Kevin Morgan, Principal Consultant at GreenBirch Group, discusses how firms are looking to benefit from the value in their own data:

In the coming years, organisations will look to their data not only as a source of insight for their existing business, but as a strategic foundation upon which to build new businesses, new client solutions and generate new revenue streams. The data many traditional enterprises create offers incredibly valuable insights for improving their current operational processes, if the data is structured and presented in a way that it can provide business benefit. 

As many firms start to build a data-driven culture to improve internal business practices, improve their customer service and derive valuable insight from their data, organisations are starting to realise that their unique data sets have value – and have looked into ways of commercialising it, e.g. for analytical research, data modelling or industry benchmarking. They are discovering strong demand for this data that can be packaged and sold to help other organisations make more informed business decisions and to help develop their customer relationships and trading strategies.  

However, many organisations lack the standardised processes and the technological infrastructure capable of collecting and transforming that data – so distributing that data internally across the organisation is challenging enough – let alone selling it externally.  

So why is this such a challenge? 

Current Landscape  

 Organisations have been sharing data since the 1850s when Reuters first started sending stock prices between London and Paris. Things have come a long way since then: tech giants such as Amazon and Google are leveraging data analytics to drive customer insight to target advertising and sales opportunities. 

However, recent scandals involving Cambridge Analytica have brought to the public’s attention how valuable data can be, especially when it gets into the wrong hands. Cambridge Analytica collected and analysed user data from Facebook and used artificial intelligence to predict and influence US elections and Brexit. This level of data awareness coupled with a number of high-profile data breaches and the introduction of GDPR means data is never far from the front of people’s minds. 

Data now takes centre stage in many organisations‘ corporate strategy as they strive to provide digital platforms to improve the customer experience and stay ahead of the competition. A great example of this is the digitisation of data products across the tier 1 global banks: opportunities and customer insight are being identified across business divisions through data aggregation strategies. Opportunities are being identified through data analytics and realised across commercial, private and investment banking divisions that previously worked in silos. An example could be where an SME has a growth strategy that can be supported through funding and access to the financial markets where multiple divisions combine to provide skills, expertise and knowledge of the customer to facilitate the client interaction.  

In April 2020, Burton Taylor reported that global market data spend for 2019 was up 5.6%, topping the 32-billion-dollar mark for the first time. This underlines the growing demand for premium datasets across the industry as hunger to consume data continues to grow.  

Organisations create and collect data as part of their core business, and often have transacted time series data collated and stored over long periods of time. This data can have significant value in the market place for both buy–side, sell–side institutions and corporates as they leverage data solutions to build competitive advantage and analytical data capabilities. Data is now seen as a valuable asset for institutions, and significant revenue opportunity for organisations prepared to evaluate the risk vs reward through a defined commercialisation process.  

Aligning Data Strategy and Technology 

Many organisations generate vast amounts of structured and unstructured data but struggle with the seamless provision of data to their internal and external customers. Innovation needs to be embedded in the culture of an organisation to drive thought leadership and implementation of new ideas and technologies. One of the causes of this problem is the lack of alignment and common objectives between the data and technology teams. There needs to be a strong partnership that works towards a common set of objectives to deliver an agreed data architecture and data strategy.  

Implementing a Data Business Strategy  

With the right level of support and sponsorship from the Executive Management Committee, and a clear mandate and vision to grow the data business there are great opportunities for the business to succeed and grow a data revenue stream. Valuing data as a strategic asset across the enterprise that is aligned to the corporate strategy and the operating model allows organisations to commercialise data assets through new data products and white labelling of data and analytics solutions to the market.  

By defining a data business strategy which is aligned to the corporate strategy there will be additional internal benefits that can be driven through enhanced analytic capabilities to improve operational efficiencies across the enterprise.  

Any commercialisation of the data product within an organisation needs to include stakeholders from the business, IT and the data teams to ensure success. Implementing strategy sessions and working groups will lead to identification of new opportunities that can help build a roadmap for prioritisation across the organisation for both data and the IT architecture. This will ensure that internal business problems and issues are captured and a balanced approach can be taken between improving the business and generating sales and revenue driven opportunities.   

As new product opportunities are identified and prioritised for investment such as new data sets and analytical tools, a common agreed framework and approach across the business lines will help to achieve the corporate and data strategy. 

With the roadmap agreed and investment prioritised, the introduction of next gen technologies, visualisation tools and machine learning will aid the development of new data products and enhanced analytic capabilities. This will not only help to create products for commercialisation for external consumption but will also allow organisations to improve operational efficiency through improved processes and reduce the number of manual interventions and reconciliations required to provide a clean data set for use across the enterprise.  

Building Enhanced Data Analytic Capabilities  

Data analytics and technology is one of the main building blocks of data commercialisation. As mentioned, analytics can be used to improve operational effectiveness, reduce costs and create revenue growth opportunities. By cultivating innovation across the enterprise and investing in state-of-the-art analytical capabilities, this allows organisations to compete in the digital age and develop products to serve the internal business and customers across the enterprise with their data needs. 

The introduction of machine learning and artificial intelligence can aid the speed in which organisations can transform their data offering and reduce the time to market for products.  

By evaluating technologies that solve specific identified business problems it will be possible to create a self-service analytical portal (user interface) where the business can develop data models and insights into their business processes that can drive data quality improvements, process improvements and identify cost optimisation opportunities. It will also provide customer insight and generate ideas as to how organisations can continue to grow and develop its business.  

Data Governance 

Within any data commercialisation programme, data governance is a key building block to achieving success and requires careful consideration. There are many key areas that need to be explored and fully understood to enable an organisation to leverage and maximise the commercial potential of their assets: 

• Clear ownership of the data and data model. 
• Rights Management and Commercial Policy, ensure that it is clearly defined with customer use cases to protect the data and its usage. 
• Protocols and policy defined around robust governance and security for internal data creation.  
• Critical data elements and quality metrics defined to report key performance, risk and control issues. 
• All new data products to follow the clearly defined protocols, policy and quality standards. 

Leveraging Partnerships 

Strategic partnerships can be a highly effective way to build business capabilities and products. These partnerships can offer unique competitive advantages where two organisations can leverage the strengths of each other to grow market share.  

By combining unique data sets with third party news, data analytic tools and other market information there will be opportunities to create derived data sets that can be used for quantitative analysis, forecasting and data modelling into applications that will have mutual benefits to both parties. There are also a number of opportunities that could be explored with alternative data providers and technology companies, there are many alternative data sets that are emerging and firms such as Quandl and Eagle Alpha who provide alternative data solutions to the financial markets and beyond.  

As well as the well–known distribution channels there are also opportunities to use vendor agnostic distribution platforms and technologies and it has never been easier or cheaper for data to be aggregated and distributed using off the shelf technologies, APIs and cloud–based solutions. 

Next Steps 

If you would like to start your data journey and discuss your data strategy and commercialisation opportunities, then please reach out to GreenBirch and we can help!