Archive for the ‘Placement’ category

Five Myths about Hadoop

February 20th, 2018

Apache™ Hadoop helps businesses solve one of their toughest challenges—profiting from massive volumes of data. Its popularity stems from its ability to enable organizations to gain value from big, diverse data types. As the Forrester Research, Inc. report The Forrester Wave™: Big Data Hadoop Solutions, Q1 2014, notes, “Hadoop is unstoppable as its open-source roots grow wildly and deeply into enterprises. Its refreshingly unique approach to data management is transforming how companies store, process, analyze and share big data.”

Evolving Technology

The technology has justifiably received accolades for the benefits it delivers, yet at the same time it’s been dogged by misinformation and overpromising of exactly what it can offer. Having the wrong expectations—or believing misconceptions—when implementing Hadoop can result in wasted time, inflated expenses and lacklustre performance.

Understanding what Hadoop can and can’t do, and then planning the installation accordingly, will help the implementation reach its full capability. To be successful, learn the truth about the technology and avoid these common myths:

Myth 1

Hadoop Can Replace a Data Warehouse

Truth: Hadoop is not a complete data or analytics solution by itself. It is a framework or platform that cannot serve as or replace the data warehouse. As such, Hadoop offers a cost-effective solution as a big data platform that can share its information with other databases, making it an ideal complement to a data warehouse. This gives organizations new ways to use and exploit large, diverse data volumes.

Myth 2

The Technology is a Passing Trend

Truth: Hadoop is popular and its momentum seems unstoppable, so don’t expect it to go away. The Forrester Wave™: Big Data Hadoop Solutions, Q1 2014, believes that Hadoop is a “must-have data platform for large enterprises, forming the cornerstone of any flexible, future data management platform.” To take advantage of it, next-generation data warehouses are supporting deeper Hadoop integration to manage larger and more complex data sets.

Myth 3

Hadoop is Free

Truth: Sure, Hadoop is an open-source product that anyone can download for free, but the cost to use the technology is not free or even cheap. It requires highly trained expertise to use effectively, and storing the data long term can be expensive. In fact, a data warehouse can cost less than Hadoop when considering analytics and multiple users. And besides the open-source technologies, vendors sell specific applications with various features to support and extend Hadoop to make it more beneficial to businesses.

Myth 4

The Solution is a Data Integration Tool

Truth: The technology is actually a distributed file system designed for specific data types and workloads. It lacks data integration capabilities. If the solution is not integrated with a larger data management ecosystem, is it likely to become another data silo that isolates information. But once it’s part of a data warehouse environment, information from the warehouse and from Hadoop can be used for queries.

Myth 5

Hadoop is a Single Open-Source Product

Truth: It is a library of products and technologies, including the Hadoop Distributed File System, MapReduce, Pig, Hive, Falcon, Knox and others. Hadoop products are available from a variety of vendors that add differentiating features, such as the Hortonworks® Data Platform that lets organizations capture, process and share data in any format at any scale. Some Hadoop products are open source—others are not. The demand for the products has created what Forrester calls a “cutthroat” market for vendors seeking to capitalize on selling unique options.

Unlock the Full Potential

Hadoop delivers a proven solution for storing and processing large data sets, enabling businesses to leverage the big, diverse data that was previously too expensive or complex to use effectively. Despite its purposes and advantages, the technology is not a replacement for a data warehouse or data integration tools. Instead, the value of Hadoop can be increased by integrating it with other data or analytics solutions.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Brett Martin.

By 2020, Artificial Intelligence Will Touch Everything — But How?

February 13th, 2018

We are all equipped with the best minds in the known universe. Between ourselves and our predecessors, humans have created virtually everything we touch. We achieved this using a tool which represents the absolute pinnacle of intelligence; our brain.

This is not hyperbole. On an intelligence-scale of zero to one, despite our individual variety, we all fit within the microscopically small space occupied by the right-edge of the ‘e’. We’re up there alone, magnitudes away from the also-rans we share this planet with.

Now we’re having a concerted stab at creating computer programs which are as intelligent as we are. Artificial Intelligence (AI) –for lack of a better definition– is where software acts human-like. Assuming we’re doing something right, and forgive our big-headedness, this sounds marvellous. The power of human intelligence in a predictable, non chemically-burdened chassis which is always powered-on and free from other responsibilities or predilections.

Follow this idea onwards through a few evolutions and the concept has perhaps too much potential. What if the intelligence-scale goes from zero to one hundred and we are simply not capable of handling the outcome?

Thankfully, we’re at the comforting level where AI means sitting back and relaxing while stuff gets done better or easier without us having to put in so much effort.

What we recognize as AI is most of the time a shortcut through processes to reach a conclusion faster or more productively. By 2020 more or less everything will be touched by AI. Truth is, this comes down to a handful of activities:


There are not suddenly new things to do. AI just helps us to transition between discrete actions.

It means we can couple workflows together faster like ordering more toilet roll when it runs low or translating a Chinese message into English. Since computers began we’ve been used to them handling our actions based on our rules (every app has a settings page). Dialling this up into AI involves removing the rules and training a computer algorithm to link up the actions.


There did not become more variables in the world to work with. AI just helps us to process all the variables in a very programmatic way with a transparent level of confidence.

It may be controversial but guessing what will happen in the future is most accurate when performed within the scientific realm of statistics. Since day one computers have excelled in math and we have almost entirely handed over this responsibility to them. Calculating statistical significance is the building block of machine learning, whether that is assessing weather patterns, detecting diseases or playing chess. We are on a continuous journey of increased data and processing power which make computers better.


We have not been making all the wrong decisions. AI just helps us be presented with the available options based on more data and with less instinct than we are accustomed to.

Assessing situations without perfect information is not just a human trait but a daily necessity and something we may not realise is constantly happening. From negotiations to ranking new business opportunities we make informed decisions towards our desired outcomes. Moving the needle upwards involves being furnished with more relevant details from more relevant sources.


There are not undiscovered ethereal ways to interact with computers. AI just opens new interface methods with less effort.

Since the mouse and keyboard, we have been on a journey to use computers with less hurdles. Learning to type is something we have all needed to achieve for the exclusive task of programming computers and digitally communicating with each other. Now with code to process human sentences and interpret input from cameras and sensors, we can make this interaction as natural as with each other.

The significance of AI

Yet, given these down to earth realities, AI is still a huge deal. We are right now in a world where things get done faster, with more accuracy, based on better knowledge and with increased ease. Statista made a very poignant chart of how Smartphone users benefit from artificial intelligence. In short, everything we do is touched in some way by AI.

So, if AI is becoming more and more ingrained into our lives, when do we get close to the machines taking over?

Impact to humans

Gartner may have calculated that by 2020 AI will create 2.3 million jobs while eliminating 1.8 million but this is not destined to last. As we’ve progressed through the last three industrial revolutions on the way to the digital revolution our working lives have fundamentally changed and they will again. Imagine a world where the work-life balance is two days of ‘work’ and five days of ‘life’. It’s quite likely.

Where I stand is that the alarm bells will ring when we morally question turning off a simulation because it has become too intelligent to be considered just code. This is still very firmly in the realm of science fiction. If the evolution of computing power is due to reach Zetta-scale (considered the minimum for human brain simulation) by 2030, at least we will have the hardware capability to make it happen. Right now, we’re firmly at the lizard/mouse stage.

In their AI Open Letter, many of the world’s greatest minds including Stephen Hawking, Mustafa Suleyman, Steve Wozniak and Elon Musk have made their pledge to ring the alarm before the power of AI is taken out of our grasp.

Where you stand is up to you. I’ll leave it with a quote from one of the world’s finest science fiction writers, the late Iain Banks: “We provide the machines with an end, and they provide us with the means.”

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Drew Bates.

Seven Unexpected Blockchain Uses That Will Improve Business

February 6th, 2018

Blockchain is the new darling of the technology world, and it’s easy to see why. Its transparency, reliability and decentralized nature make it the clear choice for securing intellectual or virtual assets like cryptocurrency.

But, blockchain’s uses extend far beyond security, and experts believe we’ve only just scratched the surface of this technology’s full potential. As the tech world continues to speculate about blockchain’s future, we asked members of Forbes Finance Council to discuss some unexpected applications of blockchain that will positively impact businesses.

All photos courtesy of Forbes Councils members.

Forbes Finance Council members discuss what applications could be ahead for blockchain technology.

  1. Reducing Carbon Footprints

Blockchain is an electronic ledger technology that has many applications, including cryptocurrencies, like Bitcoin and Ethereum. The concept behind this provides a basis for technologically sound and secure payments without leaving behind a paper trail. This new form of managing transactions makes way for industries to facilitate merchant-customer interactions while reducing the carbon footprint. – Ibrahim AlHusseini, the Husseini Group

  1. Internet of Things Security

Keep an eye on blockchain integration with Internet of Things technology. Incorporating the security of blockchain with IoT devices will give customers a new level of security and comfort. This should lead to faster adoption of concepts like “smart home” technology and has the potential to spur growth in the IoT space. – Ismael Wrixen, FE International

  1. Contract Fraud Reduction 

Expect to see a reduction in fraud. Blockchains are going to digitize the world. Contracts are going to be embedded in digital code and stored in transparent, shared databases. Every agreement in this world will be protected from deletion, tampering, revisions, etc. – Justin Good bread, Heritage Investors

  1. Secure, Real-Time Payment 

Blockchain will allow for real-time payments, which will result in faster speeds of service from every aspect. For example, wire transfers usually take three days, and that can represent a huge deficiency for a business. With blockchain-enabled instant payments, they can move on to other things much faster. – Chad Otar, Excel Capital Management, Inc.

  1. Supply Chain Efficiency

Blockchain will enable companies to communicate with vendors efficiently, viewing available inventory in the vendors’ warehouses and tracking that inventory as it moves through the supply chain, all the way to delivery of a sales order to their customer. – Ben Taylor, SoftLedger

Forbes Finance Council is an invitation-only organization for executives in successful accounting, financial planning and wealth management firms. Do I qualify?

  1. Credit Accessibility For Small Business

Blockchain will undoubtedly have a tremendous effect on trust in business transactions. While external auditing is rarely done for small businesses, the ability of blockchain accounting software to provide transparency to third parties will allow for easier access to capital for these businesses without the large expense of audits or the large premium charged for uncertainty. – Vlad Rusz, Vlad Corp. USA

  1. Clarity In Business Agreements And Transactions

Blockchain helps to clarify and document business transactions. Things previously done on a handshake will be codified and subject to the smart contract the parties have agreed to. Once the smart contract is fulfilled, the counterparty will be paid. Think about a world with no more accounts receivable float or collections calls. You do what you agreed to, and the payment is released. – Matthew May, Acuity

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Forbes Finance Council.


Software Testing 101: 8 Practices Successful App Testers Embrace

January 29th, 2018

Millions of people use mobile and desktop apps every day–usually without thinking about the behind-the-scenes work that went into creating them. And though users certainly notice when a bug makes it through and negatively impacts their experience, not many people stop to think about how nice it is when an app isn’t buggy.

Habits of successful software testers

The unsung heroes who work to make sure you don’t ever think about them: app testers. They’re the people who put an app through various challenges before it’s released, in order to identify potential vulnerabilities or features that don’t work properly.

Interested in becoming an app tester? Here are a few tips for doing it successfully.

  1. Keep user experience at front-of-mind.

The best testers think like users, envisioning all possible scenarios that could occur. What will potential users want from the app? How will they use it? Will anything confuse them? Is there anything they could do that would “break” the app? Answering these questions first is important for a successful app test.

Ragu Masilamany, Vice President of Network Virtualization at Amdocs, notes that this isn’t always easy. “Imagining the user’s experience is especially challenging when developing new types of products that do not have established usage patterns or a defined user population,” he says. However, he continues, “Those who aspire to be software testers need to be able to imagine how the product will be used and the perceived user experience.”

  1. Design your tests to fail.

In school, nobody wants to take a test they’re meant to fail. App testing is a whole different story. The harder the test is to pass, the more effective it will be at rooting out weaknesses.

Leo Laskin, senior solutions architect at Sauce Labs, says, “Don’t get upset or lose excitement when something you’ve been coding for a while does not work. In fact, design tests to fail. This will help ensure you’ve attempted to eliminate all bugs and problems when your product is released.”

Titus Fortner, fellow senior solutions architect at Sauce Labs, says to design the test in such a way that it imparts useful information whatever the outcome: “Make sure that any test written gives valuable information both when it passes and when it fails.”

  1. Keep tests small but more frequent.

There’s no need to create long, complicated tests. Smaller tests can be just as effective, and they make it easier to actually test while the app is still being developed (and adjust accordingly) rather than waiting for it to be done. Laskin says, “Keep tests small. After you’ve written code, it means time to test. Don’t think you have to run a large elaborate test every time. Keeping tests small allows a quick turn around and gives you more time to focus on creating code.”

  1. Automate testing when possible.

If frequent app testing is required at a company, automation can save a lot of time and work. Many teams are already adopting this process. “Recent software development practices are adopting a high degree of automation, using robots to test software products,” explains Masilamany. “As a result, software testers not only need to identify ‘sunny day’ and ‘rainy day’ scenarios to test; they also need to develop the software that automates these test scenarios so that the product can be tested by machines many, many times during the development and quality assurance phases for the best results.”

Laskin agrees with the benefits of automation: “Today, automated testing can provide speed and efficiency and test a variety of scenarios quickly. The ease of automated testing also means that beginner developers can learn to execute and master tests without expert knowledge.”

A few of Laskin’s tips for successful automatic tests: “When writing automated tests, keep them small so that they run quickly–you want to be able to provide results to your teams as fast as possible. Align with your developers and confirm they’re on board with writing code that is optimized for automated testing.”

However, Fortner notes that not every test can (or should) be automated. “Be judicious in what and how you decide to automate,” he says. “Automated tests should be short, focused and repeatable.”

  1. Involve the team.

Sometimes the developers are also the app testers, but even when they’re separate roles, the two should be working together for optimal results.

“Teamwork makes the dream work,” says Laskin. This is especially important when automated testing is incorporated: “When coding or working with a coder, make sure everyone is on the same page and writing code for automated testing. This eliminates any confusion and ensures that the testing process is expedited.”

After a test is conducted, important information and results must be shared with the dev team. “Collect metrics and show them to your team,” says Fortner. “Don’t allow them to be ignored.”

  1. Test both the look and function of the app.

Some developers and testers may prioritize look over function, and others might focus on function but ignore aesthetics. Striking a balance is key.

“First impressions matter. But you shouldn’t just focus your tests on the visual and design aspects of your app,” says Laskin. “Features need to be thoroughly tested as well–especially the workflows that are most important to the overall user experience. If you don’t deliver what users expect (and what the business requires), the eye-pleasing app you were working on will still be uninstalled…and good luck with winning consumers back when that happens.”

  1. Test across multiple systems and devices.

Testing an app on only one device/system rules out the vast amount of experiential variety that different ones can present. Even if your app is only available on one type of device (e.g. it’s only in the Apple Store), you still have to account for a different experience across specific devices–whether it’s the iPad or all the different generations of iPhones.

“The fact is, apps will appear and function differently on every device and platform so you want to ensure your code works as desired across all devices,” says Laskin. “Developers need to ensure their apps function well (and look pleasing) not only across mobile and desktop devices, but different operating systems, and the overwhelming array of devices from the latest iPhone X to iPhone 4 and the Samsung Galaxy to Google Pixel. ”

  1. Take advantage of open-source tools.

Software testers have their own communities, and you can find plenty of open-source tools to make your life easier. “There are a number of open source tools and technologies available for software testers,” says Masilamany. “They need to be familiar with those tools and assemble them to create test suites built specifically for the product being tested.”

Fortner advises to look at open-source resources when creating general automated tests as well. “Leveraging open source communities and tooling allows you to get the most out of your test-automation efforts.”

Laskin finishes by reminding us of the stakes involved. “The behind-the-scenes process of testing is what ensures that products work for users,” he says. “While it sounds simple enough, as today’s users become more discerning (and impatient), ensuring a seamless, fault-free and delightful experience is imperative to app success.”

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Laurence Bradford.


15 Great Project Management Tools for Agencies

January 23rd, 2018

In an increasingly digital world, having the right project management tool is essential to keep teams on track. You need a shared digital system so everyone involved in a project can stay up to date on to-dos, in-progress items, notes and follow-ups. This is especially true in an agency setting, where everyone is dealing with a wide range of tasks for internal and external stakeholders.

The best tool for your company is going to depend on your size and your needs, but it doesn’t hurt to get recommendations from other agency professionals who have tested a few of them out.

All photos courtesy of individual Forbes Agency Council members

Fifteen Forbes Agency Council members share the tools they rely on.

  1. Asana

Asana is a favorite of mine because it can be used across different departments. It easily allows us to start a project with one team, set sub-tasks and manage deadlines efficiently. Then if the project needs to be passed to another team, it can easily be assigned to them. Not only can they add their own notes but they also benefit from having access to the full history of the project.   – Katie Jansen, AppLovin

  1. Brandpoint

Brandpoint is effective for content management because it creates an easy project flow between co-workers. In our experiences with the content management team, there used to be a lot of juggling between who produces, curates, and eventually posts content such as blogs and press releases. Since we started using Brandpoint, the project funnel has become clearer more organized.   – Jeff Grover,

  1. Breeze

We love because it’s super simple, fast and doesn’t get in the way of our work. Additionally, they are super responsive to changes. We’ve regularly emailed about feature requests and multiple times they’ve implemented that feature within a day or so. It’s great!   – Ryan Short, MODassic Marketing

  1. G-Suite

We coordinate a lot using Google Spreadsheets for link-building campaigns, content marketing calendars and we sometimes incorporate Google analytics and campaign data directly into Spreadsheets. We also use Google Docs to collaborate on different content-based projects. For meetings and external communication, we collaborate with Google calendar and Gmail.   – Kristopher Jones,

  1. InVision

We recently made the switch from Moqups to InVision to handle collaboration between our design team and sharing work with our clients. InVision helps us take the typical wire framing process one step further with their prototyping capabilities, which allow us to link and click between multiple pages like we would on the live site.   – Chapin Herman, Herman-Scheer

  1. Mavenlink

Mavenlink is my go-to for project management. You can add in customers, upload latest files, easy-to-use, report to finance, and track time management. You can also see which of your team members are being over-utilized, so you can better manage workflow and team support.   – Jaymie Scotto Cutaia, Jaymie Scotto & Associates

  1. MeisterTask

After much analysis and internal debate, my team landed on MeisterTask to efficiently manage our projects. MeisterTask does a fantastic job of providing key data and functionality without being too overwhelming or difficult. The tool easily allows us to create lists to organize our workflow, assign tasks and due dates to team members while tracking all progress in real time.   – Jody Resnick, Trighton Interactive

  1. Podio

Podio is an amazing all-in-one solution for project management. Since it’s app-based, you can customize its app set for specific types of deliverable-based PM and reporting. Secondly, we can manage a project from start to finish with internal agency discussion and auto tasking for our 50-plus team members and then the project can evolve to be client facing, for review of drafts, content and reporting.   – Loren Baker, Foundation Digital

  1. Slack

Slack may not be a project management tool per se, but it’s definitely project management-adjacent. Since it integrates with Trello, Github and Google Docs, it’s become the one place our whole team can go to find out what’s up. For us, it has decreased emails and made collaboration with remote team members a breeze.   – Sarah Mannone, Trekk

  1. SmartDraw

There are dozens of project management tools. Some are super simple but fail at anything more complex than planning a birthday party. Others are powerful enough to plan a moonshot, but require a Ph.D. to operate. SmartDraw gives us the power we need for even complex projects in a super-simple app that all our staff can use.   – Neil Myers, Connect Marketing

  1. Teamwork

I like Teamwork for its power and simplicity. Many tools that can handle complex project management needs like task dependencies are often too technical and cluttered for what an agency needs. Many simple project management tools lack the level of depth and customization an agency needs. Teamwork sits in the middle as a practical tool to manage projects and track time.   – Dan Golden, Be Found Online

Forbes Agency Council is an invitation-only community for executives in successful public relations, media strategy, creative and advertising agencies. Do I qualify?

  1. Trello

Oftentimes, I find people (and myself) getting inundated because the idea of “how much work there is to do” prevents people from getting started. With Trello, you can map out each initiative that’s a part of a campaign, assign project leads and set deadlines. Also, it’s such a great feeling when you can check an item off the to-do list or mark an item as completed.   – Chi Zhao, Hokku PR

  1. Wrike

Wrike is an excellent project management tool that serves us well. Employees can project-manage, create tasks and subtasks, and bring others in with a few clicks. It includes time tracking, chat, reporting and comprehensive search. It keeps a record of everything happening, everything that has happened, and what will happen in the future. You can even bring clients into the mix.   – Bernadette Coleman, Advice Local

  1. A Combination of Tools

We believe successful project management has three key components: tracking the project tasks to meet the deadline (Teamwork), communicating to the client the project status (Slack) and outlining the ideal process for the project (Google Suite). A standalone tool many times will miss at least one of these key parts. Find the right specialized tools for your business, as it likely won’t be just one.   – Todd Earwood, MoneyPath Marketing

  1. One That Works the Best For Your People And Processes

For our company, it’s Jira. Our people are technical by nature. Also, our processes are well defined. Therefore we can match workflows associated with a task to our processes. If your people are creative and collaborative, you might need something more like a forum board. Your project management tool needs to match your people and processes.   – Alan Morte, Three Ventures Technology, Inc

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Forbes Agency Council.


Big Data, Small Target: The Smart Approach To Artificial Intelligence

January 18th, 2018

Companies that have invested heavily in big data solutions want to know how to make smart, strategic investments that will distinguish them from the competition and enable the best possible return before making the decision to go all in. In the past, not all enterprise big data initiatives went as planned. These failures are not usually published, but the big data failure rate is unusually high.

According to Gartner, only 15% of businesses make it past the pilot stage of these projects. Our fear, as leaders of technology companies, is that with so much attention surrounding AI, the pressure is on to apply the technology or risk falling behind the many decision makers who are adopting technologies without first establishing clear business goals and understanding the differences between AI and ML and how they should be applied.

It’s easy to get caught up in the allure of artificial intelligence as well as its hype, including breakthroughs like deep learning, but those looking to make an outsized impact should instead focus on its more practical counterpart: good old-fashioned machine learning — or “cheap learning,” as my colleague Ted Dunning and Ellen Friedman explain in their guide Practical Machine Learning: Innovations in Recommendation.

The distinction is simple: Cheap learning is about leveraging basic machine learning techniques on straightforward data sets en masse to generate a large number of small, incremental improvements. Deep learning, on the other hand, is a specific subset of machine learning. Deep learning is a collection of sophisticated and highly intensive machine learning approaches that make business decisions based on highly complex data sets possible.

For tasks that involve analyzing raw data, such as images and voice recordings, deep learning is best. But when it comes to working on simplified, structured types of data, we’ve found cheap machine learning will do the trick. When you consider that the majority of data flowing through enterprises falls into this second category, it’s clear which tool makes the most sense.

As you chart a course forward, here’s what you should be doing today to set your company up for success tomorrow:

Capture More, Better Data

Artificial intelligence is fueled by data. Pick an approach, and you’ll find data at the center. Why? Because large volumes of complete data sets are needed to accurately recognize significant patterns of behavior with people, events or other characterizations, and that’s what AI is all about.

Having access to more data — especially a range of contributing or related data sources —  is usually an advantage. This is why companies like Google (a leading investor in our company), Amazon, Facebook, Alibaba and Baidu are so powerful from an AI perspective. These companies have enormous data sets that they’ve been capturing for decades across a wide variety of trended patterns. This data has fed into their algorithms for years, making them increasingly more refined, accurate and targeted.

For most enterprise companies, the big challenge is that it’s not always clear at the time data is collected what’s going to matter down the road. This makes it hard to know what to measure today and if that measurement will be valuable in the future. This line of thinking represents the old-school way — it presumes there is only a finite amount of data one can feasibly capture and store, but that’s no longer the case with the advent of new technologies. Furthermore, the ability to connect this data, at a meta-schema level, allows a completely new perspective on previously unrelatable data sources. In addition, big data has seen its fair share of innovation in recent years with storage becoming increasingly smarter and cheaper.

Establish Clear Business Objectives

Successful machine learning isn’t just about choosing the right tool or algorithm and feeding it tons of data. Context matters. Putting machine learning to work on large data sets will yield little value without clear objective goals guiding the efforts.

Do you know what success looks like today? How about five or 10 years from now? Machine learning can help you get a clear baseline today and empower data scientists and engineers to point it in the right direction based on data visibility that is continuously being reviewed and refined.

There’s a sense that AI techniques like machine learning will offer businesses a magic bullet that turns everything into a smarter, more efficient version of itself. This is wishful thinking. Today, these tools work best in narrow frameworks; in the long term this will not be true, but it’s today’s reality. The more specific the objective, the more effective the tool and the higher likelihood of success. Operationalizing a vast number of simple but powerful techniques can deliver enormous business value with relatively short development times and ease of deployment and maintenance.

Stay Grounded

The path to real business value is a well-crafted strategy. Once you have a business roadmap with goals and well-defined objectives, the application of AI techniques will make more sense and align with the overall business strategy. There is no worse feeling or decision more career-limiting than using advanced techniques and technologies that are not aligned to your business goals and strategy. These projects are, typically, the most strategic and have the greatest visibility and highest expectations.

Every business wants demonstrated improvements based on hard data to support the results. The bottom line: Use the appropriate technique for the assignment given. Truthfully (and based on our practical experience), deep learning will come in handy and may be the right strategic technological choice. But for most applications in the enterprise, cheap learning will offer a more practical — and effective — solution.  Don’t be afraid to recognize the difference.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Tom Fisher


A Look At SAP’s 2017 So Far

January 9th, 2018

Software giant SAP continued its strong performance in 2017, with its top line growing at more than 9% in the first three quarters of the year and beating market expectations. Aided by a phenomenal increase in new cloud bookings, revenues from its Cloud business remained the primary growth driver.

The company’s cloud and software gross margins saw a marginal decline, but a substantial improvement in its services gross margin led to a slight improvement in overall margins. We expect cloud margins to continue declining in the near term, as the company faces tough competition from software behemoths like Microsoft, Oracle and Salesforce. Operating profit and EPS grew by 19% and 35% year-over-year, respectively, due to lower share-based compensation, acquisition-related charges and restructuring costs.

Owing to a good performance in the first three quarters of the year, as well as overall stock market gains, SAP’s stock is currently trading 16% higher than its price in January. While the revenue growth was seen across all business segments, SAP’s Cloud business, aided by a strong increase in new bookings, was the standout performer.

The company continued its dominance in the Enterprise Resource Planning software market, with more than 1,500 customers adopting its S/4HANA platform this year, taking the overall count to over 6,900 customers. This should assuage some investor concerns about the long-term value of this platform, the sheer power of which is reflected in its cost.

Moreover, with 80% of its customers still using the earlier platform and expected to shift to the newer one in the near future, there is tremendous potential which the company expects to tap.

Cloud Offerings Continue Phenomenal Growth Under Increased Adoption

As more and more companies adopt cloud services, the overall cloud market size has been expanding at a rapid rate. Aided by a 30% increase in new cloud bookings, the SAP’s revenue from Cloud Support and Services grew 28% in constant currency. The growth was evident across all geographies, with its revenues growing by 9% year over year in EMEA, 7% in the Americas and a robust 12% in Asia-Pacific.

SAP is also rapidly expanding its presence in the Internet of Things (IoT) space with new products and partnerships. This is a multi-billion dollar market, which could very well be responsible for driving the next phase of SAP’s Cloud revenue growth.

The recent addition of multiple Internet of Things (IoT) solutions to the SAP Leonardo digital innovation system highlights SAP’s renewed focus on bolstering its foothold in the IoT domain, which could drive the company’s top line in the future.

Combined with its ongoing efforts to strengthen its offerings in the machine learning space, SAP is likely to fare well going forward despite the heavy competition.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributorTrefis Team


Artificial Intelligence and The Future Of Jobs

December 28th, 2017

The job of IoT Evangelist working for SAP is  to go around and speak about how the Internet of Things is changing the way to live, work, and run our businesses. IoT Evangelist is a job title that didn’t exist 5 or 10 years ago – mainly because the Internet of Things wasn’t a “thing” 5 or 10 years ago. Today it is, so is the job of an IoT Evangelist.

The fact is, technological change has a tremendous impact on the way we spend our working lives. Many of today’s jobs didn’t exist in the past. Of course, the reverse is true as well: a lot of jobs – mostly tedious/manual labor of some variety, think miners, lift operators, or similar – have gone away.

Robots and much more

Much of the discussion today about the relationship between technology and jobs is a discussion about the impact of artificial intelligence (AI). Robots in manufacturing is the most obvious example. A lot of AI has to do with big data analysis and identifying patterns. Thus, AI is used in data security, financial trading, fraud detection, and those recommendations you get from Google, Netflix and Amazon.

But it’s also used in healthcare for everything from identifying better subjects for clinical trials to speeding drug discovery to creating personalized treatment plans. It’s used in autonomous vehicles as well – to adjust, say, to new local conditions on the road. Some say it’s also coming for professional jobs. Think about successfully appealing parking fines (currently home turf for lawyers), automated contract creation, or automated natural language processing (which someday could be used to write this blog itself – gulp!).

The spinning jenny

Will AI continue to take jobs away? Probably. But how many new jobs will it create? Think back to the spinning jenny – the multi-spindle spinning frame that, back in the mid-18th century, started to reduce the amount of work required to make cloth.

By the early 19th century, a movement known as the Luddites emerged where groups of weavers would go around smashing these machines as a form of protest against what we’d now call job displacement. But these machines helped launch the industrial revolution.

As a result of the spinning jenny’s increased efficiency, more people could buy more cloth – of higher quality, at a fraction of the cost. This led to a massive uptick in demand for yarn – which required the creation of distribution networks, and ultimately the need for shipping, an industry that took off in the industrial revolution.

As the spinning jenny came into use, it was continuously improved – eventually enabling a single operator to manage up to 50 spindles of yarn at a time. Other machines appeared on the scene as well. This greater productivity, and the evolution of distribution networks also meant there was a need for increasingly comprehensive supply chains to feed this productivity boom.

Muscle vs caring

Economists at Deloitte looked at this issue of technological job displacement – diving into UK census data for a 140-year period stretching from 1871 to 2011. What they found, not surprisingly perhaps, is that over the years technology has steadily taken over many of the jobs that require human muscle power.

Agriculture has felt the impact most acutely. With the introduction of seed drills, reapers, harvesters and tractors, the number of people employed as agricultural laborers has declined by 95% since 1871.

But agriculture is not alone. The jobs of washer women and laundry workers, for example, have gone away as well. Since 1901, the number of people in England and Wales employed for washing clothes has decreased 83% even though the population has increased by 73%.

Many of today’s jobs, on the other hand, have moved to what are known as the caring professions, as the chart below shows. The light blue bars represent muscle-powered jobs such as cleaners, domestics, miners, and laborers of all sorts; the dark blue, caring professions such as nurses, teachers, and social workers. As you can see, these have flipped.

The Deloitte study also points out that as wealth has increased over the years, so have jobs in the professional services sector. According to the census records analyzed, in England and Wales accountants have increased from 9,832 in 1871 to 215,678 in 2015. That’s a 2,094% increase.

And because people have more money in general, they eat out more often – leading to a fourfold increase in pub staff. They can also afford to care more about how they look. This has led to an increase in the ratio of hairdressers/barbers to citizens of 1:1,793 in 1871 to 1:287 today. Similar trends can be seen in other industries such as leisure, entertainment, and sports.

Where are we headed now?

Will broader application of AI and other technologies continue the trend of generating new jobs in unexpected ways? Most assuredly. Already we’re seeing an increased need for jobs such as AI ethicists – another role that didn’t exist 5-10 years ago.

The fact of the matter is that technology in general, and AI in particular will contribute enormously to a hugely changing labour landscape. As mentioned at the start of this post that the role in SAP as an IoT Evangelist – this is a role to no longer exist in 5 years time, because by then everything will be connected, and so the term Internet of Things will be redundant, in the same way terms like “Internet connected phone”, or “interactive website” are redundant today.

The rise of new technologies will create new jobs, not just for people working directly with the new technologies, but also there will be an increasing requirement for training, re-training, and educational content development to bring people up-to-speed.

Will there be enough of those jobs to go around – and will they pay enough to support a middle-class existence for those who hold them? That’s another question – but it’s one that’s stimulating a lot of creative, innovative ideas of its own as people think seriously about where technology is taking us.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and writer Tom Raftery



Get SAP Certification from your home ONLINE and at 1/6 of the cost!

December 18th, 2017

To my surprise, SAP has started delivering most of the certifications on SAP Cloud hub, a SAP Portal for Certification and Education, where you can register, pay and appear for the online proctor monitored exam from the comfort of your own home!

The best part is that you get a package of 6 exams in one fee of CAD$ 720 or USD$535 (subject to change as per SAP policies).

You can appear up to 3 times for the same exam (just in case you fail) or 6 separate modules of SAP.

Compare this cost with SAP training center or Pearson Vue based certification, where you pay CAD$ 720 for each exam or attempt irrespective if you fail or pass.

Here is more information:

  1. Find your SAP certification: List of SAP Certifications (Cloud certifications are marked by cloud)
  2. Book Cloud Certification (Canada)

Book Cloud Certification (US)

3. Appear for exam

Here are more blogs on similar topics:

SAP Cloud Certification

A general blog, how to pass SAP Certification

Source: All the above opinions are personal perspective on the basis of information provided by Praveen Kumar





A Complete Beginner’s Guide to Blockchain

December 12th, 2017

You may have heard the term ‘blockchain’ and dismissed it as a fad, a buzzword, or even technical jargon. But blockchain is a technological advance that will have wide-reaching implications that will not just transform the financial services but many other businesses and industries.

A blockchain is a distributed database, meaning that the storage devices for the database are not all connected to a common processor.  It maintains a growing list of ordered records, called blocks. Each block has a timestamp and a link to a previous block.

Cryptography ensures that users can only edit the parts of the blockchain that they “own” by possessing the private keys necessary to write to the file. It also ensures that everyone’s copy of the distributed blockchain is kept in synch.

Imagine a digital medical record: each entry is a block. It has a timestamp, the date and time when the record was created. And by design, that entry cannot be changed retroactively, because we want the record of diagnosis, treatment, etc. to be clear and unmodified. Only the doctor, who has one private key, and the patient, who has the other, can access the information, and then information is only shared when one of those users shares his or her private key with a third party — say, a hospital or specialist. This describes a blockchain for that medical database.

Blockchains are secure databases by design.  The concept was introduced in 2008 by Satoshi Nakamoto, and then implemented for the first time in 2009 as part of the digital bitcoin currency; the blockchain serves as the public ledger for all bitcoin transactions. By using a blockchain system, bitcoin was the first digital currency to solve the double spending problem (unlike physical coins or tokens, electronic files can be duplicated and spent twice) without the use of an authoritative body or central server.

The security is built into a blockchain system through the distributed timestamping server and peer-to-peer network, and the result is a database that is managed autonomously in a decentralized way.  This makes blockchains excellent for recording events — like medical records — transactions, identity management, and proving provenance. It is, essentially, offering the potential of mass disintermediation of trade and transaction processing.

Some people have called blockchain the “internet of value” which is a good metaphor.

On the internet, anyone can publish information and then others can access it anywhere in the world. A blockchain allows anyone to send value anywhere in the world where the blockchain file can be accessed. But you must have a private, cryptographically created key to access only the blocks you “own.”

By giving a private key which you own to someone else, you effectively transfer the value of whatever is stored in that section of the blockchain.

So, to use the bitcoin example, keys are used to access addresses, which contain units of currency that have financial value. This fills the role of recording the transfer, which is traditionally carried out by banks.

It also fills a second role, establishing trust and identity, because no one can edit a blockchain without having the corresponding keys. Edits not verified by those keys are rejected.  Of course, the keys — like a physical currency — could theoretically be stolen, but a few lines of computer code can generally be kept secure at very little expense.  (Unlike, say, the expense of storing a cache of gold in a proverbial Fort Knox.)

This means that the major functions carried out by banks — verifying identities to prevent fraud and then recording legitimate transactions — can be carried out by a blockchain more quickly and accurately.

Why is blockchain important?

We are all now used to sharing information through a decentralized online platform: the internet. But when it comes to transferring value – money – we are usually forced to fall back on old fashioned, centralized financial establishments like banks. Even online payment methods which have sprung into existence since the birth of the internet – PayPal being the most obvious example – generally require integration with a bank account or credit card to be useful.

Blockchain technology offers the intriguing possibility of eliminating this “middle man”. It does this by filling three important roles – recording transactions, establishing identity and establishing contracts – traditionally carried out by the financial services sector.

This has huge implications because, worldwide, the financial services market is the largest sector of industry by market capitalization. Replacing even a fraction of this with a blockchain system would result in a huge disruption of the financial services industry, but also a massive increase in efficiencies.

But it is the third role, establishing contracts, that extends its usefulness outside the financial services sector. Apart from a unit of value (like a bitcoin), blockchain can be used to store any kind of digital information, including computer code.

That snippet of code could be programmed to execute whenever certain parties enter their keys, thereby agreeing to a contract.  The same code could read from external data feeds — stock prices, weather reports, news headlines, or anything that can be parsed by a computer, really — to create contracts that are automatically filed when certain conditions are met.

These are known as “smart contracts,” and the possibilities for their use are practically endless.

For example, your smart thermostat might communicate energy usage to a smart grid; when a certain number of wattage hours has been reached, another blockchain automatically transfers value from your account to the electric company, effectively automating the meter reader and the billing process.

Or, let’s return to our medical records example; if a doctor or patient issues a private key to a medical device, say a blood glucose monitor, the device could automatically and securely record a patient’s blood glucose levels, and then, potentially, communicate with an insulin delivery device to maintain blood glucose at a healthy level.

Or, it might be put to use in the regulation of intellectual property, controlling how many times a user can access, share, or copy something. It could be used to create fraud-proof voting systems, censorship-resistant information distribution, and much more.

The point is that the potential uses for this technology are vast, and that more and more industries will find ways to put it to good use in the very near future.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and writer Bernard Marr