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12 Pitfalls to Avoid on the Path to Managing Reputational Risks

Authors: Henisz, Witold J.

http://www.ieseinsight.com/review/articulo.aspx?doc=130424&idi=2&seccion=4&issue=28&idioma=2

Date: First Quarter 2016

Tags: risk management, corporate diplomacy, stakeholder engagement, DIPLOMat, due diligence

Engaging with your external stakeholders is an essential means by which companies not only reduce risk and preempt future crises, but also generate value for shareholders and society alike. So why do executives appear to ignore “corporate diplomacy,” which in many ways is one of the best resources in the executive toolkit to help manage risk effectively? Drawing on his DIPLOMat framework, the author answers this question by showing the myriad ways that executives are frequently stymied in their efforts to maximize their stakeholder potential. He sketches out the 12 biggest risks to avoid. By identifying the main risks in the stakeholder environment, executives can mitigate their adverse impacts and seize their upside opportunity.

Tools and Frameworks:
> “Becoming a Corporate DIPLOMat” defines six elements that are key to winning the hearts and minds of external stakeholders in support of an organizational mission.
> The article sketches out the 12 biggest pitfalls to avoid when designing and implementing corporate diplomacy.

Examples Cited:
Shell, oil and gas prices, Indian government, Chevron Nigeria, Tintaya mine in Peru, community leader in Peru who closed down road access to a local mine, Rio Tinto mine in Bougainville, Papua New Guinea, a hotel in Bali

Research Basis:
Based on research by the author contained in his book Corporate Diplomacy: Building Reputations and Relationships With External Stakeholders (Greenleaf, 2014).

About the Author:
Witold J. Henisz is the Deloitte & Touche Professor of Management at The Wharton School, University of Pennsylvania, and Principal at the political risk management consultancy PRIMA LLC.

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Introduction to Functional Programming in Scala

Learn more about Scala, a programming language that supports both object-oriented and functional paradigms.

Scala is a multi-paradigm programming language in the sense that it supports both object-oriented and functional paradigms. It runs on the JVM and can be installed using the instructions found here: http://scala-lang.org/download/install.html.

Let’s explore some of its functional features.

Hello, world!

Who am I not to respect the classic “Hello, world!” program presented when introducing a programming language? So with my utmost respect for Brian Kernighan who created this tradition here’s the “Hello, world!” in Scala:

 

Listing 1: Hello, world!

object HelloWorld {
 def main(args: Array[String]): Unit = {
  println("Hello, world!")
 }
}

The structure of this program consists of a singleton object, HelloWorld, which contains only one method called main. It takes the command line arguments and call the predefined method println passing in the “Hello, world!” string.

From this simple program, you may have already noticed that types in Scala follow the variable name (or parameter name in case of function). Indeed the args parameter is of type Array[String] and the main function returns a value of type Unit. For simplicity’s sake think of Unit as the typical void of other languages even if it’s not exactly the same.

Immutability

In Functional Programming you tend not to have classes whose state can be changed — the so-called mutable classes. Rather, your model is represented by immutable classes. Scala offers a nice syntax to create them that falls under the name of case classes. Here is a simple example:

Listing 2. Case classes

case class Person(firstname: String, lastname: String, age: Int)

// create instances of the Person class
val bob: Person = Person("Bob", "Smith", 39)
val alice = Person("Alice", "Brown", 31)

// access fields
val aliceAge = alice.age

If you come from a Java background, you’ll notice much less boilerplate code. Note also the use of the val keyword. In Scala it is used to create immutable variables, such that once you assign a value to a val reference you can no longer change it. For example something like the following is disallowed:

val a = 42
a = 3 // error: reassignment to val

There’s another peculiarity to consider in Listing 2. I didn’t forget to declare the type of the alice variable, I left it out on purpose to demonstrate another nice feature of Scala: type inference. In fact, I could have omitted it for bob as well since Scala’s type inferer is smart enough to understand its type as it did for alice.

Case classes provides many other goodies along with pattern matching, another fundamental pillar of functional programming languages.

Pattern Matching

If this is your first encounter with pattern matching, you could consider it as an enhanced switch statement, as an oversimplification. Actually it’s much more than that. For example, examine the following code:

Listing 3. Pattern Matching

import Shape._

trait Shape

case class Rectangle(base: Double, height: Double) extends Shape
case class Circle(radius: Double) extends Shape

object Shape {

  def area(shape: Shape): Double = shape match {
    case Rectangle(b, h) => b * h
    case Circle(r) => r * r * Math.PI
  }
}

val rectangle: Shape = Rectangle(4, 5)
val circle: Shape = Circle(4)

val rectangleArea = area(rectangle)
val circleArea = area(circle)

The code in bold shows pattern matching. It matches against the shape object passed as a parameter. If it’s of type Rectangle it extracts its base and height, whereas if it’s a Circle it extracts its radius. In both cases it computes the area of the shape. The extraction part is called deconstruction.

Note also the trait keyword. It’s used to define “interfaces” in Scala. I used quotes for the word since they are actually different from, say, Java interfaces but for now this similarity will do.

The import Shape._ part lets you use Shape’s area function without fully referencing it as Shape.area.

One more thing. You may have noticed that the name Shape is used both for the trait and object definition. In Scala that’s possible and frequently used. In that case the object is called a companion object of the trait and, practically, it has some implications I won’t cover here for the sake of brevity.

Pattern matching sounds good, but could a functional language be defined as such if it didn’t provide functions as first-class citizens? No, of course it couldn’t!

Functions as First Class Citizens

In FP you tend to write pure functions, functions that, given the same input, always return the same output, without producing any side effect. They are functions in the mathematical sense of the term.

A function in a language is first-class if it can be used like any other type, such as Int, String, Double, and so on. This means that it can be assigned to a variable, passed as a parameter to another function or returned by a function. Consider the following code snippet:

val double: Int => Int = x => x * 2

val increment: Int => Int = x => x + 1

def applyFunc(x: Int, f: Int => Int): Int = f(x)

In this case, double is a function from Int to Int. It takes an integer and doubles it. Notice the type definition: Int => Int. The function body — what follows the equals sign — means: “take the integer x provided by the client code and return its value multiplied by 2”. Similarly the increment function takes an integer and adds 1 to it.

The applyFunc function takes an integer and a function that takes an integer and returns an integer. The return type of applyFunc is still an integer. This function just applies the function f, passed as a parameter, to the value x, also passed as a parameter. So if you want to apply the double function to your integer you can use this function as follows:

applyFunc(21, double) // the result will be 42

Now you need to apply the increment function instead. Easy:

applyFunc(21, increment) // the result will be 22

In the FP world functions such as applyFunc are called higher-order functions (HOFs). A HOF is a function that takes another function as a parameter and/or returns a function as its result.

Conclusions

Of course the space instantiated for an article is not enough to cover functional programming in all its entirety or all the features of a language about which you could easily write a 1000 page book. This article didn’t even scratch the surface of Scala but, hopefully, tickled your brain a little bit.

Big companies such as Twitter, LinkedIn, Netflix, and many others, are already using Scala successfully in production. Now might be a good time to jump on board before you get left out of the boat.

Final Note: In case you’re worrying about your beloved Java library, no problem, you can use any Java API from Scala seamlessly.

Alessandro Lacava is a software designer and developer. At the moment, he is mainly interested in functional programming and languages such as Scala, Haskell and the like. He also has fun playing with Domain-Specific Languages (DSLs).

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How do I write an effective elevator pitch?

https://www.quora.com/How-do-I-write-an-effective-elevator-pitch/answer/Elliot-Loh
Elliot Loh

I think of an elevator pitch as more of a state of mind than an actual script. But if I had to propose a formula, it would look something like this:
We solve [problem] by providing [advantage], to help [target] accomplish [target’s goal].
Depending on the stage you’ve reached, you might follow it up with a second sentence about your business model:
We make money by charging [customers] to get [benefit].
So for Geni, I used to say, “We solve the problem of genealogy by matching possible relatives, to help genealogists create one accurate world family tree. We make money by charging enthusiasts for enhanced search and other premium features.” For Yammer, the pitch might be, “We make companies more efficient by providing a live feed of comments and questions, so employees can find answers more quickly. We make money by charging companies that want administrative control on their employee networks.” (Note that I no longer work for either company, so my examples shouldn’t be taken to represent them.)

My approach tends toward simplicity. In an elevator or conference environment, you only have a moment to deliver your pitch. Best to craft a clear statement that can lead to a question by the receiver. And for that question, you should have plenty of material lined up to demonstrate value:

•You’re the first to do this, or
•Why you’re different and therefore better than similar products
•Why your market is worth pursuing
•How much traction you have
•Why this is difficult for others but easy for your team
•etc.

As others have said, this is something that should be expressed rather than memorized. If you are passionate about your product, this will probably be a matter of whittling your message down, rather than building it up.

The data-driven businesses solving problems you didn’t know existed

Just two years ago, data scientist Alex Pentland proclaimed that we’re just beginning a ‘decade of data’. He wasn’t wrong: the world now produces 2.5 quintillion bytes of data every day.

It’s made up of everything from the photos we take on our smartphones to the sensors that track our weather. And it’s given rise to a whole new sector – businesses that use data in innovative ways, whether it’s working out which route we should drive or helping companies understand why a customer is browsing but not buying.

“In 2000, it cost ten dollars per gigabyte to store data,” says Mike Upchurch, founder of analytics software company Fuzzy Logix. “By 2005 it was 50 cents a gigabyte. Now it’s three cents a gigabyte. So people are just storing astronomical amounts of data.

data_foot_getty.jpg

“But it’s all completely worthless unless you can do two things: ask it a question and take actions on the result. We worked with a major UK supermarket, for example, using weather patterns to help determine what should be on their shelves. This resulted in millions of dollars’ worth of savings in perishable food.”

Business benefits

Conversion optimisation agency PRWD, uses data to help companies improve the percentage of website customers who complete the desired goal – whether that’s filling in a survey or buying something.

“There is a big emphasis on investing in data tools. But a lot of companies aren’t investing in the people who will actually interrogate that data and provide the stories for the business to drive an actionable output,” says PRWD’s optimisation strategist Chris McCormick.

“For example, we might look at ‘success data’ – what’s going well for an ecommerce website. Perhaps the data shows that presenting reviews to customers makes them more likely to buy. So let’s make more of those reviews, and bring them to the forefront.”

tube_map_getty.jpg

Enabling app creation

Data’s also behind the innovation success story of the last ten years – apps. Transport apps have seen massive growth – Mapway’s London Tube Map, for example, has been downloaded more than 15 million times. But that data needs work before app developers can use it, and that’s where companies like Transport API come in.

In 2010, the UK government began releasing transport data. But this isn’t always easy to use. It’s around 60 different data feeds, including everything from bus times to tube delays.

Read: Ultrafast boost for businesses from Virgin Media Business

“We saw a business opportunity there,” says Emer Coleman, TransportAPI’s business development director. “If you’re a developer and you want to make an app, you have to do a lot of work to integrate those feeds together and clean up any anomalies. We do that for them, and put it out as a single source.”

And it’s not just big business which benefits from Transport API’s source: the company’s three levels of pricing include a freemium model. “You can access 30,000 data hits a month for free,” says Emer. “That means people can experiment, do some research and development and some crucial concept testing – and they can do it for nothing.”

Making things better

Big data specialists Mastodon C’s Witan project is also aiming to use data to drive innovation. It’s building an open source platform that enables cities to use their data better, to help them plan for their future education, employment, housing and energy needs.

data_london_getty.jpg

The UN estimates that three billion people will live in cities by 2050. So planning cities for the future is a big challenge.

“Many cities don’t have data by city: they have it by ward, or by national level,” says delivery manager Elisabeth Weise. “Another problem is that they might have the data but work in silos: population projects are done by one team, then employment projections are done by another.”

The first tool, allowing boroughs to run their own population projections, has just been launched across the 33 London boroughs. “It’s really freeing up resources and giving much more analytic power to the boroughs,” says Weise.

“There’s been a lot of enthusiasm. This is really been missing: there are a lot of niche products out there but they are very expensive. There’s no big buy-in: you can start with demography, as this underlies so much city planning. Cities are such drivers of growth – but they need to have the proper tools to be able to do it.”

This is a guest blog and may not represent the views of Virgin.com. Please see virgin.com/terms for more details. Thumbnail from gettyimages.