Big (and Smart) Data for Digital Globalization

Data is all around us whether we use it or we are part of it. More than another trend, data is the way to move with agility and make every step and achievement tangible for those who do not see or believe it. One of the most transformational and accelerating factors of digitization is precisely how data is considered, leveraged, valued, and distilled. As data mining is not new it has become more than just a back office type of activity. It is all about turning facts into more than facts, figures into more than figures, and content into more than content.

For digital globalization practitioners and leaders, data shines like a glittering prize. That is why they face similar challenges to all business leaders when it comes to making the most of data. With the world to conquer and a number of diverse audiences to engage, they have to transform big data into smart data to focus on what enables making–and avoids breaking–the digital experiences local customers require. Specifically they must pin down the right data at the right time in the content supply chain to convert it into reliable indicators and valuable assets in the long run. In addition, due diligence is required to cover the cost and efforts of funneling, acquiring, and maintaining data. While the amount, the nature, and the scope of data depend on digital globalization targets and priorities, several categories may help establish a good base line to identify smart data and agree on a starting point for global expansion.

  1. Customer understanding data-Ranging from general (e.g. census) to segmentation data these data enable you to bear in mind what customers do at all times as prospects, decisions, buyers, or users.
  2. Usage data-As typical performance data this remains crucial in any proper mix of smart data for digital globalization.
  3. Content effectiveness data-Capturing and measuring the real impact of content on experiences is tricky and must reflect the nature and ecosystem of the content.

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Big Data: The Latest Rage in Supply Chain Management

Early uses of big data were concentrated in two areas: customer segmentation/marketing effectiveness, and financial services, particularly in trading. Recently, supply chain has become the “next big thing.”

Why? A company’s supply chain is rich with data, and it’s also a large cost component. Combined, those facts mean that advanced analytics can become a strategic weapon for optimizing the supply chain.

However, many companies can’t see the forest for the trees. They are optimizing, but not strategically. When applying data to supply chain, it’s critical to step back and look at what truly drives business value.

“They’re Digging in the Wrong Place”

As every fan of “Raiders of the Lost Ark” knows, Indiana Jones found the Ark of the Covenant first. The Germans had far greater manpower and resources and they were more efficient, but they were competently digging a hole in the wrong place. The same goes for using big data in supply chain optimization. You could have the most efficient process in the world, but if you’re making the wrong amount of the wrong product, it will hurt your business.

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