Four key data monetisation opportunities

What if you could predict with near-perfect accuracy what you’re going to sell and when your customer is going to buy?  Right supply, right time is the goal German manufacturers have set themselves, without reducing the configuration options customers expect.DM4 pic_lr

Having almost completed stage 1 of their plan – changing processes and ways of working off the back of internally monetising their data – they’re now looking externally to close the gap.

So where are these opportunities to monetise data?  They can be broken down into internal and external activities.

I’m often asked why we consider internal activities as data monetisation, rather than simply better business management.  There are several important reasons why we need to look at internal data monetisation efforts. For example, government agencies don’t have the ability to sell their data, however, the value of combining different agency data sets is evident for understanding fraud, terror activities, tax evasion and student-loan default.

Large siloed organisations have data sources that, once combined, can provide insights that are incredibly rich. Several years ago, a large petroleum retailer brought together their geographically siloed customer data and as a result, realised their largest selling product was not petroleum, but rather a liquid refreshment.  Subsequently, they discovered they were the largest global purchaser of this secondary product, and negotiated a much more lucrative global purchasing agreement.

A third reason to look at internal data monetisation is that it’s a great way for organisations to learn before opening themselves up to the legalities of monetising data externally – it’s essentially a risk mitigation strategy at the beginning of an agreed data monetisation strategy.  And in the words of Julie Andrews, that’s often a very good place to start.  So let’s have a closer look at where we can find these opportunities:


Business optimisation
Purely a cost reduction or productivity gain that couldn’t be achieved any other way than by changing the way the business views and uses its own data.  The emphasis is not so much on the initial analysis of data, which everyone is already doing, but more on using different data, or data differently, to both identify and answer the next few questions in the chain of analysis

Increased market share
The focus is on increasing market reach, whether it be in current markets or widening the reach for current products outside of their markets. It requires going outside the comfort zone and trying something different.  Finding the right something different will require new data and that internal entrepreneurial mindset I referred to in my last post The 3 P’s of Data Monetisation.

External, for those who dare, requires having the data strategy to meet the needs of both the business and a monetisation capability, and full trust in your data:

New products, services or channels
Here we’re identifying and solving our customer’s business problems through our own customer intelligence data. Often times we’re looking to collect more than just the transactional data within our systems.  The end goal is to anticipate and meet the needs of the group of customers we’ve identified, or identify a solution based on variables we know hold value, and then identify the customers who will need it.

New business models
A new business model is usually required when an organisation shifts from looking at the answer to a problem from an internal business perspective to that of the customer’s perspective.  It sounds easy in theory but often politics or traditional ways of working provide large roadblocks. This also often requires the business to put part of its traditional revenue at risk while it realigns to create that customer-focused perspective.

So now you have a few places to start, however, these attempts can only be successful if your overarching data strategy takes into account three key requirements of data collection, packaging and delivery.  For more on the framework that turns these opportunities into reality, take a look at Turning Data into Dollars: A Framework for Successful Data Monetisation written by Anne Buff.

If you would like to know more about how to use the framework to identify and deliver either internal or external data monetisation capabilities please feel free to register for the webinar here with global thought leader Anne Buff.

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'Data for good is in the DNA of this organization'


Jake Porway, DataKind


Wheat rust. You may have never heard of it, but in a matter of days, this fast-moving, silent-killing plant disease can completely annihilate a critical wheat farm in Ethiopia. Wheat rust’s newest nemeses? A legion of volunteer superheroes in the Data for Good movement.

When Jake Porway, Founder and Executive Director of DataKind, bounded onto the stage at Analytics Experience this week, he spoke of a few of these superheroes who recently came together, hackathon style, in Seattle, Washington, to look at satellite data and infrared models just to locate all of the likely wheat farms throughout Ethiopia. Integrations with Google Maps and visual analytics allowed them to find and predict which of those farms had wheat rust – with 80-percent accuracy, by the way – and where this devious disease was headed next. This information is now allowing partner Bill & Melinda Gates Foundation to contact farmers in practically real time to suggest shifting crops or applying preventative pesticides.

All this from a team of volunteers working together for just two days.

“This work has bigger impact than you might imagine,” said Porway. “For organizations that don’t have access to this stuff, it’s like magic. It’s incredible. It’s like sci-fi.”

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What’s your big data problem?

topics in big dataAnalytics Experience 2016 featured more than 100 breakout sessions and talks covering numerous topics in big data. You can watch many of those talks from our Analytics Experience 2016 video portal, where select keynote and session talks are archived. To give you a taste of the content you'll find there, here’s a look at two of those sessions from two very different companies – Dow Jones and UnitedHealthcare – which transformed their data strategies with SAS®. For Dow Jones, the answer was the cloud. For UnitedHealthcare, Hadoop. When it comes to data problems, there’s no one-size-fits-all solution. The beauty of SAS is that it’s adaptable to all different sorts of problems.

Building practical analytics at The Wall Street Journal

In many ways, Dow Jones, which publishes The Wall Street Journal, MarketWatch and Barron’s, is a traditional publisher trying to transition to a new media landscape. In response to a shift in how its customers consume news today, the company has been forced to make its own changes. Jeff Parkinson, Vice President of Customer Operations at Dow Jones, spoke during an Analytics Experience breakout session about how the business has transformed the way it uses data to understand and support its customer base.

Working from legacy systems, Dow Jones faced enormous challenges, with multiple analytics teams working on the same analysis. “To get a standard data model out there – how many subscribers we had in a particular region – would sometimes take us weeks,” said Parkinson.

Parkinson knew the company needed a major change, but he didn’t want to lose the code that his team had already written in SAS®. After consulting with their SAS Account Executive, the company decided to supercharge their program with SAS/ACCESS® Interface to Amazon Redshift on AWS.

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What’s your digital health care plan?

digital health care plan

Jared Cohen, president of Jigsaw, at Analytics Experience 2016

Historically, generations could expect to experience one – maybe at best – major technology disruption or transformation in their lifetime. That’s simply not the case today. Today it is more difficult than ever before to know what will transform the technology landscape in the next month or the next year. And that dilemma is what Jared Cohen, president of Jigsaw, calls “the new world disorder.” Cohen took the main stage at Analytics Experience 2016, to explore the unparalleled advances and unprecedented threats we need to be prepared for as the physical and digital worlds merge.

“Technology is changing the world, but the world is also changing technology,” said Cohen. “But while those changes are extraordinary, they only tell us a fraction of what’s happening. We really need to be asking ourselves how all of this technology is impacting our world.”

Consider North Korea, a place where citizens receive the death penalty if caught with a smartphone, yet tens of thousands still risk their lives daily to have access to mobile devices. At the same time, in Brazil, where the Amazon jungle is being used by criminals and traffickers to hide slave-labor camps, drones are being used to fly over the jungle to uncover such camps, and in the last year 700 camps have been discovered. These environments force us to rethink the power of technology, its capabilities and the risks and lengths people will go to maintain access to the digital world.

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An analytics revolution underway: Insights from Analytics Experience 2016


“What we are experiencing from analytics today is nothing short of a revolution.” SAS CEO Jim Goodnight at Analytics Experience 2016

“What we are experiencing from analytics today is nothing short of a revolution,” said CEO Jim Goodnight, who spoke at Analytics Experience 2016 and set the stage for the conference’s executive panel. “Right now, my primary mission is to ensure people understand the limitless possibilities that lie before us, given the power of analytics.”

After decades of growth, the field of analytics has truly arrived, maturing into a discipline deeply embedded into all of the best decision making. The executive panel, which was moderated by Executive Vice President and Chief Marketing Officer Randy Guard, took a deep dive into analytics and what it will take for companies to move from the promise of analytics into the reality of its competitive advantage.

Here are some of the highlights from the session.

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Nine years (and still together)

plastic bride and groom on a wedding cakeThe IT industry is littered with examples of short-lived corporate partnerships and alliances that often appear impressive but regularly end or are withdrawn after the initial enthusiasm wanes. The old adage that “actions speaker louder than words” is especially pertinent and I regularly encourage clients to look for tangible examples of co-operation beyond the text of a press release.

I wanted to use this blog post to highlight the existence of a relationship that began in late 2007 and bucks the industry trend. Cast your mind back to 2007: Can you remember when Vista was the predominant PC operating system, the Apple iPhone had just been launched, Android had yet to be announced and Uber didn’t even exist ?

This lengthy preamble is simply a way to introduce the SAS partnership with Teradata, a relationship which is now nine years old and has been responsible for some significant achievements: Read More »

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Winning Olympic gold: My story

Gold medal

Great Britain's men's eight rowing team won gold in Rio to reclaim the Olympic title for the first time since 2000.

When you read people’s stories of winning Olympic medals, they often fall into cliché. It's hard not to. In my experience, the nerves, the expectations, the emotions are all heightened beyond what I've ever experienced, so it becomes necessary to use all the hyperbole at your disposal. That said, winning gold felt more muted than I imagined it would.

Please don't misunderstand me. Everything up to winning was more exaggerated, volatile and exciting than anything else in my life up to that point. In the days preceding the final, I went from being silent and morose to loud and optimistic following a single training session. I challenged our chief coach about changes to the boat; I was sure we would lose given the wrong weather conditions; and I was certain we would win after doing a short practice piece.

In the hours before the final, I used every trick to try and calm nerves that threatened to overflow and wash me away with them. Even writing this now, I can feel my jaw clench and my arms start to shake. In the seconds before, the realisation that in five minutes this single goal I had trained for four years would be over, battled to be a comfort and a torture, crushing all other considerations in their path.

See? It's very hard not to revert to cliché. Read More »

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Disrupting risk management: how financial models are changing

In many ways financial services is about risk management. Regulatory pressures such as BCBS 239, stress-testing, IFRS9, Solvency II and the Fundamental Review of Trading Book have hugely strengthened that focus.

But there are other concerns too. Cost pressures are increasingly important, as is the rise of challengers to the status quo, including online-only providers and new entrants to the market, often more specialist and more targeted than the incumbents. Digital transformation and the drive towards online services, and the rise of the internet of things (IoT) are other challenges.

But perhaps the most difficult area for financial services is the connection between digital transformation and regulation.  The challenge is to maintain current risk processes and systems, but integrate them with new platforms such as Apple Pay, in a way that is compliant with regulations. Are risk managers looking at these issues? Our discussions with a cross section of risk managers suggest that the answer is a resounding no. Read More »

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How to manage the IoT Analytical lifecycle

Since the idea of an “IoT analytical lifecycle,” may be understood in many different ways, let’s start with a definition. Performing analytics at the data center and the cloud is well established practice, and is still quite relevant. With growing numbers of connected devices and availability of computing capabilities at various layers of the network, however, there is an opportunity to extend internet of things (IoT) analytics towards the edge of the network.

How can you extend analytics closer to the edge of the #IoT network? Click To Tweet

The kind of analytics that can be done closer to the edge will be more localized in nature since it would only be applied towards data emerging from one or a few specific end points in the business eco-system, such as a fleet vehicle, a manufacturing plant floor, an offshore drilling station etc. After the application of these localized analytics, the resulting data still would need to flow back to the data center or cloud to allow more comprehensive, enterprise level analytics to be applied and used in decision-making, which renders the IoT data more valuable.

For example, cloud or data center analytics may move from analyzing data for a single vehicle to analyzing data at the fleet level. This data center or cloud based analytics would not only drive decision making at the enterprise level but will also result in insights that can improve the localized models that need to be pushed back towards the edge. Establishing this feedback loop from the edge to the cloud or data center and back to the edge is how we create an analytical lifecycle for IoT. Read More »

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How can cognitive computing improve air travel?

travelers pulling suitcases through an airport security lineIf cognitive computing were adopted widely by companies in the travel industry, what would a typical trip look like for a business traveler? Might some of your biggest travel frustrations be relieved? Let’s find out.

It’s a beautiful day, and you have an important customer briefing in LA. You’ve already received a text that your flight is on time.  Monday morning is one of the busiest times at the airport, and naturally you’re running a tad late. You start to worry about finding a spot to park in the packed airport garage, but then your navigation system uses image detection to direct you right to the best open spot.  Without realizing it, you got one of the last four spots in the main parking lot.

How did this happen? Using convolutional networks, the computer can analyze photos of the parking lot in real time and detect images with a 6 percent error rate, which is better than the human eye.

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