Gauri Chhabra
We live in the best of times, and the worst of times…to borrow from Charles Dickens.
That pretty much describes the data analysis time in which we live. The best part is businesses are flooded with huge quantities and varieties of data on one hand, and the ironically worst part- ever-faster expectations for analysis. The vendor community is responding by providing highly distributed architectures and new levels of memory and processing power. Upstarts also exploit the open-source licensing model, which is not new, but is increasingly accepted and even sought out by data-management professionals.
With thedata flow increasing across companies, there is a growing demand for a niche specialization among IT professionals called big data analysts. These are the people who know how to read, manage and optimize big data environments.
What is Big Data Analytics?
Simply put, big data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis.
Elaborately, big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Such information can provide (SCA) Sustainable Competitive Advantage over rival organizations and result in business benefits, such as more effective marketing and increased revenue.Using techniques such as text analytics, machine learning, predictive analytics, data mining, statistics and natural language processing,the businesses can study data to understand the current state of the business and track customer behavior.
Besides, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in leaner processes and effective decision making thus making it graduate from increasing wallet share to strategic thinking.
Skills and technology expertise:
Big data analytics can be done with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics and data mining.The unstructured data sources used for big data analytics may not fit in traditional data warehouses. Also, traditional data warehouses may not be able to handle the processing demands posed by big data. As a result, a new class of big data technology has emerged and is being used in many big data analytics environments. The technologies associated with big data analytics include NoSQL databases, Hadoop, Linux, Python, Hive and MapReduce. These technologies form the nucleus of an open source software framework that supports the processing of large data sets across clustered and diversified systems.
Key Platform Capabilities:
Hadoop-based analytics:nine-year-old open-source data-processing platform first used by Internet giants including Yahoo and Facebook, leads the big-data revolution that processes and analyzes any data type across commodity server clusters.
Stream Computing: Drives continuous analysis of massive volumes of streaming data with sub-millisecond response times.
Data Warehousing:Develops deep operational insight with advanced in-database analytics
Reality check –
How are companies using Big Data?
More and more companies are using Big Data to increase their Marketing Return on Investment.It is paying them through increased productivity and enhanced customer relationship management.
Gaining new insights:
Companies are building a data advantage by pulling in relevant data sets from both within and outside their domains. They have witnessed a paradigm shift from mass analysis of data to selective cherry picking where specific objectives are laid down and only a specific behavior of the consumer is analyzed. This helps them take tough decisions like cannibalizing their own brand or ditching one product in favor of another. Most sales leaders deploy resources, for example, on the basis of the current or historical performance of a given sales region.
P2P is the new B2B:
A decade ago, marketing activities were only confined to B2C. Today, there is no water tight compartmentalization and B2B transaction is in fact a person to person interaction so much so that companies face a double challenge- winning new customers and keeping existing customers from defecting. To stay competitive, they need to invest in Search Engine Optimization and Google Analytics to make sure potential customers are finding them, and social media monitoring to spot new sales opportunities. They need to analyze both the pre and post purchase behavior of the consumer and also his cognitive dissonance if any. For instance, the Retail giants tailor their offers and discounts based on predictions of how likely a valued customer areabout to defect and also by perceptually mapping it with competitors’ products.
Automated linear marketing:
Companies invest in an automated linear marketing that processes vast amounts of data, mines it and linearly uses it to create better and more relevant interactions with consumers. That can include predictive statistics, machine learning, and natural language mining. These systems track key words automatically, for example, and make updates every 10seconds based on changing search terms used. It can also make price changes across thousands of products based on customer preferences, price comparisons, inventory, and predictive analysis. For instance, banks capture and provide credit card payments and online fed next-product-to-buy steps to call centers which the front desk employees use to make suitable offers during the customer’s next interaction.
Career options:
Today, the term Big data analyst is used somewhat loosely and ubiquitously;however, it forks into three specialty fields -technologists, who write the algorithms and code to transverse the large amounts of data; statisticians and quantification experts; and artist-explorers, creative people who can navigate content and find something others don’t see.
A word of caution: People are just slapping buzzwords on résumés and looking to get 50 or 100 percent more, and they’re getting it. There are a number of people who can window dress their resume throughHadoopand call themselves big data analysts and they are polarized from the field.
Has academics caught up with the growing demand?
Universities are now waking up to the need for big data related curriculum. Coursera, which offers free online courses, holds a course on Web Intelligence and Big Data. Teradata has also recently announced free certification for candidates seeking analytic jobs. EMC too has been active in sponsoring certifications for the Business Intelligence.
The Road Ahead:
The Chief Operating Officer (COO) and co-founder of Technossus LLC,Giri Kalluri who also cofounded the resource center in Ludhiana opines,”With the total data growth expected to increase exponentially by 2020, there is going to be a surge for big data analysts and companies in India as well as US are feeling the talent crunch in the area”.
With BigData analysis becoming a do-or-die requirement for today’s businesses, now is the time to script a promising career ahead…
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