Data hunting
Just like headhunting, you need to know which data has value and when it is available. Data brokerage for new — monetization — products is upcoming fast.
Understanding which data is available and how it can be of value for e.g. AI, data scientists or new products is not new. The last 5 years, data scientists performed their analysis based on mostly internal data with added value from either open data and/or data from trusted partners.
Data specialists will recognize this, organisations have made great strides in identifying and mapping INTERNAL systems, processes and its data, sometimes also making it centrally available. Aided by a central data catalog. However, data developments are going faster than ever. Companies are looking for available external data to identify new values and new products. This definitely goes beyond the open data initiatives. And also beyond single cooperation with trusted partners , e.g. within clinical health sector or for the greater good to resolve energy & climate challenges. Companies are looking for consistently available, accessible and trusted data which they can use for new products.
There are multiple ways to enable data availability, e.g. within a shared environment with equal rights, or providing data on subscription base (i.e. the facto monetizing data). That is not what this article is about, you can find more info in of my other articles here. There are also multiple ways to enable technologies for data availability. Often this is the first focus of companies. And technology also requires sufficient attention. Being flexible in a transforming SAAS market, with rapidly evolving standards and legislation means constant development and deployment. Technology capabilities are therefore highly valued. However, it should not be the only focus. Make sure there is a strategy and road map to ensure that the investment for data availability (and the underlying technology) is worth it.
Knowing the landscape of available data and potential obstacles (e.g. bad data quality or data privacy issues) is a hot topic, as it is a valuable asset. Companies focusing on data ecosystem play have an advantage, they are understanding this value. These companies are already started to cultivate a vendor & collaboration network. Often starting within their own sector and fast extending towards a network of motivated parties with similar interests and pursue similar objective. Participating multiple categories of data suppliers enables this objective, because this way the data they share through their network is often similar and/or complementary.
The first step is to identify value generation from data and/or analytics. There are multiple ways to monetize them, ranging from aggregated data to practical dashboards (see also: A brief guide to productize data. There has never been a better time to… | by Marinka Voorhout | Medium). Find the best business model for it.
Multiple data products can be obtained through a multitude of channels — including data brokers, data aggregators, and analytics platforms — and this number is growing fast. Identifying available data therefore requires considerable effort, given that the external-data environment is fragmented, suffers from data inconsistency between suppliers and is expanding quickly.
Analyzing the quality and economic value of data products also can be difficult. Moreover, efficient usage and deployment of external data may require updating existing data environment, including changes to systems and infrastructure.
The challenges of understanding the availability of data and tapping external sources are considerable but surmountable. Making data available from and to partners is needed to ensure participation. Choosing the right partners, companies need to be able to create and explain their unique value proposition which are beneficial for customers and potential partners. The latter should complement and support each others strategic ambitions.
For companies, it also means that data capabilities will need to change. Current capabilities often focus on data quality, identifying internal data and/or AI pilots with limited external data. And mostly on a tactical level at best. New capabilities for data hunting needs to become in place, very similar to the traditional head-hunting. This includes establishing an external-data strategy team that scouts data, develops relationships with data brokers and trusted partners for external data sourcing. The Chief Data Officier, as executive sponsor of a data effort and its data-focused teams must rigorously analyse, evaluate and test external data before using and deploying data at scale. As well as understand and define the value of company data to act as data broker themselves. The team must cooperate with partners. And finally develop an up-to-date EXTERNAL map of available data aligned with the above mentioned internal data map.
Data hunting and brokering goes beyond the data domain. This means that data teams must cooperate closely with its companies’ purchasing/procurement experts who can also enable trial usage, data engineers for relevant data ecosystem requirements and adjustments, data scientists and analysts to identify relevant monetization opportunities. The CDO should guard whether the defined objectives, strategy and business case for purchasing and share data are constantly being met.
Data sharing — on any ecosystem play environment — must of course always be in accordance with e.g., security, privacy, FAIR use, data consistency and data quality. Find out more on trusted data here.
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