Disney, Depp and the cyber supply chain risk management problem

One field-tested security strategy for information systems and digital content is to address the problem through processes, people and technology. On the process front, all companies involved in the production of digital IP should, by now, be adhering to a proven information security framework that fully addresses supply chain risks. That includes making sure your digital IP is protected at all times, even during post-production (or maybe we should say especially during post-production, given recent incidents).

Fortunately, there is a ready-made cybersecurity framework that companies can use, at no charge, thanks to the US federal government, which has done some sterling work in this area, namely the NIST Cybersecurity Framework.

The current version is a great way to get a handle on your organization’s cybersecurity, and the next version, currently in draft, goes even deeper into the need to maintain cybersecurity throughout the supply chain. For that reason, the draft is worth quoting at length:

“The practice of communicating and verifying cybersecurity requirements among stakeholders is one aspect of cyber supply chain risk management (SCRM). A primary objective of cyber SCRM is to identify, assess and mitigate “products and services that may contain potentially malicious functionality, are counterfeit, or are vulnerable due to poor manufacturing and development practices within the cyber supply chain.”

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A Tale of Two Disciplines: Data Scientist and Business Analyst

data scientist and BA

The ability to use data to achieve enterprise goals requires advanced skills that many organizations don’t yet have. But they are looking to add them – and fast. The question is, what type of big data expert is needed? Does an organization need a data scientist or does it need a business analyst? Maybe it even needs both. These two titles are often used interchangeably, and confusion abounds.

Business analysts typically have educational backgrounds in business and humanities. They find and extract valuable information from a variety of sources to evaluate past, present, and future business performance – and then determine which analytical models and approaches will help explain solutions to the end users who need them.

With educational backgrounds in computer science, mathematics, and technology, data scientists are digital builders. They use statistical programming to actually construct the framework for gathering and using the data by creating and implementing algorithms to do it. Such algorithms help businesses with decision making, data management, and the creation of data visualizations to help explain the data that they gather.

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How IoT logistics will revolutionize supply chain management

As with many other areas of the economy, the digital revolution is having a profound effect on delivery logistics.

The combination of mobile computing, analytics, and cloud services, all of which are fueled by the Internet of Things (IoT), is changing how delivery and fulfillment companies are conducting their operations.

One of the most popular methods for fulfilling deliveries today is through third-party logistics, which involves any company that provides outsourced services to move products and resources from one area to another. Third-party logistics, or 3PL, can be one service, such as transportation or a warehouse, or an entire system that maintains the whole supply chain.

But the IoT is going to change how this process operates. Below, we’ve outlined the impact of IoT on supply chain, and how IoT management will transform inventory, logistics, and more.

Internet of Things Supply Chain Management

One of the biggest trends poised to upend supply chain management is asset tracking, which gives companies a way to totally overhaul their supply chain and logistics operations by giving them the tools to make better decisions and save time and money. Delivery company DHL and tech giant Cisco estimated in 2015 that IoT technologies such as asset tracking solutions could have an impact of more than $1.9 trillion in the supply chain and logistics sector.

And this transformation is already underway. A recent survey by GT Nexus and Capgemini found that 70% of retail and manufacturing companies have already started a digital transformation project in their supply chain and logistics operations.

Asset tracking is not new by any means. Freight and shipping companies have used barcode scanners to track and manage their inventory. But new developments are making these scanners obsolete, as they can only collect data on broad types of items, rather than the location or condition of specific items. Newer asset tracking solutions (which we’ll get into shortly in the next section) offer much more vital and usable data, especially when paired with other IoT technologies.

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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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Walmart and Target are refusing to surrender to Amazon

While many public companies focus their attention on embellishing their quarterly results, Amazon has always taken the long view.

The online retailer leader has invested heavily in infrastructure including a nationwide network of warehouses, robots which help ship orders, and even predictive technology that helps the company know what a customer plans to buy before he or she orders it.

Amazon even has a pioneering deal with the United States Postal Service which allows for Sunday delivery in some markets.

All of this has not come cheap, and it has hurt Amazon’s short-term profitability in some quarters, but it has helped the company build a strong competitive advantage over its chief rivals Wal-Mart and Target.

Those two physical retailers are struggling to change their supply chains to meet the needs of individual digital customers rather than stores. That’s a radical switch that requires major changes to how both brick-and-mortar chains operate.

But if either Wal-Mart or Target can hope to compete with Amazon, they have to recreate the digital leader’s ability to ship millions of products in a two-day window efficiently. Both companies seem to at least understand the problem and are taking steps to catch up.

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Managing risk in the digital supply chain

You may be aware of risks and problems in your own business, but increasingly it’s possible to be exposed to issues by other organizations that you deal with, particularly if you’re buying in IT services.

How can enterprises deal with these threats and ensure that their data and that of their customers is kept safe at all stages of the supply chain? We spoke to Dean Coleman, head of service delivery at service management and support specialist Sunrise Software, to find out.

BN: How difficult is it for larger organizations to manage problems that might occur further down the supply chain?

DC: It can be quite difficult, historically most organizations have a handle on risk in terms of what’s going on in the business, financial targets and so on. But when it comes to IT risks and the supply chain providing IT they don’t have the same visibility. These days IT is everywhere and businesses depend on it so IT problems have a larger impact. The understanding of risk needs to be something that key decision makers are more aware of.

BN: Is this a particular problem when dealing with smaller companies who might not have resources in house?

DC: Yes, from the supplier side of the fence we see that smaller organizations often don’t have the skills in house to deal with security, infrastructure, and so on. They rely heavily on these services but don’t see them as a core part of their business. Because they don’t have the skills and resources they will often turn to third parties to manage these things for them. However, in some cases the third parties also don’t do a very good job, they’ll be providing reactive services rather than the proactive ones that are really needed to predict problems based on risk.

Read more at Managing risk in the digital supply chain [Q&A]

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