Why Big Data?
The term Big Data can be described as a large volume of data, both structured and unstructured. The term big data is quite new. even before it comes to a term, companies have been dealing with a large scale of data sets around for decades using spreadsheets, feedback forms, and graphs to track customer insights and trends. The only difference today is we have the right tools and technical experts to gain the benefits of big data.
What launches the term of Big Data…
Around 2005, when social media started to grow in popularity and Around 2010 there were 5 billion mobile phones in use people realized that how much data is generating every day. We can be sure that there are more data generating today and hope you can understand. Those billions of social media users in every social media platform like Facebook, Twitter, LinkedIn, etc generating around 2.5 million terabytes in a day. And phones, the apps we install on the phone also big sources of big data which is contributing to our core every time, every day. And Google now processes more than 40,000 searches every second (3.5 billion searches per day) on average!. Does it make you think about how many searches you have made in google last month, last year? All of these leads to projections of serious growth. 40% of global data per year, and 5% of global IT spending. This much data has sure pushed the data science field to start remaining itself and the business world of today.
So what makes big data valuable?
It’s the applications and how big data can serve human needs that makes it valued. Big data allows us to build better models, which produce higher precision results. We are witnessing hugely innovative approaches in how companies market themselves and sell products. How human resources are managed. How disasters are responded to. And many other applications that evidenced based data is being used to influence decisions.
As an example, all of you might have experience on YouTube that they keep details about videos that we have been watching and showing videos to watch next based on our interest and behavior of using YouTube. Which is narrowing down the huge raft of options that we have to go through. So as YouTube other businesses also can leverage technology to make better informed decisions that are based on signals generated by actual consumers. Big Data enables business people to hear the voice of each consumer as opposed to consumers at large.
And you might have noticed while using Facebook the advertisements which are popping up to you are based on the things you used to search via your browser and the things which are you sharing on posts and discussing in comments. This is how amazing big data works. Now, many companies, including Walmart and Target, Amazon use this information to personalize their communications with their costumers, which in turn leads to better met consumer expectations and happier customers. Which basically is to say, big data has enabled personalized marketing. Consumers are copiously generating publicly accessible data through social media sites, like Twitter or Facebook. Through such data, the companies are able to see their purchase history, what they searched for, what they watched, where they have been, and what they’re interested in through their likes and shares.
So by examining large scale and varied data sets so called big data, can uncover information such as hidden patterns. unknown correlations, market trends and customer preferences which can help organizations to make informed business decisions. This will leads organization to smarter business movements, more efficient operations with happier consumers and higher profits.
Thomas H.Davenport, In his report “Big Data in Big Companies” mentioned Companies got value from the big data in the following ways:
Using big data technologies such as Hadoop and cloud-based analytics companies can reduce their cost insignificant amount when storing large scale of data. also, they can identify more efficient ways of doing the business. Businesses can handle analyze information immediately with the speed of Hadoop and in-memory analytics together with their ability to identify new sources of data which helps businesses to make immediate decisions based on the learning. As an example, if a customer has changed their preferences targeted the services are likely to less effective. And the other thing an organization can do with big data is developing new products and service offerings based on data. Most of the online firms employ this approach because they have an obvious need to employ data based products and services. One of example is LinkedIn, they are using big data and data scientists to develop a huge list of product offerings and features, including people you may know, groups you may like, jobs may be interested in, who viewed my profile, etc. These offerings can attach millions of new customers to the LinkedIn. And finally, business decisions with big data can also involve other areas for analytics such as supply chains, risk management, or pricing. The reason which makes big data decisions make smarter is, use of the external data sources to improve the analysis. As an example, in the supply chain decisions companies are increasingly using external data to capture and measure supply chain risks.
Applications of Big Data
Big data has a huge potential to enable models with higher precision in many application areas. And these highly precise models are influencing and transforming business. I’ll mention only a few examples here. One area we are all familiar with is the recommendation engines. These engines leverage user patterns and product features to predict best match products for enriching the user experience. If you ever shopped on Amazon, you know you get recommendations based on your previous purchases and searches. Similarly, Netflix would recommend you to watch new shows based on your viewing history.
Another technique that companies use is Sentiment Analysis, or in simple terms, analysis of the feelings around events and products. As an example, in Amazon, I can read reviews before purchasing, and also, I can write a review. This way, other customers can be informed. The most important thing is Amazon can keep track of the product reviews and trends for a particular product. As an example, they can judge if a product review is positive or negative. Since these reviews are written in English or any other language, it uses a technique called Natural Language Possessing, And other text analytical methods. Likewise, Amazon can analyze the general opinion of a person or public about such a product. This is why sentiment analysis often gets referred to as opinion mining. News channels are filled with Twitter feed analysis every time an event of importance occurs, such as elections. Brands utilize sentiment analysis to understand how customers relate to their product, positively, negatively, neutral. This depends heavily on the use of natural language processing.
Mobile devices are ubiquitous and people almost always carry their cellphones with them. So Mobile advertising is been a huge market for businesses. Platforms utilize the sensors in mobile devices, such as GPS, and provide real time location based advertisements, offer discounts, based on this deluge of data. This time, let’s imagine that I bought a new house and I happen to be in a few miles range of a Home Depot. Sending me mobile coupons about paint, shelves, and other new home related purchases would remind me of Home Depot (One of the largest home improvement retailer). There’s a big chance I would stop by Home Depot.
Every business wants to understand their consumer’s collective behavior to capture the ever-changing landscape. Several big data products enable this by developing models to capture user behavior and allow businesses to target the right audience for their product. Or develop new products for uncharted territories. Consider this example. After an analysis of their sales for weekdays, an airline company might notice that their morning flights are always sold out, while their afternoon flights run below capacity. This company might decide to add more morning flights based on such analysis. Notice that they are not using individual consumer choices, but using all the flights purchased without consideration to who purchased them. They might, however, decide to pay closer attention to the demographic of these consumers using big data to also add similar flights in other geographical regions.
With rapid advances in genome sequencing technology, the life sciences industry is experiencing an enormous draw in biomedical big data. This biomedical data is being used by many applications in research and Personalized Medicine. Before personalized medicine, most patients without a specific type and stage of cancer received the same treatment, which worked better for some than the others. Research in this area is enabling the development of methods to analyze large scale data to develop solutions that tailor to each individual, and hence hypothesize to be more effective. A person with cancer may now still receive a treatment plan that is standard, such as surgery to remove a tumor. However, the doctor may also be able to recommend some type of personalized cancer treatment. A big challenge in biomedical big data applications, like many other fields, is how we can integrate many types of data sources to gain further insight into the problem.
Another application of big data comes from the interconnected mesh of a large number of sensors implanted across Smart Cities. Analysis of data generated from sensors in real time allows cities to deliver better service quality to inhabitants. And it helps to make daily lives better, such as managing traffic flow more efficiently or maximizing energy savings.
As a summary, it hasn’t been long since the advent of big data, but these attributes take us to a data era. Large organizations across industries are joining this data economy. Most companies are not keeping traditional and big data separate, but combining them to form a new synthesis. And finally, it’s important to remember that the primary value of big data doesn’t come from the raw form, but from the processing and analysis of it and the insights, products, and services that emerge from the analysis.
The motivation for this article is from the Coursera course series, “Big Data Specialization” by the University of California San Diego. If there is anything to add or change feel free to comment below.
