Various Reasons
Reasons for slower progress are various. In retail, an increase of 60% in net margin was forecasted, while the realized value so far ranges between 30% and 40%. In manufacturing, cost reduction in product development and operating costs have reached only 20-30% of the estimated target. In the EU public sector, an increase of 0,5% in productivity due to analytics was forecasted, with actual value captured being at 10-20%. In health care, the forecasted productivity growth of 0,7% has reached only 10-20%. Reasons for failing to meet the potential in full include the lack of analytics talent, the difficulty in integrating and sharing siloed datasets and lack of incentives.
The scarcity of skilled data analysts seems in fact to be a main bottleneck. While the number of graduates in data science programs has been increasing by about 7% per year, the demand is forecasted to grow by 12%, leading to a shortfall of around 250,000 data scientists.
Among the six most disruptive business models enable by analytics technologies for the years to come, McKinsey sees the following:
- Business models enabled by orthogonal data: bringing in external data sources to supplement existing data sets to deliver added value. A relevant data source is the sensor data available as part of the internet of things (IoT), allowing real-time monitoring of many variables.
- Hyperscale, real-time matching: deploying analytic technologies to match need and demand in real time, e.g. in logistics, smart cities, mobility, etc.
- Radical personalization: allowing to tailor products and services to the preferences and needs of groups or individuals, an example would be the ability to deliver treatments tailored to smaller, targeted patient groups in healthcare / pharma. In addition, personalization would allow to invest in wellness and prevention instead of only on disease treatment. The impact of personalized medicine is regarded to range from $2 to $10 trillion.
- Massive data integration capabilities: enabling better cross-selling, development of personalized products, dynamic pricing, better risk assessment, and more effective marketing, e.g. in the retail banking industry
- Data-driven discovery: supporting product innovation in materials science, synthetic biology and life sciences. Leading pharmaceutical companies are using analytics to aid drug discovery, for instance.
- Enhanced decision making: analytics-based decision making could prevent errors (e.g. in medical diagnosis and treatment), e.g. by flagging allergies or dangerous drug interactions, as well as contribute to a more transparent labour market by providing access to data on the demand and supply for particular skills, salaries, value of degrees, etc.
http://www.semalytix.de/