My latest posts on Active Information:
Data Scientists & Business Intelligence Pros, what’s the difference?
In a way, you could say data science leans towards innovation, while business intelligence leans towards optimization. Each are critical for business, government and societal progress.
For the vast majority, Lin’s breakthrough is a complete surprise. However, for numbers hobbyist Ed Weiland, Lin’s breakthrough was merely a matter of time.
My latest posts on Active Information. It’d be fair to say I’m more focused on raising ideas than hit counts.
Software Architect Lessons from Amazon’s DynamoDB
I took a bit of a tangent (shocking!) on the DynamoDB announcement, pulling lessons from Werner Vogels’ recounting of the DynamoDB genesis that every software architect should embrace.
More data, more collaboration, more power.
In another showing of me being me, I ferret out a counter intuitive idea on the human change of data-driven decision-making.
The official excerpt:
“When you think about the human resistance in adopting data-driven decision-making, or really any change, at the root is the me question. What is the impact on my job, my span of control, my future opportunities?”
This week on Active Information, I expanded on a random thought that popped into my head while watching the Patriots-Broncos game. Go Pats!
Football and Weekend Data Warriors
This week on Active Information I riffed on a WSJ article that riffed on Daniel Kahneman‘s Thinking, Fast and Slow, which led me into the data scientist shortage and analytics-as-a-service.
Alas, as I didn’t lead with any of those buzzwords in the title, the post is sadly under-read. Anyway, the link and blurb follow. I’m off to hone my buzzword skills.
Quite possibly, we will find ourselves in a “there’s an algorithm to decide that” world. But, until the talent shortage is stemmed, we’ll need to get our rationality delivered.
My latest posts on the HPIO Active Information blog:
Streaming through a Computational World — (most popular post to date)
To take advantage of the computational world, or the nearer term internet of things, we need to infuse smarts throughout our data collection networks. We need to employ up-front and intermediate filters, traffic cops, aggregators, pattern detectors, and intelligent agents. We need to get over being data hoarders, and have the astuteness to leave data behind.
Busting cultural resistance via experimentation platforms — (changing change)
Culture, mistrust of the data, lack of interest. These very human factors are adoption barriers for 46% of the respondents. Yet, these barriers aren’t new. Nor, confined to big data and advanced analytics. To change a culture, you need to bring proof to the table. And proof requires hands-on experimentation and real-world data. We need data to prove that we need data. How will we get that?
My latest posts on the HPIO Active Information blog:
Ready or not, here comes Big Data
Sometimes though, a trend is so compelling (e-commerce, mobility), in-your-face (social media) or simple to comprehend (cloud), that it leaps into mainstream media and takes on a life of its own. Instead of playing the role of serial advocate, corporate IT leaders and architects are suddenly in a game of catch-up.
Rx for AstraZeneca: Real-world evidence
Rethinking their prelaunch process, and data needs, AstraZeneca proposed a data collaboration with customers.
And one of my favorites:
Why do we still have titled CIOs, yet no clear candidate C-level executive to manage the organization’s information agenda?
5th Anniversary Edition
Event-Driven Architecture Overview
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