Are you a data alchemist? Are your data initiatives adding millions of dollars to your company’s bottom line in cost savings or new revenue? Our webinar, The Alchemy of Data Monetization: Turning Your Data into Gold, showed how to start doing that by presenting data monetization examples and tips on how to deploy your data monetization strategy.
That is what having a data monetization business model is all about: leveraging your data to enhance your bottom line. You do this in two ways:
- Direct monetization: It’s a way to create new revenue streams by providing access to your data (this is when you sell your data)
- Indirect monetization: Use insights to improve your business (usually resulting in quick wins)
The Five Big Data Monetization Challenges
- Let the data scientists figure it out: Data scientists can prepare data, create a data monetization business model, and fit the data to the model to predict future trends. However, unless you know your business challenges you want to address, this won’t actually help you.
- Inability to work across business silos: Business units can often run independently of one another for positive reasons, often having their own commitments and goals. But this creates data siloes that are isolated from one another, limiting your ability to discover and orchestrate data monetization strategies that could save you millions.
- Inefficient/disconnected infrastructure: This includes sales, marketing, customer support and many others. Like siloed business units, siloed data systems limit you from being able to pursue your big data monetization initiatives.
- Investing in the latest algorithms instead of the most useful: It’s all about matching algorithms to what you want to solve, so you can maximize where your dollars go. Data scientists with advanced degrees may be mathematically and statistically talented, but will you get actionable insights that increase ROI out of their data initiatives?
- Data science is just the start of the work: Once you get data, that’s just the beginning. You need useful data that keeps on giving more and more insights. You need the power of technology – a production IT system to take care of ingesting, cleaning up and transforming data into something insightful. It’s all about getting results from your data that can be applied to business problems.
Get started by asking yourself these four questions – use the answers to form your “elevator pitch” – to explain how your data analysis generates business insights whose implementation yields bottom line results:
- What is your approach to data monetization?
- What are the risks of your approach?
- What are the benefits to the company?
- What are the costs to build capability and execute your data monetization strategies?
Like all alchemists, before you will be able to turn data into gold (and achieve data monetization success), you must transform yourself by broadening your skillset and experience. You must reach beyond knowledge algorithms and approaches so you can sell projects to your executives and speak their vocabulary. Can you describe how to measure success? Can you deliver on time and with a realistic budget? This is what your executives will care about. And you will always be competing with other projects for budget, having crisp and concise answers to these questions is a must.
But how do you know you’ve become a data alchemist? It’s when you are able to:
- Identify and understand key business problems
- Apply data science to make progress on those problems
- Explain how your project will improve the bottom line
- Show how you achieved your success metrics
Approach data alchemy like this:
And don’t forget – the “we” is important, as data alchemists don’t work in isolation.