Executive Summary

AA Report in 1 Minute

Agile Data & DataOps in Advanced Analytics

In Advanced Analytics, Agile methodology is necessary with its wider approach than usual: it considers not only the different aspects in a web/mobile development, but also every dimension included in Data Management: Data Security, Data Quality, traceability, Master Data. There is, however, an acute need for highly qualified personnel as a result.

However, it poses work-related difficulties for the DataOps & DevOps team —endlessly looking for more storage server space— stemming from the great quantities of data generated.

Firms need solutions to overcome: a) the great quantity of data; and b) the shortage of tech talent:

  • For a): they need some ‘tin’ to store it in.
  • For b): they need an end-to-end solution –SQL, R, PYTHON–.

Adjuvant Technologies

Reasons to pool data in Credit Unions: 1) they do not have enough data to build accurate predictive models. 2) if a data scientist works on a Data Pool OF 50 Credit Unions, they split the cost of the data scientist.

The union of these technologies is a result of the need to give access to data to as many people as possible within the firm, making Analytics and Machine Learning friendlier.

Combining these technologies enables and extends digital transformation projects at the Edge. Edge Computing devices provide connectivity and protection for new and existing Edge devices. 5G provides the Edge with far better connectivity and lower latency to cloud based applications. But cost of processing and storing the data is still there. A hybrid edge compute/5G solution reduces these costs.

Artificial Intelligence (AI) & Automation

Top AI and Analytics trend for 2019: use of advanced algorithms to identify and optimize business insights.

Algorithms: use of advanced computing algorithms to identify and optimize business insights that humans cannot spot. AI will deliver 2 trill $ in business value worldwide in 2019.

Algorithms are the future of business. And Machine Learning and Deep Learning –the tools for processing algorithms—are the doorway into a new era of Artificial Intelligence (AI).

Machine Learning and AI generate higher quality predictive and prescriptive insights, where the important goals are: a) accuracy in recommentations and predictions; and b) explainability of the predictions.

Examples

It is possible to deliver new insights through Natural Language Processing (NLP) & Analytics from the huge trove of phone-based consumer interaction . Firms can use NLP to surface keywords and topics to make recorded content discoverable, allowing access to key insights.
Actionable Analytics
AI + Deep Learning are tackling common problems in healthcare. Smartphone-based cognitive behavioral therapy and integrated group therapy are treating depression, eating disorders, and substance abuse.
Healthcare