Upgrading Parallels Desktop 8 (or older) to Parallels Desktop 11 for Mac Time flies by fast and technology changes every day. It’s not surprising some of us are left behind with older software versions that aren’t up to date on our computers. In most cases, the most recent version of almost any app is more […]
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The global software as a service (SaaS) market is expected to break $50 billion in terms of product spending by 2024, from $12bn this year, according to a report released by price comparison checker Better Buys.
The report, an examination on the state of the SaaS market which takes its cues from various industry reports, argues almost two thirds (64%) of small and medium businesses rely on cloud technologies to drive growth and workforce efficiency, while more than three quarters (78%) of companies polled expect to expand the number of SaaS platforms they use in the next three years, taking the number of apps up with it.
In terms of vendors, Salesforce (11% of market share in 2015) reigns supreme, according to the Better Buys verdict, with Microsoft (8%), Adobe (6%) and SAP (5%) behind. The researchers argue that despite an increase in horizontal SaaS development, such as Salesforce and Slack, vertical-specific software remains the cornerstone of the overall market: “Subscription growth in business intelligence, security, IT, and enterprise vertical applications continue to rise.”
The researchers also give an idea as to how the ‘software as a service’ landscape has evolved, arguing that the first such example was Dun & Bradstreet, founded back in 1824, whose mission was to create “a network of correspondents to serve as a source of reliable, objective credit information.” Take that into account with Moodwire, founded in 2015, with a goal of deriving actionable insights for businesses from a plethora of data, analytics, web pages and social postings, and the evolution is fascinating.
The report also gives good news as to the amount of jobs in the market, at least for US-based IT professionals. The top states for SaaS jobs per 100,000 people were Massachusetts (172), Maryland (162), Washington (150), Virginia (146) and Colorado (144), according to the researchers.
“SaaS providers are finding that fewer customers require education on the benefits of SaaS versus on-premise solutions as more SMBs are used to working with SaaS,” the report concludes. “This has forced SaaS providers to shift their focus from customer education to nurturing and maintaining customer relationships to keep them and minimise loss to growing competition.”
You can read the Better Buys analysis here.
EMC has recently released its Global Data Protection Index 2016 where it claims only 2% of the world would be considered ‘leaders’ in protecting their own assets, reports Telecoms.com.
Data has dominated the headlines in recent months as breaches have made customers question how well enterprise organizations can manage and protect data. Combined with transatlantic disagreements in the form of Safe Habour and law agencies access to personal data, the ability to remain secure and credible is now more of a priority for decision makers.
“Our customers are facing a rapidly evolving data protection landscape on a number of fronts, whether it’s to protect modern cloud computing environments or to shield against devastating cyber-attacks,” said David Goulden, CEO of EMC Information Infrastructure. “Our research shows that many businesses are unaware of the potential impact and are failing to plan for them, which is a threat in itself.”
EMC’s report outlined a number of challenges and statistics which claimed the majority of the industry are not in a place they should be with regard to data protection. While only 2% of the industry would be considered leaders in the data protection category, 52% are still evaluating the options available to them. Overall, 13% more businesses suffered data loss in the last twelve months, compared to the same period prior to that.
But what are the over-arching lessons we learned from the report?
Vendors: Less is more
A fair assumption for most people would be the more protection you take on, the more protected you are. This just seems logical. However, the study shows the more vendors you count in your stable, the more data you will leak.
The average data loss instance costs a company 2.36TB of data, which would be considered substantial, however it could be worse. The study showed organizations who used one vendor lost on average 0.83TB per incident, two vendors 2.04TB and three vendors 2.58TB. For those who used four or more vendors, an average of 5.47TB of data was lost per incident.
Common sense would dictate the more layers of security you have, the more secure you will be, however this is only the case if the systems are compatible with each other. It should be highlighted those who lost the larger data sets are likely to be the larger companies, with more data to lose, though the study does seem to suggest there needs to be a more co-ordinated approach to data protection.
And they are expensive…
Using same concept as before, the average cost of lost data was $900,000. For those who have one vendor, the cost was $636,361, for those with two, 789,193 and for those with three vendors the cost was just above the average at 911,030. When companies bring in four or more vendors, the average cost of data loss rises to 1.767 million.
China and Mexico are the best
While it may be surprising, considering many of the latest breakthroughs in the data world have come from Silicon Valley or Israel, China and Mexico are the two countries which would be considered furthest ahead of the trend for data protection.
EMC graded each country on how effective they are were implementing the right technologies and culture to prevent data loss within the organizations themselves. 17 countries featured ahead of the curve including usual suspects of the UK (13.5% ahead of the curve), US (8%), Japan (1%) and South Korea (9%), however China and Mexico led the charge being 20% and 17% respectively ahead.
While it may not be considered that unusual for China to have a strong handle on data within its own boarders, Mexico is a little more surprising (at least to us at Telecoms.com). The country itself has gone through somewhat of a technology revolution in recent years, growing in the last 20 years from a country where only 10% of people had mobile through to 68% this year, 70% of which are smartphones. Mexico is now the 11th largest economy in terms of purchasing power, with the millennials being the largest demographic. With the population becoming more affluent, and no longer constrained by the faults of pre-internet world, the trend should continue. Keep up the good work Mexico.
Human error is still a talking point
When looking at the causes of data loss, the results were widespread, though the causes which cannot be controlled were at the top of the list. Hardware failure, power loss and software failure accounted for 45%, 35% and 34% respectively.
That said the industry does now appear to be taking responsibility for the data itself. The study showed only 10% of the incidents of data loss was blamed on the vendor. A couple of weeks ago we spoke to Intel CTO Raj Samani who highlighted to us the attitude towards security (not just data protection) needs to shift, as there are no means to outsource risk. Minimizing risk is achievable, but irrelevant of what agreements are undertaken with vendors, the risk still remains with you. As fewer people are blaming the vendors, it would appear this responsibility is being realized.
Human error is another area which still remains high on the agenda, as the study showed it accounts for 20% of all instances of data loss. While some of these instances can be blamed on leaving a laptop in the pub or losing a phone on the train, there are examples where simple mistakes in the workplace are to blame. These will not be removed, as numerous day-to-day decisions are based off the back of intuition and gut-feel, and a necessity for certain aspects of the business.
An area which could be seen as a potential danger would be that of artificial intelligence. As AI advances as a concept, the more like humans they will become, and thus more capable of making decisions based in intuition. If this is to be taken as the ambition, surely an intuitive decision making machine would offer a security defect in the same way a human would. Admittedly the risk would be substantially smaller, but on the contrary, the machine would be making X times many more decision than the human.
All-in-all the report raises more questions than provides answers. While security has been pushed to the top of the agenda for numerous organizations, receiving additional investment and attention, it does not appear the same organizations are getting any better at protecting themselves. The fact 13% more organizations have been attacked in the last 12 month suggests it could be getting worse.
To finish, the study asked whether an individual felt their organization was well enough protected. Only 18% believe they are.
The US Securities and Exchange Committee has released its filings outlining the road to Microsoft’s acquisition of LinkedIn, during which $5 billion was added to the value of the deal, reports Telecoms.com.
Five parties were involved in the saga, which eventually led to the news breaking on June 13, with Microsoft agreeing to acquire LinkedIn in an all-cash deal worth $26.2 billion. Although it has not been confirmed by the companies themselves according to Re/code Party A, which kicked the frenzy, was Salesforce. Party B was Google, which was also interested in pursuing the acquisition.
Party C and Party D were contacted by LinkedIn CEO Jeff Weiner to register interest however both parties declined after a couple of days consideration. Party C remains unknown, though Party D is believed to be Facebook, who even if had shown interest in the deal, may have faced a tough time in passing the agreement by competition authorities.
In terms of the timeline, a business combination was first discussed by Weiner and Satya Nadella, Microsoft’s CEO during a meeting on February 16, with Party A being brought into the frame almost a month later on March 10. Salesforce CEO Marc Benioff has confirmed several times in recent weeks his team were in discussions with LinkedIn regarding the acquisition. In the following days, Party B was brought into the mix, also declaring interest. Once the interest of Party A and B were understood, Microsoft was brought back into the mix on March 15 with the report stating:
“Mr. Weiner called Mr. Nadella to inquire as to whether Microsoft was interested in discussing further a potential acquisition of LinkedIn, and explained that, although LinkedIn was not for sale, others had expressed interest in an acquisition. Mr. Nadella responded that he would discuss the matter further with Microsoft’s board of directors.”
Prior to the agreement LinkedIn was valued at roughly $130 per share, with the initial offer recorded at $160. Microsoft eventually paid $196 per share, though this was not the highest bid received. The company referred to as Party A in the document put an offer forward of $200 per share, though this would be half cash and half shares in the company. Weiner negotiating skills have seemingly added approximately 50% to the value of LinkedIn shares and bumping up the total value of the deal by $5 billion.
The exclusivity agreement was signed on May 14, though pressure had been put on LinkedIn by both Microsoft and Party A in the weeks prior. It would appear Party A had not been deterred by the agreement, as additional bids were made, once again driving up the perceived value of LinkedIn shares. Microsoft’s offer of $182 was no longer perceived high enough, and encouraged to match Party A’s offer of $200. The report states LinkedIn Executive Chairman Reid Hoffman was in favour of an all cash deal, allowing Microsoft extra negotiating room. Nadella was eventually informed on June 10 the offer had been authorized by the LinkedIn Transactions Committee.
Although Microsoft could be seen to overpaying on the price, it would be worth noting LinkedIn has been valued at higher. The company initially launched its IPO in 2011 and had a promising year in 2013 increasing the share price from $113.5 to over $200 across the 12 month period. Share prices rose to over $250 last November, following quarterly results in February, share prices dropped 44% after projected full-year revenues at $3.6 billion to $3.65 billion, versus $3.9 billion expected by analysts. Considering the fall in fortunes, it may be fair to assume shareholders would be pleased with the value of the deal approaching $200 per share.
While Microsoft has been a relatively quiet player in the social market prior to the acquisition, though this could be seen as a means to penetrate the burgeoning market segment. Although the place of social media in the workplace remains to be seen, Microsoft has essentially bought a substantial amount of data, including numerous high-net worth individuals and important decision makers throughout the world. LinkedIn currently has roughly 431 million members and is considered to be the largest professional social media worldwide.
Another explanation for the deal could be the value of Microsoft to IT decision makers. A report from JPMorgan stated Microsoft would be considered the most important vendor by CIOs to their organization due to the variety of services offered. AWS is generally considered to be the number one player in the public cloud market, though Microsoft offers a wider range of enterprise products including servers, data centres, security solutions, and cloud offerings, amongst many more. Now social can be added to the list. As Microsoft increases its offerings, it could penetrate further into a company’s fabric, making it a much more complicated decision to change vendor.
Australian telco Telstra has continued efforts to bolster its Network Applications and Services (NAS) business unit through acquiring Readify, reports Telecoms.com.
The company has been vocal about its aims for the NAS business unit as it has sought to expand through numerous acquisitions in recent years. Aside from the Readify deal, the company has also incorporated O2 Networks, Bridge Point Communications, Kloud and North Shore Connections, as well as numerous partnerships including with cloud security start-up vArmour.
“This arm of the business (NAS) has been a strong growth area for Telstra, achieving double-digit growth in revenue driven by business momentum in Asia, as well as advances in technology in the cloud computing space,” said a statement on the company website. “We are well equipped to continue to capitalise on this growth and ensure our focus on NAS continues to drive revenue.”
Readify, which currently offers enterprise cloud application solutions as well as Big Data and IoT, will provide an additional platform for Telstra to drive digital transformation for its enterprise customers in domestic and global markets. The offering builds on the January acquisition of Kloud which offers cloud migration services, as well as unified communications solutions and contact centre provider North Shore Connections in 2013, network integration services provider O2 Networks in 2014 and security, networking, and data management provider Bridgepoint, also in 2014.
“Readify will provide application development and data analytics services, nicely complementing Kloud’s existing services,” said Telstra Executive Director Global Enterprise and Services, Michelle Bendschneider. “It will enable Telstra to add incremental value to customers in enterprise cloud applications, API-based customisation and extensions as well as business technology advisory services.”
Back in April, the company announced a business multi-cloud connecting solution, which supports numerous offerings hybrid cloud offerings including Azure, AWS, VMware, and IBM. The one-to-many “gateway” model will enable Australian customers to connect to Microsoft Azure, Office365, AWS, IBM SoftLayer, and VMware vCloud Air, while international customers can only connect to AWS and IBM SoftLayer for the moment.
The cloud and enterprise services market has been a long-ambition of the company, though it did get off to a slow start. Back in 2014, its national rival Optus Business stole a march on Telstra through acquiring Ensyst, winner of Australian Country Partner of the Year at the Microsoft Worldwide Partner Awards during the same year, as it looked to grow its own cloud proposition. It would appear Telstra is making up for lost time through an accelerated program of product releases and acquisitions.
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Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production.
Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customised, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimised outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.
The 10 ways machine learning is revolutionising manufacturing include the following:
Smart manufacturing systems designed to capitalise on predictive data analytics and machine learning have the potential to improve yield rates at the machine, production cell, and plant levels. The following graphic from General Electric, and cited in a National Institute of Standards (NIST), provides a summary of benefits that are being gained using predictive analytics and machine learning in manufacturing today.
Source: Focus Group: Big Data Analytics for Smart Manufacturing Systems
In many manufacturing companies, IT systems aren’t integrated, which makes it difficult for cross-functional teams to accomplish shared goals. Machine learning has the potential to bring an entirely new level of insight and intelligence into these teams, making their goals of optimising production workflows, inventory, Work In Process (WIP), and value chain decisions possible.
Source:GE Global Research Stifel 2015 Industrials Conference
Integrating machine learning databases, apps, and algorithms into cloud platforms are becoming pervasive, as evidenced by announcements from Amazon, Google and Microsoft. The following graphic illustrates how machine learning is integrated into the Azure platform. Microsoft is enabling Krones to attain their Industrie 4.0 objectives by automating aspects of their manufacturing operations on Microsoft Azure.
An automotive OEM partnered with Tata Consultancy Services to improve their production processes that had seen Overall Equipment Effectiveness (OEE) of the press line reach a low of 65 percent, with the breakdown time ranging from 17-20 percent. By integrating sensor data on 15 operating parameters (such as oil pressure, oil temperature, oil viscosity, oil leakage, and air pressure) collected from the equipment every 15 seconds for 12 months. The components of the solution are shown here:
Source: Using Big Data for Machine Learning Analytics in Manufacturing
The series of acquisitions Salesforce is making positions them to be the global leader in machine learning and artificial intelligence (AI). The following table from the Cowen and Company research note, Salesforce: Initiating At Outperform; Growth Engine Is Well Greased published June 23, 2016, summarizes Salesforce’s series of machine learning and AI acquisitions, followed by an analysis of new product releases and estimated revenue contributions. Salesforce’s recent acquisition of e-commerce provider Demandware for $2.8B is analyzed by Alex Konrad is his recent post, Salesforce Will Acquire Demandware For $2.8 Billion In Move Into Digital Commerce. Cowen & Company predicts Commerce Cloud will contribute $325M in revenue by FY18, with Demandware sales being a significant contributor.
Manufacturers often are challenged with making product and service quality to the workflow level a core part of their companies. Often quality is isolated. Machine learning is revolutionising product and service quality by determining which internal processes, workflows, and factors contribute most and least to quality objectives being met. Using machine learning manufacturers will be able to attain much greater manufacturing intelligence by predicting how their quality and sourcing decisions contribute to greater Six Sigma performance within the Define, Measure, Analyse, Improve, and Control (DMAIC) framework.
Machine learning is making a difference on the shop floor daily in aerospace & defense, discrete, industrial and high-tech manufacturers today. Manufacturers are turning to more complex, customised products to use more of their production capacity, and machine learning help to optimise the best possible selection of machines, trained staffs, and suppliers.
Manufacturers whose production processes are designed to support rapid, highly customized production runs are well positioning to launch new businesses that provide a subscription rate for services and scale globally. Consumer Packaged Goods (CPG), electronics providers and retailers whose manufacturing costs have skyrocketed will have the potential to subscribe to a manufacturing service and invest more in branding, marketing, and selling.
For many complex manufacturers, over 70% of their products are sourced from suppliers that are making trade-offs of which buyer they will fulfill orders for first. Using machine learning, buyers and suppliers could collaborate more effectively and reduce stock-outs, improve forecast accuracy and met or beat more customer delivery dates.
Machine learning is extending what enterprise-level price optimization apps provide today. One of the most significant differences is going to be just how optimising pricing along with suggested strategies to close deals accelerate sales cycles.
Sources:
Lee, J. H., & Ha, S. H. (2009). Recognizing yield patterns through hybrid applications of machine learning techniques. Information Sciences, 179(6), 844-850.
Mackenzie, A. (2015). The production of prediction: What does machine learning want?. European Journal of Cultural Studies, 18(4-5), 429-445.
Pham, D. T., & Afify, A. A. (2005, July). Applications of machine learning in manufacturing. In Intelligent Production Machines and Systems, 1st I* PROMS Virtual International Conference (pp. 225-230).
Priore, P., de la Fuente, D., Puente, J., & Parreño, J. (2006). A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engineering Applications of Artificial Intelligence, 19(3), 247-255.
The cloud promises new levels of agility and cost-savings for Big Data, data warehousing and analytics. But it’s challenging to understand all the options – from IaaS and PaaS to newer services like HaaS (Hadoop as a Service) and BDaaS (Big Data as a Service).
In her session at @BigDataExpo at @ThingsExpo, Hannah Smalltree, a director at Cazena, provided an educational overview of emerging “as-a-service” options for Big Data in the cloud. This is critical background for IT and data professionals, as experts estimate that “as-a-service” cloud sourcing will increase from today’s 15% to 35% by 2021. Learn more and hear important considerations to help evaluate and rank your options.
Cloud Expo 2016 New York at the Javits Center New York was characterized by increased attendance and a new focus on operations. These were both encouraging signs for all involved in Cloud Computing and all that it touches.
As Conference Chair, I work with the Cloud Expo team to structure three keynotes, numerous general sessions, and more than 150 breakout sessions along 10 tracks. Our job is to balance the state of enterprise IT today with the trends that will be commonplace tomorrow.
Mobile Computing, Big Data and Analytics, the Internet of Things, Platforms and Microservices and Containers, DevOps, and WebRTC are all part and parcel of Cloud Computing today.
A strange thing is happening along the way to the Internet of Things, namely far too many devices to work with and manage. It has become clear that we’ll need much higher efficiency user experiences that can allow us to more easily and scalably work with the thousands of devices that will soon be in each of our lives.
Enter the conversational interface revolution, combining bots we can literally talk with, gesture to, and even direct with our thoughts, with embedded artificial intelligence, which can process our conversational commands and orchestrate the outcomes we request across our personal and professional realm of connected devices.
«We work in the area of Big Data analytics and Big Data analytics is a very crowded space – you have Hadoop, ETL, warehousing, visualization and there’s a lot of effort trying to get these tools to talk to each other,» explained Mukund Deshpande, head of the Analytics practice at Accelerite, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.