HP Enterprise Spins Software Division into Micro Focus International
October 23, 2017
It would seem that the saga of HP’s lamented 2011 Autonomy acquisition is now complete—Reuters announces, “Hewlett Packard Enterprise to Complete Software Spin-Off.” Reporter Salvador Rodriguez explains:
The enterprise software businesses, which include the widely used ArcSight security platform, have been merged with Micro Focus International Plc (MCRO.L), a British software company. HPE was formed when the company once known as Hewlett-Packard split into HPE and HP Inc in November 2015.
The spin-off comes as HPE adjusts to the rapid shift of corporate computing to cloud services offered by the likes of Amazon.com Inc (AMZN.O) and Microsoft Corp (MSFT.O). HPE aims to cater specifically to customers running services both on their own premises and in the cloud, said Ric Lewis, senior vice president of HPE’s cloud software group, in an interview.
The spin-off marks the end of HP’s unhappy tangle with Autonomy, which it acquired for $11 billion in an aborted effort to transform HP into an enterprise software leader. The ink was barely dry on the much-criticized deal when the company took an $8.8 billion writedown on it.
But wait, the story is not over quite yet—the legal case that began when HP sued Autonomy ’s chief officers continues. Apparently, that denouement is now HPE’s to handle. As for Micro Focus, Rodriguez reports it will now be run by former HPE Chief Operating Officer Chris Hsu, who plans to focus on growth through acquisitions. Wait… wasn’t that what started this trouble in the first place?
Cynthia Murrell, October 23, 2017
MarkLogic Aims to Take on Oracle in Enterprise Class Data Hub Frameworks
October 10, 2017
MarkLogic is trying to give Oracle a run for its money in the world of enterprise-class data hubs. According to a recent press release on ITWire, “MarkLogic Releases New Enterprise Class Data Hub Framework to Enhance Agility and Speed Digital Transformations.”
How does this Australian legend plan on doing this? According to the release:
Traditionally, integrating data from silos has been very costly and time consuming for large organizations looking to make faster and better decisions based on their data assets. The Data Hub Framework simplifies and speeds the process of building a MarkLogic solution by providing a framework around how to data model, load data, harmonize data, and iterate with new data and compliance requirements.
But is that enough to unseat Oracle, who has long had a seat at the head of the table? Especially, since they have their own new framework hitting the market. That is still up for debate, but MarkLogic is confident in their ability to compete. According to the piece:
Unlike other databases, NoSQL was specifically designed to ingest and integrate all types of disparate data to find relationships among data, and drive searches and analytics—within seconds.
This battle is just beginning and we have no indication of who has the edge, but you can bet it will be an interesting fight in the marketplace between these two titans.
Patrick Roland, October 10, 2017
AI Is Key to Unstructured Data
October 5, 2017
Companies are now inclined to keep every scrap of data they create or collect, but what use is information that remains inaccessible? An article at CIO shares “Three Ways to Make Sense Out of Dark Data.” Contributor Sanjay Srivastava writes:
Most organizations sit on a mountain of ‘dark’ data – information in emails and texts, in contracts and invoices, and in PDFs and Word documents – which is hard to automatically access and use for descriptive, diagnostic, predictive, or prescriptive automations. It is estimated that some 80 percent of enterprise data is dark. There are three ways companies can address this challenge: use artificial intelligence (AI) to unlock unstructured data, deploy modular and interoperable digital technologies, and build traceability into core design principles.
Naturally, the piece elaborates on each of these suggestions. For example, we’re reminded AI uses natural language processing, ontology detection, and other techniques to plumb unstructured data. Interoperability is important because new processes must be integrated into existing systems. Finally, Srivastava notes that AI challenges the notion of workforce governance, and calls for an “integrated command and control center” for traceability. The article concludes:
Digital technologies such as computer vision, computational linguistics, feature engineering, text classification, machine learning, and predictive modeling can help automate this process. Working together, these digital technologies enable pharmaceutical and life sciences companies to move from simply tracking issues to predicting and solving potential problems with less human error. Interoperable digital technologies with a reliable built-in governance model drive higher drug quality, better patient outcomes, and easier regulatory compliance.
Cynthia Murrell, October 5, 2017
Trust the Search Black Box and Only the Black Box
September 21, 2017
This article reads like an infomercial for a kitchen appliance. It asks the same, old question, “How much time do you waste searching for relevant content?” Then it leads into a pitch for Microsoft and some other companies. BA Insights wrote, “The Increasingly Intelligence Search Experience” to be an original article, but frankly it sounds like every spiel to sell a new search algorithm.
After the “hook,” the article runs down the history of Microsoft and faceted search along with refiners and how it was so revolutionary at the time. Do not get me wrong, this was a revolution move, but it sounds like Microsoft invented the entire tool rather than just using it as a strategy. There is also a brief mention on faceted navigation, then they throw “intelligence search” at us:
Microsoft’s definition of “intelligence” may still be vague, but it’s clear that the company believes its work in machine-learning, when combined with its cloud platform, can give it a leg up over its competitors. The Microsoft Graph and these new intelligent machine-learning capabilities provide personalized insights based on a user’s personal network, project assignments, meeting schedule, and other search and collaboration activities. These features make it possible not only to search using traditional methods and take action based on those results, but for the tools and systems to proactively provide intelligent, personalized, and timely information before you ask for it – based on your profile, permissions, and activity history.
Oh! Microsoft is so smart that they have come up with something brand new that companies which specialize in search have never thought of before. Come on, how many times have we seen and read claims like this before? Microsoft is doing revolutionary things, but not so much in the field of search technology. They have contributed to its improvement over the years, but if this was such a revolutionary piece of black box software why has not anyone else picked it up?
Little black box software has their uses, but mostly for enterprise and closed systems-not the bigger Web.
Whitney Grace, September 21, 2015
The Secret to Success for Apple and Google
July 25, 2017
What makes them so special? As part of their 10 Lessons from 10 Years of The World’s Most Innovative Companies series, Fast Company explains “Why Apple and Google Are Titans.” The article examines what these two historically very different companies hold in common. In a nutshell, each was built around purposeful innovation at breakneck speeds. Writer Robert Safin observes:
Innovation is not a onetime activity. It is a philosophy and culture. The fruits of innovation do not unfold on schedule, in a single year, along a straight line. To stay up with—and ahead of—the changes in today’s world, you need to be always moving, trying new things, fueled by an internal restlessness. This is at the heart of both Apple and Google. …
The critical corollary, then, to that need-for-speed: a need for purpose. Having a clearly understood mission behind an enterprise allows everyone to prioritize, in real time, to quickly assess which changes are worth responding to and what lens to use in addressing them. Apple and Google have always had this framework, from Steve Jobs’s mission of ‘making tools for the mind that advance humankind’ to Google founders Larry Page and Sergey Brin’s pledge to ‘do no evil’ while ‘organizing the world’s information.’
It is easy to underestimate each of these companies, Safin notes. It can seem as though Apple has been simply riding the connectivity wave in its iPhone surfboard, but we’re reminded how Apple has had to evolve and pivot to get and stay, at the fore. As for Google, one might think it has simply been lucky that internet search has become ubiquitous. However, Google has actually taken risk after risk, many of which turned out to be valuable only for their lessons. See the article for more examples about each company.
Cynthia Murrell, July 25, 2017
Darktrace Attracts Lucrative Clients, Releases New Product
July 7, 2017
We are happy to see innovative cyber-security firm Darktrace meet with success. Access AI informs us that “Darktrace Reports $125 Million in New Contracts.” The post lists some of the company’s diverse customers, including the Blackhawk Network, Rakuten Securities, the Church of England, and Birmingham International Airport. We also learn:
A well-performing final financial quarter has left the company with a revenue increasing 600 percent year on year. 60 countries worldwide are currently using the company’s Enterprise Immune System technology. Darktrace reports that customer renewals are at 90% and that with over 360 employees, the company has doubled inside over the past year.
What makes Darktrace so special? The company is bringing machine learning and other AI tech into the cybersecurity field, where they are sorely needed. Leveraging the mathematics chops of certain Cambridge University specialists, their unique model takes inspiration from biological immune systems. The press release,“Darktrace Cyber ‘Immune System’ Fights Back” at PR Newswire, heralds the arrival of their product Antigena, which offers a sort of self-defense system for networks. The write-up tells us:
Darktrace is the first company in the world to arm the defenders with proven machine learning and mathematics that work without any prior knowledge of attacks, rules or signatures. With Antigena, Darktrace now spots and inoculates against unknown threats, as they germinate within organizations in real time.
‘The battlefield is the corporate network – we cannot fight the battle on the border anymore. We are living through a new era of threat which is relentless and pernicious – and it’s inside our networks now. Today, we have arrived at new detection that reacts faster than any security team can,’ said Nicole Eagan, CEO, Darktrace. …
Darktrace Antigena is a new product innovation, which replicates the function of antibodies in the human immune system. As the Enterprise Immune System detects a threat in its tracks, Antigena modules act as an additional defense capability that automatically neutralize live threats, without requiring human intervention.
Besides automatically fending off potential threats, there’s another big, but perhaps underappreciated advantage to Antigena—no time-wasting false alerts. To learn about the rest of Darktrace’s products, navigate here. The company was founded in Cambridge in 2013, and now keeps a second headquarters in San Francisco and offices around the world. Darktrace puts the utmost confidence in their team’s considerable expertise not only in mathematics and software development but also in intelligence gathering and cyber operations.
Cynthia Murrell, July 7, 2017
HPE IDOL Released with Natural Language Processing Capabilities Aimed at Enterprise-Level Tasks
June 16, 2017
The article titled Hewlett Packard Enterprise Enriches HPE IDOL Machine Learning Engine With Natural Language Processing on SDTimes discusses the enhancements to HPE IDOL. The challenges to creating an effective interactive experience based on Big Data for enterprise-class inquiries are related to the sheer complexity of the inquiries. Additional issues arise around context, specificity, and source validation. The article examines the new and improved model,
HPE Natural Language Question Answering deciphers the intent of a question and provides an answer or initates an action drawing from an organization’s own structured and unstructured data assets, in addition to available public data sources to provide actionable, trusted answers and business critical responses… HPE IDOL Natural Language Question Answering is a core feature of the new HPE IDOL 11.2 software release that features four key capabilities for natural language processing for the enterprise.
These capabilities are the IDOL Answer Bank (with pre-set reference questions), Fact Bank (with structured and unstructured data extraction abilities), Passage Extract (for text-based summaries), and Answer Server (for question analysis and integration of the other 3 areas). The goal is natural conversations between people and computers, an “information exchange”. The four capabilities work together to deliver a complex answer with the utmost accuracy and relevance.
Chelsea Kerwin, June 16, 2017
Salesforce Einstein and Enterprise AI
May 5, 2017
One customer-relationship-management (CRM) firm is determined to leverage the power of natural language processing within its clients’ organizations. VentureBeat examines “What Salesforce Einstein Teaches Us About Enterprise AI.” The company positions its AI tool as a layer within its “Clouds” that brings the AI magic to CRM. They vow that the some-odd 150,000 existing Salesforce customers can deploy Einstein quickly and easily.
Salesforce has invested much in the project, having snapped up RelatelQ for $390 million, BeyondCore for $110 million, Predicition IO for $58 million, and MetaMind for an undisclosed sum. Competition is fierce in this area, but the company is very pleased with the results so far. Writer Mariya Yao cites Salesforce chief scientist Richard Socher as she examines:
The Salesforce AI Research team is innovating on a ‘joint many-task’ learning approach that leverages transfer learning, where a neural network applies knowledge of one domain to other domains. In theory, understanding linguistic morphology should also accelerate understanding of semantics and syntax.
In practice, Socher and his deep learning research team have been able to achieve state-of-the-art results on academic benchmark tests for main entity recognition (identifying key objects, locations, and persons) and semantic similarity (identifying words and phrases that are synonyms). Their approach can solve five NLP tasks — chunking, dependency parsing, semantic relatedness, textual entailment, and part of speech tagging — and also builds in a character model to handle incomplete, misspelled, or unknown words.
Socher believes that AI researchers will achieve transfer learning capabilities in more comprehensive ways in 2017 and that speech recognition will be embedded in many more aspects of our lives. ‘Right now, consumers are used to asking Siri about the weather tomorrow, but we want to enable people to ask natural questions about their own unique data.’
That would indeed be helpful. The article goes on to discuss the potentials for NLP in the enterprise and emphasizes the great challenge of implementing solutions into a company’s workflow. See the article for more discussion. Based in San Francisco, Salesforce was launched in 1999 by a former Oracle executive.
Cynthia Murrell, May 5, 2017
How Data Science Pervades
May 2, 2017
We think Information Management may be overstating a bit with the headline, “Data Science Underlies Everything the Enterprise Now Does.” While perhaps not underpinning quite “everything,” the use of data analysis has indeed spread throughout many companies (especially larger ones).
Writer Michael O’Connell cites a few key developments over the last year alone, including the rise of representative data, a wider adoption of predictive analysis, and the refinement of customer analytics. He predicts, even more, changes in the coming year, then uses a hypothetical telecom company for a series of examples. He concludes:
You’ll note that this model represents a significant broadening beyond traditional big data/analytics functions. Such task alignment and comprehensive integration of analytics functions into specific business operations enable high-value digital applications ranging far beyond our sample Telco’s churn mitigation — cross-selling, predictive and condition-based maintenance, fraud detection, price optimization, and logistics management are just a few areas where data science is making a huge difference to the bottom line.
See the article for more on the process of turning data into action, as illustrated with the tale of that imaginary telecom’s data-wrangling adventure.
Cynthia Murrell, May 2, 2017
Comprehensive, Intelligent Enterprise Search Is Already Here
February 28, 2017
The article on Sys-Con Media titled Delivering Comprehensive Intelligent Search examines the accomplishments of World Wide Technology (WWT) in building a better search engine for the business organization. The Enterprise Search Project Manager and Manager of Enterprise Content at WWT discovered that the average employee will waste over a full week each year looking for the information they need to do their work. The article details how they approached a solution for enterprise search,
We used the Gartner Magic Quadrants and started talks with all of the Magic Quadrant leaders. Then, through a down-selection process, we eventually landed on HPE… It wound up being that we went with the HPE IDOL tool, which has been one of the leaders in enterprise search, as well as big data analytics, for well over a decade now, because it has very extensible platform, something that you can really scale out and customize and build on top of.
Trying to replicate what Google delivers in an enterprise is a complicated task because of how siloed data is in the typical organization. The new search solution offers vast improvements in presenting employees with the relevant information, and all of the relevant information and prevents major time waste through comprehensive and intelligent search.
Chelsea Kerwin, February 28, 2017
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