Strategizing Content Management Applications

The material that has been gathered and preserved contains vast data that may be utilized for analytics, including machine learning and artificial intelligence (AI) applications.

FREMONT, CA: In the modern world, every firm has placed a high priority on gaining insights from data gathered from various systems (internal and external). Companies now have a competitive advantage in decision-making and company operations thanks to these insights. The Enterprise Content Management team (ECM), which is responsible for gathering structured and unstructured content from various sources and storing high volumes of content along with its metadata, is responsible for a significant portion of gathering and curating the data. This, however, continues. The material gathered and preserved contains vast data that may be utilized for analytics, including machine learning and artificial intelligence (AI) applications.

Although format, volume, form, and quality are important, the best practices apply to all three types of data—structured, unstructured, and semi-structured—regardless of these factors. The approaches listed below will assist in improving the content data and can be highly beneficial for the teams using the ECM's data:

Efficiency: Centralizing the content management repository has been the accepted pattern across IT. However, many significant, diverse businesses are still hybrids, with some of their contents stored in different applications. Besides the economic advantage, having a centralized content repository has many more advantages. All departments benefit from its consistency in content, tools, and formats. Departments can reuse the information many times thanks to the simpler maintenance.

Increasing revenue: Take measures to guarantee that the data and the content are of the highest caliber. The organization should have set appropriate data definition standards and quality guidelines. A process for ongoing improvement should also be devised. The organizational culture should include frequent monitoring, quality inspections, and prompt resolution of quality concerns. Before the data is stored in any system, the users who index the data must be taught to produce high-quality data. Although this may take some time, the high-quality data it produces, in the end, will be valuable for other systems further down the line.

Data streamlining: Before being sent to the analytics and AI team, the structured and unstructured data held in the ECM system has to be filtered, enhanced, and confirmed. It will make the data more accurate, trustworthy, and helpful for any application. Several tools are available for extracting information from unstructured data, such as Content Search Services, Optical Character Recognition (OCR), Natural Language Processing (NLP), text mining, labeling, and multimedia analysis tools. These technologies aid in extracting important data from unstructured data and make it accessible for additional research.

Accuracy: A data dictionary is a crucial tool for enterprise content management (ECM) since it offers a central repository of knowledge about the data being stored and managed inside a company. Every attribute, including name, data type, and description, is shown in full for each data piece. Users may discover any possible flaws or inconsistencies in the data with this information, enabling them to better comprehend the meaning and intended application of the data. A data dictionary may also assist in increasing the effectiveness and efficiency of ECM systems by ensuring that the data is used most suitably and efficiently as possible and by raising the general caliber of the information the ECM system is managing.