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Friday, March 11, 2016

Brushing mind with BigData and its problems.

Before getting started with BigData, 

I have to find the answer for what is Analytics and Insights?

This what google says ..

plural noun: analytics
  1. the systematic computational analysis of data or statistics.
    "content analytics is relevant in many industries"
    • information resulting from the systematic analysis of data or statistics.
      "these analytics can help you decide if it's time to deliver content in different ways"

Wiki says..
Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics,computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight.
Firms may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analyticsprescriptive analyticsenterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modelingweb analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.[1]

Analytics vs. analysis[edit]

Analytics is multidisciplinary. There is extensive use of mathematics and statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data—data analysis. The insights from data are used to recommend action or to guide decision making rooted in business context. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective.[citation needed]. There is an increasing use of the term advanced analytics,[citation needed] typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks to do predictive modeling.

Now what is statistics?
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data.[1] In applying statistics to, e.g., a scientific, industrial, or societal problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal". Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.[1]

  1. the capacity to gain an accurate and deep understanding of someone or something.
    "his mind soared to previously unattainable heights of insight"
    apprehensionappreciationcognizancepenetration,acumen, astuteness, perspicacity,
     perspicaciousness, sagacity,sageness, discriminationjudgement, shrewdness,
     sharpness, sharp-wittedness, acuity, acuteness, flair, breadth of view, vision, far-sightedness, prescienceimaginationMore

    • an accurate and deep understanding.
      plural noun: insights

      "his work provides important insights into language use"

What is BigData?
Big data is a buzzword, or catch-phrase, meaning a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and softwaretechniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity.
Despite these problems, big data has the potential to help companies improve operations and make faster, more intelligent decisions. This data, when captured, formatted, manipulated, stored, and analyzed can help a company to gain useful insight to increase revenues, get or retain customers, and improve operations.

Big Data: Volume or a Technology?

While the term may seem to reference the volume of data, that isn't always the case. The term big data, especially when used by vendors, may refer to the technology (which includes tools and processes) that an organization requires to handle the large amounts of data and storage facilities. The term big data is believed to have originated with Web search companies who needed to query very large distributed aggregations of loosely-structured data.

Big Data (huge unstructured data in petabyte and exabyte) has to be analysed  and gain the insights which can be to enhance marketing and sales etc.,
Because big data takes too much time and costs too much money to load into a traditional relational database for analysis, new approaches to storing and analyzing data have emerged that rely less on data schema and data quality. Instead, raw data with extended metadata is aggregated in a data lake and machine learning and artificial intelligence (AI) programs use complex algorithms to look for repeatable patterns. 
Big data analytics is often associated with cloud computing because the analysis of large data sets in real-time requires a platform like Hadoop to store large data sets across a distributed cluster and MapReduce to coordinate, combine and process data from multiple sources.


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