{"id":9004111223059983,"date":"2013-10-24T04:00:00","date_gmt":"2013-10-24T04:00:00","guid":{"rendered":"https:\/\/www.pragmaticinstitute.com\/uncategorized\/tackling-big-data\/"},"modified":"2013-10-24T04:00:00","modified_gmt":"2013-10-24T04:00:00","slug":"tackling-big-data","status":"publish","type":"resources","link":"https:\/\/www.pragmaticinstitute.com\/resources\/articles\/tackling-big-data\/","title":{"rendered":"Big Data: How Do You Tackle it?"},"content":{"rendered":"

While the term \u201cbig data\u201d may be overused these days, the problem it defines is no less a reality. Frankly, hardware and software products developed by some of the people reading this helped create this monster. It\u2019s become faster, easier and cheaper for our customers to churn out more and more data. This has created a problem for our customers\u2014and an opportunity for us.<\/p>\n

The two problems associated with all big data are:<\/p>\n

Cost.<\/strong> It costs money to host, store, find, secure, back up, move, archive and recover, just to name a few. And by its very nature, unstructured data is hard to find, organize, prune and protect.<\/p>\n

Risk.<\/strong> The risk factor lies in holding on too long to sensitive data, such as credit card numbers, social security numbers, business transactions, email conversations and anything else that could spell trouble when landing in the wrong hands.<\/p><\/blockquote>\n

Data typically does not increase in value with time; it grows in obsolescence each day. The more unstructured (and mostly obsolete) data to weed through for discovery and review, the greater the cost and risk.<\/p>\n

THE BIG DATA OPPORTUNITY<\/h2>\n

The good news is that from every customer pain is born new solutions, product features and capabilities that drive even greater value for your products. And increased value means greater differentiation and revenues for your products.<\/p>\n

\"data\"<\/a>
Photo By Franki Chamaki on Unsplash<\/figcaption><\/figure>\n

Text analytics engines, which are designed to automatically mine and analyze a large volume and variety of text-based data, can help you capture that value by turning big data into usable data.<\/p>\n

But if they aren\u2019t already part of your product\u2019s capabilities, you\u2019re not alone. In an October 2012 report1, Gartner reported that 2011 IT spending driven by big data functional demands totaled $27 billion. The report also stated, \u201cIn 2016, IT spending driven by big data will reach $55 billion.\u201d<\/p>\n

The big data wave is still in its relative nascence, and the agile product team still has a chance to gain a first-mover advantage in bringing a more intelligent, big-data proof product to the market well ahead of the competition.<\/p>\n

Four primary inflection points have turned big data into an opportunity for technology marketers: the doubling of memory size; advances in network speed, capacity and reduced pricing; solid state storage; and continued CPU performance increases.<\/p>\n

To put a finer point on the opportunity text analytics provide in the world of big data, Gartner also clearly recommends that organizations engage in early adoption of machine data and text mining through 2015, and look to embedded capabilities from traditional vendors thereafter.<\/p>\n

TEXT ANALYTICS ENGINES EXPLORED<\/h2>\n

First, it\u2019s important to realize that not all text analytics engines are created equal. Let\u2019s look at the three main types of text analytics engines:<\/p>\n

    \n
  1. Boolean keyword<\/li>\n
  2. Lexical techniques<\/li>\n
  3. Concept-aware technology<\/li>\n<\/ol>\n

    Boolean keyword<\/strong>. Anyone who\u2019s ever done a Google search should have a pretty good grasp of how Boolean keyword search works. Enter keywords with arguments such as \u201cAND,\u201d \u201cOR,\u201d \u201cNOT\u201d or \u201cNEAR\u201d to narrow the results that you might get by simply entering a word or phrase. This works well if the user knows exactly what to look for, and the search term or phrase is the right amount of unique to yield few false positives without omitting results that should be produced. Conversely, the shortcomings of Boolean keyword search are caused by synonyms, acronyms, abbreviations, misspellings and multi-lingual limitations.<\/p>\n

    For example, searching for \u201cdog\u201d will yield anything containing the term \u201cdog,\u201d including hot dog, Snoop Dogg, dog stocks, bird-dogging, Elvis Presley\u2019s 1956 hit single \u201cHound Dog\u201d and the Charleston RiverDogs minor league baseball team. However, an entry that talks about a German shepherd might not come up if the entire entry omits the term \u201cdog.\u201d<\/p>\n

    Lexical techniques.<\/strong> This includes using word lists, dictionaries and thesauruses. It can be more accurate than Boolean keyword search, because it is based on predefined lists that contain related terms. Using the \u201cdog\u201d example, someone would need to build a list of all the possible representations of the word \u201cdog,\u201d such as doberman, German shepherd, beagle, hound, etc. In this case, searching for \u201cdog\u201d will reference the dog library and produce results containing any of the terms in the library. The shortcoming of this technique is that it\u2019s labor intensive to build libraries, and they need to be updated constantly as new terms like \u201clabradoodle\u201d emerge. They also need to be populated with common abbreviations (such as \u201cCardi\u201d for Cardigan Welsh corgi), acronyms (such as BMD for Bernese mountain dog) and misspellings (in the off chance a user can\u2019t correctly spell dachshund, shih tzu, schipperke or chihuahua). And if the set of searchable content is in more than one language, a separate table needs to be created for every search term in every language.<\/p>\n

    Concept-aware analytics engines.<\/strong> This last type is used by U.S. Federal Government Intelligence Agencies, as well as thousands of law firms, because it is the most effective approach to managing unstructured big data. It organizes unstructured content conceptually, the way humans do, without the need to build complex libraries or keyword lists.<\/p>\n

    Concept-aware analytics engines work, ironically, like a hound dog that gets trained on a scent\u2014searching for anything containing that scent. Concept-aware engines are fed sample content (the \u201cscent\u201d) that conceptually represents a category, such as dogs. Sample content could be documents, emails or web content (in whole or part). Using our dogs example, a user would identify several sample documents about dogs. The engine maps the concepts in those documents, converts the text into mathematical algorithms and calculates the relationships between words in a document using a high-dimensional mathematical space. It can then find conceptually related results (i.e., \u201cfind more like this\u201d), regardless of misspellings, acronyms, abbreviations, synonyms and even language.<\/p>\n

    No passages or documents about hot dogs, Snoop Dogg or the Charleston RiverDogs would return, unless there was also a reference to the animal (such as the RiverDogs\u2019 mascot, Snoop Dogg\u2019s canine pet, or the description of a Dachshund). Entries containing abbreviations (Cardi), acronyms (BMD), misspellings (\u201cDockshound\u201d) would all be included, even if they do not contain the word \u201cdog\u201d entirely. The system can be trained in any language by simply using example text in the language desired.<\/p>\n

    Weeding through millions of unstructured documents and emails is a big task, but concept-aware text analytics provide a fast and effective way to get right to the pertinent conceptually related items.<\/p>\n

    Once the desired categories are defined using example text, concept-aware auto-categorization sheds light on the \u201cdark data\u201d comprising a large amount of big data. Using examples of what the user is looking for, the system can \u201cfind more like this\u201d and the user can take whatever action is desired.<\/p>\n

    GET STARTED<\/h2>\n

    Regardless of your product\u2019s market, you can drive increased value for your customers by using text analytics engines to reduce the big data problem\u2014and to increase the big data benefits. Here are eight use cases where concept-aware text analytics engines can tackle big data.<\/p>\n

    1. Archiving.<\/strong> Sample documents of old email newsletters and outdated marketing documents can be used as examples to find similar documents that can be considered for defensible deletion, dramatically reducing the clutter without having to manually inspect each document and email.<\/p>\n

    2. Compliance.<\/strong> Once the junk has been pared down, concept-aware categorization can be used to enable greater precision in determining exactly which documents and messages are required to be archived\u2014and for how long\u2014according to retention policies and regulatory requirements.<\/p>\n

    3. Collaboration.<\/strong> Auto-categorization dramatically improves the ability of users to consume and properly apply internal research assets and intellectual property that can be leveraged elsewhere in the enterprise or for external consumption. It makes documents easier to find, dramatically improving collaboration, sharing and syndication of valuable content.<\/p>\n

    4. Social media\/brand management.<\/strong> Social media is a big part of the \u201cbig data boom.\u201d Highly unstructured, it lends itself to organization, grouping similar content together, finding related terms and instantly identifying new terms as they evolve.<\/p>\n

    5. Content management.<\/strong> Web content that\u2019s not categorized limits web visitors\u2019 ability to find what they\u2019re looking for, and limits the publisher\u2019s ability to monetize valuable content (even if it\u2019s crowdsourced content). Auto-categorizing web content as it\u2019s produced enables web publishers to apply far richer categories to content than humans typically do and is far more cost effective.<\/p>\n

    6. Databases (ERP, CRM, etc.).<\/strong> Unstructured content exists even within structured databases. Concept-aware analytics engines provide users with a far more effective way to slice through mountains of unstructured content in databases and organize it for greater business value and more informed business decisions.<\/p>\n

    7. Records management.<\/strong> Platforms that categorize company records have been exposed to the challenges of unstructured big data for some time. Concept-aware categorization can be applied to records management as a more effective way to conceptually group or tag documents and apply the appropriate policies to those document sets. Whether the policy relates to archival, defensible deletion, retention, sharing, restricting or any other action, concept-aware categorization analytics engines can help address records management challenges in a highly effective, cost-efficient way.<\/p>\n

    8. Security, privacy and forensics.<\/strong> Content that either no longer has value for the organization or is not marked for retention through compliance could be an unnecessary liability. Sensitive customer data, such as medical records, Social Security numbers, credit card numbers or (worse yet) illicit materials are a virtual time bomb. Concept-aware auto categorization can reduce risks by enabling you to identify these materials, dispose of them in a highly defensible way and demonstrate that your company\u2019s information-governance policies are enforceable and consistent.<\/p>\n

    Despite the hype around big data, it poses both challenges and potential benefits for your customers. The pace at which big data is increasing is nothing short of mind boggling, creating market opportunities that are increasing in size daily.<\/p>\n

    Turning unstructured big data into reduced risk, reduced cost and increased value depends on visionary product teams who have identified customer challenges and have made a commitment to addressing these challenges with innovative solutions.<\/p>\n

    Concept-based auto-categorization has proven itself as a highly effective, extremely fast and incredibly precise approach. The possibilities are endless for applying it to big data to address its major obstacles and to harvest its broad benefits.<\/p>\n","protected":false},"excerpt":{"rendered":"

    How to use text-analytics engines to your customers’ advantage<\/p>\n","protected":false},"author":255,"featured_media":9004111222545962,"menu_order":0,"template":"","categories":[9004111222501021,1],"tags":[],"content-series":[],"content-format":[9004111223037711],"framework-box":[],"vertical":[],"ppma_author":[1184],"class_list":["post-9004111223059983","resources","type-resources","status-publish","has-post-thumbnail","hentry","category-product-marketing","category-uncategorized","content-format-article","author-steven-toole"],"yoast_head":"\nBig Data: How Do You Tackle it? | Pragmatic Institute<\/title>\n<meta name=\"description\" content=\"The possibilities are endless for applying it to big data to address its major obstacles and to harvest its broad benefits.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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