Aggregated enterprise architecture wisdom
Customers’ perception of a company depends on their experiences with the organization at every point of contact. Companies can try to change how customers view a brand in a number of ways, such as a new mobile app or an improved complaint-handling proc…
Huawei Technologies started out nearly 30 years ago as a small private company with 14 employees and 140,000 yuan in capital. By 2015, its total revenue exceeded $60 billion. Huawei is already a global company, but its globalization journey has been a …
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…
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:
Predictive analytics has become the key to helping businesses — especially those in the highly dynamic Chinese market — create differentiated, individualized customer experiences and make better decisions. Enterprise architecture professionals must t…
Predictive analytics has become the key to helping businesses — especially those in the highly dynamic Chinese market — create differentiated, individualized customer experiences and make better decisions. Enterprise architecture professionals must t…
Three of four architects strive to make their firms data driven. But well-meaning technology managers only deal with part of the problem: How to use technology to glean deeper, faster insight from more data — and more cheaply. But consider that only 2…
Three of four architects strive to make their firms data driven. But well-meaning technology managers only deal with part of the problem: How to use technology to glean deeper, faster insight from more data — and more cheaply. But consider that only 2…
You can’t bring up semantics without someone inserting an apology for the geekiness of the discussion. If you’re a data person like me, geek away! But for everyone else, it’s a topic best left alone. Well, like every geek, the semantic geeks now have their day — and may just rule the data world.
It begins with a seemingly innocent set of questions:
“Is there a better way to master my data?”
“Is there a better way to understand the data I have?”
“Is there a better way to bring data and content together?”
“Is there a better way to personalize data and insight to be relevant?”
Semantics discussions today are born out of the data chaos that our traditional data management and governance capabilities are struggling under. They’re born out of the fact that even with the best big data technology and analytics being adopted, business stakeholder satisfaction with analytics has decreased by 21% from 2014 to 2015, according to Forrester’s Global Business Technographics® Data And Analytics Survey, 2015. Innovative data architects and vendors realize that semantics is the key to bringing context and meaning to our information so we can extract those much-needed business insights, at scale, and more importantly, personalized.