Fast growing data

You are probably already feeling some of the impact of living in a data-driven world. You rarely come across a Google search that doesn’t answer your question, and often enough you find more than enough information to write your own tome on any topic you can imagine.

Moreover, your hard drive is probably filled with so much data accumulated over the years that you might wonder what would happen to all your data if it crashed and you hadn’t backed up everything. Fortunately, hard drive data recovery experts can quickly resolve this issue.

In four years from now, there will be 5,200 GB of data per person. International Data Corporation, a research group, believes that there will be 50 times more data in the next decade

When computers were invented, experts first talked about bytes, which is 8 bits, then kilobytes, megabytes, gigabytes, terabytes, petabytes, exabytes and now they are talking about zettabytes((1 000 000 000 000 000 000 000 bytes). For instance, this year, Internet traffic is estimated to be about 1.3 zettabytes.

Why Data Science?

To bring out valuable insights out of such huge data we need Data Science.

It’s been said that Data Scientist is the“sexiest job title of the 21st century.” Why is it such a demanded position these days? The short answer is that over the last decade there’s been a massive explosion in both the data generated and retained by companies, as well as you and me. Sometimes we call this “big data,” and like a pile of lumber we’d like to build something with it. Data scientists are the people who make sense out of all this data and figure out just what can be done with it.

Facts & Preditions

A 2013 McKinsey report predicted that by 2018, there would be a shortage of 190,000 data scientists in the United States, and a shortage of 1.5 million analysts capable of doing something about the big data flood headed their way.

IBM Predicts Demand For Data Scientists Will Soar 28% By 2020. The number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000.

The data analytics market will soon surpass $200 billion.

Why Data analytics is the FUTURE of everything?

How Data Scientists can bring the change?

Data science can definitely add value to business by the addition of statistics and insights across workflow, be it hiring new candidates to helping senior staff make better and informed decisions. Data science can add value across all industries.

1

Empowering management and officers to make better decisions

2

Data Scientists direct the actions based on trends which in turn help in defining goals

3

Data Scientists challenge the staff to adopt the best practices and focus on the issues that matter

4

Identifying opportunities

5

Decision making with quantifiable data driven evidence

6

Testing these decisions

7

Identification and refining of target audiences

8

Recruiting the right talent for the organization

How Data Science/Analytics makes a difference

Pinpoint

They are used to identify trends, patterns and anomalies that help to explain why business practices don’t yield the results that they should. They are used to pinpoint hard to find process fallouts and blockages in seconds and subsequently, identify areas for business improvements.

Predict

They are used to predict ahead of time where the next issue is likely to occur where agreed contingency or mitigation plans could be executed. They are used to empower decision makers with options and ‘what-if’ choices to take action on possible turn of events.

Big Data Processing

Big Data Processing

Big Data processing is applied in-conjunction with analytics to help accelerate time to results, reduce risk exposure, predict earlier and mitigate events ahead of time to guarantee customer service levels and end user experiences.

Analytics Offerings Options

Data

  • Centralized, Distributed
  • Structured, unstructured
  • Machine-generated, People-generated
  • Internal, External
  • Descriptive analytics
    • What happened?
  • Diagnostic analytics
    • Why did it happen?
  • Predictive analytics
    • What could happen?
  • Prescriptive analytics
    • What could we do?
  • Decision Automation

Decision

  • Real-time, Batch
  • Standard, Custom

Action/Impact

  • Financial, Non-financial
  • One-time, Ongoing