There is a very significant but somewhat hidden barrier between vision and data-driven business execution. To carry out a meaningful data analytics plan, you first need access to the data itself. That does not seem like a problem. Isn’t all the data that needs to be analyzed sitting in various databases, “data lakes,” and the like? Not really.
Large amounts of data, in fact some of the most important information needed to get the most out of data analytics, are locked away in unstructured formats such as PDFs and other files. Data discovery provides the answer. The goal of the process is to find the data that is essential for effective analysis and turn it into meaningful insight.
How do you find important unstructured data?
This is a multi-step process that begins with accessing as much relevant data as possible. It continues by uncovering insights about the data and then makes sure those insights reach the stakeholders who can use them.
Data discovery is necessary in today’s businesses for a variety of reasons. Beyond the tendency for important information to be unavailable because it sits in unstructured data formats, there is also the people problem. Not everyone in the organization knows how to prepare data for analysis. In fact, most employees probably lack those skills. Most staff in organizations do not know how to collect data and prepare it for analysis. It is a highly specialized skill, it is expensive, and in many cases the work never ends.
This is where the need arises for a tool that can find information not only in metadata or structured data, but in the content of the information itself. According to industry research, much data remains untouched by traditional tools: more than 80% of content is never analyzed, and the loss of the information contained in that data translates into a global opportunity cost of millions of dollars.
The IFindIT intelligent tool addresses this problem. It enables non-technical people to access complex data sets and extract the information they need with a single search, without first having to prepare the information through large content templates, metadata rework, or extensive document management tables. Rethinking data preparation options will also allow business users and analysts to expand their self-service capabilities, including information management and extract, transform, and load (ETL) capabilities. Data preparation is one of the most difficult and time-consuming challenges facing business users of data discovery tools, BI, and analytics platforms. IFindIT enables users to prepare data for BI and data analytics tools. Specific activities that can now be performed include data analysis, integration, management, modeling, and enrichment.
The intermediate stage of data discovery, knowledge discovery in data, deals with the intangible but extremely important aspects that allow data to reach its potential. Even if you can collect all the data you need for analysis, that is not enough to gain real insight. You need to turn the data into useful information.
In some cases, the transformation is a matter of pattern and correlation analysis. For example, a company may have three seemingly unrelated data sets: PQRS (petitions, complaints, claims, and suggestions), customer orders, and employee sick leave. Using a similar analytics tool, companies can learn that when a certain percentage of employees are sick, customer complaints rise as a result of late deliveries.
A knowledge engine is a data discovery engine. It could take the form of an enterprise search platform that can aggregate data from multiple sources, including unstructured data, and empower employees to gain insights based on what they can discover, no matter where in the document it is located.
Without IFindIT, companies could spend a great deal of time searching for information and trying to integrate and understand what they find, only to discover that it is incomplete or missing data. Or they may be unable to find the information that is relevant to their needs at all. IFindIT can collect almost any type of data needed for data discovery. The tool uses built-in connectors and converters developed by Creangel to ingest, accumulate, and consolidate content. This can be done for multiple document formats, making them all accessible through a single search catalog. It then applies natural language processing (NLP), which gives meaning to the text rather than performing a plain, simple keyword search. Natural language processing (NLP) is a branch of artificial intelligence that helps systems understand, interpret, and manipulate human language. NLP draws on computer science and computational linguistics to bridge the gap between human communication and computer understanding. In addition, you can boost your data discovery process with machine learning (ML) algorithms that simplify and accelerate data analysis. Beyond its goal of empowering non-technical people to take part in data discovery, ML helps search tools and information engines learn to improve their workloads. Over time, employees can discover more data faster, contributing more to the business through the insights that meaningful information provides.
In conclusion, turning a company’s or organization’s CORE information into simple automation has always seemed very complex, whether because of the need for programming, the delay in finding information, the belief that only highly trained staff can do it, or simply the collective illusion that the information cannot be found, even after a search tool has been implemented. However, IFindIT can break this paradigm by collecting unstructured data, such as that found in company content and PDF files, and running a search-based business intelligence engine that can suddenly bring data to life. Data can be turned into information. Armed with information, people can work with important knowledge about the business. This is the power of data discovery delivered by intelligent enterprise search