Early adapters are deploying systems of intelligence that will integrate data lakes, machine learning, systems of engagement and systems of record, and will self-tune themselves to business objectives writes Wikibon Big Data Analyst George Gilbert. Wikibon’s big data community is talking about deployment patterns that build on each other, including mastering data lakes, designing intelligent systems of engagement and evaluating intelligent self-tuning systems.

Wikibon community members are applying data they previously could not collect to find answers to questions they did not know they had. Approximately 80 percent of these enterprises have had at least one good experience applying the data lake pattern to a previously intractable application problem. However, success is hit or miss because of the challenges of working with multiple new data sources and analysis tools and administering multiple analytic engines in Hadoop.

Some enterprises are enhancing systems of engagement to manage individual consumer experiences across multiple channels in real time. For instance, a telco might prioritize call connections in real-time for high-value customers in cell zones suffering high congestion.

A few organizations are applying big data technologies to applications to tune predictive models. The goal is to sustain quality by minimizing drift, as new data points are added, without requiring frequent interventions by data scientists. These systems often must also coordinate multiple systems of record simultaneously.

A new report from Gilbert, “Enhancing Systems of Intelligence”, available to Wikibon Premium subscribers, discusses these trends, which Gibert documented in multiple interviews with Wikibon community members who are actively involved in big data projects. The report discusses the goals and challenges of these advanced projects.

Graphic  © Wikibon 2015