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Five Myths That Shroud Big Data

Five Myths That Shroud Big Data

Understanding the myths around big data is key to gaining an advantage over competitors, writes Jean-Marc Bonnet,
 consulting architect, EMEA, Teradata Corporation.

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Myth #3: Big data is only for tech companies like Google, Facebook, and Amazon

Whether a company is a large internet firm, a Fortune 500 or smaller organization, the explosion of big data matters. Operating a business today without serious insight into business data is simply not an option. It is all about competitive advantage, regardless of the industry. Competitive advantage depends on the ability to manage and analyze all the critical data entering a business environment.

Organizations in every major market are turning to a new generation of advanced analytic applications that leverage huge volumes of data in new ways to provide deeper, smarter insights. The new world with different types of big data requires deep analytic insights gathered from many data sources and large volumes of data. These insights enable data-driven business decisions that can deliver competitive advantage with less work and lower costs.

Myth #4: Data scientists and big data analytics are 2012’s IT fad

Big data analytics is not a one-time event – it’s here to stay. According to Tim O’Reilly, founder of O’Reilly Media, “We are at the beginning of an amazing world of data-driven applications. It’s up to us to shape the world.” Data scientists, now an established profession, are at the forefront of shaping the business world, and data-savvy business professionals are required as a part of the new era.

The data scientist must be curious with an investigative mind, highly motivated, and a critical thinker. They often combine a deep understanding of business processes with mathematical, statistical, and technical skills like the use of Excel, SQL, and analytical workbenches. There is incredible demand for these professionals who blend business acumen with technical know-how.

Myth #5: The value of big data resides with the technical processing capabilities of Hadoop and similar software.

There isn’t one single technology that does it all. Building big data analytic capabilities requires the right mix of people, processes, and a variety of technologies depending on the business problem that the organization is working to solve. Unlocking the business value hidden within the data is the key. This requires sophisticated analytic applications, and some of the new, sophisticated applications include digital marketing optimization, fraud detection and prevention, and social network analysis.

Hadoop adds value and has its place in the big data technology arsenal. Hadoop is a framework, and is a very good platform for filtering, transforming, and consolidating multi-structured data. It can be compared to the frame of a sports car without an engine or body. The sports car frame can support the engine, body and other components like Hadoop supports the data and enables on-the-fly data exploration and analysis to rapidly uncover new and changing patterns in data.

The Key to Success

The key is to integrate new types of data with traditional business data that businesses already have. By opening up access to the entire corporate ecosystem and incorporating data from all sources, businesses can use big data analytics to achieve a super-charged view of the customer to improve customer service and sales.

 

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