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4 Basics for Making the Move From Corporate Job to Entrepreneur

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Why do so few of us follow our passions in life? Most of us go to work, to jobs we don’t love, working tireless hours for someone else’s vision and dream. If you’re one of the lucky ones, you’ll have a great boss who motivates and inspires you to do better and friendly colleagues that support each other, as you all face daily internal and external challenges. And when your day is done, you get home feeling neither high nor low, just fortunate to be employed.

For those that aren’t so lucky, it’s a challenge to even get through each day. You dread waking up and having to go into work, your boss has an inferiority complex and makes your life very difficult, you’re part of a culture where you always feel like your job is at risk and are surrounded by colleagues that are so infused with fear, they stab each other in the back to get ahead.

While the first example sounds much better than the second, neither should be acceptable. Life is too precious and short for us to spend even one second of our day not doing something we love.

So why don’t people take “leaps of faith” to do what they love? Fear. Fear of failure. Fear of not knowing where to begin. Fear of not making money. Fear of losing insurance coverage. Fear of upsetting their family. The list of fears goes on and on. Those fears are certainly warranted, but at what cost?

“It took me twelve years of sprinting up the corporate ladder, living to work, moving six times, relationships being destroyed and compromising my values and beliefs to finally wake up and realize, I had jeopardized everything I was passionate about: traveling, getting married, having children, and becoming an entrepreneur. Now, I don’t want to come across as a victim because I definitely could’ve had better balance in my life. However, I was so fixated on getting to the next level each time, despite feeling constant stress and anxiety, that I lost all sense of what was important to me,” explains James Adamy, CEO of Ju-mp. He, like me, left a cushy corporate job to start a business he is passionate about, helping professionals network with the right business mentors and coaches to solve professional problems.

Six years ago I took the leap from someone else’s dream to start building my own company, Due. Here are tips I’ve learned from experience and from the mentors and coaches who have guided me through the process.

1. Mitigate fear.

Why do we feel fear? Fear is a product of uncertainty, and any entrepreneur knows that a startup is filled with uncertainty. So, in order to mitigate fears, we must put strategic plans in place that have tangible outcomes. Plan several months prior to help ensure a seamless transition.

Sometimes the best way to mitigate fear is by walking through each worst case scenario, and realizing that the failure isn’t so bad. As long as you plan for the worst case, you can take better measures to avoid them.

Sometimes we even fear success. Starting a new business may lead to more work, more money, more opportunities, which can also lead to less time, losing friends, and a dwindling social life. Think about creating a work-life balance to embrace the success rather than fear it.

2. Develop a network.

Immediately become immersed in your new passion outside of work at every opportunity. Attend local meetups, seminars and conferences, and reach out to people on various professional and social networks.  Do everything possible to start meeting people who can potentially become clients and/or business colleagues. You’ll need to start thinking strategically about experts and the right people to be as lean as possible.

3. Learn and develop new skill sets.

While many of the skills you learned in corporate America are transferable, a startup is a whole new challenge. Read books and blogs, take online courses and find mentors/coaches who will help you through the challenging times.

Often following a passion means trying something you’re familiar with, but the business and leadership aspects are a little more challenging. Managing a team in corporate America and rallying under-paid, equity owners in a startup are two very different challenges. Skilling up in finance, negotiation, operations, and every hat a CEO needs to wear is a surefire way of guaranteeing success.

4. Run your personal finances like a startup.

This will never be exact, but put some rough numbers together for the following: How much are my startup costs? When do I anticipate generating revenue? How much? Overhead? Can I get a loan?

Once you come up with your number, add a few more months for your runway and use that to establish how much money you’ll need in the bank before you part ways with your current organization.

There are two costs you need to consider, your own personal financial situation, and the finance of your startup. Start cutting back all the unnecessaries in your life to get your personal “burn rate” down to a minimum, then calculate a year’s worth of runway to provide a windfall. Bootstrap your own personal life to start adapting to what it will be like owning a startup and applying principles to your own finances.

Having a support system is so important for success. Knowing that you have family and friends cheering you on can make all the difference in the world. If for some reason that’s not the case, you can always fall back on the like-minded individuals you met at conferences, meetups, etc.

 

source: http://www.entrepreneur.com/article/244754

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Careers

THE 7 MOST IN-DEMAND TECH JOBS FOR 2018

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The 7 most in-demand tech jobs for 2018

CIO | Jun 6, 2018

From data scientists to data security pros, the battle for the best in IT talent will wage on next year. Here’s what to look for when you’re hiring for the 7 most in-demand jobs for 2018 — and how much you should offer based on experience.

 

 

 

 

Source: Computer World

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Industry

AUTOMATION WILL MAKE LIFELONG LEARNING A NECESSARY PART OF WORK

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As more companies adopt and learn through digital solutions, and as new forms of employment and investment opportunities strengthen the demand recovery, we expect productivity growth to recover, write James Manyika and Myron Scholes in Project Syndicate.

For years, one of the big puzzles in economics has been accounting for declining productivity growth in the United States and other advanced economies. Economists have proposed a wide variety of explanations, ranging from inaccurate measurement to “secular stagnation” to questioning whether recent technological innovations are productive.

But the solution to the puzzle seems to lie in understanding economic interactions, rather than identifying a single culprit. And on that score, we may be getting to the bottom of why productivity growth has slowed.

Examining the decade since the 2008 financial crisis – a period remarkable for the sharp deterioration in productivity growth across many advanced economies – we identify three outstanding features: historically low growth in capital intensity, digitization, and a weak demand recovery. Together these features help explain why annual productivity growth dropped 80%, on average, between 2010 and 2014, to 0.5%, from 2.4% a decade earlier.

Start with historically weak capital-intensity growth, an indication of the access labor has to machinery, tools, and equipment. Growth in this average toolkit for workers has slowed – and has even turned negative in the US.

In the 2000-2004 period, capital intensity in the US grew at a compound annual rate of 3.6%. In the 2010-2014 period, it declined at a compound annual rate of 0.4%, the weakest performance in the postwar period. A breakdown of the components of labor productivity shows that slowing capital-intensity growth contributed about half or more of the decline in productivity growth in many countries, including the US.

Growth in capital intensity has been weakened by a substantial slowdown in investment in equipment and structures. Making matters worse, public investment has also been in decline. For example, the US, Germany, France, and the United Kingdom experienced a long-term decline of 0.5-1 percentage point in public investment between the 1980s and early 2000s, and the figure has been roughly flat or decreasing since then, creating significant infrastructure gaps.

Intangible investment, in areas such as software and research and development, recovered far more quickly from a brief and smaller post-crisis dip in 2009. Continued growth in such investment reflects the wave of digitization – the second outstanding feature of this period of anemic productivity growth – that is now sweeping across industries.

By digitization, we mean digital technology – such as cloud computing, e-commerce, mobile Internet, artificial intelligence, machine learning, and the Internet of Things (IoT) – that is moving beyond process optimization and transforming business models, altering value chains, and blurring lines across industries. What differentiates this latest wave from the 1990s boom in information and communications technology (ICT) is the breadth and diversity of innovations: new products and features (for example, digital books and live location tracking), new ways to deliver them (for example, streaming video), and new business models (for example, Uber and TaskRabbit).

However, there are also similarities, particularly regarding the effect on productivity growth. The ICT revolution was visible everywhere, the economist Robert Solow famously noted, except in the productivity statistics. The Solow Paradox, as it was known (after the economist), was eventually resolved when a few sectors – technology, retail, and wholesale – ignited a productivity boom in the US. Today, we may be in round two of the Solow Paradox: while digital technologies can be seen everywhere, they have yet to fuel productivity growth.

MGI research has shown that sectors that are highly digitized in terms of assets, usage, and worker enablement – such as the tech sector, media, and financial services – have high productivity. But these sectors are relatively small in terms of share of GDP and employment, whereas large sectors such as health care and retail are much less digitized and also tend to have low productivity.

MGI research also suggests that while digitization promises significant productivity-boosting opportunities, the benefits have not yet materialized at scale. In a recent McKinsey survey, global firms reported that less than a third of their core operations, products, and services were automated or digitized.

This may reflect adoption barriers and lag effects, as well as transition costs. For example, in the same survey, companies with digital transformations under way said that 17% of their market share in core products or services was cannibalized by their own digital products or services. Moreover, less than 10% of the information generated and that flows through corporations is digitized and available for analysis. As these data become more readily available through blockchains, cloud computing, or IoT connections, new models and artificial intelligence will enable corporations to innovate and add value through previously unseen investment opportunities.

The last feature that stands out in this period of historically slow productivity growth is weak demand. We know from corporate decision-makers that demand is crucial for investment. For example, an MGI survey conducted last year found that 47% of companies increasing their investment budgets were doing so because of an increase in demand or demand expectations.

Across industries, the slow recovery in demand following the financial crisis was a key factor holding back investment. The crisis increased uncertainty about the future direction in consumer and investment demand. The decision to invest and boost productivity was correctly deferred. When demand started to recover, many industries had excess capacity and room to expand and hire without needing to invest in new equipment or structures. That led to historically low capital-intensity growth – the single biggest factor behind anemic productivity growth – in the 2010-2014 period.

But, as more companies adopt and learn through digital solutions, and as new forms of employment and investment opportunities strengthen the demand recovery, we expect productivity growth to recover. Myriad factors contribute to productivity gains, but it is the twenty-first century’s steam engine – digitization, data, and its analysis – that will power and transform economic activity, add value, and enable income-boosting and welfare-enhancing productivity gains.

 

 

 

 

Source: Project Syndicate

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Business

WHY AI ISN’T THE DEATH OF JOBS

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Companies using AI to innovate are more likely to increase employment, writes Jacques Bughin in MIT Sloan Management Review.

When pundits talk about the impact that artificial intelligence (AI) will have on the labor market, the outlook is usually bleak, with the loss of many jobs to machines as the dominant theme. But that’s just part of the story — a probable outcome for companies that use AI only to increase efficiency. As it turns out, companies using AI to also drive innovation are more likely to increase head count than reduce it.

That’s what my colleagues and I recently learned through the McKinsey Global Institute’s broad-based research initiative aimed at understanding the spread of AI in economies, sectors, and companies.1 We polled 20,000 AI-aware C-level executives in 10 countries to compile a sample of more than 3,000 companies (mostly large), identified distinct clusters within that pool, and ran a variety of scenarios on those clusters to project the effects of AI on employment, revenue, and profitability.

This research and analysis suggest that although AI will probably lead to less overall full-time-equivalent employment by 2030, it won’t inevitably lead to massive unemployment. One major reason for this prediction is because early, innovation-focused adopters are positioning themselves for growth, which tends to stimulate employment. (See “How AI-Based Innovations Drive Employment.”)

Here’s how we expect things to play out in the five clusters of companies we examined.

Enthusiastic innovators, or pioneering companies that make early investments in AI and embrace the disruption it can create in the quest for advantage, adopt a full range of AI technologies and use them to bolster innovation and efficiency. These companies are analogous to what sociologist and communication theorist Everett Rogers called “early adopters” back when he coined the term — they’re intrinsically motivated to use new technology to shape and open markets.2 While this approach is potentially complex in the short term, our analysis shows that by 2030, the profitability of enthusiastic innovators will grow 8% faster than that of the average company on an annual basis, their revenue will grow 4% faster, and their head count will rise 2.2% faster.

Source: MIT Sloan Management

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