Features of future AI-Powered Data Science

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Domain specialization of Analytics platforms: The next-generation of Analytics will rely heavily on domain specialization, thus delivering solutions for target industry sectors. Data Science is Changing and Data Scientists will Need to Change Too – Here’s Why and How from Data Science Central describes Advanced Analytics platforms with access to third-party GIS and consumer data. The current market trends in Business Analytics indicate that the platform strategy will soon shift from being a “one-stop, general purpose” platform to a domain-specific solution geared to industry sectors such as e-commerce, finance, HR, manufacturing and so on.

Source: Google Images


Automation of Analytics processes: More than 40 percent of Data Science tasks will become automated by 2020. Significant Analytics processes like Data Preparation or Data Modeling will become automated in most cases. Automation tools like SPSS and Xpanse Analytics are already in wide use. The learning algorithms of ML-powered, AI solutions will provide quicker and better results over time. The McKinsey article What’s Now and Next in Analytics, AI, and Automation provides a clear vision of the digitized future, where advanced digitization of business processes will be a differentiator between businesses that survive and those that perish.

The Middle Layer of the Analytics stack will absorb the Data Science: The Data Science smarts will be hidden in the middle layer of the Analytics platforms, as is evident in many VC-funded startup Analytics solutions.

Multi-skilled Data Scientists will be required: In addition to being highly skilled in their fields, future Data Scientists will be knowledgeable in industry domains to succeed in their jobs. Without the adequate domain knowledge, the future Data Scientists will not be able to quickly translate a business problem into a Data Science.

Predictive Analytics will require divergent skills for different industries: Predictive Analytics is becoming so specialized and divergent across industry sectors that the future Predictive Analytics tools and features will be tuned for industry-specific applications.

Citizen Scientists will perform sophisticated Analytics: Analytics platforms will become so well-equipped that Citizen Data Scientists will be able to execute Advanced Analytics tasks without the help of experts.

 Deep Learning will be simplified and operationalized: Deep Learning (DL) requires more simplification for full adoption into Business Analytics platforms. DL techniques hold groundbreaking promise for significant applications in forensic science through highly accurate facial recognition, and the wide adoption of this technology into Analytics platforms will be a game-changer for the Business Analytics solution provider market.

Source:http://www.dataversity.net

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