexity. The modern establishment transmits a fragmented scene of various types of data and unintended quality, especially between organized and unorganized sources.

In simple phrases, structured data indicates information organized in uniform and easily research coordination, enabling effective treatment and analysis by software systems.

Non -structured data is information that does not follow a specific format or an organizational model, which makes it more complicated for processing and analysis. Unlike structural data, it includes various formats such as emails, social media, videos, photos, documents, and sound files. Although it lacks the clear organization of organized data, the non -structured data carries valuable visions, when managed effectively through advanced and AI analyzes, can pay innovation and report strategic work decisions.

Henrik stated, “Currently, less than 1 % of institutions data is used by obstetric intelligence, and more than 90 % of this data is unorganized, which directly affects confidence and quality.”

The confidence element in terms of data is an important element. Decision makers in the organization need a fixed (TRST) belief that the information that is within their own reach is complete and reliable and obtained properly. But there is evidence that countries that are less than half of the data available to companies are used in artificial intelligence, while often ignored or marginalized data due to the complexity of their processing and examination for compliance – especially on a large scale.

To open the way to make better decisions based on a complete set of experimental data, the calmness of the consumed information should be easily converted into castor. Henrik said that automatic swallowing is the solution in this regard, but the rules of governance and data policies – on both unorganized and structured data.

Henrik has identified the three operations that allow institutions to benefit from the inherent value of their data. First, swallowing on a large scale. It is important to automate this process. Second, governance and information thirty [is when] You can allow this to AI Tawylidi. We achieve more than 40 % of the return on investing in any traditional use. “

IBM provides a unified strategy, rooted in a deep understanding of the institution’s AI trip, as well as advanced software solutions and field experience. This enables organizations to convert structural and unorganized data efficiently and safely into ready -made assets for AI, all within the limits of current governance and compliance frameworks.

He said: “We combine people, operations and tools. It is not simple by nature, but we simplify them by harmonizing all basic resources.”

With the expansion of business and conversion, the diversity and size of its data increases. To keep pace with this, the process of swallowing artificial intelligence data must be developed and flexible.

“[Companies] Facing difficulties when scaling because their artificial intelligence solutions were initially built for specific tasks. When they try to expand their scope, they are often not ready, data pipelines grow more complicated, and data management becomes necessary. This drives the increasing demand for effective data governance. “

The IBM approach is to understand the journey of every agent of artificial intelligence, creating a clear road map to achieve an investment return through an effective AI implementation. Henrik said: “We give priority to the accuracy of data, whether organized or unorganized, in addition to swallowing data, proportions, governance, compliance with the regulations related to the industry, and the necessary ability. These capabilities enable our customers to expand through multiple cases of use and fully benefit from the value of their data.”

Like anything worthwhile in technological implementation, it takes a long time to put the correct operations in place, and to attract the right tools, and it has a necessary vision of how the need to develop.

IBM provides institutions a set of options and tools to enable the burdens of artificial intelligence work in the most organized industries, on any scope. With international banks, financial homes, and international multinationals between their customer list, there are few alternatives to large blue in this context.

To learn more about enabling data pipelines for Amnesty International that pays business and providing a quick and important investment,, Go to this page.