The Internet already takes the lead as the prime source of information for most data workers. With its ability to sort data and absorb large numbers of data points,the Internet is augmenting to give people personal and particular information. However, most of this data comes in large volumes at high velocity from multiple venues and in various formats. Moreover, this vast amount of valuable data is mostly not in structured forms, making it easy for you to drown under the hundreds, thousands, and even millions of search results for even the simplest of questions.
Artificial intelligence and machine learning have transformed everything in the last few years as technology has reached a critical acceleration point. When the interface that enables accessing information and completing tasks changes, it's only natural that the search will also alter. And true to the expectation, search has improved because it now understands purpose and context, and it has become a key component of artificial intelligence.
Search is Everywhere
Search is a regular part of a knowledge professional's daily routine, the day-to-day basics of working on and around large volumes of data. But, with the humungous transformation in a short span in the way knowledge workers collected, processed, and retrieved data, the means to search data also witnessed a significant change. Since search in data management has transformed, it is only fair to say that search is now found everywhere.
Searching in data management has been fragmented as the practice of searching more natively in more and more selective ways has grown. Yet, in its millennial years, search has matured and integrated numerous aspects of its identity, even as it fragments.
Applying searching techniques to check for or extract an element from any stored data set can be done in structured data. These algorithms are categorised based on the type of search operation they execute. Almost all kinds of searches can be put into two major categories:
- Sequential search like linear search
- Interval search like binary search
When you consider searching in data management, you come across some standard and quite beneficial tips to follow,which include:
- Planning the search
- Identifying useful keywords
- Making decisions about where you plan to look for information
- Making the best use out of a range of resources
Following these basic steps can help data workers operate on desired data sets with ease.
But, the most obvious question here is:Can the steps mentioned above be used for unstructured data? Can you plan your search and use a set of specific keywords and look for the exact information you need if the data is not in a pre-defined data model or is not organised in a pre-specified manner.
It's pointless to guess that the answer is a resounding no. And this makes it important to address the role of search in unstructured data!
Searching in Unstructured Data
Unstructured data is usually text-heavy,but it can also include dates, statistics, and facts. Compared to data recorded in fielded form in databases or annotated (semantically labelled) in texts,unstructured data leads to irregularities and ambiguities that make it difficult to analyse using standard methods. Furthermore, when data is so scattered, the role of search becomes ten times more important.
There's no doubting that structured data benefits businesses in various ways, but the unstructured data that an organisation collects has untapped potential and business-critical insights. For example, work descriptions, résumés, emails, text documents, research and legal reports, voice recordings, videos, photos, and social media posts account for about 80% of data in the company. While unstructured content used to be difficult to analyse and use, advances in neural networks, search engines, and machine learning are improving our capacity to use it for enterprise knowledge discovery, search, business insights, and actions.
The Way Forward
Consider this:
One department of your organisation is responsible for maintaining a repository of all the old and new prospects you come across over the years. There can be multiple instances where data workers in a different department, say, HR, require data for one or more of these prospects. Finding someone's phone number on the system or a particular business's address can become cumbersome, especially when the data is gathered and compiled from multiple sources both within and outside the organisation. If there are no specialised means to search, you'd have to manually look at each piece of data - each phone number or business location – to determine if it's what you're looking for. It will take a long time to perform this with a large collection of data.
The average knowledge worker continually operates on a data landscape that is fragmented and segregated. As a result,instead of extracting value, professionals spend more time organising,searching, and processing data. In a competitive environment where informed business decisions are vital to competitive advantage, this results in a lack of concentration, effective, and intelligent work.
To make the most of the search knowledge workers perform in their day-to-day lives, Needl is a system that pulls together prominent productivity tools and content sources on one platform,allowing users to find the proverbial needle in a lot of digital haystacks. We create a dynamic two-way data flow by layering deep integrations with cloud and AI-based unified processing and collaboration. We also offer a real shared data model and shared cloud computing platform capabilities, allowing for deep data integration across all apps. This permits information flows to be agnostic to location and the limits of the apps they reside in, allowing users to process and collaborate on these linked data sets utilising typical cloud computing-based workflows.
In every knowledge-intensive business, Needl is reinventing how unstructured data management should function.
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