INFO GRAPHICS WITH EXCEL

I’m not always the biggest fan of info graphics. Many of the posters-sized info graphics released these days have issues. But lately I’ve also received several requests on how to do info graphics with Excel. Many people don’t know where to start.

How Info Graphics are Different
Info graphics differ somewhat from your usual dashboard-style reporting. When we report with business tools, we use the data points–charts, tables, etc–to investigate a problem or monitor a system. That is, we use data to find results. Info graphics are used when we already know the results and we want to present it in an interesting, sometimes even artistic, way. Info graphics, then, are more about style and appearance–they wouldn’t necessarily find a good home on a dashboard. But they do work well in magazines, newspapers, and some student projects.

Info Graphics and Excel
Many info graphics are made with graphic editing programs like Adobe Illustrator. As far as I know, these illustrations are static. So each change in the underlying data won’t be automatically updated in the graphic. You would just have to redraw the graphic. Excel provides a benefit here: if we use Excel’s charts to make our info graphics, we can update the underlying data and the result appears automatically.

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Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

Big-data analysis consists of searching for buried patterns that have some kind of predictive power.

But choosing which “features” of the data to analyze usually requires some human intuition.

In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too.

To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets.

Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions.

In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

“We view the Data Science Machine as a natural complement to human intelligence,” says James Max Kanter, whose MIT master’s thesis in computer science is the basis of the Data Science Machine.

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The Future Of Performance Management Is Not One-Size-Fits-All

In 2013, CEB research found that 86% of organizations had recently made significant changes to their performance management system, or were planning to. In 2014, a Deloitte survey found that 58% percent of companies surveyed did not think performance management was an effective use of time, and many media outlets jumped on the opportunity to air their grievances.

Finally, the rising wave of discontent seemed to crash in 2015, as a slew of large organizations like GE, Accenture, Netflix, and Adobe all scrapped their age-old annual performance management processes in favor of more continuous feedback systems. And many others followed suit.

But, was it the right move for everyone?

Last summer, I wrote an article on this topic myself, urging business leaders to really consider the implications of following these organizations. The issue, in my opinion, is not that these organizations did something wrong. Rather, the risk is that many leaders misinterpreted these stories to mean that they should abandon performance management altogether.

One thing is clear: the future of performance management in the American workplace is still very much in question.

For more insight into this important topic, I recently sat down with a handful of thought leaders in the performance management space, including Rob Ollander-Krane, Senior Director of Organizational Performance Effectiveness at Gap, Inc., Nigel Adams, Global Chief Talent Officer at Razorfish Global, and Amy Herrbold, Senior Director of Organizational Development at Kellogg. Together, we discussed the future of performance management to understand, from their perspective, why changes to this process are long overdue.

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Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

A new and unique computer system from MIT has outperformed human intuition using its algorithms, and it’s amazing, and perhaps a little frightening: the Data Science Machine beat out over 600 human teams in finding predictive analysis.

Big-data analysis consists of searching for buried patterns that have some kind of predictive power.

But choosing which “features” of the data to analyze usually requires some human intuition.

In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too.

To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets.

Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions.

In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

Read more at Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

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Close the Loop on Supply Chain Risk: 5 Strategies to Move Product, Boost Sales and Automate Efficiency

Supply chain management is a critical function for any small to mid-sized business. Yet, too often companies rely on spreadsheets to manage supply chain activities — a risky prospect that’s labor-intensive and error-prone.

A better option is to bring these activities into your financial management or ERP system. Centralizing tasks such as order filling, inventory management and delivery tracking can positively impact sales, improve cash flow and keep you tax compliant.

Here are five ways that ERP supply chain management benefits your bottom line.

Right-sized Inventory

Getting inventory right can be tricky: too low, you risk losing customers; too high and you’re left holding the bag, so to speak.

Control Quality

Dealing with defective materials or products can be a drain on your business. Not only can it hurt sales, but it can also damage your reputation.

Optimize Shipping

Web sales have made fast, affordable shipping a must-do for all businesses. Keeping track of goods coming and going can become burdensome, not to mention the hassle of dealing with lost or late shipments.

Improve Cash Flow

Invoicing practices can greatly impact your cash flow. Moving from a manual process to automation allows you to process invoices faster and shorten the order-to-cash cycle.

Be Compliant

Navigating complex and ever-changing trade and tax rules can be daunting. Being part of a supply chain compounds that risk.

 

Read more at Close the Loop on Supply Chain Risk: 5 Strategies to Move Product, Boost Sales and Automate Efficiency

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