Aggregated enterprise architecture wisdom
By Steve Nunn, President and CEO, The Open Group Every holiday season, there is always one gift everyone just has to have. This past year, that honor went to the hoverboard, a self-balancing scooter reminiscent of the skateboards many of … Continue reading →
The Forrester Wave for Master Data Management went live today. The results may surprise you.
MDM tools today don’t look like your father’s MDM. No longer an integration hub between applications and DBMSs, today’s tools are transitioning or have reinvented MDM to handle the context missing from system traditional implementations. Visualizations, graph repositories, big data and cloud scale, along with application like interfaces for nontechnical users, mean MDM and master data gets personal with stakeholders.
Semantics and insight are not an outcome of MDM but an integrated part of the engine and hub. Three MDM evolutions stand out:
The Forrester Wave for Master Data Management went live today. The results may surprise you.
MDM tools today don’t look like your father’s MDM. No longer an integration hub between applications and DBMSs, today’s tools are transitioning or have reinvented MDM to handle the context missing from system traditional implementations. Visualizations, graph repositories, big data and cloud scale, along with application like interfaces for nontechnical users, mean MDM and master data gets personal with stakeholders.
Semantics and insight are not an outcome of MDM but an integrated part of the engine and hub. Three MDM evolutions stand out:
Businesses must be designed for change. Otherwise they may fall behind and fail.
Businesses must be designed for change. Otherwise they may fall behind and fail.
Greger Wikstrand and I have been trading posts about architecture, innovation, and organizations as systems (a list of previous posts can be found at the bottom of the page) for quite a while now. His latest, “Technology permeats innovation”, touches on an important point – the need for IT to add value and not just […]
We’ve seen another acquisition in the shifting eDiscovery market this week as kCura, the developer of Relativity, announced its acquisition of Content Analyst Company, the brains behind the CAAT analytics engine (kCura’s press release is here). The acquisition is not entirely surprising. kCura has been relying on the CAAT engine to power its analytics offering for eight years. According to kCura, use of its Relativity Analytics offering “has grown by nearly 1,500 percent” since 2011, with more than 70% of current kCura’s customers with licenses.
What does this acquisition mean for kCura, its customers, and Content Analyst Company customers?
We’ve seen another acquisition in the shifting eDiscovery market this week as kCura, the developer of Relativity, announced its acquisition of Content Analyst Company, the brains behind the CAAT analytics engine (kCura’s press release is here). The acquisition is not entirely surprising. kCura has been relying on the CAAT engine to power its analytics offering for eight years. According to kCura, use of its Relativity Analytics offering “has grown by nearly 1,500 percent” since 2011, with more than 70% of current kCura’s customers with licenses.
What does this acquisition mean for kCura, its customers, and Content Analyst Company customers?
This is more than just one vendor acquiring a partner to bring its tech in-house. The markets kCura competes in are changing. Customers want better predictive coding workflows, reporting, and visualization capabilities. The momentum around technology-assisted review (TAR) in eDiscovery is growing globally. In February 2016, the Pyrrho Investments Limited v. MWB Property Limited case gave the green light to predictive coding software in the UK, with the decision (PDF) citing acceptance in US and other jurisdictions. Interest and adoption of analytics for eDiscovery and other investigative use cases will only grow. Now that machine learning and technology-assisted review processes have been OK’d by the courts, many of the objections to using software for automated categorization, security classifications, and other analysis of textual data will dissipate.
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:
I was recently attending a business event and I was conversing with a gentleman who happened to be the head of human resources of his company, he was sharing with me that among his employees 15 percents were top performers. I happened to have in mind the statistics by Gallup[1] of the level of employees […]
This week’s episode of Tom Cagley’s Software Process and Measurement (SPaMCast) podcast, number 385, features Tom’s essay on Agile portfolio metrics, Kim Pries talking about the value of diversity, and a Form Follows Function installment on sense-making and decision-making in the practice of software architecture. Tom and I discuss my post “Architecture and OODA Loops […]