By Shirish Nadkarni
The COVID-19 pandemic has spawned an economic crisis that is causing traditional business income streams to go into long-term jeopardy or to dry up altogether. Organisations have felt the acute need to identify new sources of value such as from the surfeit of available data.
Data monetisation, according to McKinsey & Co, is the process of using data to increase revenue. The highest-performing and fastest-growing companies have adopted data monetisation, and made it an important part of their business strategy.
Direct data monetisation involves selling to third parties direct access to your data. The data can be sold in raw form, or in a form that is already transformed into analysis and insights. Typical examples may be contact lists of potential business prospects or findings that impact on buyers’ industries and businesses.
Indirect data monetisation is where things get interesting. Firstly, there’s data-based optimisation that involves analysing data to reveal insights that can improve our organisation’s business performance. Data can identify how to reach customers and understand customer behaviour so we can drive our sales. Data can also highlight where and how to save costs, avoid risk and streamline operations.
Secondly, there are data-driven business models, where we use our data to discover new business opportunities and customers. We can embed analytics into our products or services, providing advantages for us and our customers. Customers benefit from direct access to usage analytics and other data generated by each product they already use. We can benefit from offering this as a value add-on or as a new tier of service.
Information assets have relatively low inventory carrying costs and transit costs compared with other assets, making monetising them a high-margin venture. Businesses should monetise data in any and all ways in which it can generate measurable economic value, internally or externally.
Data can assume infinite forms and therefore be monetised in endless ways.
The idea that we should monetise our data is not a new one. No doubt we have seen charts that explain Alternative Data’s massive revenue growth, but we have likely considered our data “too sensitive” to actually monetise.
Monetising sensitive data
There are four common questions that customers ask when invited to monetise their sensitive data:
Is my data too sensitive to monetise?
Perhaps surprisingly, the answer to this question is almost always no. In the vast majority of cases, a data consumer wants the statistical insights from your data, rather than the data itself. If this is true, we can actually bring solutions to bear that allow even the most sensitive data to be analysed, without any risk of data exfiltration or exposure. Let’s change our mindset: we are selling insights, not data.
Is my data unique?
In this question, the word “unique” can be replaced with “valuable”. But consider a more pointed question: “Is my data valuable to systematic investors”? The good news is that there are specific and detailed considerations that will give clear guidance as to whether our data is unique, and by proxy valuable.
How much money can I make?
If we are comfortable with the idea of safely monetising our data, and we have determined there is potential value to data consumers, then we come to the next question.
How much is this value?
Our data’s value can and should be determined before any significant investments are made to activate monetisation programs. A time frame of 1-3 months is standard to get empirical evidence of our data’s value.
Which monetisation approach fits with larger data strategy
As our world has become increasingly data-driven, so the ways of monetising data have developed. As our organisation grows, we need to decide which monetisation approach fits best with our larger data strategy, and which BI (business intelligence) and analytics platform can provide us with the data monetisation tools that are right for our needs.
Data as a service:
This is the simplest, most direct data monetisation method. Data is sold directly to customers or intermediates. The data is either raw, aggregated or anonymised. Buyers don’t benefit from receiving insights, nor do they benefit from advanced analytics.
Insight as a service:
This involves combining internal and external data sources and applying analytics to provide insights, though these are limited to specific datasets or contexts that the buyer has purchased.
Analytics-enabled platform as a service:
This is a more flexible type of data monetisation that provides much more value to customers. Analytics and BI platform is installed and implemented to provide customers with highly versatile, scalable data analytics in real-time.
Embedded analytics:
This is the most advanced and exciting way of monetising data that provides the most value to customers. Simply put, embedded analytics means adding features normally associated with BI software – such as dashboard reporting, data visualisation, and analytics tools – to existing applications.
According to Sisense, a leader in embedded business intelligence, product teams can build and scale customised actionable analytic apps and seamlessly integrate them into other applications, opening up new revenue streams and providing a powerful competitive advantage.
A Sisense spokesperson claims, “Ours is the only embedded analytics platform built from the ground up that provides both agility and performance with scale.
“This accelerates the time to market, lowers TCO, and creates an embedded analytics solution that is fully customised for specific needs.
Questions relating to privacy and security
Predictably, the rise of large-scale data collection and machine learning has been accompanied by pressing questions related to privacy, security and data access. Working with sensitive data is not trivial. The right combination of capability to monetise data and meet a clients’ risk tolerance has historically proven hard to implement.
Most approaches either significantly devalue your data, expose you to risk, or both. The risks are real. No amount of incremental revenue is worth landing in the headlines or losing customers due to privacy or regulatory infringement.
Maximising the value of your data
Start treating data as an asset and gain the benefits from maximising its value, says Ondrej Kulhanek, Principal, Data Management with KPMG in the Czech Republic, adding that too many companies lack metadata – i.e. data about data – such as the data quality, where it is stored and what it means. In fact, many companies are more likely to have a more detailed inventory of their office furniture than their own data!
“Before thinking about monetising data, companies need to discover what kind of data they hold about their partners, customers, products, assets or transactions and what publicly available data can be called on to increase the value of their proprietary data,” says Kulhanek.
“They must also work out whether that data is of value internally to cut costs, streamline operations or improve sales processes, or as an external revenue stream such as customer intelligence as a service, or both.”
Kulhanek advises businesses to embed data monetisation into business strategy and get the right structures in place.
“Too often corporate strategy is not supported by related data management initiatives and vice versa,” he asserts. “Executives should evaluate their key business goals and strategic initiatives through the lens of how data can support them.
“Once you understand the quality of data and have tied it to business strategy then you can put the right structures in place to monetise it. Often, this involves assembling a multi-disciplinary, cross-functional team – including information product leads, data management experts and executives from sales, marketing and operations – to create a platform for business innovation powered by the data.
“Together they can determine ownership structures for different data sets while making sure that sense of ownership doesn't result in data bottlenecks or siloes.”
Question: As broadcasters and media operators impacted by the pandemic, having re-evaluated your business goals, assets and strategies, are you partnering with online and e-commerce companies to help them ‘get reach quick’?
APB+ welcomes any case studies or ideas on how best the media can monetise its information assets that have relatively low inventory carrying costs and thereby data can be a high-margin source of revenue.
Please contact shirish.nadkarni@editecintl.com or maven@editelintl.com if you would like to share your story.