Four key data monetisation opportunities

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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:

Internal:

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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Carmen Gallagher

Carmen Gallagher is the head of Information Management and Big Data at SAS Australia and New Zealand. Carmen has spent many years focusing on helping companies achieve maximum benefit from data kept within their systems and has long been championing the need for a data quality and governance focus within all organisations, especially as the world of big data is redefining how we make business decisions.

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