In a recent blog, we talked about Attivio’s “Sherlock” cognitive search campaign, which takes aim at IBM’s Watson. We noted that organizations deploy cognitive search platforms to boost employee productivity, foster innovation, and gain greater insight from their data. But to achieve those goals, they often take on huge professional services from “mega vendors” like IBM that don’t deliver an effective cognitive solution.
So, of course, we were very pleased to see in the just released Forrester Wave™ for Cognitive Search And Knowledge Discovery Solutions (Q2 2017) that we were in the leader category. And well, Watson didn’t make it.
Benedict Cumberbatch, star of the BBC series “Sherlock,” has a problem. Sherlock’s stream-of-consciousness deductive speeches must be delivered at warp speed— “100 miles an hour”—and that’s hard to pull off without mistakes.
But, of course, all that speed makes sense. Holmes observed, processed, and bang! Insight. That’s rapid time to value.
Time to Value: The Missing Ingredient
Organizations deploy cognitive search platforms to boost employee productivity, foster innovation, and gain greater insight from their data. But to achieve those goals, they often take on huge professional services from “mega vendors” that don’t deliver an effective cognitive solution.
Take a look at this unbelievable Marketing stack from Cisco. I’m assuming that this all stands behind Cisco.com and is representative, but not exhaustive. Even so, have you ever seen anything so well thought out, open, and cutting edge – let alone so well laid out? Can you imagine the infrastructure budget that Marketing Ops team has – mercy!
Sir Arthur Conan Doyle’s 1886 fictional “consulting detective,” Sherlock Holmes, was a great mind renowned for his highly advanced powers of observation and reasoning. He was often assisted by Dr Watson, who was unfailingly loyal, if noticeably less bright. At the end of each thrilling tale starring the duo, the anxious reader would always be delighted to hear Sherlock announce that he had solved the latest mind-bending riddle, inevitably characterizing the solution to his trusty helper as, “Elementary, my dear Watson!”
Once Chief Data Officers have identified, confronted, and hopefully overcome the challenges at the bottomand in the middleof the Big Data stack, what’s next? As Andrew Brust of Datameer notes, “At the top of the stack, there are seemingly endless choices. Whether Enterprise BI stalwarts, BI 2.0 challengers, or big data analytics players, the number of vendors and their similar positioning makes it really hard for customers.”
In my last post, I wrote about what Chief Data Officers tell us are the most pressing challenges they face at the bottom of the Big Data stack —mainly creating a hybrid data architecture that can accommodate modern and legacy data sources. Once that architecture is in place and we move up the stack, CDOs encounter another set of challenges.
Writing on the O’Reilly.com site back in August, CEO Jessie Anderson of Smoking Hand, a training company for Big Data technologies, commented on the overall complexity of Big Data, NoSQL technologies, and the distributed systems that deploy them.
Chief Data Officers certainly have first-hand knowledge of this complexity and the hurdles it presents to extracting the maximum value out of business data. Complexity takes a variety of forms throughout the Big Data stack. Let’s start at the bottom.
Chief Data Officers have a lot of things on their plates. And one of those things is giving users freer access to the data they need. This is what we call "data democracy." Most CDOs like the idea of data democracy in theory. But in practice, the CDOs we talk to find that efforts to create a data democracy face at least four common barriers:
Search professionals from across the nation are gathering in Washington DC this week for the Enterprise Search and Discovery Summit at KMWorld. For some, the looming abandonment of Google’s Search Appliance (GSA) solution will be a topic of discussion.
When Google announced plans to withdraw its appliance from the market earlier this year, many companies found themselves catapulted into a time-sensitive hunt for a Google search appliance replacement and upgrade. According to this CMSWire story, the clock is ticking and “you could have from 12 to 24 months before your search servers become bright yellow room warmers” and that’s not even factoring in the time to investigate, test, and implement a new solution.
