What is Big Data? Big data is a term that describes the large volume of data – both structured and unstructured

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What is Big Data?

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

While the term “big data” is relatively new, the act of gathering and storing large amounts of information for eventual analysis is ages old. The concept gained momentum in the early 2000s when industry analysts articulated the now-mainstream definition of big data as the four Vs:

Volume – Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.

Velocity – Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.

Variety – Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.

Veracity — Veracity refers to the trustworthiness of the data and to develop mechanisms that reduces the inherent discrepancies in all the data collected.

Why Big Data Matters?

All companies, without undermining their size or industry, should start looking into Big Data initiatives and cloud-based storage as cost-effective strategies to improve their business performance. This, no doubt, applies to utility companies since they are entering an era of new challenges in providing consumers with all the information they want at a fast pace to keep them satisfied in order to build strong customer relationships.

To cater to this huge demand, many companies have sprung up to offer services to other businesses, enabling them to launch big data initiatives of their own. In other words, to leverage the information they have available in order to improve productivity and efficiency, and ultimately increase profits. This also enables companies to minimize infrastructure investments for their big data initiatives or avoid them completely by using cloud-based storage and analysis tools that can be rented when needed.

How it works and key technologies

There’s no single technology that encompasses big data analytics. Of course, there’s advanced analytics that can be applied to big data, but in reality several types of technology work together to help you get the most value from your information. Here are the biggest players:

Data management- Data needs to be high quality and well-governed before it can be reliably analyzed. With data constantly flowing in and out of an organization, it’s important to establish repeatable processes to build and maintain standards for data quality. Once data is reliable, organizations should establish a master data management program that gets the entire enterprise on the same page.

Data mining- Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions. With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what’s relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.

Hadoop- This open source software framework can store large amounts of data and run applications on clusters of commodity hardware. It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop’s open source framework is free and uses commodity hardware to store large quantities of data.

In-memory analytics- By analyzing data from system memory (instead of from your hard disk drive), you can derive immediate insights from your data and act on them quickly. This technology is able to remove data preparation and analytical processing latencies to test new scenarios and create models; it’s not only an easy way for organizations to stay agile and make better business decisions, it also enables them to run iterative and interactive analytics scenarios.

Predictive analytics- Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It’s all about providing a best assessment on what will happen in the future, so organizations can feel more confident that they’re making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, risk, operations and marketing.

Text mining- With text mining technology, you can analyze text data from the web, comment fields, books and other text-based sources to uncover insights you hadn’t noticed before. Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.

Using big data analytics, 3rd Eye Advisory® was able to identify customer insights patterns for an organization in FMCG sector which helped them in better customer choice decision making, cost reduction and development of customer specific products and services.

www.3rdeyeadvisory.com

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