Hadoop and Big Data

During last ten years the volume and diversity of digital information grew at unprecedented rates. Amount of information is doubling every 18 months, and unstructured information volumes grow six times faster than structured.

Big data is the nowadays trend. Big data has been defined as data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time.

Hadoop was created in 2005 by Doug Cutting and Mike Cafarella to address the big data issue. Doug Cutting named it after his son's toy elephant It was originally developed for the Nutch search engine project. Nutch is an effort to build an open source web search engine based on Lucene and Java for the search and index component.

As of 2013, Hadoop adoption is widespread. A number of companies offer commercial implementations or support for Hadoop. For example, more than half of the Fortune 50 use Hadoop. Hadoop is designed to process terabytes and even petabytes of unstructured and structured data. It breaks large workloads into smaller data blocks that are distributed across a cluster of commodity hardware for faster processing.

Ventana Research, a benchmark research and advisory services firm published the results of its groundbreaking survey on enterprise adoption of Hadoop to manage big data. According to this survey:

• More than one-half (54%) of organizations surveyed are using or considering Hadoop for large-scale data processing needs.
• More than twice as many Hadoop users report being able to create new products and services and enjoy costs savings beyond those using other platforms; over 82% benefit from faster analysis and better utilization of computing resources.
• 87% of Hadoop users are performing or planning new types of analysis with large scale data.
• 94% of Hadoop users perform analytics on large volumes of data not possible before; 88% analyze data in greater detail; while 82% can now retain more of their data.
• 63% of organizations use Hadoop in particular to work with unstructured data such as logs and event data.
• More than two-thirds of Hadoop users perform advanced analysis such as data mining or algorithm development and testing.

Today, Hadoop is being used as a:

• Staging layer: the most common use of Hadoop in enterprise environments is as “Hadoop ETL” — pre-processing, filtering, and transforming vast quantities of semi-structured and unstructured data for loading into a data warehouse.
• Event analytics layer: large-scale log processing of event data: call records, behavioral analysis, social network analysis, clickstream data, etc.
• Content analytics layer: next-best action, customer experience optimization, social media analytics. MapReduce provides the abstraction layer for integrating content analytics with more traditional forms of advanced analysis.
• Most existing vendors in the data warehousing space have announced integrations between their products and Hadoop/MapReduce.

Hadoop is particularly useful when:

• Complex information processing is needed.
• Unstructured data needs to be turned into structured data.
• Queries can’t be reasonably expressed using SQL.
• Heavily recursive algorithms.
• Complex but parallelizable algorithms needed, such as geo-spatial analysis or genome sequencing.
• Machine learning.
• Data sets are too large to fit into database RAM, discs, or require too many cores (10’s of TB up to PB).
• Data value does not justify expense of constant real-time availability, such as archives or special interest information, which can be moved to Hadoop and remain available at lower cost.
• Results are not needed in real time.
• Fault tolerance is critical.
• Significant custom coding would be required to handle job scheduling.

Does Hadoop and Big Data Solve All Our Data Problems?

Hadoop provides a new, complementary approach to traditional data warehousing that helps deliver on some of the most difficult challenges of enterprise data warehouses. Of course, it’s not a panacea, but by making it easier to gather and analyze data, it may help move the spotlight away from the technology towards the more important limitations on today’s business intelligence efforts: information culture and the limited ability of many people to actually use information to make the right decisions.

Benefits of Automatic Content Classification

I had few questions about my posts about automatic content classification. I would like to thank my blog readers for these questions. This post is to follow up on those questions.

Organizations receive countless amounts of paper documents every day. These documents can be mail, invoices, faxes, or email. Even after organizations scan these paper documents, it is still difficult to manage and organize them.

To overcome the inefficiencies associated with paper and captured documents, companies should implement an intelligent classification system to organize captured documents.

With today’s document processing technology, organizations do not need to rely on manual classification or processing of documents. Organizations that overcome manual sorting and classification in favor of an automated document classification & processing system can realize a significant reduction in manual entry costs, and improve the speed and turnaround time for document processing.

Recent research has shown that two-thirds of organizations cannot access their information assets or find vital enterprise documents because of poor information classification or tagging. The survey suggests that much of the problem may be due to manual tagging of documents with metadata, which can be inconsistent and riddled with errors, if it has been done at all.

There are few solutions for automated document classification and recognition. Some of them are: SmartLogic's Semaphore, OpenText, Interwoven Metatagger, Documentum, CVISION Trapeze, and others. These solutions enable organizations to organize, access, and control their enterprise information.

They are cost effective and eliminate inconstancy, mistakes, and the huge manpower costs associated with manual classification. Putting an effective and consistent automatic content classification system in place that ensures quick and easy retrieval of the right documents means better access to corporate knowledge, improved risk management and compliance, superior customer relationship management, enhanced findability for key audiences and an improved ability to monetize information.

Specific benefits of automatic content classification are:

More consistency. It produces the same unbiased results over and over. Might not always be 100% accurate or relevant, but if something goes wrong, it is at least is easy to understand why.

Larger context. Enforces classification from the whole organizations perspective, not the individuals. For example, a person interested in sports might tag an article which mentions a specific player, but forget/not consider a team and a country topic.

Persistent. A person can only handle a certain number of incoming documents per day, whilst an automatic classification works round the clock.

Cost effective. Possible to handle thousands of documents much faster than a person.

Automatic document classification can be divided into three types: supervised document classification where some external mechanism (such as human feedback) provides information on the correct classification for documents; unsupervised document classification (also known as document clustering) where the classification must be done entirely without reference to external information, and semi-supervised document classification where parts of the documents are labeled by the external mechanism.

Automate your content classification and consider using manual labor mainly for quality checking and approval of content.

In my next post on this topic, I will describe the role of automatic classification in records management and information governance.