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Analytics 3.0 and Data-Driven Transformation

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The advent of mobile, the Internet of Things, and cloud has reinforced the need for a new era of analytics to solve challenges in the customer, product, operations, and marketing domains. And new niche startups armed with data and digital weapons are all set to shake up the market with a wave of digital disruptions. The established companies need to restructure their business and technology landscape. Such transformation helps them to increase their sales and is a matter of survival in the current market conditions, where “data and digital” is the new word of mouth.

A complete data-driven transformation is nearly impossible unless the organization is prepared to deconstruct old-school methodologies and pave the way for establishing a data culture. Like business transformation, data-driven transformation must provide an end-to-end solution. Data-driven transformation calls for a This is especially important for companies that have IT and marketing services departments heavily outsourced because stringent service-level agreements with vendors often kill innovation and don’t provide the space to take risks and “fail fast.”

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Treat your Data as your Company’s Most Valuable Asset

To be most effective and efficient, methodologies for combining business and technology transformations require both initiatives running in parallel and complementing each other.

Gartner Research statistics show that data-driven transformation can be initiated beyond the IT domain. The 2015 percentage increase in business unit heads driving initiatives that formerly were championed by a small group of enthusiasts indicates that companies are recognizing the value of big data. Big data is no longer just another IT project. Companies seeking to gain a foothold as a market leader benefit when everyone gets involved.

Why Companies Have Struggled with Traditional Analytics

Analytics has evolved in three stages: Analytics 1.0 was data warehousing and business intelligence; Analytics 2.0 was big data, Hadoop, and NoSQL. We are in the era of Analytics 3.0, when tools make decisions and measure the impact. A true data-driven transformation requires moving to Analytics 3.0.

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Prior eras utilized traditional description analytics and data warehousing. The problem with this approach is that descriptive analytics is the postmortem analysis: It tells what happened before. In Analytics 3.0, prescriptive analytics (what should I do?) minimizes human input and eliminates descriptive analytics (what happened?), using technology to facilitate decision support and automation. This creates disruption and allows companies to catapult ahead of the competition.;

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