The era of Big Data has brought with it the landmarks of petabytes (1,024 terabytes) and exabytes (1,024 petabytes). Big Data refers to either the humungous amount of data present in structured and unstructured form which is ever-expanding or the technology required to be able to deal with data of this measure. Once the Big Data is laid out, predictive analytics are the strategists who analyze them to deduce the ongoing trends in eMarket and form forecasts about the upcoming events. The forecasts are displayed in probabilities. Depending on the domain, sources and method of research is selected. SAS (Predictive Analytics Suite), Microsoft (Microsoft Dynamics CRM Analytics Foundation) and IBM (IBM SPSS Statistics) are some of the companies which provide solution for Big Data and Predictive Analytics. One can choose between proprietary and open source technologies for data mining and predictive analytics. The five basic elements of any predictive analytics model are being shown in the presentation. Read the full blog here: http://suyati.com/5-steps-to-master-big-data-and-predictive-analytics/ To get in touch, write to us at: jghosh@suyati.com

2. Big Data refers to either the
humungous amount of data present
in structured and unstructured form
which is ever-expanding or the
technology required to be able to
deal with data of this measure.
What is BIG DATA?

3. The era of Big Data has brought with it the landmarks
of petabytes (1,024 terabytes) and exabytes (1,024
petabytes) and the future is yet to come.
How Big is BIG DATA?

4. Once you have the data in your hands, you
need to employ predictive analytics to gain
insight from the data to predict the
upcoming trends.
How Predictive Analytics help?

5. Since service providers are too many; it
is advisable to know the basic 5 steps
which should be included in every
predictive analytics model.
What’s Fundamental?

6. 1. Storm-Forecasts From Dust-Data
 Learn to differentiate Between
Relevant And Irrelevant Data

7. How to proceed?
 The overflowing data must
be sorted and categorized.
 Develop tools that make process
of collection and classification of
data easy.

8. 2. The Data-Spinal Column for Brain of Analytics
 Use Predictive Algorithms
than Blind Deductions for
accurate conclusions

9.  The ground for innovation in
predictive analytics lies not in
coming up with new formulas
but in emerging with
unprecedented forecasts.
 This is possible when the
data is strategized and
analyzed to furnish exactly
the kind of trend forecasts
the company needs.
How to proceed?

10. 3. Spearhead the Data-Army
 Position a Chief Data Officer to
execute the plans

11.  It is important to recruit a leader
who can direct the center on basis
of the predefined algorithms
sprinkled with a dash of ingenuity.
 The Chief Data Officer (CDO) will
be responsible for executing
the Big Data Model depending on
needs of enterprise.
How to proceed?

12. 4.Diagnosing the Stakes of Data-Move
 Have an idea about Supply
Chain Risk Management

13.  Follow up a description of your supply chain with self-
assessment of vulnerabilities, and lastly, the
evaluation of implication and identification of actions.
 See the bigger picture and recognize how your supply
chain fits into this.
How to proceed?

14. 5. The Anatomy of Your Analytic Model
 Selecting a Core Platform for
storing data and performing
analysis.

15.  The ideal platform would be a cloud-based one
such as AWS which will avoid the costs involved
in building from the ground your supply chain
management or in bringing together point
solutions.
 Gartner reports that through 2015, about 80%
of Fortune 500 companies will be disabled from
extracting benefits from big data.
How to proceed?

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18. For any queries on BIG DATA?

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