Data is the new oil has become an old saying now. However, unlike oil, you don’t see it being traded as much. The biggest players trading in data are still the same players as from 10 years back. One would have expected that the firms generating data would be trading it directly with data consumers. Why then, has the exchange of data primarily been dependent on syndicated providers? Where is the spread of data marketplaces that many had predicted? There is one answer – data quality and data reliability.
Data quality is why most enterprises still don’t use their own data for business gain to the extent they should. Their decision-makers don’t completely trust their data. Sharing it with subscribers i.e., creating marketplaces, is a relatively distant objective. That is where the syndicated data providers come in to fulfill an important function, of data quality assurance, and data alignment across sources.
Having said that there are some firms out there that have created revenue streams by sharing data, and as a collateral benefit, data-driven decision-making has increased within their own organizations as well. There is a retailer in Europe that leverages its POS and loyalty data to offer insights on subscriptions to CPG brands. The marketplace platform created has many of their own merchandisers, marketers, and logistics managers using it (for free of course). While the CPG subscription revenue pays for the platform and more.
Similarly, there is a European telecom firm that provides anonymized and aggregated behavior data on a marketplace. And many B2C marketers are subscribers today. In fact, they also allow individuals or small businesses to provide their personal data for a fee, on the marketplace.
A third example is a large sourcing firm in Asia, that provides data and insights on a commercial basis to subscribers. The buyers of such data are those who are interested in the growth of industries in Asian regions. Information like – are apparel manufacturers growing less in South China? Is the Philippines catching up in electronics manufacturing? Is the Indonesian export economy expected to grow? How is India disrupting Asian markets? – are available for a price. Subscribers include banks, analysts, and now governments.
When marketing wastage or product design failure is reduced, or when governmental strategies are more data-based, everybody gains. The Bureau of Transportation Statistics in the US provides significant aggregated and collated data that can help any logistics analyst. Now we find some large logistics providers have started collating such data across the bureau’s data, their data, and their fleet suppliers’ data and are offering insights based on the same. This is the evolution of the data economy.
To operate in this economy, there are seven essential elements to consider:
- Data – Reliable data, automation in data alignment, cleansing, and correction.
- Insights – Models deriving insights provided on the platform so that it is not just data but an insight marketplace.
- Self-service – Aggregated data provided with self-service capabilities to slice and dice.
- Partitions – Subscription level-based data and insights access. Implement checks to ensure subscribers (and through them competition) do not get access to insights that can be used to the disadvantage of the entity sharing the source data.
- Operating income – The pricing should take a cue from cloud menu costing and could even leverage the elasticity of the cloud to provide the services.
- Regulations – Privacy, HIPAA, and AI laws are prevalent in this kind of business. Ensuring data or insights do not violate any law requires adequate metadata and monitoring.
- Governance – This is not just about creating a platform and allowing subscription on it. The governance mechanism must ensure automation and audits in load balancing, peak handling, metric definitions, numbers reconciliation, complaint handling, and more.
Sooner rather than later, businesses will invest in better data quality for their internal decisions and for AI adoption. And as their confidence in their data increases, they will start exploring how to commercialize the insights they can get from it. The data economy is taking time to materialize, and it will be a gradual sunrise rather than a sudden burst of light. But it is only logical that it will be around us soon.