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Big data analytics

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Big data analytics

  1. 1. Name of the Book:Understanding Big Data: Analytics for Enterprise Class Hadoop and streaming Data Author:Paul C. Zikopoulos, Chris Eaton, Dirk deRoos, Thomas Deutsch, George Lapis and Steven Sit. Date:
  2. 2. Introduction • Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every year. some researchers states that Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. Which makes it extremely important to at least know the basics of the field of big data and data analytic and knowing the difference between the two terms.  source: Big Data: 20 Mind-Boggling Facts Everyone Must Read, Forbes magazine
  3. 3. Plan 1. What Is Big Data? 2. Why Is Big Data Important? 3. What Is Data Analytic? 4. Where Is It Used ?
  4. 4. What IsBigData? • the term Big Data applies to information that can’t be processed or analyzed using traditional processes or tools.
  5. 5. WhyIs Big Data Important? Key Big Data Principles Big Data solutions are ideal: 1. For analyzing not only raw structured data, but semistructured and unstructured data from a wide variety of sources. 2. When all, or most, of the data needs to be analyzed versus a sample of the data 3. For iterative and exploratory analysis when business measures on data are not predetermined. 4. Aalysis and achieving synergy with existing solutions for better business outcomes.
  6. 6. Data Analytics involves automating insight into a certain dataset Big Data is something that can be used to analyze insights wich can lead to better decision and stratigic busness move
  7. 7. What Is DataAnalytic? Analytics Is in Our Blood: o I need to go to campus faster! o Hmm.. Looking at the sky today, I think it’ll be rain
  8. 8. What Is DataAnalytic? Data analytics (DA) is the process of Examining, Inspecting, Cleansing, Transforming, and Modeling data s in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software like “SAP Analytics”, “Google Analtics”, “Microsoft power BI”…
  9. 9. Analytics Lifecycle Explore Elaborate Model Plan Model Use Prepare Data Comunicate •Explore the avaible data •Formulate the questions •Determine the necessary Data •Collect Data •Clean Data •Formulate the questions •Determine wich variables to explain & wich one sto predict •Select the possible model and algorithmes •Implement the model •Validate it after choosing the best one •Interpret result •Make decision based on results •Goals to reach  Source: Schmarzo(2012):Data analytic lifecycle
  10. 10. Data Analytics Domains
  11. 11. Unsupervised analytics :when we don’t know the truth about something in the past. The result is segment that we need to interpret Example: We want to do segmentation over the student, based on the historical exam score, attendance, and late history. Supervised analytics: when we know the truth about something in the past Example: we have historical weather data. The temperature, humidity, cloud density and weather type (rain, cloudy, or sunny). Then we can predict today weather based on temp, humidity, and cloud density today TypesOf Analysis Do we need only descriptive analysis? Or we need to go with predictive analysis? Predictive Method
  12. 12. TypesOf Analysis Do we need only descriptive analysis? Or we need to go with predictive analysis? descriptive analysis does exactly what its name implies to "describe" or summarize the raw data and make them interpretable by humans. Descriptive Method
  13. 13. TypesOf Analysis Do we need only descriptive analysis? Or we need to go with predictive analysis? Examines data or content to answer the question “Why did it happen?”, and is characterized by techniques such as data discovery and data mining. Diagnostic Method
  14. 14. Conclusion Big Data Analytics is a hot research topic among the database researchers as well as the business community. However, currently we have different methods to analyse big data which we have mentioned in our paper but there is a lot of scope to create or invent new method of analytics. There are different tools and open source software available. Some of which we have mentioned briefly in the paper. There is a scope for the future research to compare the tools and find out the best in a particular situation by applying it. Also new can always be searched and invented. There are many more issues which can be further investigated like: Big data privacy and security, completeness, Data Quality etc
  15. 15. References

Notas do Editor

  • Data coming from various human & machine activity

    Big data is just data with:
    More volume
     
    Faster data generation (velocity)
     
    Multiple data format (variety)
  • For example, typically, data bound for the analytic warehouse has to
    be cleansed, documented, and trusted before it’s neatly placed into a
    strict warehouse schema


    When you stop and think about it, it’s little wonder we’re drowning in
    data
  • When you stop and think about it, it’s little wonder we’re drowning in
    data
  • When you stop and think about it, it’s little wonder we’re drowning in
    data
  • ANALYTICS IS IN YOUR BLOOD
    As human being we always try to predict and analyse the world around us so we applicate the same thing for data
  • with the goal of discovering useful information
  • When you stop and think about it, it’s little wonder we’re drowning in
    data
  • Telecom: They are one of the most important contributors to Big Data. The telecommunications industry improves the quality of service and routes traffic more efficiently. By analyzing real-time call data records, these companies can identify fraudulent behavior and act immediately.
    Education:By using Big Data technology as a learning tool instead of traditional conferencing methods, we have improved student learning and helped teachers better track their performance.
    Healthcare: Health care uses massive data analysis to reduce costs, predict epidemics, prevent preventable diseases, and improve quality of life in general.
    banks and financial services companies use Analytic systems to suggest immediate actions, such as blocking irregular transactions, stop fraud before it happens, and improve profitability.
  • Whay might happen in the future
    There is 2 types of
  • What’s happening now ?
  • Diagnostic analytics takes a deeper look at data to attempt to understand the causes of events and behaviors. .
  • When you stop and think about it, it’s little wonder we’re drowning in
    data
  • When you stop and think about it, it’s little wonder we’re drowning in
    data

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