Cannabis enjoys a nebulous legal status right now in the United States. It is classified a Schedule I drug by the federal government, meaning it has no medical or productive use whatsoever. This seems to fly in the face of reality and the many established medical uses of the product. About half the states have legalized some form of cannabis use at this point with more seeming to happen every year. This obviously puts the states at odds with the federal government, which has the potential to lead to problems. In addition, each state has different rules and regulations regarding the growth, store, transportation, exchange and sale of the cannabis products. While I personally have no interest in the recreational use of cannabis, I have seen near miracles in the medical use with friends and family – from creams for arthritic conditions to CBD pills for sleep disorders, to helping with chemotherapy side effects from cancer treatment. I’m one of a large number of people that would like to see serious medical research opened up with regard to cannabis, but in the meantime, people are working with what is available. Blockchain-built smart contract technology I spoke with Paragon CTO Vadym Kurylovich and CSO Chuck Bogorad for some insight. Let’s take a look at what Paragon plans to do to modernize the exploding cannabis industry. Paragon has many interesting plans, but to begin with, the ParagonCoin (PRG) was originally going to be issued as an ERC20 token on the Ethereum network . However, as development proceeded, they discovered it wasn’t satisfying their needs. Ultimately, they are going to use IOTA , and they have a special discount for payments in their pre-sale and crowdsale for any payments using the IOTA coin. Most importantly, this allows Paragon to issue “smart contracts” that tie into their larger business, which breaks down as follows: Create an immutable ledger for all industry-related data via ParagonChain Use ParagonCoin to pay for industry-related services and supplies Establish niche co-working spaces via ParagonSpace Organize and unite global legalization efforts through ParagonOnline Standardize licensing, lab testing, transactions, supply chain and ID verification through apps built in ParagonAccelerator The first part, as illustrated above, is to connect a consumer with a supplier through proper channels. Using a blockchain ledger ensures that each step of the process is accomplished before the next step can happen. This includes the supply chain for the product, from growing to processing to testing and eventually to the dispensary where it is connected to the consumer who has gone through their own process. Some examples of the smart contract uses in the cannabis industry include verification of medical marijuana IDs and prescriptions. Track cannabis products through the supply chain and ensure that expiration dates are honored, for example. Link physical goods to serial numbers, barcodes, RFID, etc. In connection with their PRG cryptocurrency, you are able to solve much of the impediment to cannabis innovation and growth – the banking, payment and visibility for regulation issues. Co-working spaces After the PRG token gets rolling, the plans for Paragon get really big with their ParagonSpace. The concept is to set up physical centers similar to WeWork where cannabis is legal and there is a strong interest in the product. This will give startups easy access to a network of resources to set up, manage and grow their business with the help of like-minded individuals. They have a lot of interesting concepts around this in their white paper , and from their timeline, it looks like the first ones will be near me here in California. Online community Finally, they have their ParagonOnline concept since not everyone can physically get together in a ParagonSpace. This is designed as a “community-based” online space where tasks, functions, goals and priorities are defined and worked on in real time by the community. The structure of the Paragon platform will encourage individuals to take long-term interest in a project or speculate on a number of ideas. Token crowdsale This is an interesting space with a lot of money involved and global implications. It is also ripe with opportunity to ensure regulatory compliance, safe products and guarantee of funds. Paragon is in the process of the pre-sale of their PRG token right now until September 15 at which time the open crowdsale will start. The pre-sale has so far raised over $25 million in two weeks. Their upcoming token crowdsale will run from September 15 to October 15 UTC with a maximum number of 100,000,000 of the PRG token to be sold. To stay up to date on developments and the upcoming crowdsale, follow Paragon on Twitter or talk with them on Telegram . By getting involved in the token sale, you are able to participate in the company early. The company will post clear instructions on how to acquire their PRG token before crowdsale. Article written by Shawn Gordon Image credit by Paragon Want more? For Job Seekers | For Employers | For Influencers
Our blockchain focus this time is on bitJob , a company that is focused on helping college students earn money, which as we know, they are perpetually short of. Having two kids in college myself with a third going next year, this subject is near and dear to me, so I wanted to understand what bitJob was doing that would “disrupt” this ecosystem. I spoke with Elad Kofman, company CMO and co-founder, to get some insights. bitJob can be looked at as being similar Fiverr and Freelancer , but the focus is on college/university students getting part-time, limited-scope work that can be done online. This allows them to work around their school schedules, earn money and gain experience. The platform is being launched on the Ethereum blockchain and is issuing their own STU token to be used in the system. Roadmap phases In the first phase of their rollout scheduled for first quarter 2018, bitJob intends a partially decentralized hybrid model to help speed adoption and get people familiar with the platform using interfaces and currencies they are familiar with, in addition to supporting cryptocurrency. The second phase scheduled to roll out late 2018 and will encourage users to interact through the blockchain section of the system. The beauty of the pure blockchain model is that it is self-running in essence. Since agreements are executed in Ethereum “smart contracts,” it means that you are able to essentially automate the accounting within the transaction. You take away the expense and burden of transaction fees and a human being calculating your pay rate and processing payment. Everything happens in nearly real time, and there is no question of being stiffed by the company because the funds are tied to the smart contract, like an escrow account, and are released on completion. There are a variety of ways work can be acquired in the system. A company can put up a job posting that either has a fixed rate, or a bid rate, and we assume that the company will engage the person who has the best experience for the work and/or lowest bid. A student can also post their profile with their skill set and companies can search for candidates and approach them for work and the two parties can come to an agreement. You can see the bitJob dashboard below: What about costs? For the students, it will always be free for them to use the system with no fees associated with any student activity on it. For companies, they can list a position and the listing is free, but if they hire someone, there will be a flat rate that has yet to be determined. What about conflict resolution? What if a student completes the task but the company says that they don’t accept the quality of the work? In this case, there is a review board that is staffed with top professionals at the universities that will review the conflict and present a ruling on it. The review board is compensated for their effort in STU coins, and these come from the store of coins that bitJob maintains. Over time, students in the system will be receiving ratings as to their performance. After enough 5-star ratings, the students could also be eligible to be a reviewer for conflict resolution. Affiliate program What is going to help bitJob grow quickly is their affiliate program. This is necessary for rapid adoption, otherwise, they would have to talk to every university and company directly. Look at the education market in India, for example. It’s the biggest in the world with about 300 million students. That is a big market to address, and bitJob is offering rewards to entice participation. bitJob has officially partnered with the following education institutions already: Blockchain at Berkeley at UC Berkeley (Berkeley, California). McGill Cryptocurrency Club at McGill University (Montreal, Canada). University of Florida Bitcoin Club (Gainesville, Florida). Concordia Fintech Society at Concordia University (New York City, New York). The Ivey Business School at Western University (Ontario, Canada). Infolab at The Cyprus International Institute of Management. The Blockchain Education Network (Global). They are in the process of signing up Michigan University. They will be getting early adopter access to the system and should be fully live in the rollout scheduled for first quarter 2018. ICO crowdsale bitJob just did a pre-sale of their STU token during the first two weeks of August and raised $1.2 million. Their upcoming Initial Coin Offering (ICO) will run from September 12 at 5pm UTC to October 13 at 5pm UTC with a max cap on the raise at $40 million. To stay up to date on developments and the upcoming ICO, follow bitJob on Twitter or talk with them on Telegram . Some of these terms are possibly unfamiliar, but this is a way to participate in the company early. The company will post clear instructions on how to acquire their STU token before the ICO sale. Make sure to sign up for updates on platform development, as well, so you are able to use it once it becomes available. Article written by Shawn Gordon Image credit by bitJob Want more? For Job Seekers | For Employers | For Influencers
As the Associate Director of the Master of Science in Analytics program and an assistant professor in the School of Computational Science and Engineering at Georgia Tech, I have learned many helpful lessons over the years from working with tech companies including the likes of juggernauts such as Google, eBay, Symantac and Intel, among others. Here are my top 10 lessons for anyone considering entering analytics and pursuing a career in this ever-growing, increasingly essential field: Lesson 1: You need to learn many things. This is good news, as it will lead to many job opportunities. Most companies are looking for data scientists. According to Gartner, “the data scientist role is critical for organizations looking to extract insight from information assets for ‘big data’ initiatives and requires a broad combination of skills that may be fulfilled better as a team.” I emphasize the “broad combination” aspect here, as breadth of knowledge is important. In today’s world of data, in which millions of emails are sent every second and households consume approximately hundreds of megabytes of data per day, we must think (a lot) about the challenges that come with that, including storage, complex system design, scalability of algorithms, visualization techniques, interaction techniques, statistical tests, etc. Enabling us to address these challenges are the building blocks of analytics — collection, cleaning, integration, analysis, visualization, presentation and dissemination. With these building blocks as a foundation, you will find that data types inform visualization design; data informs choice of algorithms; visualization informs data cleaning for dirty data; and visualization informs algorithm design, for when the user finds results that don’t make sense. Lesson 2: Learn data science concepts to future-proof yourselves. An essential data analytics skill is the ability to decompose a problem into smaller pieces and identify which ones already have well-known, effective solutions, so as to focus our energy on the remaining pieces that require innovation. A good book on this topic is Foster Provost and Tom Fawcett’s "Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking" which highlights key data science concepts that I think all data scientists should know, e.g., classification, regression, similarity matching, clustering, co-occurrence grouping, etc. By understanding these core concepts well, we would be able to generalize to related techniques that may come about in the future. Lesson 3: Data are dirty. Always have been, and always will be. You will likely spend the majority of your time cleaning data. It’s important work! Just how dirty are real data? Think of all the different ways a date can be written — Jan 19, 2016; 1/19/16; 2006-01-19; 19/1/16. Think about duplicates, empty rows, different kinds of abbreviations, typos, missing values, trailing spaces, incomplete cells, synonyms, bad formatting, the list goes on. Data scientists can expect to spend 80 percent of their time on data preparation, which can lead people to feel like data janitors. There is a silver lining, however, with tools such as OpenRefine (formerly Google Refine), which is a free, open source tool for working with dirty data. Lesson 4: Python is the king. Python is one of the top programming languages at tech firms like Google (the other two are Java and C++). It’s easy to write, read, run, debug and works well with others — it’s a great “glue” language, meaning your python program/script can easily call code or libraries written in other languages. Python is often the very first language popular libraries would support. Certainly, R is also very popular and has strong community support. And if you are thinking about writing production code (e.g., where speed is very important), then C++/C/Java will be much better options. Lesson 5: You’ve got to know SQL and algorithms. Even if job descriptions may not mention them, you need to know SQL and algorithms because: 1) many datasets are stored in databases, and 2) you need to know if an algorithm can scale to a large amount of data. Lesson 6: Learn D3. With data-driven documents, seeing is believing. This is a huge competitive edge. Lesson 7: You should know the “basic” big data technologies. Given that “big data” is so common and machines and disks die — according to Google, about 3 percent of 100,000 hard drives fail within the first three months — companies expect you to know the basics of big data technologies, such as Hadoop and Spark. Hadoop and Spark are open-source software for reliable, scalable, distributed computing. Fortune 500 companies and many research groups use it, and the cost is low to set up. It will be an essential skill, like SQL. Lesson 8: Spark is now pretty popular. The Spark project started in 2009 at UC Berkeley’s AMP lab and was open sourced in 2010. You might ask why you should consider a new programming model. MapReduce greatly simplified big data analysis, but as soon as it became popular, users wanted more complex, multi-stage applications (e.g. iterative graph algorithms and machine learning) and more interactive ad-hoc queries. This requires faster data sharing across parallel jobs. Some Spark programs can run more than 10 times as fast as their Hadoop MapReduce counterparts. Lesson 9: Industry moves fast. So should you. Be cautiously optimistic. And be careful of hype. Every day, you would probably hear about new technologies from popular press. We should learn about them. Yet at the same time, we should dig deeper into them, if possible, to understand why and if they indeed are promising. Lesson 10: Your soft skills can be more important than your hard skills. If people don’t understand your approach, they won’t appreciate it. So, practice your networking and presentation skills! By doing so, you can influence others, and tell them about the great work that you have done! Learn more about Georgia Tech's Master of Science in Analytics > Article published by Duen Horng (Polo) Chau Want more? For Job Seekers | For Employers | For Influencers //
This is the first in my new series to focus on companies in the exploding blockchain environment ( read my beginner's guide article as a primer ) and describe their business to people that aren’t necessarily engulfed in the blockchain ecosystem. I’m really excited about Dragonchain , I’ve been watching it since it was released as open source late last year, and now they have made a big announcement so we can now find out what the Disney hybrid blockchain known as Dragonchain is about. Dragonchain is taking a fresh and mature view to the idea of blockchains and smart contracts, and to my mind, has solved many of the issues associated with wide scale and generic deployment of blockchain based applications. For example, if you were holding a Homeowners Association election, you really don’t need that vote distributed across the entire Ethereum network as you have a high degree of trust and can deploy it close to home. Anyway, to get to the heart of Dragonchain, I talked with Principal Architect Joe Roets and Business Strategist George Sarhanis to get some details. To start, Dragonchain is essentially a blockchain of blockchains. It is abstracting almost everything about working with blockchains, so you can almost look at it in an OOP programming model. If you want to create a cryptocurrency in the Dragonchain commercial platform, you can instantiate one by using a currency library smart contract. You just select various parameters like mining or minting algorithm, token supply, how far a coin/token can be split, etc. The flexibility is fantastic. Next, you are free to use Python , Java, NodeJS and C# ( GO is coming soon), unlike Ethereum which requires you use their custom Solidity language. Since the datastore is abstracted, you can use whatever data repository you wish, the reference implementation is PostgreSQL. Many blockchains use a NoSQL model. Most of the excitement in blockchain right now is over Initial Coin Offerings (ICO). This is basically an easy and unregulated way to sell cryptocurrency for a particular project. People buy the coins in the hopes that they will have a higher value later when the project is live. There is an insane amount of money going on in this space – hundreds of millions in just the last couple months. Most of the ICOs are being done with nothing more than a website and a white paper, and for an idea that the company can’t explain very well, but they are usually focused on general use systems to the public. For instance, some are looking at ways to deploy advertising across blockchain. Dragonchain can be thought of more like Ethereum in that it is a platform, not like bitcoin which is a currency. Dragonchain also intends an ICO token sale in late September, but in their case, they actually have working technology that is useful for anyone wanting to do blockchain applications. To understand why this kind of platform is important, you need to understand what inherently goes on with blockchain data. First, in most cases, the blocks that get mined are generated through complex computer calculations that chew up a tremendous amount of energy and is slow. Next, that transaction is replicated across every computer that is on the network. The upside to those two tasks is that you have resilient data that you are pretty comfortable is secure, but for many uses it is overkill. With Dragonchain’s hybrid model, you don’t need to rely on that energy usage, also known as Proof of Work (PoW). There is an alternative to PoW that is typically referred to as PoS (Proof of Service/Space/etc). Dragonchain has the ability to provide a reputation-based notary alongside bitcoin and Ethereum trustless proof that something has happened; they call it a spectrum of trust which you can read more about in the Dragonchain Architecture document . A user or consumer of data can decide for their own system and purposes their acceptable risk for a transaction (or class of transactions). For some, having their own enterprise validation will be enough. For others, having an independent and separate 3rd party notarization will do it. For very high value or important transactions, it may be prudent to wait for a measurable level of security such as bitcoin can provide, where one can specify that, for example, a $2 million transaction must wait until it would take $200 million to attain a 0.05% chance to cheat the system. In our opening example I illustrated an election for a Homeowners Association, this could be deployed internally to a particular network where you have a high amount of trust and don’t need the information replicated everywhere, but in the case of a statewide general election, you’re going to want more guarantees and security. This flexibility to handle any of those situations is part of the power of the Dragonchain framework. The code available in the Dragonchain Github repository is functional, well-documented and includes the ability to transact on Dragonchain, query Dragonchain and process the blockchain. The organization is based in the USA, which gives me a higher confidence level than some I’ve seen that are in countries I’ve never heard of. There is going to be a token sale in either late September or early October for Dragonchain – this is that initial coin offering (ICO) we discussed. You can follow them on Twitter or join the conversation on Slack . I’m very excited by this platform and the future for blockchain based product development. There is a lot of interesting plans at Dragonchain, like a commercial side and an incubator to help grow Dragonchain-based projects. The future looks very bright for Dragonchain. Article written by Shawn Gordon Image credit by Dragonchain Want more? 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In my customer discussions, I have learned that organizations are increasingly choosing to do their big data in the cloud. At the same time, many organizations that initially implemented their big data on premises are looking to move their big data instances to the cloud, as well. Amazon Elastic MapReduce (EMR) represents an alternative to on premises by providing a Hadoop framework that actually eliminates some of the work out of initiating a Hadoop instance from scratch. This matters because 32% of business executives in a recent IDG survey found a lack of technical depth to be a major impediment to success with big data. EMR allows businesses to process data across a dynamically scalable Amazon EC2 instance and run distributed frameworks, including Apache Spark, HBase, Presto and Flink. At the same time, it can make use of data within AWS data stores, including Amazon S3 and Amazon DynamoDB. Since the big data software is already installed and configured by Amazon, businesses can spend their time improving the quality of their big data without worrying about infrastructure and administrative tasks.  Advantages of doing big data in the cloud include: Ease of use Lower cost Elasticity Reliability Flexibility However, Amazon EMR’s native security prevents many users from taking their big data to the cloud, and as importantly, making use of Amazon’s out-of-the-box solutions, including log analysis, web indexing, data transformations (ETL), machine learning, financial analysis, scientific simulation and bioinformatics. Securing the data According to the IDG survey mentioned earlier, significant concerns exist for public cloud big data around compliance, data governance and data security. These concerns are exacerbated unless sensitive data is permanently deidentified. And many are in fact doing this. This approach, of course, does not work for many use cases, especially those where the data becomes tied to a predictive model. So wouldn’t it be great if you could take advantage of Hadoop while not increasing the potential of an unwanted data release? If you could eliminate the data security risk, you could follow what is becoming a well-proven path of data analysis. In the last few years, there has been a dramatic increase in number of organizations running their big data workloads in the public cloud. This movement of big data workloads from on-premises to the cloud is due to the high total cost of ownership for on-premises Hadoop clusters. To my surprise, Amazon claims that AWS EMR is now running more workloads than both Cloudera and Hortonworks. By leveraging Amazon S3 as central data hub for big data workloads, users can process big data sets at scale. The problem is that big data implementations typically contain significant amounts of sensitive PII or PHI data. To operate with these data in the public cloud or even on premises, it is essential that this data is protected. What is needed is an architecture where EMR can consume protected data from S3. In this model, we can also consider EMR as a transient compute platform that is spun up and spun down as analytical results are required. The advantage being that you have massively powerful compute platform on an as-needed basis. What is missing, as we have been saying, is ability to protect data flows as they move in and out of EMR. Parting remarks It always makes business sense to start by using the native protections provided within EMR and S3. But adding to them the ability to directly protect the data flow in and out of EMR, which provides the protection that regulated industries require. In other words, if hackers break into Amazon, they do not get your valuable sensitive data. For those in regulated industry, this provides the kind of protection needed to securely lift and shift data your big data to the cloud. And the good news is that there are now multiple providers that can help you secure your data in Amazon. So what is stopping you? Article written by Myles Suer Image credit by Amazon Want more? For Job Seekers | For Employers | For Influencers
Building a company from scratch is not easy, period. For a few years now, Indian media has begun to extoll entrepreneurship as that sexy thing you do to get fame and make money. Nothing could be further from the truth! It was 10 years ago that I left HDFC Bank to start Cequity . From the structured environments of companies like P&G, Shoppers Stop and HDFC Bank, now I was learning to create something from nothing. An old Ladakhi saying motivated me to jump – "You cannot solve all problems before you start, paths will show paths." Now, after a decade of being an entrepreneur, I have realized that creating a “sexy halo” around entrepreneurship is a big mistake. Robert Kiyosaki said something interesting – “For being an entrepreneur you need two things: ignorance and courage.” In fact, if I knew how hard it was to grow a company, I may not have seen the trade off between the corporate world and entrepreneurship in a very positive way. And if I really knew, I may not have started. Fighting the hill Coincidentally, I started long distance running around the same time as I started a company. Who knew that the metaphor of the marathon runner, who battles many “internal walls” before he is successful would so mirror my entrepreneurial journey? Running makes you strong mentally; you learn to stay the course. Many of life’s greatest learning’s have come to me on the road. I still remember that Mumbai half marathon, many years ago, when I was pushing myself to get to a sub two-hour timing. I reached Peddar road doing good time, but still a bit behind my target timing. I started climbing the Pedder road hill with another unknown runner on my side, and just as I started to open my throttle on the hill, he said something that I now remember for life. He looked at me and said, “stop fighting the hill.” His point was to go easy on the uphill, and you will more than make up on the downhill. What a metaphor it was for me and not just for running but for life itself! Be kinder to yourself, don’t fight the hill and you can still make your target happen. For the record, I did my first sub two-hour run in that race and now whenever I am in a tough spot, I remind myself to not fight the hill. Before analytics and big data Ten years ago, I first decided to leave the safer environment of the corporate world and take a plunge at creating something new. At that time, starting up was not really a sexy option, and I was leaving a large corporate brand. Was I scared? I most definitely was. I think what really drove me was the passion for the area that I was going to focus on. Remember, this was also before analytics became sexy and the word big data became wildly popular. For some strange reason, being able to effectively use data for marketing besotted me. I think it was this blind passion that kept me going right from when I started out to this day. Without that, I think I would have given up very soon. The perfect combination I think we survived and then thrived because I didn’t start the business alone. Entrepreneurship is lonely and no one is perfect. Creating something from scratch needs all the skills that one can assemble! The perfect combination for a startup is when one founder is always chasing “perfection” and the other is passionately chasing “speed.” This creates a lovely natural balance, which is almost like an entrepreneurial equilibrium. Almost like a perfect ballet! One founder ups the ante on quality and the other at the same time pushes the pedal on speed. The two efforts ensure that the startup does not miss any opportunities. It's also critical to be clear about what makes you feel like you are working – dirtying yourself in the market or getting into your company's micro moments. One founder may like to look inward and one may be more focused on the outside view. Learn what turns you on. This is actually great for the business because the complementary passions enhance value. But it creates havoc at a personal level because both the entrepreneurs are like chalk and cheese – it was true in my case! It takes grit to go through this phase, and actually this phase never ends. Over time together, founders learn from each other. Almost like the process of osmosis, there is the gradual or unconscious assimilation of ideas, knowledge and competencies. Yet, it is incredibly important to not let your true "winning style" be eclipsed by this osmosis. Importance of others Moreover, I think that we thrived because of the people who invested in us. Sundar and Shekar Swami of the Hansa group were always superbly supportive. 2007/8 was a difficult year, and we had just started then. No one knew analytics in those days, and we struggled to find our feet. Now I realize that it takes a special quality of people who let you live your mistakes and find your feet. Hats off to both Sundar and Shekar for believing in us then and letting us evolve. Also, a few years back we invited a private equity firm to invest in us. Again, I respect them for having trusted us and feel accountable for helping to deliver value for them, as well. We thrived because of our mentors, too. People who believed in me gave us business. They trusted us when we didn't have the capabilities. They gave us their valuable customer data. They believed we had the gumption to make it happen.They became wonderful sounding boards for me as we created our company. They know who they are, I don't need to name them! But mainly we thrived because of our people. We started in an office opposite Arthur Road jail – no glitzy office experience for a new joinee. But people joined us, and they were the true heroes from the early days. In the words of the great Urdu poet, मजरूह सुल्तानपुरी : “मैं अकेला ही चला था जानिबे मंजिल, मगर लोग साथ आते गए और कारवांबनता गया." Early challenges (and successes) uniquely impacted my leadership style. Earlier I worked for large brands, now my new context shaped me as a person and as a leader. People watch what you do, and that is far more important than what you say. Limitless expedition My entire entrepreneurial journey reminded me of a bicycling expedition that I had undertaken in college. We took on an audacious challenge of cycling across the Himalayas, a distance of 4,500 kilometers crossing the Jalori pass at a height of 10,300 feet! It hadn't been done before at that stage. It took us 20 cycle tires, many spare parts and almost broken bones to complete the challenge. This month I complete the 30th anniversary of this expedition, and I cannot help but marvel at the similarities with my last decade of being an entrepreneur. I think any one who starts a company has to have a bit of madness in him or her. I learned that sometimes we stop ourselves from being audacious. This expedition reminded me about not thinking about limits. Life is beautiful when you don't let constraints imprison your imagination! Can this be taught? I don't know but it sure can be learnt. After this expedition, the Himalayas have always reminded me to honor this madness in myself. Entrepreneurial phases I break down my 10 years of being an entrepreneur into the following four phases – a) Survival, b) Making money, c) Proving your business model by creating a market reputation and a brand position and d) Scaling up. They were not distinct phases, and they seemed to overlap into each other, like living pieces having a voice that kept reappearing at different points. You start a business because you want to be successful and have a passion that wants to “tell its story.” Yet the conflicting ideas in each of these stages kept us in a deep state of equilibrium. Growth, yes, but not at all costs. Profit, yes, but not at the cost of growth! Growth, yes, but not at the cost of destabilizing us. Reputation, yes, but not before we actually started making a mark! The interaction of these different voices was not only in the form of conflict. There was also dialogue, as different voices learned from each other. As an entrepreneur, one learnt to hold contradictions. Each phase has distinct challenges, and you have to be quite crazy to even imagine the journey, let alone actually do it. In fact, if I had any clue about what it would take to really build Cequity to this stage, I wouldn’t have done it. At a personal level, it also forces you to keep learning to become the “expert” that you have to be to be successful. Cognitive psychologist Anders Ericsson has specialized in studying how experts acquire world-class skills. He has a crucial insight – Experts don't just practice their skills a lot; they do "deliberate practice". Having a goal and keeping deliberate track of it is deliberate practice. As an entrepreneur, you learn this very fast. For 16 years before I started Cequity, I was always the client, so I had to learn to wear the other shoe (become a consultant) and at the same time learn to bring on new clients. This needed massive “deliberate practice”. The good news is that it can be done. As entrepreneurs, you mustn't sacrifice speed even as you grow. Some consultants have called this “the growth paradox” – growth creates complexity and complexity kills growth. Speed is one of the few advantages a young company enjoys relative to bigger players. Using speed as a lens for making organizational decisions can help defeat the growth paradox. The Real McCoy For years as we built Cequity, I saw ourselves rapidly picking up opportunities and building a business out of them. How do we sustain this culture, how do we bring in “big company” processes and retain the “entrepreneurial heart”? L.D. DeSimone, Chairman, and CEO of 3M has this interesting take – “Entrepreneurs, like dancers, need to be light on their feet. They need a sense of timing and room to move. However, they also must balance their freedom to explore opportunities against the discipline of the marketplace”. Entrepreneurs can create a company fabric, with a soul, that is able to play with these contradictions – Freedom and discipline. And they can do it only if they are able to step into their own shoes comfortably and allow their entrepreneurial spirit to be a nucleus around which grow “enabling processes”. Only then culture forms around you as an entrepreneur and others feel enabled to bring their strengths to the party. Only then can you chase EBITDA targets and be agile enough to not lose out on rapidly evolving opportunities. You have to be the “real McCoy” first, and only then does culture form around you. People are actually very good at reading you. They can spot the fake "McCoy" from a mile! Like I said earlier, it's what you do that matters more than what you say. One of the lessons I have learnt is that companies grow only when people do. Unless we grow as people, our company would not grow. And as Napolean Hill once said, "The starting point of all achievement is desire." Still learning So how do you surround yourself with “hungry” people all looking for growth? And not all growth has to be long term — I would respect someone who comes in to create massive learning opportunities, contributes and then moves on, maybe to become an entrepreneur! Or the company keeps growing to show him the next step in the ladder. Greeks often describe doing something with soul, creativity or love. When you put “something of yourself” into what you're doing, whatever it may be as “meraki”. How does one inspire others to put meraki in their work? Entrepreneurs carry this spirit, how do you infect others? I am still learning. Escape velocity is the speed at which an object must travel to break free of a planet or moon's gravitational force and enter orbit. A spacecraft leaving the surface of Earth, for example, needs to be going about over 40,000 kilometers per hour, to enter orbit. How can companies enter their escape velocity phase? How do you manage the short term with the long-term EBITDA every month and invest for the future? We still have some way to go. But I suspect the only way to change it for the next decade would be to again create some insurgency — "Insurgency, so you remain at war against your industry on behalf of underserved customers.” But not at war, we entrepreneurs need to be kinder to ourselves — Don’t fight the hills! In a recent trip to Israel, I had the privilege of meeting Mooly Eden, an iconic entrepreneur. Interestingly, he said, "If you have the same business model now as in the last 10 years, then you are dead." The dancer Martha Graham said that it takes about 10 years to make a mature dancer. I guess the same applies to entrepreneurs, too, and I am looking forward to the next decade! (Swami and I started Cequity in 2007, and this is the year we finish 10 years.) Article written by Ajay Kelkar Image credit by Getty Images, Blend Images, Erik Isakson Want more? For Job Seekers | For Employers | For Influencers
Technology is advancing at an unbelievable speed, and every few years, there comes a new technology or technical concept that promises to impact important parts of computing in a significantly positive way. Some of these technologies that came in the past have either truly transformed the way computing works or the way we interact with technology. Others just turned out to be a hype with lots of great promises and little substantial impact. Chatbot is one of the newest set of technologies that re-introduces the promise of a huge positive impact on our lives by making technology more like us. Famous words that have been used to brand chatbots and associated technologies are these: “Bots are new apps.” Many people, including myself, started watching this new cluster of technology last year and started taking them seriously a few months ago. Well, you have to take a particular set of technology seriously when the likes of Google, Microsoft, Facebook, Amazon and Apple all say in one voice that “Bots are the future of computing,” and they do this in the same year. I cannot remember another time when all of these tech giants agreed on a common topic before. This write-up is my effort to share some personal thoughts on an intriguing concept. I like to share my thoughts on bots by putting some context around usage, design considerations, their potential future place in everyday computing and our interaction with technology. So “bots” – what are they? For those not totally familiar with the concept, let me provide a quick overview of what “bots” are. In a simple sense, chatbots (or bots) are a new kind of access and delivery channel. They are considered to be part of what is called “conversational User Interface”, which means they are a kind of user interaction unit that can engage with a user in a conversation, either via an interface where users can chat with the system or an interface that is voice enabled, which means a user can engage with the system in a natural, voice-enabled conversation. Just like how we speak with each other. And, this is the big deal – the fact that we can engage with a technology system in a more human-like conversation style. This is promised to be an actual game changer. Of course, there’s no new concept here. Products like Siri, Alexa and Cortana have been doing this for a few years now. In my view, the big deal is that now computing, platforms, talent pool and tools are making it possible for a vast number of people and organizations to build such systems. You don’t have to be Apple, Amazon, Microsoft or Google to build a bot anymore. There are many different kinds of functional categories that bots fit into. There are those bots that are chat enabled and work within your favorite chat platforms, such as Facebook messenger and Skype. There are those that are voice enabled and listen to you in your natural voice (even if you have an accent, like I do) and talk to you in a human-like voice. Further, there are bots that are only informational and provide you information based on a question that you asked. There are also bots that can take actions based on your commands. We also have bots that are rule driven. Rules are pre-defined and when a particular rule is met, a bot can take an action or can provide you relevant information. Finally, we have smart bots. These are bots with a personality. These bots are driven by big data, advanced analytics and artificial/augmented intelligence. They learn a lot about you as they interact with you to help you make right decisions. They pro-actively take actions and even talk to other bots if needed. An important thing to recognize here is given the way bots are designed today, they act as both input and output channels. Most of the processing, computation and analysis is still done on existing compute infrastructure, and that is a great thing because we do not need a new bot-specific compute layer. A single compute system can serve your enterprise applications, websites, mobile applications and bot at the same time. A use case – Bot for doctors? Bots – more particularly analytics, machine learning and AI-driven bots – can have a huge number of practical implementations. Value that bots can add in research, clinical trial setups, medical record analysis, recruiting, disease symptom logs, interaction with governments and many other areas can be significant if we use technology carefully. I like to present as a case study a situation here that I experienced first-hand recently. This is a based on a personal experience. Last week, I visited my doctor for a regular checkup. I also wanted to get my medical records from her since she does not visit the town I stay in anymore. So, upon my request, her office gave me a stack of printed sheets that had my medical records and visit logs that she created at the time of my visits. I have been seeing her for the last three years, and I must have seen her five times in this period, yet my visit logs were close to 40-50 pages. When I read the documentation while waiting for her assistant to call me in, I was taken aback with the amount of details and text that she had put in for each visit. On an average, this was close to 10-12 pages of details about each visit that I had. When I saw her, I had to ask – When does she type all this information? And does she do this for each of her patients? She explained that she does this for each patient (so I wasn’t the special one). It is mostly done as soon as a patient leaves, and that is why I wait 20-30 minutes to see her because she had to write these logs for her previous patient and then needed some time to review my data from previous visits. Now imagine if she had a voice-enabled bot or maybe an Alexa that she could talk to right after I leave and dictate my visit log in her natural voice. She could have activated the bot by using an activation phrase (“Hey, my smart bot, make a log for patient XYZ.”), and the bot starts listening to the information about my visit. Because these reports are broken down into multiple segments, a conversational style that bots offer will be extremely helpful for such a situation. Further, right before my doctor gets ready to see me, she can prep by again activating the bot and asking it to tell her about my previous visits. Using this bot will not only save her a lot of time and effort but will also give her more time to spend with me and will reduce my wait time from 30 minutes to 10 minutes (hopefully). Moreover, these bots can be used during my visit to take ad-hoc notes, asking for newer treatments (e.g. a bot can search for newer clinical trials for the doctor by way of a voice command), adjusting treatments and even sending prescriptions to pharmacies, all at once during the visit. One last point before concluding this example. What would happen if, as a patient, I have a counterpart bot at my home where I record my daily status, feelings and symptoms and this data is transported, in a secured way to a system that my doctor’s bot can access? Now, my doctor does not have to wait for me to come in after three months and ask me how I felt during that time. Doing so anyways gives her only a snapshot of how I felt in the last 24-48 hours because of memory limitations that we all have. It does not provide a multi, data point-driven trajectory. With the help of these two bots, now she knows how I felt over a period of time. This may give her an opportunity to call me in sooner, suggest me to not show up for a scheduled visit and maybe she could also adjust my treatment plan based on the data that my bot is exchanging with the system that her bot can access. This is a simple story but hopefully, it puts some perspective around how chatbots and conversational systems can make a real impact on real-world problems. What’s needed to build impactful bots? Here are four things to keep in mind when designing bots for various purposes. 1. Start with “Why?” This probably is the most important thing to consider before exploring bots. You must always start with the question, “Why do I need bots?” or “Why do I think they can make a difference?” instead of asking, “What can I do with bots” or “How do I start using bots?” It is easy to get carried away with the coolness of technology, but keep in mind that even though bots can have a massive positive impact on many situations, they are not a great fit for every situation. There are a huge number of use cases where a visual user interface will leave bots miles behind. 2. Analytics and data Smart bots require intelligence, and intelligence comes from constant data points, access to historical data, useful algorithms, machine learning and big data. Wouldn’t it be great if my doctor’s bot could tell her on its own that bi-weekly B12 shot that she has prescribed me is not working for me and has also not worked for 78% other patients in her care with a profile similar to me? 3. Strong integration It is extremely important to ensure that bots can integrate with existing systems and can leverage existing computing infrastructure. My doctor’s bot must be able to integrate with her patient database, discs and other systems that she uses. It should be able to integrate with the software she uses to send electronic prescriptions to my pharmacy. They should also be able to integrate with public APIs and with other bots. Most of the bot architecture available today can do this as long as the target system supports an open and secure integration architecture. 4. Privacy and security Privacy and security must still be top priorities for designing bots. It becomes more important when we start thinking about using bots in medical, clinical and healthcare settings. How to activate a bot, how to integrate them with other systems, how to have a bot integrate with other bots and how to make sure that a smart bot, when activating itself in an automated fashion, is not giving information to someone who does not or should not have access to this information. All of these security design considerations must be thought of carefully. Conclusion So are bots really new apps? Perhaps not yet, but they certainly have their own value preposition, which if used for the right kinds of problems or opportunities, can add far more value than an app in a classic sense. However, for a huge number of other situations, bots may not be as effective as an app could be. Even though bots are not yet in a position to replace all kinds of user interfaces and apps, aren’t you happy that they are here to help us achieve things that one could only hope for a few years ago? I think bots demonstrate a huge amount of promise today. Advancements in big data, machine learning and augmented intelligence will one day in the near future get us very close to the original premise of bots – that “technology can be more like human beings.” Article written by Manoj Vig Image credit by Getty Images, Moment, Yuichiro Chino Want more? For Job Seekers | For Employers | For Influencers
(Read Part 1 of this Space Analytics series.) Saudi Arabia has recently given Qatar 13 demands to end a trade and diplomatic embargo – and one of them is pulling the plug on broadcaster Al Jazeera. But it's not the first time that Al Jazeera has been in the crosshairs. In September 2013, when Al Jazeera had its sports broadcast on, the football scores went out uninterrupted. Next, the weather was without any issues. But soon as the news anchor began to talk about the Muslim Brotherhood – the screen froze, fought back to show pixels and finally faded to black. After the report concluded, the screen unfroze and Al Jazeera returned to its normal broadcast. Meanwhile, on the Cairo-Alexandria Desert Highway, west of the capital, several satcom jammers in a non-descript SUV with antennas pointing to the sky, check their television schedule print outs. Like television producers, they flip switches and turn knobs when the clock strikes the exact minute and second the Muslim Brotherhood broadcast is set to begin. And they un-flip the switch when it's set to end. The main characteristic of an intentional interference of satellite communications is the preciseness – almost like clockwork that a signal is jammed and then returned. Al Jazeera suspected the new government of Egypt was behind the jamming. But they needed to prove it. Enter the space cowboys Space cowboys are those that using data science and geolocation techniques to identify possible locations of the source of jamming. They use a mixture of math and Google Maps. Intentional interference or satcom jammers usually keep moving. They never jam in the same place twice for fear of being pinpointed. The key is correlating radio frequency trajectory information to the finding the ellipses of satellite dishes on Google Map images. The catch is – what if the satellite imagery – is too old? Especially if the satellite jamming equipment is attached to a vehicle. However, in the case for Al Jazeera, the space cowboys identified multiple locations: Cairo-Alexandria Desert Highway close to Al-Natrom Valley prison. A large military installation annexed to a building equipped with satellite antennas and a telecommunications tower near to the Cairo-Suez highway. East Cairo, specifically in the densely populated Heliopolis area on the junction between Airport road, El-Thawra street and El Mergheni Street near the Egyptian military intelligence headquarters and the army’s public-relations department. The trick is to predict the jammers next movement. Now taking the coordinates of where the jammers were in order to create a perimeter – a measurement of the distance around something; calculating the length of the boundary where they were found to have jammed previously. Normally, satcom jammers stay in safe-zones where no one will report them to the authorities unless the authorities are paid off. But even then, like creatures of habit, they tend to circle around. So the goal is to anticipate them when they return to a former location. As the jamming locations were given to Al Jazeera and the circle was broken, Al Jazeera is now back on the air uninterrupted. Now the question is if Qatar does not give into the demands – will Saudi Arabia resort to the same tactics? Futhermore, with SpaceX having double-header rocket launches this past month placing telecommunication satellites in the geo orbit, the opportunity grows for more satcom jammers. But so does the demand on space cowboys to geolocate and block the sat blockers. And as satcom jammers get more sophisticated – also armed with data science – the old Waylon Jennings' and Willie Nelson's 1978 cover " Don't Let Your Babies Grow Up To Be Cowboys " seems more pertinent than ever: Mamas' don't let your babies grow up to be (space) cowboys 'Cause they'll never stay home and they're always alone Even with someone they love But if the space cowboys are successful, the goal is for us all to ride happily into the sunset. Article written by Gary Jackson Image credit by Getty Images, Vetta, sharply_done Want more? For Job Seekers | For Employers | For Influencers
If you’re reading this, then you’ve likely heard one of the following terms: blockchain , cryptocurrency or bitcoin . Each of those are related as bitcoin is a cryptocurrency that is built using blockchain technology. Bitcoin really took off when the dark website Silk Road started using it. Before that, it was a nerdy curiosity. You might have heard that most Ransomware asks for payment in bitcoin; this is because it is secure and anonymous. I think we can all pretty easily understand the idea of an electronic transaction, but where it breaks down for most people is understanding what technology is running under bitcoin and how to potentially take advantage of it. This infographic I found is supposed to help explain it, yet this is probably why most regular people don’t understand it: There is around $30 billion in cryptocurrency floating around out there now, so this is a big deal. At its heart, blockchain is simply a ledger, like an old style accounting ledger. However, each transaction contains a key value that links it to the previous block, which means you can’t change an entry in the ledger as it will distort the key value. It will be dropped from the chain, and the other computers in the network won’t allow it. Here's an example of how blockchain technology would detect and prevent a node from hacking the blockchain and changing database transactions: When a node submits a blockchain update that contains an altered block, all other nodes will be able to detect that a change has been made and reject the update. What bitcoin, Ethereum and other cryptocurrencies use to create those blocks is something called Proof of Work . Basically, they chew up a bunch of computer time to generate these hash values to create a new block (that’s what all that mining is you hear about). So over time, this takes longer and longer to do, which means a Proof of Work system isn’t a great way to try and make an application that relies on response time, but you still need to have a level of consensus so you know the transaction is secure. In this article, I’m going to try and uncover some of the mysteries with a small, real world type example that doesn’t have to do with exchanging money. The other accepted method of blockchain consensus is Proof of Stake  and that working can be referred to as “minting” as opposed to “mining” (there is also Proof of Space ) in the PoW model and is several thousand times more cost effective. PoS makes a lot more sense for an application-based system, one popular blockchain platform that supports both methods is Emercoin . There is an emerging system called Dragonchain that came out of Disney (of all places) and embraces a more modular system that allows for virtually any method to be implemented, which is really perfect for building applications. Let’s use a voting system as an example. I found a number of projects built on Ethereum that were unfinished and one on Emercoin that I couldn’t figure out if it worked. This is the biggest problem with all the blockchain projects I saw – no one seems to be able to clearly explain what they are doing and/or make it simple to install and use. They all seem to assume some level of understanding or expertise. But back to the voting system. The 2016 election for President of the United States has been filled with accusations of hacking and influence and various malfeasance, and while we can’t do anything about influence, we can do something about hacking and fraud. We will take a couple liberties with voter registration and assume everyone has a unique voter token for the election they are going to vote in. This could be a stockholders vote, a Homeowners Association election, a school board or a statewide election (we have no direct nationwide votes). Now you would use whatever device has been set up for voting – this could be your computer at home through a webpage, an app for your smartphone or a voting center like we currently use. You use that voter token (a smartcard would be a good application here), log in to the system, do your voting and then that is confirmed through the consensus (proof) system that you are using. This confirms your ID and that your votes are valid for that election. Then your vote is essentially locked in an immutable block, and the next vote block as added in the chain from someone else until the election is done. Each block in the chain has a key to the block in front of it. This is what keeps it from being altered because that key is a hash value of the contents (in theory). At the end of the election, you are able to log in and check your private ID and see that your votes are in the election chain, and you can see the value of all the other votes, but not who did them. In this way, you can confirm that your vote counted and that all votes were counted in the election. If someone tries to use your voter token, then the consensus system will reject that attempt to vote, which prevents fraud or hacking. So you have total visibility and transparency while still having complete anonymity. There are an incredible array of applications for blockchain technology, and if you are a fan of the HBO series “Silicon Valley”, what they were doing with his new distributed next generation internet, is pretty much what blockchain does with data. There is a LOT of information available on this topic. It can get confusing very quickly, but this is the quiet storm that is coming and will transform how we interact with just about everything. It will provide the security that we’ve been missing in so much of today’s modern world. Article written by Shawn Gordon Image credit by Getty Images, Photographer's Choice, William Andrew Want more? For Job Seekers | For Employers | For Influencers
Customers today, whether B2B or B2C, demand a personalized experience. They expect a choice of engagement channels and flexibility to access information or connect with companies at any time, from any place, using any device. But as businesses adopt newer technologies, systems and channels to support the changing needs of customers, the customer data becomes fragmented. For example, customer data may consist of the profile information, demographics, omnichannel interactions, e-commerce transactions and analytical insights. This information is scattered across CRM, marketing automation, financial, logistics, support and business intelligence systems. Additional data sources include social networks to understand customer preferences and sentiments, data from connected devices as well as data purchased from third-party data providers. Companies realize that, in order to deliver the best customer experience and create relevant engagement, they need to process data from various sources and make informed decisions. They recognize the need to understand the complete customer journey and are becoming more customer- rather than product-centric. Customers are becoming more sophisticated and tone-deaf to one-size-fits-all messaging and promotions. They look for messages that are relevant to them. To deliver personalization at scale, a deep customer understanding with accurate and complete customer information is a must. Such digital transformation requires you to have data management capabilities that are responsive to the customer in real time, and help identify the right engagement, with the right message and at the right time. Unfortunately, traditional master data management has not delivered on the promise of meeting the needs of organizations in this new age of the customer. We need to think beyond traditional master data management and into the realm of modern data management where we not only think of “golden records” of customer profiles but also about their relationships, their past interactions and transactions. We need to process these to glean deeper insights and help customer-facing teams with intelligent recommended actions. Building a reliable data foundation Ensuring data reliability involves blending the data from all internal, external and third-party sources to create complete customer profiles. Modern data management platforms provide connectivity to ingest data from all sources in any format. For third-party data sources, that includes Data as a Service capabilities through which data-driven customer 360 applications can subscribe to such sources, search based on any available attribute and enrich the internal customer information. The next step is to blend the data together and create a single-source-of-truth about all B2B accounts and customers. The data from all sources is matched and merged using various survivorship rules. Data owners decide what will be the surviving sources for each profile attribute. Next-generation modern data management platforms also use machine learning to identify hidden match rules. Multiple high-velocity data streams make ensuring data quality hard. It requires ongoing data stewardship including matching, merging and cleansing of the data. Modern data management supports high-volume data management with cleaning, matching, merging, unmerging and verification capabilities while maintaining full audit trails, history and data lineage, where compliance is required. Such consolidated and accurate customer data is then provisioned to all operational and analytics systems. A single source of truth of complete customer data ensures consistent customer experience across channels and functional groups. Understanding relationships Just consolidating customer information is not enough. Discovering relationships between people, products and places is fundamental to understanding customers. For consumers, you want to know the many-to-many relationships they have with products, channels, stores, family members, friends and devices. Retailers, for example, want to bundle customers into households. This is only possible if you know the relationship between various customers and locations. Understanding relationships is important in account-based B2B marketing, as well. Marketers must understand the organizational structure of the account, key influencers, places and products of interest. Modern data management platforms leverage hybrid data stores with graph technology to uncover relationships between all entities like contacts, accounts, products and places. With this information, marketers can design very specific campaigns and offers for the accounts. Furthermore, using an advanced analytics environment, like Apache Spark, data teams can uncover hidden relationships or gather other interesting insights about relationships. Business owners can find the key influencers, understand product penetration and get to the key member of procurement committee. This information helps them get to the right person at the right time, with the right offer. Navigating the complex organizational hierarchies in large accounts can be tricky. It takes a lot of effort and resources to figure out the business units, their locations, key contacts and product penetration across the account. Companies use third-party data sets such as Dun & Bradstreet data to acquire hierarchy information, but that information may be limited to the legal structure. Sales need hierarchies that can provide information about product penetration, competitive coverage, credit risk roll ups and business value across the account. Graph capabilities can help in creating personalized hierarchies that provide a roll-up of such contextual information for account planning and execution. Building data-driven customer 360 applications Once you have all customer information, complete with relationships, you can visualize the information as contextual data-driven applications. A reliable data and relationship foundation allows data-driven applications to provide relevant and consistent information for sales, marketing or support. The information is utilized for accurate segmentation, campaign design or to run analytical models like customer value or churn propensity and includes such insights in the data-driven application. Relevant insights, delivered in the context of a user’s role and objective is essential for marketing success. Modern data management provides capabilities for near-real-time analytics and recommends next-best actions using predictive analytics and machine learning. You can determine trends in customer preferences, the effectiveness of marketing programs, the business value of an account and the influence of a contact. Armed with this information, marketers can provide personalized information and offers to the customer, using the right channel of engagement, at the right time, delivering superior customer experience. Enabling collaborative data curation Data clean-up is not a one-time job. It’s an ongoing undertaking that requires collaboration from all operational and customer-facing teams. Data-driven customer applications must incorporate an easy way to collaborate via tools like discussion threads and voting to gain more information about a client, and keep it current. Business users may also need to use structured processes to source additional information from a data stewarding group, flag suspected bad data or request data updates. Bringing all teams together in a collaborative workspace helps build a sound customer engagement strategy and supports high data quality. As customer data volume, variety, and sources increase, managing data will continue to challenge business and IT teams. To make sense of vast customer information and ensure that all operational and analytical systems are using accurate data, you need to establish a reliable customer data foundation. Blending data from all sources, cleansing and curating it, and discovering relationships provides a better customer understanding and enables personalized engagement. Agile modern data management allows organizations to build reliable customer views and ensures that, as more information sources, systems and channels are introduced, those can quickly be included to create a fuller customer picture. Article written by Ajay Khanna Image credit by Getty Images, DigitalVision Vectors, gobyg Want more? For Job Seekers | For Employers | For Influencers