Ag 3.0 : Why IoT, Big Data & Smart Farming is the Future of Agriculture

The farming industry will become arguably more important than ever before in the next few decades.

The world will need to produce 70% more food in 2050 than it did in 2006 in order to feed the growing population of the Earth, according to the UN Food and Agriculture Organization. To meet this demand, farmers and agricultural companies are turning to the Internet of Things for analytics and greater production capabilities.

Technological innovation in farming is nothing new. Handheld tools were the standards hundreds of years ago, and then the Industrial Revolution brought about the cotton gin. The 1800s brought about grain elevators, chemical fertilizers, and the first gas-powered tractor. Fast forward to the late 1900s, when farmers start using satellites to plan their work.

The IoT is set to push the future of farming to the next level. Smart agriculture is already becoming more commonplace among farmers, and high tech farming is quickly becoming the standard thanks to agricultural drones and sensors.

Below, we've outlined IoT applications in agriculture and how "Internet of Things farming" will help farmers meet the world's food demands in the coming years.

High Tech Farming: Precision Farming & Smart Agriculture

Farmers have already begun employing some high tech farming techniques and technologies in order to improve the efficiency of their day-to-day work. For example, sensors placed in fields allow farmers to obtain detailed maps of both the topography and resources in the area, as well as variables such as acidity and temperature of the soil. They can also access climate forecasts to predict weather patterns in the coming days and weeks.

Farmers can use their smartphones to remotely monitor their equipment, crops, and livestock, as well as obtain stats on their livestock feeding and produce. They can even use this technology to run statistical predictions for their crops and livestock.

And drones have become an invaluable tool for farmers to survey their lands and generate crop data.

As a concrete example, John Deere (one of the biggest names in farming equipment) has begun connecting its tractors to the Internet and has created a method to display data about farmers' crop yields. Furthermore, the company is pioneering self-driving tractors, which would free up farmers to perform other tasks and further increase efficiency.

All of these techniques help make up precision farming or precision agriculture, the process of using satellite imagery and other technology (such as sensors) to observe and record data with the goal of improving production output while minimizing cost and preserving resources.

Future of Farming: IoT, Agricultural Sensors, & Farming Drones

Smart agriculture and precision farming are taking off, but they could just be the precursors to even greater use of technology in the farming world.

BI Intelligence, Business Insider's premium research service, predicts that IoT device installations in the agriculture world will increase from 30 million in 2015 to 75 million in 2020, for a compound annual growth rate of 20%.

The U.S. currently leads the world in IoT smart agriculture, as it produces 7,340 kgs of cereal (e.g. wheat, rice, maize, barley, etc.) per hectare (2.5 acres) of farmland, compared to the global average of 3,851 kgs of cereal per hectare.

And this efficiency should only improve in the coming decades as farms become more connected. OnFarm, which makes a connected farm IoT platform, expects the average farm to generate an average of 4.1 million data points per day in 2050, up from 190,000 in 2014.

Furthermore, OnFarm ran several studies and discovered that for the average farm, yield rose by 1.75%, energy costs dropped $7 to $13 per acre, and water use for irrigation fell by 8%.

Given all of the potential benefits of these IoT applications in agriculture, it's understandable that farmers are increasingly turning to agricultural drones and satellites for the future of farming.

Courtesy of BI

How To: 4 Ways to Make Big Data Actionable

The oil and gas industry can be a volatile and unpredictable marketplace. However, this sector plays a fundamental role in global commerce, and if it is not running efficiently, the effects ripple throughout our economy.

There's good reason why: Demand and consumption have been growing steadily over the past few decades. Now, the United States is the third largest producer, generating more than 12 percent of the world’s oil.

So, how did oil and gas operations manage to eliminate inefficiencies, expedite production and improve processes, all the while driving profitability? Somewhat ironically, these operatons become technology companies.

According to Shiva Rajagopalan, CEO of Seven Lakes Technologies, at the end of the day, the COO lives and breathes numbers, and the numbers all need to match.

Rajagopalan is an oil and gas industry expert, who developed his enterprise software solution to give oil and gas operators better insights into the data they need to eliminate inefficiencies in their drilling and production operations. 

In an interview he told me that he believes that companies as large as those in oil and gas, or as small as a five-person startup, can make their data actionable to drive down costs and increase production. Here's how.

1. Be disciplined with big data.

According to a recent survey by The Economist Intelligence Unit, companies that master the emerging discipline of big data management can reap significant rewards and separate themselves from their competitors.

Big data is a term that describes the large volume of data -- both structured and unstructured -- that inundates a business on a day-­to-­day basis. And, thanks to constant tech innovations, such as sensing capabilities, the volume of data produced by a drilling operation is enormous.

“But it’s not the amount of data that’s important. It’s what organizations do with the data that matters,” Rajagopalan said.

For the oil and gas industry, superior data discipline reduces the inaccurate reporting of active well counts and decreases incorrect reserves reported, and penalties assessed due to incorrect updates in the forecasting system.

While oil and gas is an enormous case study for how companies can integrate data into their operations, the benefits they receive hold true for all companies. Implementing proper workflow analytics helps to automate processes and streamline manual business operations to make every arm of the company more efficient.

“With software, organizations can shrink the time it takes to convert data into value in the hands of its operators, Rajagopalan said. "Software improves the quality of analytics and creates confidence within the organization, so those on the front lines are making informed decisions.

"Being disciplined with big data means you get accounting, production and budget systems all communicating effectively."

2. Scale successful results.

The best way for the corner office executive to be on the same page as those in the field is via access to the same information. However, this requires the organization to trust that the data is flowing smoothly across the organization without multiple sources causing errors or bad reporting.

“Repeatability provides employees with a level of trust in the data and systems with which they make decisions,” Rajagopalan said. “They reconcile information less and take accountability more. They continue to respond in this way, and the system continues to shrink their time to achieve business value.”

3. Avoid the garbage-in, garbage-out effect.

In order to balance production and expenses, companies with field operations will require a more surgical effort than in previous decades. The unstructured data, which may not have been considered in the past, becomes relevant.

Working in industries with field workers, all of this data needs to be tagged, integrated and synced together with the appropriate validation checkpoints so everyone from the COO to the asset manager has the exact same view of operations.

4. Work smarter, not harder.

Yes, it’s an age-old cliché, especially for entrepreneurs. But clichés are only repeated so much in the first place because they are so relevant. Oil and gas is an important part of the world’s energy balance. It is simply time to rethink and refocus to outwit and outmaneuver the current market forces.

“Equip your team to drive significant and lasting value. Disparate source systems, ungoverned information and unreliable data block their view to operational excellence. Give them tools to turn meaningful insights into shared action,” Rajagopalan said.

By employing and utilizing intuitive new technology that keeps the company moving in the same direction while enabling actionable insights across the organization, COOs can set aggressive goals and lead their companies to the end zone time after time in a sustainable manner.

Courtesy Of Ayodeji Onibalusi

5 Key Tips On How To Succeed In Your Social And Digital Marketing Campaigns

By Marcel Masaga

Social Media Networks have evolved rapidly in the past decade or so that they have been in existence and have cumulatively made it easier for everyone to connect and share as the slogan of one of the more popular services says. So the fact of the matter is that they have made it possible for people to connect across both space and time. But the real value of these networks for business are only just beginning to get unlocked thanks to the power of digital marketing and as such this type of marketing has become a must for any enterprise that wants to stay ahead of the curve.

The formal definition of Social Media Marketing is that it entails the use of social media platforms and websites in order to promote a product or a service. Thanks to the highly measurable nature of digital media the social media platforms often contain a motley of tools which are useful in offering a wide-ranging set of actionable information that is of immense value to the platform as a whole. Some of these tools include; data analytics, geographical information, progress reports, demographics, success rate and engagement with your specific digital campaign.

Social Media Channels

This whole sub field has created a new marketing professional who is referred to as the digital marketer. Social Media Marketing which is a sub field of Digital Marketing is largely concerned with targeting potential customers on social media networks where they congregate in their droves. The combined total number of people who interact daily on all the major social media networks is estimated at 2 billion people, slightly under 30% of the total world population. Facebook is the largest player in this genre and on its own it registers traffic of 1.2 billion daily active users.

Used well and employing a predefined method your business stands to reap huge rewards from these platforms. Let us briefly explore the key concepts that are a must have for any expert digital marketer of long standing who wishes to work with some of the biggest brands in helping transform their user engagement and converting website visitors into loyal customers who will purchase the products that you have on offer. Without much further ado let us get down to the key insights that we gleaned;

The Key Takeaways

·         Assemble The Team - As with any campaign, especially a military campaign which is a matter of life and death, what happens in the boardroom before even the first shots are fired is pivotal to the success or otherwise of the campaign. The first step in any campaign is to assemble a winning team.

The dream team should expect social media savvy above any other skill, they should be comfortable using theses platforms to communicate and communicate effectively. This means that they should possesses skills in writing and editing wining copy, photo and video editing, and the knowledge of using metric tools and responding to feedback in real time.

·         Plan The Campaign – Now that you have an enthusiastic team that is armed with the minimum communication skills mentioned above, you need to get into the actual social media marketing campaign planning phase. You must employ a tactical strategy if there is any hope of you getting anywhere fast.

The tactical strategy is hinged upon organizing your campaigns short-term objectives and goals for each of the social media platforms that you will be using. Assign tasks to team members who have the innate potential and skills to see the tasks through. Generate and brainstorm on content ideas and on the channels most appropriate for each piece of content.

·         Choose The Platforms – There are various social media channels and platforms and each is designed for specific content and specific engagements goals. For instance Twitter is a micro-blogging platform is designed for sharing real time information and offering links to the full content sources.

Instagram and SnapChat are image sharing platforms, YouTube and Vimeo are video sharing platforms and Facebook comes across as a veritable jack of all trades geared towards connecting family and friends. So you must exercise great care in choosing the channels well suited for your content. An easy way to go about this is to use social interaction reporting tools to see where your audience is and target them where they are already at.

·         Establish Timelines – Set your goals carefully and realistically and do make sure that you strive to stick to them as much as possible. This will keep the entire team focused on the tasks at hand and help them to direct their efforts and energy to make sure that not only everything runs efficiently but also that the tasks get completed on time.

Some elements which you must include in your calendar include; categories, keywords, article types, content formats, promotion and marketing, tracking dates and the optimal times to promote certain types of content. Ideally you should have a worksheet where you manage all these tasks.

·         Stick To Your Voice - This point belongs to all forms of marketing regardless of whether it is old school or new school marketing and it cannot be over emphasized. The fact of the matter is that when your target audience interacts with your content (audio-visual or textual) they are not only hearing your message but they are also interacting with you in a very rich way.

Personal and Emotional

They are hearing the voice of the company and trying to make sense of and assimilate many points of information including tone, language, delivery, intent and much, much more. In effect they are interacting with your company on a highly personal and emotional level.

Make sure that this interaction is a true reflection of your company and that the message will make them want to do business with you. The end game of the social marketing or any digital marketing campaign for that matter is to convert people into paying customers, never lose sight of this in your efforts, if you adhere to the above points then you will be well on your way to success.

By Marcel Masaga

The Top 10 AI And Machine Learning Use Cases Everyone Should Know About

Machine learning is a buzzword in the technology world right now, and for good reason: It represents a major step forward in how computers can learn.

Very basically, a machine learning algorithm is given a “teaching set” of data, then asked to use that data to answer a question. For example, you might provide a computer a teaching set of photographs, some of which say, “this is a cat” and some of which say, “this is not a cat.” Then you could show the computer a series of new photos and it would begin to identify which photos were of cats.

Machine learning then continues to add to its teaching set. Every photo that it identifies — correctly or incorrectly — gets added to the teaching set, and the program effectively gets “smarter” and better at completing its task over time.

It is, in effect, learning.

       1.       Data Security

Malware is a huge — and growing — problem. In 2014, Kaspersky Labsaid it had detected 325,000 new malware files every day. But, institutional intelligence company Deep Instinct says that each piece of new malware tends to have almost the same code as previous versions — only between 2 and 10% of the files change from iteration to iteration. Their learning model has no problem with the 2–10% variations, and can predict which files are malware with great accuracy. In other situations, machine learning algorithms can look for patterns in how data in the cloud is accessed, and report anomalies that could predict security breaches.

        2.      Personal Security

If you’ve flown on an airplane or attended a big public event lately, you almost certainly had to wait in long security screening lines. But machine learning is proving that it can be an asset to help eliminate false alarms and spot things human screeners might miss in security screenings at airports, stadiums, concerts, and other venues. That can speed up the process significantly and ensure safer events.

        3.      Financial Trading

Many people are eager to be able to predict what the stock markets will do on any given day — for obvious reasons. But machine learning algorithms are getting closer all the time. Many prestigious trading firms use proprietary systems to predict and execute trades at high speeds and high volume. Many of these rely on probabilities, but even a trade with a relatively low probability, at a high enough volume or speed, can turn huge profits for the firms. And humans can’t possibly compete with machines when it comes to consuming vast quantities of data or the speed with which they can execute a trade.

        4.         Healthcare

Machine learning algorithms can process more information and spot more patterns than their human counterparts. One study used computer assisted diagnosis (CAD) when to review the early mammography scans of women who later developed breast cancer, and the computer spotted 52% of the cancers as much as a year before the women were officially diagnosed. Additionally, machine learning can be used to understand risk factors for disease in large populations. The company Medecision developed an algorithm that was able to identify eight variables to predict avoidable hospitalizations in diabetes patients.

5.      Marketing Personalization

The more you can understand about your customers, the better you can serve them, and the more you will sell.  That’s the foundation behind marketing personalisation. Perhaps you’ve had the experience in which you visit an online store and look at a product but don’t buy it — and then see digital ads across the web for that exact product for days afterward. That kind of marketing personalization is just the tip of the iceberg. Companies can personalize which emails a customer receives, which direct mailings or coupons, which offers they see, which products show up as “recommended” and so on, all designed to lead the consumer more reliably towards a sale.

3.      Fraud Detection

Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.

4.      Recommendations

You’re probably familiar with this use if you use services like Amazon or Netflix. Intelligent machine learning algorithms analyze your activity and compare it to the millions of other users to determine what you might like to buy or binge watch next. These recommendations are getting smarter all the time, recognizing, for example, that you might purchase certain things as gifts (and not want the item yourself) or that there might be different family members who have different TV preferences.

5.      Online Search

Perhaps the most famous use of machine learning, Google and its competitors are constantly improving what the search engine understands. Every time you execute a search on Google, the program watches how you respond to the results. If you click the top result and stay on that web page, we can assume you got the information you were looking for and the search was a success.  If, on the other hand, you click to the second page of results, or type in a new search string without clicking any of the results, we can surmise that the search engine didn’t serve up the results you wanted — and the program can learn from that mistake to deliver a better result in the future.

6.      Natural Language Processing (NLP)

NLP is being used in all sorts of exciting applications across disciplines. Machine learning algorithms with natural language can stand in for customer service agents and more quickly route customers to the information they need. It’s being used to translate obscure legalese in contracts into plain language and help attorneys sort through large volumes of information to prepare for a case.

7.      Smart Cars

IBM recently surveyed top auto executives, and 74% expected that we would see smart cars on the road by 2025. A smart car would not only integrate into the Internet of Things, but also learn about its owner and its environment. It might adjust the internal settings — temperature, audio, seat position, etc. — automatically based on the driver, report and even fix problems itself, drive itself, and offer real time advice about traffic and road conditions.

 Courtesy of Bernard Marr

Why Big Data is a Game Changer for Marketers

Over the last couple of years Big data has revolutionized businesses in a much bigger way. It has started to make a marked difference in the way marketers think and take decisions. Marketers believe that Big data and analytics are the sure way of surging profits higher and capturing a larger share of the market. With the help of Big data technology they are able to get a 360-degree view of their customers. This is helping the marketers to build robust marketing plans and improve results.

Big data technology is revealing many unknowns to the marketers and hence most of the data friendly companies are seriously planning its implementation. Why not! It has not only enabled to get a full view of the customer insights to make better business strategies but companies can analyze the different set of data and predict results. With such visibility, predictability, and flexibility it is hard to ignore the impact of Big data in business. In fact with it’s implementation, many upheavals have happened and many businesses changed diametrically. There are numerous such examples in diverse industries.

How Big data can make a difference?

A few years back a top marketing executive of a large sized US retailer found the company’s market share being constantly lost to the competitor. One of their major competitors was steadily eating away the market share across a range of lucrative market segments. The company started to combat this with online promotions with improvements in merchandising but even then the competitor went on gaining share of the market. With this incident becoming a threatening issue, the company started delving deeper into it and discovered something far more startling than anything that they could have imagined. Their competitor has made huge investments in their ability to collect, collate, integrate and analyze the data from every sales unit and store. They had been using this data to conduct real world experiments.

Simultaneously, the competitor used the same data to adjust prices in real time. They also started reordering the hot selling items at the right time with the availability of right goods at the right location. What the retailer discovered was a game-changing phenomena that baffled their imagination. They found that their competitor has become a far more nimble organization by constantly analyzing and synthesizing data and circulating it throughout all sections of the organization. This is fairly the reason why the challenge of Big data cannot be ignored and it’s definitely a game changer in the industry.

Big data is not a temporary trend and it is here to stay for quite some time until it changes the way the world thinks, completely. The reason is it has a predictive element in, not only how the customers would behave but in all aspects of business including finance, Human Resources, operations and other realms as well. It is also making a mark in other areas like sports and entertainment to get more engagement.

The key role of data

History says that it’s not only the adoption of technology that made organizations surge their productivity and profitability higher. Rather, their ability to alter the management practices and adopt the change to maximize the potential was more crucial. Research has found out that few industries have an advantage in terms of market incentives to implement Big data and unleash its benefits. It is believed that the era of Big data can create a whole new way of managing things and a renascence of management principles. For organizations, capturing data is important but more important would be to use it diligently and optimize productivity.

Big data is certainly going to be a game changer as it could challenge the companies, using proprietary data, as information is now more readily available. In this aspect few of the industries have an advantage already and few others don’t. For example real estate industry does have information asymmetries like privileged access to buyer’s information and transactional data. It requires significant investment and efforts to get information like ask behavior of buyers and bid information. In recent times the real estate teams are bypassed by permitting the buyers to engage directly with the sellers. Not only real estate but cost and pricing data are now accessible across many industry verticals.

With the advancement of GPS technology, information like clues about competitors’ physical facilities are also available. These satellite details are becoming handy in gleaning insights about business constraints, expansion plans and a plethora of activities. Such loads of information often call for further investment in making them useful and sharing them with the right people at the right time. Thus in many cases these details are getting stuck in the departmental siloes such as engineering, operations or logistics. So the need of the hour is to create an infrastructure not only for data processing and sharing but to gear the whole organization with the speed of the data and the insights shared.

Confronting the challenges

The opportunities are many as Big data is slowly making its way in the business world but there are considerable number of challenges too. Talent acquisition is one of them. A study by Mckinsey estimated that the demand of Big data professionals could outstrip supply by 50% to 60%. It was estimated that by 2018 there would be a need of 140,000 to 190,000 additional Big data specialists. There would be an additional need of 1.5 million managers and analysts with a sharp understanding of the application aspect of Big data.

Along with education and training of the data personnel of companies, there are concerns about data privacy and accessibility at the same time. There would be a surge in the life style of people as prices could become lower with better design of products according to consumers’ needs. This would also call for new open spaces for data storage and emerging security issues as well. There will be a need of new devices to gather data. The IT architectures would be more external environment oriented posing greater risks of intellectual property and security. Talking about the human interface and customer centricity of organizations there would be a sea change in the whole gamut of functioning of organizations. Nevertheless, Big data is a revolutionary technology that has already started impacting and we have to observe the impending changes in business over time.

Courtesy Of Swapnil Bhagwat

WEF The World Human Capital Report 2016 Big Data Visualizations:The Global Economy is Failing 35% of the World’s Talent

The Global Economy is Failing 35% of the World’s Talent

• The Human Capital Report 2016 finds that globally only 65% of the world’s talent is being optimized through education, skills development and deployment during people’s lifetimes
• Finland, Norway and Switzerland hold the top spots, utilizing around 85% of their human capital. Japan leads when it comes to 55 year-olds and over
• Report aims to assess how public and private sector investments in education and skills can best prepare workforces for the future and how big data and the gig economy might drive greater opportunity for workers

Tianjin, People’s Republic of China, 28 June 2016 – Rich and poor countries alike are missing huge opportunities when it comes to making the most of their populations’ economic potential, with only 65% on average of the world’s talent being optimized during all stages of the working life time, according to the World Economic Forum’s Human Capital Report 2016, which is published today.

The purpose of the report is to help countries assess the outcomes of past and present policies and investments in education and skills and provide guidance on how to prepare the workforce for the future demands of the global economy. In addition to measuring the 130 countries that comprise the Report’s Human Capital Index, it also analyzes a mix of public and private data from online platforms such as Care.com, LinkedIn, Uber and Upwork to generate insights on skills gaps and the potential of the online gig economy.

“Today’s transition to the Fourth Industrial Revolution, combined with a crisis of governance, creates an urgent need for the world’s educators and employers to fundamentally rethink human capital through dialogue and partnerships. The adaptation of educational institutions, labour market policy and workplaces are crucial to growth, equality and social stability,” said Klaus Schwab, Founder and Executive Chairman of the World Economic Forum.

Download The full Pdf of the report Here.

The Human Capital Index 2016

Across the Index, a total of 19 nations that have tapped 80% of their human capital potential or more. In addition to these 19 countries, 40 countries score between 70% and 80%. A further 38 countries score between 60% and 70%, while 28 countries score between 50% and 60%. Five countries in the Index remain below 50% in 2016.

At the top, Norway (2) and Switzerland (3) are nearly tied and gaining ground onFinland’s top position. All three are effectively utilizing about 85% of their full human capital potential. Japan (4) rises one rank in this year’s Index, with greater potential to be tapped by closing the gender gap. New Zealand (6), the other country in the top 10 from the East Asia and the Pacific region, rises three ranks since last year. Sweden (5) also rises one rank in this year’s Index, slightly outperforming its neighbour Denmark (7). The Netherlands (8) and Belgium (10) maintain their respective rankings while Canada (9) drops five ranks since last year.

Taking a regional perspective, on average only one region—North America—passes the 80% threshold, even though the United States (24) lags its northern neighbour by a considerable margin. Two regions—Western Europe and Eastern Europe and Central Asia—score in the 70% to 80% range and three others—East Asia and the Pacific, Latin America and the Caribbean and the Middle East and North Africa—in the 60% to 70% range. Two regions—South Asia and Sub-Saharan Africa—have not yet crossed the 60% average threshold.

Western Europe’s three largest economies all fall in the top twenty of the index, led by Germany (11) followed by France (17) and the UK (19). The lower range in the region comprises Italy (34), Portugal (41), Greece (44) and Spain (45). In total, the 28 current member states of the European Union collectively achieve a group average score of 78.48, with 12 member states passing the 80% threshold. The remaining 16 member states all make use of 70% to 80% of their full human capital potential.

The Index covers 22 countries from Eastern Europe and Central Asia. With an overall average score of 75.02, the region ranks in third place globally, after North America and Western Europe. It includes several remarkable success stories with regard to successful human capital potential maximization, including Estonia (15) and Slovenia (16) which both score above the 80% threshold, and the Czech Republic (25), Ukraine (26), the Russian Federation (28), Kazakhstan (29) andPoland (30) all scoring within the top 30. Ukraine’s performance is particularly remarkable relative to its GDP per capita levels.

East Asia and the Pacific scores towards the middle of the range of Human Capital Index results, with an overall average score of 69.75. The best performing countries; Japan (4), Singapore (13), and the Republic of Korea (32) are global strongholds of human capital success, while countries such as Cambodia (100),Lao PDR (106) and Myanmar (109) trail the region despite a relatively solid performance relative to their income levels. China (71) scores near the regional and overall Index average with regard to its human capital performance.

The 24 countries from the Latin America and the Caribbean region score in the middle range of the Index, just behind the East Asia and the Pacific region, with an overall average score of 66.95. With the exception of Cuba (36) and Haiti (111), the gap between the best and worst performers in the region is much smaller than for any other region. Chile (51) and Argentina (56) share similar strengths and weaknesses, passing the 70% overall human capital maximization threshold. By contrast, Brazil (83) is lagging behind the regional average.

The Middle East and North Africa region comprises 15 countries that had enough data for coverage in the Index. Of these, only one—Israel (23)—makes it into the top 30 of the Index. The Gulf states, Bahrain (46), Qatar (66), and the United Arab Emirates (69), outperform the rest of the region in terms of making the best use of their human capital potential. The North African nations of Morocco (98),Tunisia (101) and Algeria (117) make up the lower end of the region’s rankings, ahead of Yemen (129) and Mauritania (130).

The Index covers six countries from the South Asia region: Sri Lanka (50),Bhutan (91), Bangladesh (104), India (105), Nepal (108) and Pakistan (118). The overall average score for the region is 59.92, behind the Middle East and North Africa and ahead of Sub-Saharan Africa, and all but the top two are yet to reach the 60% threshold with regard to optimizing their human capital potential.

In Sub-Saharan Africa, a cluster of countries, including Mauritius (76), Ghana(84), South Africa (88) and Zambia (90) score in the 60–70% range — placing them ahead of the Middle East and North Africa regional average and on a par with the lower half of the Latin American and East Asia and the Pacific regions. Other economies, however, such as Ethiopia (119) and Nigeria (127) face a range of human capital challenges, including low survival rates for basic education. With an overall average score of 55.44, the Sub-Saharan African region is the lowest-ranked region in the Index. In total, the Index covers 26 countries from the region.

Human capital investment and planning can make a difference to a nation’s human capital endowment regardless of where it falls on the global income scale. Creating a virtuous cycle of this nature should be the aim of all countries. That said, there remains a clear correlation between an economy’s income level and its capacity to develop and deploy human capital

Results by Age Group 

One further finding of the Index is the unequal development and deployment of human capital across the age group spectrum. Of the estimated 7.4 billion people that comprised the world’s population at the start of 2016, 26% were aged under 15, a further 16% fell within the 15-24 age group, while 41% fell within the prime working age group of 25-54 year-olds. At the upper end of the world population pyramid, 9% of the world’s people fall within the 55-64 age group and 8% are aged 65 and over. Of these, the Index finds that while the world has developed on average 81% of the human capital potential of under-15s, only 66% of the human capital potential of the next age group up, 15-24, has been similarly harnessed. This group is largely being failed when it comes to preparing them with the relevant skills for a successful education-to-employment transition. Those in the 25-54 group are similarly only making use of on average 63% of their human capital potential while the older two age groups are likewise under-utilized, with an average of 67% utilization in the 55-64 age group dropping to 54% for 65 and overs.

Using Big Data to Understand Skills

“The new platforms and technologies of the Fourth Industrial Revolution present unprecedented amounts of data with which to complement official statistics, although for now these insights represent particular membership bases, composed of digitally-connected subsets of the populations of selected economies. Through a unique partnership, the Report leverages LinkedIn’s Economic Graph to generate further insights – fully recognizing that unlike international data, these insights have limitations. For example, they provide an overview of a relatively high-skilled, digitally connected subset of the populations of selected economies:

• Employers and employees need to start thinking about skill bundles, not job titles: While employees and employers often rely on academic degrees and previous job titles to determine fitness for a new role, a key finding in the report reveals that job titles can mean different things in different industries and geographies. The higher the skills overlap between two industries, the easier it is to transfer between them. For example, there is little skills overlap between LinkedIn members with the job title “data analyst” in the market research and oil & energy industries. By contrast, data analysts in the financial services and consumer retail industries exhibit very similar skills.
• Re-skilling may be easier than we thought: Taking a focus on skills rather than jobs may broaden the talent pool for employers – and create new opportunities for workers. For example, only about 84,000 of LinkedIn’s 430 million members have the job titles “Data Scientist” or “Data Analyst”, a highly in-demand profession for which many employers report shortages. However analysis of the skills reveals an additional 9.7 million members that possess one or more of the primary or sub-skills for Data Scientist and Data Analyst, among which 600,000 have at least five of these skills. While this clearly does not make them data scientists, data such as this provides a wider range of options for developing new talent through a relatively modest amount of supplemental training.
• Countries need to maximize learning at school and at work: Combining the Human Capital Index findings on skills diversity acquired through education with the LinkedIn findings on skills diversity acquired in the workforce highlights major differences across national boundaries. For example, Norway, Belgium, Spain, Switzerland and Portugal perform well on both skills diversity in both education and the workforce, while Australia and Romania perform relatively poorly on both areas. In the United States and Canada, the education system enables people to enter work with a relatively diverse set of skills, but these same people have less of an opportunity to diversify their skills in the workforce. In other countries, including France, Brazil and Colombia, opportunities to diversify skills by ‘learning on the job’ appear to be stronger than during the education system, where learning appears more concentrated around a narrower set of skills.
• Understanding data can help countries manage brain drain and gain:Whether driven by declining opportunities within a country, or growing demand within others, in-demand workers go where there is opportunity. Mapping the skills flows between economies offers an unprecedented opportunity for governments, businesses and employees alike to understand skills hotspots in near real-time. Economic Graph data analysed by LinkedIn for the Report shows how countries are gaining or losing in-demand skills. For example, Australia, Chile and the United Arab Emirates are all leading their regions in gaining technology-related skills while countries such Greece—but also Canada and Finland—are losing them.

“Creating economic opportunity for every member of the global workforce is a defining issue of our time,” said Jeff Weiner, Chief Executive Officer, LinkedIn. “We’ve charted the supply, demand, and flow of talent as we’ve mapped the Economic Graph, and we’ve uncovered clear opportunities for governments and employers to capitalize on the potential of their workforce at much higher rates. We’re committed to providing educators, employers, policymakers, and workers with insights, products and services that narrow skills gaps and improve economies.”

Mapping the “Gig Economy”

While the potential and promise of new technologies for enhancing education and lifelong learning has already been well documented, there remains ambiguity around the role of platform technologies when it comes to accelerating and enhancing opportunities for the workforce. Using unique data from LinkedIn as well as public and private data from Uber, Care.com and Upwork, the Report sheds light on the so-called “gig economy” by revealing the diversity and range of platform-enabled work.

The Report finds that although digital formats for connecting people to work are new, the act of ad-hoc work or self-employment is not. With a global average of 13% own-account workers, the world working-age population is already deeply engaged in analogue formats of “gig work”. The Report also finds that while own-account work may be growing, particularly own-account work enabled by digital platforms, digital formats remain a very small portion of own-account work in many economies. For example, of all of LinkedIn’s nearly half a billion members, less than 3% are freelancers. In addition, digital platforms are growing in both the developed, emerging and developing world, where the number of own-account and informal workers are traditionally higher. The highest numbers of freelancers are in the Media, Entertainment & Information, Professional Services and Consumer Industries and in economies such as Italy, Argentina and Colombia. While some of these freelancers are using technology, most are still relying on traditional analogue ways of building relationships and accessing markets to generate returns for their services.

Moreover, digital work platforms can span a range of both high-skilled, high-wage work and low-skilled, low-wage work. Less evident but equally illuminating is the range of skills and wages within some of these platforms. For example, Care.com data shows the pay premium placed on what is seen as more skilled work, such as tutoring, as opposed to traditional care roles. In addition, platforms such as Upwork are seeing their fastest growth in highly-developed, high-wage, specialist skills building on an already strong base in high-skilled work. The age and gender profiles of platform economy workers are highly diverse and do not always follow patterns in the traditional economy. Finally, to the extent that digital talent platforms make large segments of the labour market more easily visible and measurable, often for the first time, they also provide an unprecedented opportunity for smart regulation.

The Report concludes that instead of passive “techno-optimism” or “techo-pessimism”, it is important for policymakers and companies to begin dialogue and action to leverage opportunities and mitigate risks. “The new technologies of the Fourth Industrial Revolution are creating disruptions to work but they are also providing the tools to rapidly enhance human capital. How business and governments react today will determine which future we end up in. The Forum’s analysis seeks to provide the insights and space for leaders to understand the changes underway and adapt quickly,” said Saadia Zahidi, co-author of the Report and Head of Education, Gender and Work Initiatives.

The Human Capital Index ranks 130 countries on how well they are developing and deploying their human capital, focusing on education, skills and employment. The generational lens used in constructing the index sheds light on age-specific patterns of labour market exclusion and untapped human capital potential. In total, the Human Capital Index covers 46 indicators, using both publicly available data and a limited set of qualitative survey data from the World Economic Forum’s Executive Opinion Survey. Details of the methodology can be found on the Report website.

The Human Capital Index is among the set of knowledge tools provided by the World Economic Forum as part of its System Initiative on Education, Gender and Work. The System Initiative produces analysis and insights focused on forecasting the future of work and skills across countries and industry sectors as well as best practices from businesses that are taking the lead in addressing skills gaps and gender gaps. The System Initiative also creates dialogues and public-private collaboration on education, gender and work in several regions of the world and within industry groups.

The World Economic Forum would like to thank Adecco Group, African Rainbow Minerals, Alcoa, Alghanim Industries, AlixPartners, A.T. Kearney, The Bahrain Economic Development Board, Bank of America, Bloomberg, Hubert Burda Media, Centene Corporation, Chobani, The Coca-Cola Company, EY, GEMS Education, Heidrick & Struggles, Infosys, JLL, Johnson Controls, LinkedIn, ManpowerGroup, Mercer (MMC), Microsoft Corporation, Nestlé, Omnicom, Ooredoo, Pearson, PwC, Renault-Nissan Alliance, The Rockefeller Foundation, Saudi Aramco, Siemens, Tata Consultancy Services, Tupperware Brands Corporation, Uber, Workday, WPP and Zain for their invaluable support of the System Initiative on Education, Gender and Work and this Report.

Courtesy of World Economic Forum | Wefreports 2016