Why does big data analytics means big gains?

Everything we do online leaves a digital trail, which results in humongous amounts of data and information. With increasing cyber activity all over the world, the data being collected at every level is increasing manifolds. Most of it goes unused. But when processed and analyzed correctly, this big data can help enterprises and businesses in amazing ways including effective marketing, new opportunities for revenue generation, better customer service, competitive advantages and a lot more.

According to TechAmerica Foundation, “Big data is a term that describes large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.”

Most of the big data being collected all over is not being completely utilized. Data is only valuable if correct information is extracted out of it. Traditional data analytics or management systems are not equipped to handle huge amounts of data. This is where big data analytics comes into the picture. Big data analytics is a process using which large data sets are examined, through which hidden patterns, market trends, customer preferences, unidentified correlations, and a lot more is uncovered.

Big data is a term that is used to describe huge amounts of structured, semi-structured, and unstructured data. The main challenge of big data analytics lies in its three characteristics or three V’s namely volume, velocity and variety. Such huge or voluminous data comes from numerous different sources, existing in a variety of file types. Velocity refers to the speed at which this data is being generated, and also the speed at which it needs to be analyzed and acted upon. It requires the analyst to have a detailed understanding of the available data along with a fair idea of what results or answers are being looked for. Cost-effectiveness for an enterprise to carry big data analytics is a big challenge. Another major challenge is the dependence on human analysts to understand and process the data. There’s a dearth of experienced analysts and data scientists who have worked extensively with big data.

Data in itself is just a big mass of information but processing it gives it meaning and shape. The result can be a source of immense knowledge. Enterprises can gain rare insights into the business and can in turn take more informed actions and decisions along with gain cost-efficiency in the long run. Creating new products to perfectly meet the needs of the customers is another advantage a business can gain through big data analytics. Every sector, be it retail, hospitality, health care or even government agencies are using big data analytics to leverage business.

With increase in data there’s also a leap in the ability to organize and analyze it. For achieving these goals there’s an increasing need of well-trained and experienced data analysts, data scientists, and also data engineers. There are numerous software, programming languages, and processes that aid data processing.

As far as career opportunities are concerned, big data analytics offers a sea of opportunities for well-trained and driven individuals.

Source: greatlearning.in

7 familiar delusions regarding Big Data

Now Big Data is making an entry in our lives, we seem to be confronted with new dilemmas, unknown until recently. We suddenly find ourselves facing a variety of factors we need to take into account, the most familiar being volume, variety and speed.

For everything that ‘Big Data’ is, it is possible to come up with something that it is not. Here are six myths plus one bonus about Big Data.

Big data is:

1. New

Alas —Big Data is not a brand new hype. It may well be the oldest hype of the moment. The first research papers on Big Data and Big Data visualization date back as early as 1997, 1998 and 1999. These reports revolve around topics still current today: insight and visualization.

2. Objective

The statistics never lie. We all know the phrase. But although data may not lie, it does not have to be objective per se. A simple example: last year, Twitter users posted some 20 million tweets on hurricane Sandy. Enough to form a picture of the reactions and emotions of the average US citizen, right?

3. Does not discriminate

Data is not color blind if that’s what you thought. You can see this reflected daily if you are a Facebook user. Although the profiles and timelines on the social network are stripped of information such as names and exact residence locations within the context of data anonymization, special algorithms based on cross references can determine the race of the user behind a Facebook profile with 95%accuracy.

4. Results in smarter cities

More sensors in the road surface, new smart meters all across the street environment and all data open to everyone. It makes our cities smarter. Or maybe not. The thing is that the information that becomes available only becomes smart when it is read and used intelligently. Although we increasingly measure things, quality should supersede quality.

5. Anonymous

This is a hairy topic given the recent debate about Internet monitoring. But before people even mentioned Prism, Nature had already published a report on this in October 2012. Based on an anonymized overview of 1.5 million phone calls, investigators could narrow down a conversation to an individual with a 95% certainty by looking at just four fixed data points. Just think about it: four data points, 95 per cent.

6. An opt-out

As with newsletters, you would expect that you could indicate not wanting to participate in the new data mining processes made possible by the huge data sets currently generated. Nothing could be further from the truth. For example, last December Instagram modified its terms and conditions so that it can share our photographs on a broader scale.

The new gold

I’ve said this so often that it may well be a good idea to lay it down in pixels for once and for all: Big Data is not the new gold. If I have to summarize it in a catchy phrase, I would say Big Data is the new oil. Crude oil, in fact, that smart refinement can transform into a fuel for all sorts of engines.

 

Why Big Data Analytics is a career defining opportunity?

Big Data is everywhere and there is almost an urgent need to collect and preserve whatever data is being generated, for the fear of missing out on something important. There is a huge amount of data floating around. What we do with it is all that matters right now. This is why big data analytics is in the frontiers of IT. Big Data Analytics has become crucial as it aids in improving business, decision makings and providing the biggest edge over the competitors. This applies to organizations as well as professionals in the Analytics domain. For professionals, who are skilled in Big Data Analytics, there is an ocean of opportunities out there.

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The potential of Big Data is in its ability to solve business problems and provide new business opportunities. So to get the most from your Big Data investments, focus on the questions you would love to answer for your business. This simple shift can transform your perspective, changing big data from a technological problem to a business solution.

Big data can be analyzed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, data mining, text analytics and statistical analysis. Mainstream BI software and data visualization tools can also play a role in the analysis process. Potential pitfalls that can trip up organizations on big data and analytics initiatives include a lack of internal analytics skills and the high cost of hiring experienced analytics professionals.

What Are the Benefits of Big Data?

This infographic from Informatica walks through the risks and opportunities associated with leveraging big data in corporations.

  1. Big Data is Timely
  2. Accessible
  3. Holistic
  4. Trustworthy
  5. Relevant
  6. Secure
  7. Authoritative
  8. Actionable