Aggregated enterprise architecture wisdom
Are you lost in a confusing soup of vendor-speak about what their data analytics stack actually offers? Big data, data platforms, advanced analytics, data lakes, real-time everything, streaming, the IoT, customer analytics, digital intelligence, real-t…
Notational – Symbolic Programming Introduced for Healthcare Analytics Quantum Mechanics Firepower for Healthcare Ecosystem Studies Interoperability Analytics Public Health and Patient Health Quantum Mechanics Driven A.I Experience Deep Machine Learning Descriptive and Inferential Statistics Definite and Probabilistic Reasoning and Cognitive… Continue Reading →
Bioingine.com; Platform for Healthcare Interoperability (large data sets) Analytics Deep Learning from Millions of EHR Records 1. Payer – Provider:- (Mostly Descriptive Statistics) Mostly answers “What” Healthcare Management Analysis (Systemic Efficiencies) Opportunities for cost reduction Chronic patient management Pathway analysis… Continue Reading →
You can’t turn anywhere without bumping into artificial intelligence, machine learning, or cognitive computing jumping out at you. Our cars brake for us, park for us, and some are even driving us. Our movie lists are filled with Ex Machina, Her, and Lucy. The news tells about the latest vendor and cool use of technology, minute by minute. Vendors are filling our voicemail and email with enticements. It’s all so very cool!
But cool doesn’t build a business. Results do.
Which brings me to the biggest barrier companies have in adopting artificial intelligence. Companies are asking the wrong questions:
These questions put artificial intelligence into the traditional analytic processes and technology adoption box. These questions assume you will begin from the same starting point as you did for big data. You are wrong: Artificial intelligence starts with the problem to solve and works backward.
To succeed at artificial intelligence you need to ask the right questions:
You can’t turn anywhere without bumping into artificial intelligence, machine learning, or cognitive computing jumping out at you. Our cars brake for us, park for us, and some are even driving us. Our movie lists are filled with Ex Machina, Her, and Lucy. The news tells about the latest vendor and cool use of technology, minute by minute. Vendors are filling our voicemail and email with enticements. It’s all so very cool!
But cool doesn’t build a business. Results do.
Which brings me to the biggest barrier companies have in adopting artificial intelligence. Companies are asking the wrong questions:
These questions put artificial intelligence into the traditional analytic processes and technology adoption box. These questions assume you will begin from the same starting point as you did for big data. You are wrong: Artificial intelligence starts with the problem to solve and works backward.
To succeed at artificial intelligence you need to ask the right questions:
It’s been awhile since I’ve blogged; not because I’ve had nothing to say, but rather because I’ve been busy with my colleagues Ted Schadler, James McCormick, and Holger Kisker working on a new line of research. We wanted to examine the fact that business satisfaction with analytics went down 21% between 2014 and 2015, despite big investments in big data. We found that while 74% of firms say they want to be “data-driven,” only 29% say they are good at connecting analytics to action. That is the problem.
Ted Schadler and I published some initial ideas around this idea in Digital Insights Are The New Currency Of Business in 2015. In that report, we started using the phrase digital insight to talk about what firms were really after ― action inspired by new knowledge. We saw that data and analytics were only means to that end. We also found that leading firms were turning data into insight and action by building systems of insight ― the business discipline and technology to harness insights and consistently turn data into action.
Here is a key figure from that report:
It’s been a while since I’ve blogged; not because I’ve had nothing to say, but rather because I’ve been busy with my colleagues Ted Schadler, James McCormick, and Holger Kisker working on a new line of research. We wanted to examine the fact that business satisfaction with analytics went down 21% between 2014 and 2015, despite big investments in big data. We found that while 74% of firms say they want to be “data-driven,” only 29% say they are good at connecting analytics to action. That is the problem.
Ted Schadler and I published some initial ideas around this idea in Digital Insights Are The New Currency Of Business in 2015. In that report, we started using the phrase digital insight to talk about what firms were really after ― action inspired by new knowledge. We saw that data and analytics were only means to that end. We also found that leading firms were turning data into insight and action by building systems of insight ― the business discipline and technology to harness insights and consistently turn data into action.
Here is a key figure from that report:
Cogito ergo sum [a] is a Latin philosophical proposition by René Descartes usually translated into English as ” I think, therefore I am“. Cartesianism – is usually understood as deterministic, i.e. about definite answers, deductive logic . Actually Descartes held that all existence consists in three distinct substances, each with its own essence… Continue Reading →
Chronology of Development of Hyperbolic Dirac Net (HDN) Inference. https://en.wikipedia.org/wiki/Thomas_Bayes From Above Link:- 1. 1763. Thomas Bayes was an English statistician, philosopher and Presbyterian minister who is known for having formulated a specific case of the theorem that bears his name: Bayes’ theorem. Bayes’s… Continue Reading →
In this step we develop one of the most important interim products for our decision model; the analytic user profile. A profile is a way of classifying and grouping what the user community is actually doing with the analytic information and services produced. We develop a quantified view of our user community so we can evaluate each platform or tool for optimization quickly and produce meaningful results aligned with usage patterns. 
Data consists of symbols that represent objects, events, and their properties. Information is data that has been made useful. Information answers the questions of who, what, where, when, and how many. Information is helpful in deciding what to do, not how to do it. Knowledge consists of instructions and know-how. Knowledge answers the ‘how’ […]
