While it may seem like a long way to go before you can deploy an AgentAI on your data assets, many companies already have sufficient assets, but they are often poorly organized, poorly known and under-exploited. Data governance data governance practices need to address all the levers that will enable AI to be deployed more effectively across the enterprise.
Contents
Enhance your data assets
Within companies, projects function as autonomous units that establish their own knowledge base to achieve specific objectives. These objectives are as varied as the implementation of a KPI, the ERP implementation or setting up customer segmentations. What all these projects have in common is that they require project teams to develop in-depth expertise not only in project-specific functionalities and data, but also in the company's data and processes: this is the company's knowledge capital.
Acquiring knowledge about data assets is very, often too time-consuming in the absence of available, high-quality or simply up-to-date documentation.
At the conclusion of projects, this knowledge is often lost, as it is not part of the project scope, and this loss represents a significant cost for the company. The main reasons for this loss include :
Learn to CAPITALIZE to develop your data assets
To establish an effective documentation practice, it is essential to change the perception of documentation from a simple constraint to a genuine strategic tool. In a word, CAPITALIZE.
One of the first levers remains the acculturation of project teams, based on concrete cases encountered every day in the company. Spreading the understanding that documentation is not just a project deliverable, it is first and foremost a tool that everyone can use in the initial scoping phases to better understand a project's environment, and reduce information search time and data errors. The key to acceptance is awareness of the value of these practices.
Secondly, a curation process must ensure that data assets are managed. It will be based on data catalogs/dictionaries, and must involve all the company's players (business, IT, data) in correcting and maintaining this heritage.
Can an AI agent open up the company's data assets?
It is on the basis of this solid foundation that an AI Agent can be deployed in the organization, to truly open up all data assets (definition, dataset...) and make all the company's data assets visible.
While it may seem like a long way from being able to deploy an AI Agent on data assets, many companies already have sufficient assets, but they are often poorly organized, poorly known and under-exploited. Data governance data governance practices need to address all levers to enable AI to be deploy AI more effectively within the enterprise.
So don't wait any longer to accelerate your projects and open up your data assets!
12 human biases in Generative AI to understand and master
Generative AI has many biases, not least human ones. Our Data & AI experts can help you understand and master the biases of
Setting up an ERP project: the keys to success
This article is the continuation of 4 mistakes in ERP / data repository implementation Every problem in the implementation of ERP and data repository projects has to be solved.
Data governance: definition and key principles
Data governance is a major lever for improving performance and accelerating business transformation. Today, data is a major corporate asset, with
