Big Data Analytics is the discipline of examining and deriving understanding from large sets of raw data sources. It involves qualitative and quantitative approaches, the goal of which are to boost productivity and business advancement. The process involves extraction and categorization of data in an effort to distinguish and scrutinize behavioral data and configurations, with techniques differing in accordance with structural requisites. The employment of data analytics is most prevalent in business-to-consumer usages. Large corporations accumulate and analyze data related with consumers, corporate procedures, marketplace economics or practical involvement. The data is examined and analyzed in an effort to understand buying movements and patterns.

In its earliest forms, the science of data analytics has existed since the 1950s, when manual examinations of spreadsheets were conducted for examining market insights and trends. Now days, data analytics offer speed and efficiency for companies and organizations in making critical and urgent decisions to stay ahead of the competition. For organizations, the importance of big data analytics lies in discovering fresh opportunities and thus, more well-informed decisions, leading to smoother operations, increased profits and a more satisfied consumer base. In a study conducted, Tom Davenport, Director of Research at the Institute of Internal Auditors (IIA) discovered that businesses derive value through data analytics by achieving cost reduction, making improved and quicker decisions and creating new products and services.

For years now, IBM has moved away from lower margin and commoditized hardware businesses and has shifted its focus towards being a global technology leader in producing innovative, cutting-edge tools to implement and conduct data analytics. Since, IBM’s central focus has been upon providing hands-on applications to companies for converting raw data into practical insight, by using analytics via the cloud. IBM is proficient in most analytics functions, including risk mitigation, market opportunity and performance management. IBM’s data analytics tool range encompasses several important categories, including business intelligence, data aggregation, data management, decision management, analytic modeling and counseling.

IBM’s major investments have lately been directed towards the following areas:

  • Innovative data analytics solutions for industries
  • Modern, innovative measures to counter data security challenges
  • Remodeling enterprise IT for cloud
  • Adaptation of work for mobile and social applications
  • Setting up a new foundation for the modern social-friendly age

In 2011, IBM introduced the Watson technology platform, which utilizes Natural language processing (NLP) along with machine learning to uncover insights from huge quantities of raw data. IBM’s Watson technology has since then been utilized by many practices, including business, developers, education institutions, healthcare, telecommunications and more. Watson refers to massive amounts of information, which include dictionaries, encyclopedias, thesauri, literary works and newswire articles, as well as databases, taxonomies and ontologies, for deriving results.

Initially, Watson was used to answer questions in the game quiz show Jeopardy, and in 2011, it competed against, and defeated experts like Brad Rutter and Ken Jennings. As Ken Jennings observed “The computer’s techniques for unravelling Jeopardy! clues sounded just like mine. That machine zeroes in on keywords in a clue then combs its memory (in Watson’s case, a 15-terabyte databank of human knowledge) for clusters of associations with those words. It rigorously checks the top hits against all the contextual information it can muster: the category name; the kind of answer being sought; the time, place, and gender hinted at in the clue; and so on. And when it feels “sure” enough, it decides to buzz. This is all an instant, intuitive process for a human Jeopardy! player, but I felt convinced that under the hood my brain was doing more or less the same thing.”

IBM Watson Analytics enables users to analyze and comprehend data using cloud-based guided analytics, data visualization and predictive analysis. Watson also makes available intuitive data discovery service via the cloud, administrating data exploration, automating predictive analysis and empowering easy dashboard and infographic formation.

IBM Cognos was the next-generation venture in a data analytics, business intelligence driven, web-based suite that offers reporting, analytics, scorecarding, and supervision of events and metrics, fulfilling varying information necessities of a business. Cognos can be used to achieve a more comprehensive view of a business and increased intuitive abilities that can be utilized to make dashboards, reports and data visualizations. Cognos’ AI assistant enables the user to investigate data offering natural language capabilities and exhibiting statistical emphasis in a user-friendly dialogue. Cognos also does away with human bias by disclosing unique data patterns, while upholding an administrated data environment.

Another IBM product offering, for example, is their SPSS suite of products that provide the following capabilities:

  • Statistical analysis and reporting – Tackles the complete analytical development, using planning, data assemblage, analysis, reporting and deployment.
  • Predictive modeling and data mining – Employs robust model-building, assessment, and automation proficiencies.
  • Decision management and deployment – Initiates analytics with cutting-edge model management and investigative decision management on prem/cloud/hybrid.
  • Big data analytics – Examines big data to obtain prognostic understanding and creates efficient business strategies.

Then there is the IBM Cloud Data Services offering managed services for data and analytics. Cloud data services offer an amalgamated, open-sourced method for app developers, data scientists and IT architects to tackle their data-focused requirements. Cloud data services afford flexibility and scalability to web and mobile applications. It also addresses the ever-increasing connectivity and modern social media savvy features that are cloud-based, by offering a suite of intuitive analytical tools to manage business infrastructure and time constraints. Cloud based services also updates businesses’ online transaction processing (OLTP) databases and data warehouses into an amalgamated cloud architecture.