Editorial archive of Creangel. The references, figures and conditions correspond to the original publication.
There is a very important but somewhat hidden barrier between vision and data-based business execution. To execute a meaningful data analysis plan, you must first have access to the data itself. It doesn't seem to be a problem. Are not all data to be analysed in different databases, “data lakes” and the like? Not really.
Large amounts of data, in fact, part of the most important information needed to make the most of data analysis, are contained in unstructured formats such as digital documents and other files. Data discovery provides the answer. The aim of the process is to find the data that are essential for effective analysis and convert them into meaningful information.
How to find important unstructured data?
This process refers to a multi-step process that starts with access to as many relevant data as possible. It continues to discover information about the data and then makes sure that it reaches the interested parties who can use them.
Data disclosure is needed in today's companies for a variety of reasons. In addition to the trend of not having important information available in informal data formats, there is also the problem of people. Not everyone in the organization knows how to prepare data for analysis. In fact, most employees probably lack such skills. Most staff members in organizations do not know how to collect data and prepare them for analysis. It is a super specialized skill, it costs a lot and on many occasions never ends.
In this case there is a need for a tool that can be able to find information not only in metadata or structured data, but also in the content of the information. According to industry research many data remain intact with traditional tools, more than 80% of the content is not analyzed and this loss of information contained in these data results in a global opportunity cost of losing millions of dollars.
The intelligent IFINDIT tool addresses this problem. They allow people without technical knowledge to obtain complex data sets and extract the information they need with a single search. Without having to pre-prepared information with large formats of content, metadata reformulation or large tables of documentary management. But rethinking data preparation options will allow commercial users and analysts to expand self-service capabilities, including information management and extraction, processing and loading (ETL) capabilities. Data preparation is one of the toughest and slowest challenges faced by commercial users of data discovery tools and BI and analysis platforms. IFINDIT allows users to prepare data for BI and data analysis tools. The specific activities that can now be carried out include analysis, integration, management, modelling and data enrichment.
The intermediate stage of data discovery, the discovery of data knowledge, deals with intangible but extremely important aspects that allow data to reach its potential. Even if you can collect all the data you need for analysis, it's not enough to get a real view. You need to turn the data into useful information.
In some cases, transformation is a matter of pattern analysis and correlation. For example, a company may have three seemingly unrelated data sets: PQRS, client orders and staff disability. Using a similar analysis tool, companies can learn that when a certain percentage of employees are sick, customer complaints increase, resulting in late deliveries.
A knowledge engine is a data discovery engine. This could take the form of a business search platform that can add data from multiple sources, including unstructured data, and train employees to get information based on what they can discover no matter where in the document they are.
Without IFINDIT, companies could spend a lot of time searching for information and trying to integrate and understand what they find, just to discover that it is incomplete or dataless. Or, they can't find the information that's relevant to their needs. IFINDIT can collect almost any kind of data needed for data discovery. The tool uses integrated connectors and converters developed by Creangel to approve, accumulate and collect content. This can be done for multiple document formats, making them all accessible through a single search catalog. It then applies the natural language processing (NLP), which gives meaning to the text, not just a normal and simple search by words. Natural language processing (NLP) is a branch of artificial intelligence that helps systems understand, interpret and manipulate human language. The NLP includes computing and computational linguistics, to try to close the gap between human communication and computer understanding. Additional can boost your data discovery process with machine learning algorithms (MLs) that simplify and accelerate data analysis. In addition to its goal of training people without technical knowledge to participate in data discovery, ML helps search tools and information machines learn how to improve their workloads. Over time, employees can discover more data faster, contributing more to the business through the knowledge provided by meaningful information.
In conclusion, transforming the company's or organization's information CORE into a simple automation has always seemed very complex due to the need for programming or delay in finding the information, or that only highly trained staff can do it, or simply that there is the collective illusion that are not found, this even after the implementation of some search tool. However, IFINDIT can break this paradigm by collecting unstructured data, such as those found in the company's content and digital documents, and by running a search-based enterprise intelligence engine that can suddenly bring the data to life. It can be converted into information. Armed with information, people can work with important knowledge about the company. This is the power of data discovery provided by smart business search