Unstructured Data, Opinions, and SAS

If you are a Twitter or Facebook user, you must be familiar with the barrage of opinions that flows in with every major or minor happening in the real world. You must be also familiar with the trend of “viral” content which may be an article, a video, an audio, an infographic, or even a single picture.

Two of the major mouthpieces of the general public and the public figures alike, Twitter and Facebook are two platforms where people come out to voice themselves on any issue that matters to them. Twitter has 288 million active monthly users and counting while Facebook has 1.35 billion and up. Perhaps even imagining the data that these two create is difficult. And these are just two of the popular social media platforms, we are not even talking about the likes of Instagram, Pinterest, Linkedin, Google+, and more.

So, what happens to all the data that is created at these places? Does it just get swallowed up into the endless chasm of virtual reality? Is it just useless after a new day on the timeline begins?

Well, although the amount of data is humongous (that’s why it’s called Big Data), it is certainly not useless. At least not for business organizations, think tanks, research organizations, government agencies and anyone else for whom keeping a track of public opinion, and public actions is important.

For businesses, the tweets, posts, blogs, customer reviews, comments and similar inputs that build up unstructured data is a goldmine waiting to be exploited. This textual data is actually the way to understanding public sentiment about a particular product, service, or event that they have offered and use this sentiment to make future business decisions to improve operations and performance.

If you are a public figure, say a politician, it is possible to know how many people across the world support the statement you made last night in that event, and that will tell you whether your ideology connects with people or if they going to blast you if you continue going down the same path.

The text mining and analytics tools offered by SAS give the power to collect unstructured data and prepare it for analysis, to gain insights and actionable instructions. In light of the expansive and fastidious need of analytics in almost every domain, it has become imperative for professionals looking forward to make a leap in their career to undergo training regarding such tools and obtain relevant certifications.

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Emergence of Big Data Systems and Hadoop

To better understand the market drivers related to Big Data, it is helpful to first understand some past history of data stores and the kinds of repositories and tools that were used to manage these data stores. Most organizations analyzed structured data in rows and columns and used relational databases and data warehouses to manage large stores of enterprise information. The preceding decade saw a proliferation of different kinds of data sources — mainly productivity and publishing tools such as content management repositories and networked attached storage systems — to manage this kind of information, and the data began to increase in size and started to be measured at petabyte scales.

In the 2010s, the information that organizations try to handle has broadened to include many other kinds of data. In this era, everyone and everything is leaving a digital footprint. Organizations and data collectors are realizing that the data they can gather from individuals contains intrinsic value and, as a result, a new economy is coming forth.

As this new digital economic system continues to develop, the market sees the introduction of data vendors and data cleaners that use crowd sourcing to test the outcomes of machine learning techniques. Other vendors offer added value by repackaging open source tools in a simpler way and bringing the tools to market. Marketers such as Cloudera, Hortonworks, and Pivotal have provided this value-add for the open source framework Hadoop. It represents another example of Big Data innovation on the IT infrastructure.

Apache Hadoop is an open source framework that allows companies to process vast amounts of information in a highly parallelized way. It is an ideal technical framework for many Big Data projects, which rely on large or unwieldy datasets with unconventional data structures. One of the main benefits of Hadoop is that it employs a distributed file system, meaning it can use a distributed cluster of servers and commodity hardware to process large amounts of data.

Some of the most common examples of Hadoop implementations are in the social media space, where Hadoop can manage transactions, give textual updates, and develop social graphs among millions of users. Twitter and Facebook generates monolithic amounts of unstructured data and use Hadoop and its ecosystem of tools to manage this large amount of data.

Big Data comes from myriad sources, including social media, sensors, the Internet of Things, video surveillance, and many sources of data that may not have been considered data even a few years ago. As businesses struggle to keep up with changing market requirements, some companies are finding creative ways to apply Big Data to their growing business needs and increasingly complex problems. As organizations evolve their processes and see the opportunities that Big Data can provide, they try to move beyond traditional BI activities, such as using data to populate reports and dashboards, and move toward Data Science-driven projects that attempt to answer more open-ended and complex questions.

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What exactly is Big Data?

Data is created constantly, and at an ever-increasing rate. Mobile phones, social media, medical imaging technologies — all these and more create new data, and that must be stored somewhere for various purposes. Devices and sensors automatically generate diagnostic information that are needed and kept in real time. Merely keeping up with this huge influx of data is difficult, but substantially more challenging is analyzing vast amounts of it, especially when it does not conform to traditional notions of data structure, to identify meaningful patterns and extract useful information. 

Although the volume of Big Data tends to attract the most attention; generally the variety and velocity of the data provide a more apt definition of Big Data. Big Data is sometimes described as having 3 Vs: volume, variety, and velocity. Due to its quantity and structure, Big Data can’t be expeditiously examined using only traditional methods. Big Data problems require new tools and technologies to store, manage, and actually benefit the business. These new tools and technologies need to enable creation, manipulation, and management of large datasets and the storage environments that house them.

However, these challenges of the data flood present the opportunity to transform business, government, science, and everyday life.  For example, in 2012 Facebook users posted 700 status updates per second worldwide, which can be leveraged to deduce latent interests or political views of users and show relevant ads. Facebook can also construct social graphs to analyze which users are connected to each other as an interconnected network. In March 2013, Facebook released a new feature called “Graph Search,” enabling users and developers to search social graphs for people with same kind of interest, people and shared locations.

Big Data is the data whose scale, distribution, diversity, and timeliness demands the use of new technical analytics and architectures to alter, enable, and unlock new insights sources of business value. Social media and genetic sequencing are among the fastest-growing sources of Big Data and examples of untraditional sources of data being used for analysis.

Big Data can come in multiple forms, including structured and non-structured formats such as financial data, text files, multimedia files, and genetic mappings. Contrary to much of the traditional data analysis performed by organizations, popular varieties of Big Data are either semi-structured or unstructured in nature, which requires a lot of engineering effort and tools to process it and analyze the same. Environments like distributed computing and parallel processing architectures that enable the parallelized data ingest and analysis the preferred approach to process such complex data.

Exploiting the opportunities that Big Data presents requires new data architectures, including analytic sandboxes, new ways of working, and people with new skill sets. These drivers are causing organizations to set up analytic sandboxes and build Data Science teams. Although some organizations are fortunate to have skilled data scientists, most are not, because there is a growing talent gap that makes finding and hiring data scientists in a timely manner difficult. Still, organizations such as those in web retail, health care, genomics, new IT infrastructures, and social media are beginning to take advantage of Big Data and apply it in creative and novel ways.

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What’s in Store for Data Scientists in 2015?

Since data science and big data are dynamic, fast-paced domains that are constantly evolving, the year 2015 has some goodies up its back as well as some dampeners.

There are quite a handful of things expected this year, with one of them being Hadoop getting translated into more production uses as companies look to utilize dark data sitting inside Hadoop systems that have already been used to derive values. There will be some unique apps that will be specialists in specific uses. Markets are going to be savvy about data transformation, horizontal analytical platforms enabling, features creation, operationalization, and model development. And as a result, the rush for hiring data scientists is going to continue.

Big data technologies will be broadening their horizons, and data science innovations will be dazzling board rooms and campuses alike. As businesses, educational institutes, as well as philanthropists acknowledge the importance of big data and data science in producing valuable insights in every domain, data literacy will be given ample focus to produce qualified professionals who can process insane amounts on data for the betterment of profit-based organizations as well philanthropic avenues like disaster relief. Clearly, data science courses are going to be much sought after.

Enterprises across domains like healthcare, energy, heavy industry, and education will realize the need of sharing data sources and are likely to lower their shields to bring together data assets and facilitate meaningful insights that will benefit the industry as a whole. Open data will be the new powerhouse.

As far as big data is concerned, it is common perception that any data is good as long it is huge. But there will be a conscious shift across industries where the game will now be data that’s connected to value. However, ethical data harnessing may be shamed by some who are not prepared to use the proper safeguards associated with utilizing data science and may end up losing in front of competitors who have thoughtful technical mechanisms in place.

Data scientists in 2015 are likely to see their skills more valuable than ever. For instance, this year may see companies transferring the key to the lockers for IT related purchases from chief information officers to chief data scientists. Moreover, there are expected to be in-house data scientists for individual departments rather than data scientists cloistering together in a separate department.


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