Data Monetisation Checklist and Strategy Tips

For those who have read my posts over the last seven weeks, thank you.  This is my penultimate blog before I start a new job as Strategic Business Development Director at Experian in January.  I will continue with Profit From Data but I will not post as regularly, probably monthly.

 So, you’re interested in monetising the data available to you but you need a strategy and a checklist.

 Here are some strategy tips that you shouldn’t overlook:

• Trust - Your data strategy must benefit your current customers, the ones who gave you the data directly or indirectly in the first place; this doesn’t have to be your sole focus but it must be part of your strategy. I’ve worked with companies whose core products generate data but they are not data businesses.  Data, to them, is a serendipitous side-line which they realised they can profit from.  These companies have been through the right processes and have the correct consents in place, so they could have just forged ahead but they didn’t; smartly they re-enforced the trusting relationship with their core customers by giving them something back.  For example, using the data to provide some insights into their markets or useful observations on the strengths of their main products or even some competitor benchmarking.
• Enterprise wide - Your data strategy must be enterprise wide.  If it isn’t and not everyone knows about it, you increase the chances of ‘value leakage’ as other parts of the business give the data away to support their product or even worse you increase the chances of a data breach.

 I’ve put these two considerations before the obvious next step, which is defining the objective and assessing the data, as I believe they are essential to the project’s success.

•  Clear objective(s) - When setting the objective(s), you really need to narrow the scope of these projects and focus on specific opportunities. Initial exploratory meetings with data brokers are useful, as you’ll begin to get a sense of the value of the data.  However, you should be aware that data brokers have a hierarchy of data partners, so even if you have the best data in the market, the data broker might be bound by contract to use another partner’s data before yours.  Just make sure you ask how their hierarchy works.  Brain storming sessions with industry experts, either within or outside of your business, will also help you build a list of other ideas e.g. benchmarks.  Once you have a list of opportunities, you can rank them in order of potential profitability and then you assess exactly what data you’ll need.  Warning … you can get carried away here, amass an enormous amount of information and waste lots of time and resources looking aimlessly for an opportunity. Always have a clear view of what you’re trying to prove and then focus on a relatively narrow data set.  You can expand it later once you have some core data to work with. 

What roles and culture do you need to support a data initiative like this?

If you don’t outsource this to a third party, with whom you would normally share the upside, you’ll need to build a team that includes the following roles:

• Statisticians who are skilled in the latest statistical techniques;
• Analysts and data scientists who can be the broker between statisticians and business managers;
• IT team who provide guidance on selecting data technologies;
• Business managers and knowledge workers who own the business process and are comfortable with the language of data and analytics; and
• Sales team/ person who are/is experienced in business development and data.

The creation of a data strategy is not something you should rush through.  Some decisions will have unintended consequences so it’s important to think these through at the outset.  If you’re still intent on pursuing a data monetisation strategy it’s at this point that you should consider paying someone to help you do the research, build the strategy and implement the plan.

 Is there a data monetisation checklist that you can follow?

Yes, it’s based upon all my posts to date.  Each section could be expanded further but for brevity’s sake, I have kept it short:

• Trust – have you used a ‘privacy by design’ approach to your new data venture or have you taken the necessary steps to build trust with whomever is providing you with the data that you’ll monetise?
• Legislation and regulation – have you assessed your data in the context of the following (not all will be relevant to every data project but most will be) and have you taken legal advice:
  • Data Protection Act (DPA);
  • Privacy and Electronic Communications Regulations (PECR);
  • Direct Marketing Association (DMA);
  • Competition and Markets Authority (CMA);
  • Financial Conduct Authority (FCA);
  • European General Data Protection Regulation; and
  • European regulations on benchmarks.
• Anonymisation/ Aggregation – there are a number of techniques you can use but make sure it isn’t possible to reverse engineer your calculations, especially with other third party data sets, to reveal your sensitive raw data.
• Uses and Value – Have you conducted an adequate amount of market research?  Remember, by identifying the right problem, you’ll create a solution that stands a greater chance of success.  Open-ended questions are essential when speaking to customers. I’ve witnessed a research programme where leading questions were asked and I wasn’t surprised that the project failed, as executives were deaf to their target markets needs and more interested in their own misjudged convictions.
• Distribute the data yourself or via a third party – If you distribute it yourself it will take longer as you will need to build a reputation in your target market.  Alternatively you could work with a partner, a data broker, but then you’ll be giving away a significant proportion of the value.  Both options have benefits; you need to consider them carefully.
• Build the Data Warehouse yourself or outsource – see my ‘come together, right now, over me’ post.
• Build and communicate your data strategy – see my tips above.
• Train and equip your sales team with material that challenges your target customers.  The Challenger Sale by Matthew Dixon and Brent Adamson of the Corporate Executive Board is a great book and explains this approach brilliantly.

Next week I’ll be looking at a few of the Good Life companies who I believe monetise data the right way.  If you want to know more about data monetisation then please have a look at my website: www.profitfromdata.net.