Architecting Business Disruption with Technology and People at Data Summit 2017

Data Summit 2017 kicked off on Tuesday at the New York Hilton Midtown with keynotes by futurist, educator, and author Thornton A. May and Rob Thomas, general manager of IBM Analytics. 

In his keynote, “The Human Side of the Data Revolution," May observed that data is impacting every aspect of life today, and stressed the importance of not only technology but people, including talking to peers, querying the solutions community, and establishing advisory boards.

Critical to the process is the need to answer the needs of the C-level executives, and interact with an stakeholders while also identifying people’s roles within organizations in terms of who is holding back (blockers) or facilitating (champions) big data initiatives.

More data is not the answer – doing more with all that data is critical, and power in society will be related to creating value with data, added May, noting that “the hero of the age we are entering will be the person who effectively masters data.”

Ninety percent of the world’s data cannot be googled, and is sitting within organizations – but putting that data to competitive advantage is key, agreeed IBM's Thomas in his keynote, titled, “Architecting Business Disruption with Machine Learning.”

While some people may feel that data science and machine learning are areas that have been covered for years, Thomas said that the key difference between now and 10 years ago is the cost of big data projects that is consumed by storage and compute.  Those costs have gone down dramatically, he noted, meaning that there is far more budget left for budget for algorithm development.

The top use cases for data science and machine learning now include healthcare – for patient diagnoses, finance – for fraud detection, manufacturing – for anomaly detection, retail – for inventory optimization, insurance – for risk scoring, transportation – for demand forecasts, network – for intrusion detection, e-commerce – for recommendations, and marketing – for customer segmentation.

And today, critical elements for big data project success include factors such as unified governance, hybrid data management, data analytics and visualization, and open source technologies. It is no longer possible to base a strategy on proprietary projects, he stressed.

Machine learning and data science can’t be strictly about technology, he added. Data science has to become a team sport, and it is critical to come at it with a point of view on what you want to do with that data for the business, he stressed.

Many conference presentations have been made available by speakers at