IDC and EMC project that data will grow to 40 zettabytes by 2020, resulting in a 50-fold growth from the beginning of 2010*. … And approximately 90% of data generated is unstructured*.
Unstructured Data refers to information that cannot be quantified in the traditional row-column database. It is a generic label for defining information which cannot be stored in a pre-defined manner or contained in a data structure. It is usually in the form of text. Think of journals, reports, books, digital images, audio/video, tweets, posts, comments and more.
Structured Data on the other hand can be labeled, organized, quantified and stored in database formats, in rows and columns. Due to this, it can be easily entered, retrieved, queried and used for computation. Think of graphs, metrics, web logs, transactional data, financial data and more.
Organizations are absolutely familiar with Structured Data. They use it all the time. Excel sheets, metrics, sales figures, calculations, statistics, investments and much more.
However, and here is the reality - organizations do have a lot of Unstructured Data as well. It is produced everywhere, through all means of communication, unattained, unknown and non-aligned. This is the data that is getting generated in our computers, servers, smartphones, cloud services, emails, resumes, job descriptions, minutes of meetings and more. Every moment! It resides everywhere – in the recesses of machines, clouds and in the air.
IDC* estimates - the volume of digital data will grow 40% to 50% per year. By 2020, the number will have reached 40,000 Exabytes or 40 Zettabytes (ZB). The world’s information is doubling every two years.
Experts at Forrester also have estimates along the same observation. Somewhere around 80% of all the data in the world in all its many forms is stored on media and in file systems not managed in a database.
One of the fundamental difference is that Structured Data can be read and understood by machines, while Unstructured Data cannot be. Machines can efficiently turn, bend, sift, sort and compute Structured Data and give results for further actions.
On the contrary, has anyone come across a machine which could clearly summarize the contents of an email or a book? Or understand a resume the way humans do before matching it to a job requisition? A thousand tweets received in response to a business campaign – could machines deduce their overall emotion?
The relationship between organizations and their Unstructured Data is of sheer significance. Exploration of this unknown territory is waiting to open new doors to mankind – insights and revelations of considerable value which could alter the way in which one engages in profession, business and life. We may find the needles from the haystack.
The big question is – how does one make use of the Unstructured Data and reap the benefits?
Studies show that business data consists of 20% Structured Data and 80% Unstructured Data. This is only 20% structured enterprise data and a majority of 90% decisions are taken based on it. Only about 10% is utilized for informed decision-making.
The loss is obvious. Unstructured Data is mostly left attended today.
Analysts and strategists need new age technologies such as Machine Learning to bring life to the game. Machines need to discern data exactly the way humans do! Except that the volume of data being handled is humungous, severely layered, scattered, concatenated and encrypted in all possible forms. Speed is surely the next critical factor. By far, the biggest valuable determinant in understanding Unstructured Data is accuracy. Just how accurate could be the analytics deduced by machines to comprehend Unstructured Data? Should top honchos base their decision on gut or real-time analytics? There is no luxury of affording emotions and irrationality today. There is simply too much at stake.
Predictive analytics, rule-based analytics, natural language processing, semantics… are all in the list of enablers capable of deciphering the obscure Unstructured Data. But nothing can quite match the capabilities of contextual intelligence technology.
1) Spire’s contextual platform gives context to any form of Unstructured Data in a way that is relevant to it. For example: When applied to HR, it may not necessarily pick a resume which contains the largest number of 'Java' words in it; but rather a resume which when scanned by a human recruiter would suggest that it contains more real-time 'Java' experience data instead of just the total quantity of the word 'Java'.
2) The second distinctive feature of the platform is that it gives unmatched accuracy levels - 95% in search and 80% in demand-supply mapping. The resulting analytics are measurable and auditable.
3) The platform has Machine Learning capabilities which recalibrates systems experientially. This results in analytics with sharper insights for informed decision making.
* IDC iView "Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East," (Source: Break Through Analysis)