Smarter Information Supply Chains

Indeed, there is more to data science than combining programming skills with statistics. What is typically missed is an ability to observe and ideate with respect to business problems. There is a real risk of misinterpretation based on the assumption that ‘the data will tell me the answers’ as promises of autonomous analytics are promoted by platform providers or new automated algorithm selection or tuning frameworks such as DataRobot and Auto-sklearn. It is also likely that greater stratification and industry specialisation will occur as data science tools become more ubiquitous. An addition risk occurs with the definition and remuneration of a typical ‘data scientist’. Recent studies show that data scientist salaries have declined, which reflects the somewhat unrealistic financial outcomes expected from application of data science to business problems. At present data science is overtly orientated towards developing engineering solutions of business problems which are often undefined or poorly defined. The risk of over-promising goes hand-in-hand with the risk of undervaluing the human factors in data science.

The most immediate impact in this space is access and ready implementation of data agnostic geo-spatial and temporal analytic visualization tools. One example would be Uber Engineering’s open-sourced Kepler.gl. This heralds that maps are becoming a common layer to rapidly communicate or compile real-time streams of data to customers, partners and suppliers.

Data Marketplaces and Data Protectionism

Data has an inherent combinational value. This means that the ability to provide ‘insights-as-a-service’ APIs based on predictive or prescriptive analytics is contingent on both data quality and compounding data sources. In most cases, businesses only benefit from this ‘compounding factor’ by integrating open or licensed data with their own proprietary sources. In short, cognitive technology and more accurate predictions are powered by broader and deeper data. This exposes a major tension between data acquisition and data protectionism.

One one hand, data marketplaces, or platforms for securely and conveniently buying and licensing data or data models are proliferating. For instance, Microsoft, Adobe and SAP have recently announced an open data initiative together. From a business perspective this can allow the trading of aggregated merchant data about localised consumer purchases, insurance claims or even anomaly detection of financial transactions. However, this is tempered by a trend toward data localization whereby nations (such as China and India) or provinces are regulating cloud data to localise it at the point of origin.

This clash amongst public, government and technology companies will greatly influence how cognitive technology can take advantage of information supply chains.

Conversational Analytics

Natural language processing (NLP) is now well established across various consumer devices with estimations that already over 25 percent of queries on Android devices are voice-based. The new frontier for NLP is analytical insight tools. Reporting tools such as Tableau have recently released a product that use plain language to query complex combinations of data. The implications here are that analytics can become more accessible across the organisation driving broader adoption. Once again, the need for a data literate workforce – especially around framing inputs and interpreting outputs will be paramount.

IMPLICATIONS

It would be naïve to assume that cognitive technologies alone will seamlessly offer automated analytical capabilities and business transformation. As ever, CIOs need to balance rapid experimentation and judicious evaluation with a willingness to understand the cultural and political factors which are shaping information supply chains and the data economy. An approach which acknowledges a need for a data-literate workforce and an awareness of greater regulation will best serve the opportunities that machine learning and predictive analytics offer.