SEC filing shows LinkedIn negotiating skills are worth $5bn

Microsoft To Layoff 18,000The 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.

Telsta adds IoT and big data offering to Network and Services biz unit

Location Australia. Green pin on the map.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.

10 ways machine learning is revolutionising the manufacturing industry

(c)iStock.com/gerenme

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:

Increasing production capacity up to 20% while lowering material consumption rates by 4%

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.

typical production improvemensSource: Focus Group: Big Data Analytics for Smart Manufacturing Systems

Providing more relevant data so finance, operations, and supply chain teams can better manage factory and demand-side constraints

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.

factory and demand analytics

Source:GE Global Research Stifel 2015 Industrials Conference

Improving preventative maintenance and maintenance, repair and overhaul (MRO) performance with greater predictive accuracy to the component and part-level

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.

Azure IOT Services

Source: Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft

Enabling condition monitoring processes that provide manufacturers with the scale to manage overall equipment effectiveness (OEE) at the plant level increasing OEE performance from 65% to 85%

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:

OEE Graphic

Source: Using Big Data for Machine Learning Analytics in Manufacturing

Machine learning is revolutionising relationship intelligence and Salesforce is quickly emerging as the leader

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.

Salesforce AI Acquisitions

Salesforce revenue sources

Revolutionising product and service quality with machine learning algorithms that determine which factors most and least impact quality company-wide

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.

Increasing production yields by the optimising of team, machine, supplier and customer requirements are already happening with machine learning

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.

The vision of manufacturing as a service will become a reality thanks to machine learning enabling subscription models for production services

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.

Machine learning is ideally suited for optimizing supply chains and creating greater economies of scale

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.

Knowing the right price to charge a given customer at the right time to get the most margin and closed sale will be commonplace with machine learning

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.

Big Data Services | @CloudExpo #BigData #IoT #M2M #ML #InternetOfThings

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.

read more

Record Numbers Discuss Cloud and Ops at @CloudExpo NY | #IoT #Cloud #BigData #DevOps

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.

read more

Keynote By @DHinchcliffe | @ThingsExpo #IoT #M2M #DigitalTransformation

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.

read more

Big Data Analytics with @Accelerite | @CloudExpo #IoT #Hadoop #BigData

“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.

read more

Andi Mann @DevOpsSummit Chair | @AndiMann #DevOps #Microservices

Cloud Expo, Inc. has announced today that Andi Mann returns to ‘DevOps at Cloud Expo 2016’ as Conference Chair
The @DevOpsSummit at Cloud Expo will take place on November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
“DevOps is set to be one of the most profound disruptions to hit IT in decades,” said Andi Mann. “It is a natural extension of cloud computing, and I have seen both firsthand and in independent research the fantastic results DevOps delivers. So I am excited to help the great team at @DevOpsSummit and Cloud Expo tell the world how they can leverage this emerging disruptive trend.”
Andi Mann, Chief Technology Advocate at Splunk, is an accomplished digital business executive with extensive global expertise as a strategist, technologist, innovator, and communicator. For over 30 years across five continents, Andi has built success with Fortune 500 corporations, vendors, governments, and as a leading research analyst and consultant.
Andi is a sought-after commentator on business technology – published in USA Today, New York Times, Forbes, CIO, and Wall Street Journal; presented at Gartner ITxpo, VMworld, CA World, Interop, Cloud Expo, and DevOps Summit; participated and hosted interviews for radio, television, webcasts, podcasts, and live events; and more.

read more

The Evolution of Storage | @CloudExpo #BigData #IoT #DataCenter #Storage

One of the greatest challenges facing business and government organizations is how to store and move sensitive data around the world while maintaining security. Storing and securing critical data held within data centers (whether physical or virtual) is top of mind from the boardroom to the remote data center. According to the International Data Corporation (IDC) Worldwide Quarterly Cloud IT Infrastructure Tracker, spending on private cloud IT infrastructure in 2015 will grow by 15.8 percent year over year to $12.1 billion, while spending on public cloud IT infrastructure will increase by 29.6 percent to $20.5 billion.

read more

[slides] Best Practices in Operating Hybrid Infrastructure | @CloudExpo @RackNgo #Cloud

The pace of innovation, vendor lock-in, production sustainability, cost-effectiveness, and managing risk… In his session at 18th Cloud Expo, Dan Choquette, Founder of RackN, discussed how CIOs are challenged finding the balance of finding the right tools, technology and operational model that serves the business the best.
He also discussed how clouds, open source software and infrastructure solutions have benefits but also drawbacks and how workload and operational portability between vendors and platforms give control back to the users and drives innovation.

read more

The cloud news categorized.