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Unstructured data, in its simplest form means “Data in any form which does not easily fit into a data model or belong to a dataset of database tables.” Unstructured data prevails in formats including books, audios, videos, and even collections of documents. Such unstructured data can be measurements of a building in the form of text, or chapters within a novel, or markup on an HTML webpage.
Approximately 40% to 80% of any enterprise’s data is in unstructured format. No matter the actual percentage, there is little doubt that the amount of unstructured data continues to grow. Today many organizations find the volume of data challenging, and integrating such humongous data with enterprise systems with the help of ill-equipped Data Management tools is a challenging task. All said and done, unstructured data, for any organization doesn’t necessarily have to be a problem.
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Data scraping and text analytics are industry proven techniques used to capture an enterprise’s unstructured data. Companies that have succeeded in capturing and managing unstructured data have a competitive advantage over firms unable to do the same. Numerous enterprises are already making efforts to improve their business efficiencies and reduce costs by managing unstructured data.
Unstructured data, if managed appropriately, helps with new product development (NPD), increased sales leads, meeting compliance requirements, improved Data Governance, analyzing social media channels, and Business Intelligence system integration. But before realizing the benefits of unstructured data, companies should identify unstructured data formats.
Unstructured Data Formats
1. Text Documents
Several combinations of word documents or word files, text files, emails and alike contribute to the largest amount of unstructured data. A lot of organizations are already on the run to manage unstructured data to cull out useful information from the talisman of corporate email. Content management systems are only partially useful in helping organizations manage and derive information from the data in the form of unstructured text documents.
2. Web Pages
The immense growth of social media and interactive web are the next most contributors to unstructured data. Websites which are data-driven usually leverage fully normalized databases as their back end. Using unstructured data can keep them away from desired results. These webpages also provide opportunities, but only to organizations which are capable of managing and deriving value from that data. Graph databases, a byproduct of data visualization, are also used to find relationships between social media users and their consumer preferences. Organizations can leverage revenue from these, but only by analyzing unstructured datasets.
3. Audio, Video, Images: Media Formats
Don’t be surprised, but audio, video and images are all unstructured data formats. The only thing that is common in these file formats is that they can be stored and managed without the format of the file being understood by the system. It allows them to be stored and utilized for future purpose in an unstructured fashion because the content of such files are unorganized. Intelligent real-time analysis of audio data is on the rise in digital audio recording and processing – but is used less as compared to other two formats. Managing these unstructured data files really helps; iTunes music store is an excellent example. Band names, genres, and related artists drive Apple’s music recommendation services.
4. PowerPoint & MS Project Office: Common Software Data Formats
Files created in Microsoft Office or any other office suits have a gamut of data formats. MS Access creates and manages completely structured database files – but in their own formats. PowerPoint and Microsoft excel have a proven history of making it really challenging for organizations to include information from these formats and into their corporate reporting mechanism. Apart from office suites, the other epicenters of unstructured data formats are commercial applications such as customer management (CRM) tools, larger enterprise resource planning (ERP) applications like SAP, or even architectural drafting applications like AutoCAD; which come in with proprietary data formats.
Once any enterprise identifies the types of unstructured data to derive meaningful information from the mass of unstructured data, they are now required to capture that data. Data mining and data scraping is done for extraction of information from data at its most basic level.
Now that unstructured data formats are identified, enterprises should collect/capture that data through web scraping for text analytics.
Data Scraping for Text Analytics
Data scraping extracts human-readable information from a computer system, by another program. It is necessary to distinguish human readable aspects of data scraping from the typical data exchange between computers as it involves structured data formats too. Data Scraping becomes a prime necessity if your organization attempts to interface with any legacy system without an application programming interface (API). What started as screen scraping, web scraping of data is following similar techniques allowing meaningful data from web pages to be scraped, cleansed, and stored in relative databases. Data mining is similar to data and web scraping and involves deriving meaningful information from a collection of static, human readable reports. It also facilitates regression test result analysis.
A classic example of successful data scraping is of online data collection for software companies in a specific format. Profiling software companies to assess their potentials and invest in, globally was the main motive. The companies that were to be worked upon were the ones which earned revenues on software products sales. Collecting details as per client requirements was challenging as it involved statistics such as Revenue, Profit, Biographies of Key persons – extremely hard to find.
It is one more way to derive meaningful information from unstructured data. Apart from raw text mining, it also uses natural language processing, to turn unstructured text into data more suitable forms for Business Intelligence and other analytical uses. Refreshing the fact mentioned in the beginning of this write-up, the majority of unstructured data resides in textual format; text analytics has become one of the most important techniques to make sense of unstructured data.
Employed by companies, text analytics can monitor social media for everything and anything between monitoring human resource applications to personally targeted advertising. Artificial Intelligence, Machine Learning, and semantic processing are other disciplines of text analytics on the verge of innovation. In order to match phone numbers along with email and Web addresses from unstructured text, regular expression matching is leveraged. To an extent that text analytics also allows applying disambiguation methods to assess context and recognize different identities sharing the same name: Apple, the Beatles record company, compared to Apple, the consumer electronics giant.
So now that unstructured data formats are identified, data is collected, stored & analyzed, the organization is set to leverage it for prime business competition.
Organizations strive to know how exterior forces can impact them. What would happen if one of your big-ticket customers is now onboard a non-profit organization, where one of the executives of your top competitor is already a member? It will give you shivers as to what would be the effect of this new network connection on your relationship with your customer’s organization. Furthermore, imagine if one of your top engineers or designers is looking out to work with a professor to benefit the school/college or university he/she passed out from. I am sure your organization cannot afford to lose a top employee to such a venture. Monitoring and keeping a track of internal and external memos will certainly help you to a great extent.
New Product Development
Say you are a product manufacturer. Your designers and engineers crafted a fabulous product. But on analyzing unstructured data from social media and other sources as mentioned above, it is revealed that your customers only use it when other similar products are not available in the market, they do buy it but for presenting it to someone and not for self-use. They are on the lookout for a product which will cost them less dollars, and are fine with fewer features compared to the product newly created.
So how do you plan to segregate the features of your new product to make it cost effective for your customers; and with minimal efforts, time and expenses? Will you be able to use the existing product at all? That should be the first question. How will your teams identify entities in the product data collected? How will they create graphs, charts and fulfill the needs of data visualizations for your sales, marketing and strategy teams?
Do you feel that the unstructured Data Management activity is worth investing in? Does your organization feel the need to take help of data and analytics experts, especially experienced text analysts? Data analytics experts know how to analyze data, extract, tag, and then index it; to identify entities. These Data Management specialists will help you to launch your product for a specific customer base or customer segment and on one or several platforms.
Opportunities with Unstructured Data
Humongous amounts of unstructured data, for any organization, is not necessarily a problem. It should be considered to be an opportunity for success. Unstructured Data Management processes focused on deriving information out of apparently unrelated data in most of the cases empowers firms with a proactive attitude to gain competitive advantage over firms who ignore unstructured data.