Dataconomy

SEPTEMBER 1, 2017

Data innovation and technology are a much discussed but rarely successfully implemented in large financial services firms. Despite $480 Billion spent globally in 2016 on financial services IT, the pace of financial innovation from incumbents lags behind FinTech which received a comparatively puny $17 Billion in investment in 2016. What. The post Why large financial institutions struggle to adopt technology and data science appeared first on Dataconomy.

Data Science 196

Data Science 196

Dataconomy

AUGUST 30, 2017

Rich Miner — Android co-founder, formerly Google Ventures, now Google — asked me recently “What if companies managed their data like they manage their money?” It’s a basic but profound question that merits some thoughts based on my 25 years managing both information and financial functions in technology and data. The post What if companies managed their data as carefully as they manage their money?

Big Data 191

Big Data Big Data 191

Dataconomy

AUGUST 28, 2017

Technologies in the field of data science are progressing at an exponential rate. The introduction of Machine Learning has revolutionized the world of data science by enabling computers to classify and comprehend large data sets. Another important innovation which has changed the paradigm of the world of the tech world. The post Trends Shaping Machine Learning in 2017 appeared first on Dataconomy.

Machine Learning 185

Machine Learning Machine Learning Data Science Algorithm 185

Dataconomy

AUGUST 29, 2017

Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Semantic Graph Database technology for Knowledge Graphs, recently announced Gruff v7.0, the industry’s leading Graph Visualization software for exploring and discovering connections within data. Gruff provides novice users and graph experts the ability to visually build queries and explore.

Artificial Intelligence 150

Artificial Intelligence Artificial Intelligence Database AI 150

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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

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