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12/07/2017

For SaaS Industry Difficult for Stand as no. 1 in Competitive Market

There is no longer any doubt that Software-as-a-Service (SaaS) solutions have become the preferred method for organizations of all sizes to acquire business applications to satisfy their escalating customer and end-user demands while keeping pace with intensifying competitive pressures.

But, the SaaS industry and its growing legion of enterprise customers are falling into the same software development and implementation traps that derailed the previous generation of on-premise, perpetual license ISVs who the leading SaaS vendors successfully disrupted over the past decade.

Gartner latest forecasts estimate that SaaS revenue worldwide will increase 20.1% in 2017, and jump from $46.3 billion at the end of this year to $75.7 billion by yearend 2020. Gartner says, “…more than 50 percent of new 2017 large-enterprise North American application adoptions will be composed of SaaS or other forms of cloud-based solutions."

Corporate software acquisition preferences and policies have dramatically shifted away from traditional, on-premise legacy applications to a new generation of on-demand, Cloud-based alternatives for a variety of reasons. SaaS adoption has gained momentum as a widening array of organizations have taken advantage of the lower upfront costs and faster time-to-value of many of today’s SaaS solutions.

Some organizations won't spend more on annual SaaS subscriptions because they are stuck on a previous version of the SaaS solution and are no longer able to take full advantage of the latest features - Jeffrey Kaplan in the founder and Managing Director of THINKstrategies

Although many SaaS deployments have taken longer than anticipated and entail specialized software development and systems integration skills to connect the new applications with legacy databases, most organizations have still been pleased with the operational efficiencies and additional functional capabilities delivered by the SaaS solutions.

As a result, many organizations are expanding their SaaS subscriptions to support additional workers, and adopting additional SaaS solutions to redesign more of their business processes. However, these organizations are often finding that their SaaS implementations are getting a lot more complicated.
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There are two primary reasons SaaS deployments become harder rather than easier over time.

First, most organizations are customizing the SaaS solutions so they will support their existing operations.

And second, the SaaS vendors are more than happy to let their customers do as much customization work as they like because it locks the customers into the SaaS vendors’ solutions.

In fact, many SaaS vendors are increasingly willing to let their enterprise customers sit on an old instance of their SaaS solution to accommodate all their customizations. However, this tactic is preventing these organizations from capitalizing on the latest advancements in their SaaS solutions.

SaaS wasn’t supposed to work this way.

The pioneers in the SaaS market, such as Salesforce.com, have always promoted the virtues of a single version of their applications being able to address the common needs of their customers. But they have recognized that there are industry-specific requirements and other operational issues facing many organizations that demand specialized skills and SaaS products. As a consequence, today’s SaaS product portfolios are becoming as complicated as the previous generation of perpetual license software applications.

Third-party software development and systems integration firms are prospering in this environment as they capitalize on the rapidly growing market for SaaS customization projects. It is no wonder that the biggest booths at the front of Salesforce.com’s Dreamforce conference show floor are populated with the largest professional services firms, such as Capgemini and Deloitte.

In fact, the market for SaaS/Cloud integration services has grown so rapidly that nearly all of the most prominent Cloud integrators founded over the past decade have been acquired by the biggest professional services companies in the world. Over the past two years, Accenture gobbled up Cloud Sherpas, IBM bought Bluewolf, and Appirio was acquired by Wipro.

Although everyone expects the rapidly evolving assortment of artificial intelligence (AI) and machine learning (ML) capabilities to automate various aspects of software development, deployment and support, the reality is that most organizations need a new set of experts to help them evaluate, implement and administer these new solutions in their environments. In response, Salesforce.com and a handful of venture firms are establishing dedicated investment funds to support the next generation of AI/ML oriented systems integrators.

Even with the promise of AI and ML on the horizon, I’m now hearing from a growing number of organizations that they don’t want to spend more on annual SaaS subscriptions because they are stuck on a previous version of the SaaS solution and no longer able to take full advantage of the latest features.

If this trend continues, the SaaS industry could face a significant speedbump in the future and independent systems integrators will be the only winners in this environment.


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14/06/2017

Record Soybean Crop Supports Brazil Real as FOB and Futures Diverge - Bloomberg

Brazil’s 12-month trade balance set a record in May, and exports from a booming soybean crop have played a major role in the gains. The strong trade balance has in turn boosted Brazil’s real, and that carries a trickle-down effect on prices because soybean exports are quoted in dollars.

Brazil, Argentina and the U.S. together produce about 90 percent of the world’s soybeans. Export prices from all three have tended to move together. And even with the surging crop and record inventories globally, forecasters say the supply isn’t expected to meet the world’s demand.

“Demand is just silly good,” Dale Durchholz, senior market analyst at AgriVisor, said in May. “The world economy is improving, and as incomes rise, consumers demand more meat, dairy and eggs. Traders have underestimated demand while focusing on the big supply.”

The Issue

Global soybean prices fell on May 17 after news of a new political scandal in Brazil. Soybean prices have continued to move together since then, including free-on-board prices at the primary export ports for the Big 3 producers. However, a tools analysis shows futures prices on the Chicago Mercantile Exchange have fallen even faster, suggesting a more bearish view of the soybean market and also of the outlook for the real.



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the Money is Easy prove after Neustar’s 2017 Worldwide DDoS Attacks

Counting on the ethics of an attacker is a very bad strategy for DDoS mitigation. 

Based on the analysis in Neustar’s 2017 Worldwide DDoS Attacks and Cyber Insights Research Report, you can expect the level of havoc raised by DDoS attackers to continue soaring.

Consider these statistics from Neustar’s DDoS Security Operations Center 

  1. Comparing Q1’17 to the same period in 2016, mitigations nearly doubled
  2. Attack volume was up even higher than last year
  3. Multi-vector attacks more than tripled
  4. Average size of attacks was higher

The rising bandwidth consumed by DDoS attacks is a sign that hackers have incredibly powerful tools, which gives them plenty of “firewood” to start five-alarm blazes all over the internet. The report shows that just 17% of attacks are under a gigabyte in size. 

About half (47%) are between 1 and 20 Gbps. And 28% are over 20 Gbps.

The tools enabling these monster attacks include malware and botnet code targeting DNS vectors, DNSSEC domains and complex TCP SYN flood attacks. With so many more resources that can be easily marshaled, both economically and electronically, it’s little wonder attacks have become more determined and more dangerous.

And there’s very little out there to discourage hackers from using these readily available tools. Many hackers operate in nation states that leave hackers alone – especially when the bad guys have threatened weak or non-existent law enforcement with swift retribution if anyone goes after the criminals. In essence, DDoS attackers are globally operating at will.

Many DDoS attacks are launched for easy money. We see this almost on a weekly basis worldwide. 

The DDoS attack is really used as a smokescreen to sneak into a network for other nefarious reasons. In 43% of the attacks, malware was activated . And one of the most nefarious types of malware is ransomware, which occurred in 23% of attacks – it’s fallout is being experienced globally by all types of businesses. When ransomware strikes a target, the organization must pay electronic cash in exchange for unlocking its data. DDoS attacks are highly profitable.

In a way, the research shows that we’ve entered a new phase of DDoS attacks. Cyber criminals used to launch DDoS attacks simply for disruption and chaos as they crippled websites and stopped online commerce. Now the attackers are getting smarter, using DDoS to generate cash for their own coffers.







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Major US Banks Have Invested in Fintech

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Since 2012, the top ten US banks by assets under management have participated in 72 rounds totaling $3.6B to 56 fintech companies.

While investment activity dropped on a quarterly basis in Q1’17, four of the last five quarters have seen over $1B invested into VC-backed US fintech startups.

US banks and their venture arms have been active investors in the private company fintech ecosystem. We used CB Insights data to visualize the fintech investments of the top ten US banks by assets. Specifically, we looked at investment activity from 2012 – 2017 year-to-date.

Key takeaways

Since 2012, the top ten US banks by assets under management have participated in 72 rounds totaling $3.6B to 56 fintech companies.
Ranked by the number of unique portfolio companies, the cohort’s three most active investors are Citi, Goldman Sachs, and JP Morgan Chase — in that order. Citi (including Citi Ventures) participated in 30 rounds to 22 companies, Goldman Sachs in 31 rounds to 25 companies, and JP Morgan Chase in 14 rounds to 13 companies. We also took a different view of these three firms using CB Insights’ Business Social Graph and highlighted where all three co-invested:

Goldman Sachs is focusing on payments, investing in six companies in the space. Between 2012 and 2017 year-to-date, the firm participated in eight financing rounds totaling about $570M. Vietnam-based MoMo operates a mobile wallet and offers branchless banking services for traditionally unbanked individuals and has raised nearly $34M in two rounds with participation from Standard Chartered and Goldman Sachs. Goldman was also the only one of the cohort to invest in real estate fintech companies, namely Cadre and Better Mortgage.

All ten banks have blockchain investments. Eight of the ten became part of R3, a banking blockchain consortium, although Goldman Sachs, JP Morgan Chase, and Morgan Stanley have since exited the consortium.

Although the second largest bank by assets, Bank of America takes the sixth spot on this list, with only six fintech companies in its portfolio. additionally, Bank of America was the only member of this cohort to invest in Bill.com, participating in the company’s $38M Series E. The payments processing platform is valued at nearly $268M, having raised $123M in funding.

Kensho saw lots of overlapping interest, with six of the cohort investing in its $50M Series B, which valued the company at $500M. Kensho applies data analytics and machine learning to financial research.

Margin plus Trading for day 14 June 2017

In Day Trade i made some Margin plus activities

Target stock - 

  • Wipro
  • Lic housing 
  • BPCL
  • Just dial


with 9.45 am, i look for  chance to enter market to make large but unlikely.

On Lic housing i thought, earn some but lot of voilty cause yesterday its touch fresh high.

BPCL going down , for looking support level purchase at 691 wait for 697, unlikely only earn 3.5 per share.

Just dial - no profit no loss - but brokerage all .075% by icici direct ( all tax sum up)

And Sold LIC housing on Contract BSE-90 with INR 15 per share with less Qty.


Tommorrow pick 


  1. TATA motors
  2. Wipro
  3. Just dial


thanks, 

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Expand Your Marketing Funnel

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Organizing Your Marketing Tech Stack, Beyond BANT, and More – Raise The Bar

https://twitter.com/thinker

Boston’s Growing Startup Landscape

Entrepreneurs often flock to Silicon Valley to start a company because of the number of venture capital firms and angel investors and the community’s appetite for new companies. But other cities across the world, including Stockholm, Portland, and Boston, are gaining a stronger startup reputation and increasing the number of startups and amount of VC funding.

Boston, in particular, has startup legs, and its growth has huge implications for investors, entrepreneurs, and salespeople.



Always Be Closing Sales

Jacco Van der Kooij of Winning By Design offers what he calls a “modernized version” of BANT that can be applied to Inside Sales Teams (SDRs, AEs) that deal with recurring revenue deals in “BANT and Beyond: Advanced Sales Qualification for SDRs & AEs”

SaaS Metrics Survey

Are you ready to raise your next round? Take our survey to help measure and benchmark the metrics vital to a SaaS company’s success. Answers will be compiled and sent to participants this summer. Go here to take the survey.

Expand Your Marketing Funnel

Kobie Fuller of Upfront Ventures aims to help marketers better make sense on how to potentially organize the plethora of marketing tools and get closer to achieving cross-channel marketing in “Organizing Your Marketing Tech Stack”

Marcus Taylor of VentureHarbour focuses his in-depth, four-part guide on both customer acquisition and improving your product-market fit in “The Ultimate Startup Marketing Strategy”

Grow Up and To The Right

Darius Contractor of Dropbox presents his framework for how user psychology has driven growth at companies like Bebo, Tickle, PhotoSugar and of course, Dropbox in “Psych’d: A New User Psychology Framework for Increasing Funnel Conversion”

Edward Ford of Advance B2B digs into “The Mission Matrix”, a framework to map out your go-to-market plan, helps you understand where you are located in the matrix, and gives a case example of this matrix in the marketing automation field in “The Mission Matrix: Your B2B SaaS Go-To-Market Strategy”

Make Your Sales Data a Lot Better with a Little Discipline - Jim Fowler

Business intelligence is projected to grow to a nearly $26.9 billion industry by 2021, but its solutions are only as good as the data behind it. IBM determined that inaccurate data took a $3.1 trillion bite out of the U.S. economy in 2016. That’s why decision makers require spot-on data and efficient, streamlined systems to maintain it. Otherwise, they’ll end up with what I call a “rat’s nest”: dirty, duplicate, or dead information that obscures useful insights for making smart decisions.

Too many sales teams (and other departments) enter data by hand but create fresh entries instead of searching their systems and updating existing accounts, which muddies their data sets. Manual data entry isn’t ideal — it can be costly, time-consuming, and open to misinterpretation.

Let’s say a prospect from IBM fills out a website lead form and enters “IBM” instead of the full company name. And let’s say that an account existed under the full name, International Business Machines Corporation, so that the entry listed under the abbreviation results in data fragmentation and confusion. Next come duplicate account records with notes, tasks, and contact information haphazardly attached — a total rat’s nest.

The best way to keep data clean is to use a globally known, unique identifier, or a “data backbone.” My company prefers to use URLs as identifiers. They’re free, globally recognizable, high-quality data points that enable you to efficiently gather information on a business’s industry, online activities, and functionality. For example, Cisco is a company that also goes by Cisco Systems, Inc. and Cisco Precision Tools. 

If sales containers required users to type in one unique URL, www.cisco.com, for all those different branches, it’d be much more difficult to create duplicate accounts, which helps keep data clean. Perhaps more important, URLs facilitate communication between people, systems, and even departments. 

Whether it’s the customer relationship management platforms used by sales teams, enterprise resource planning software used by purchasing teams, or the account-based marketing technology employed by marketing teams, the business intelligence platform can recognize a unique URL and attach it to clean, usable data. Unique identifiers let you know you’re pulling from the sources and contacts you’ve intended to track.

Establishing a data backbone is one part of the business intelligence equation, but fleshing out the ribs (contact information, credit history, competitive intelligence, etc.) can make data seem overwhelming without a good process for managing it. The following strategies can help you improve your business intelligence through better data management:

Clean house on marketing and sales contacts

Organizations of all stripes can use their primary identifiers (their backbones — in the above example, URLs) to make sure their sales and marketing teams work from a unified contacts list. Businesses should remove duplicate accounts from data sets, so that marketing, sales, and other departments can work more cohesively when reaching out to prospects. For example, Amnesty International integrated its firmographic data and improved donor relations by avoiding multiple solicitations, which made for timelier campaigns. Using only the most relevant, searchable information, and then assigning it a unique identifier, helps tidy up data for more effective work.

Coordinate communication around industry news and events. 

A business’s competitive data should include opportunities to boost communication on the basis of events and industry happenings. For example, our clients in the sales enablement space draw on our competitive graph, firmographic data, and news alerts to identify trigger events for their users. Say you’re a mobile phone provider looking to roll out a new bundled internet and phone plan at a competitive price. Using data to compile national averages of usage and monthly payments, a sales team can craft its promotional material and pitches around what its product does that the competition does not. Our company’s daily snapshot uses blogs, articles, and other information to detail where a company is positioned in its competitive field. You can take a similar approach by arming sales with valuable information for engaging with prospects.

Identify potential prospects according to current clients 

Use a competitive relationship graph and firmographic data to help you find new opportunities based on your previous successes. Sales reps can identify lookalike companies, those with profiles similar to existing accounts, to discover other companies that generate similar revenue or that compete in the same space. Pinpointing these possible competitors helps identify prospects faster and more efficiently. This also works for identifying expansion opportunities and new markets. One baby clothing retailer in the UK used business intelligence on sales performance to determine which items to stock in each store and where to potentially expand to new locations.

Map and categorize incoming leads

Segmentation is critical in account-based marketing, so it’s important to accurately categorize leads entering your funnel. Attributes recorded in the data system will then direct your marketing team to which leads it should target with certain campaigns. Companies that tailor their strategies this way see increased conversion rates, lower churn, and high customer satisfaction. 

SM Marketing Convergence Inc., a retail-affiliated marketing company in the Philippines, used business intelligence and visual analytics tools to process more than 200 million transactions made across 500 stores within a year. The report showed what tactics worked and how to segment future leads.

Clean data construction is the way forward, and to ignore the need is to sacrifice your competitive edge. A strong backbone is the key to riding the growing data wave to prosperity.

Jim Fowler is founder and CEO of Owler, writer of this blog a community-based business insights platform. Prior to Owler, Jim founded Jigsaw in 2003 and was CEO until it was acquired by Salesforce in 2010.


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12/06/2017

How a Data Center Works from Data Perspective

“Data Center” 
Well judging by the above, you may see that the Data Centers sector is going BOOM as we speak! Before going further ahead, let’s consider what exactly they are. 

Data Centers are highly specialized environments specifically organized for safeguarding a company’s most valuable equipment and intellectual property. It is considered very essential in undertaking storage and management of huge data (or simply called ‘Big Data’ in this marketing day-and-age) and information. 

Nowadays, Data Centers continue to be the main service hub to drive innovation with a new paradigm for business agility and response.

Though this industry and in general the number of data centers are blossoming, information related to them is not quite handy. There are close to hundreds of thousands of data centers currently present and more than hundred being added up in a quarter across the globe which are next to impossible to track and follow up on for every organization. 

Apart from this, many facets of a data center need constant requirement of external resources such as hardware & software management, fire safety providers, electrical & power controllers, energy efficient switches, UPS & Generators, etc. to drive its day-to-day executions.

Wouldn’t it be just great if you could get all this info straight away which would help you to target and reach them ahead of your or any competition? 

This is exactly where SMARTe Inc,  Database vendor has mastered in solving these challenges. 

Though our unique DaaS suite, we have successfully helped our enterprise clients with vital insights on the upcoming data centers along with complete information about the decision makers and influences. 

This has aided our clients in reaching out to the correct people at the correct time resulting in maximum business ROI from their marketing and sales outreach. Along with this, we also help to build/refine contact data with up-to-date, relevant and accurate contact information for every targeted business, including generating the names of multiple decision makers/influences at each organization as per their demand.


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18/04/2017

Augmented Intelligence combining human and AI to change behavior


Featured in: Big Data, Big Ideas & Innovation, Entrepreneurship, Healthcare, Technology

Artificial Intelligence, or AI, is the most promising and overhyped technology of our times. AI techniques such as deep learning have allowed computers to match or even beat world experts at games like chess and Go, and even board-certified doctors at diagnosing diabetic retinopathy and skin cancer.

While AI is great for recognizing patterns in puzzles and pictures, it is much harder for AI to change the behavior of people, in all their fascinating and frustrating complexity. In an earlier post on the Keystone Habit, I introduced the concept of using goals and habit change loops for personal development. Now let’s explore how to combine these loops for product development, in order to design a system to help someone change.

Here, I argue that human intelligence is better applied to helping people form goals, while AI is better applied to helping people form habits. This is not to say that an effective fully-automated AI system could not be built. But if you already have human and software resources, they can be synergistically combined to create an “Augmented Intelligence” system for behavior change.

The Sepah Behavior Change Model:

While there are a lot of behavior change models out there, I created a new model to integrate two important concepts—the goal formation loop on the left, and the habit formation loop on the right—into a unified loop that allows for continual improvement. Let’s walk through an example of how this works:


Plan the Goal:

All behavior begins with intention. Let’s say my intention is to exercise more often. While I may want to start working out every day, I decide to work with a personal trainer who takes into account my current routine to help me set a S.M.A.R.T. goal, that is Specific, Measurable, Agreed Upon, Realistic, and Time-Bound. For example, since he knows my gym is conveniently located two blocks from my work, and I’m more motivated to exercise in the morning because I work long hours, we mutually set a goal of going to the gym next to my work at 7:30AM, to lift weights for 30 minutes on Monday, Wednesday, and Friday, and to check-in at week’s end.

Theoretically, an AI-based system could also ask me a series of questions about my routine and preferences, then creates a reasonably-tailored S.M.A.R.T. goal for me. But these systems often run into problems adjusting goals over time, as we’ll see later in the loop, which is why humans are better suited to plan and revise goals.

Act on the Goal:

Next comes an attempt to act on the goal. I’m motivated on Monday morning, and I successfully go to the gym for 30 minutes before work. Great! Action is the centerpiece of the model for good reason, since goals are merely dreams without action. It’s worth noting that inaction, such as procrastination, is counterintuitively an action itself (what the Taoists call ‘Wu Wei’, the action of inaction). So whether you do or you don’t, you’re still acting. The difference is direct action takes you towards your goals and values, while avoidance takes you away from them.

Reward the Habit:

Here we cross into the habit loop: actions only become habits when they are repeatedly rewarded over time, even if the rewards are occasional. These can be intrinsic rewards (e.g. the mood boost from the workout) or extrinsic rewards (e.g. my personal trainer telling me I did a good job). While social reinforcement is an incredibly powerful motivator—it fueled the rise of phenomena like Crossfit—AI can reward habits equally well.

Gamification, which is the application of behavioral principles to game mechanics, is the best example of AI reinforcement. It successfully gets people to play video games for hours on end using tokens such as points and badges to designate accomplishment and skill development. This would seemingly pale in comparison to the sense of satisfaction that comes from a real person giving you a heartfelt high-five after a workout. But the high-five after every single workout can become repetitive, while gamified systems use variable schedules, quantities, and types of rewards (like how slot machines randomly dole out different jackpots) to prevent habituation and continually provide the addictive dopamine hit that powerful rewards bring.

AI is also superior to human reinforcement on an economic basis. While a personal trainer is motivational, they are usually not as cost-effective as an AI system emailing me a coupon for free protein shake at my gym when I achieve 3 workouts a week, or notifying me that I am among the top 20 most active gym-goers via an automated text message. Applications like Pact, DietBet, and Stikk are examples of companies that blend social and behavioral economic rewards to get people to go to the gym more often.

Remind the Habit:

Habits not only need to be rewarded, but they need a reminder (also known as a cue or trigger) to initiate the behavior regularly. Since I am out of town for work on Tuesday, I call the hotel front desk and set a wake-up call on Wednesday morning to remind me to get up for my scheduled workout. While that is an effective reminder, it is not a good use of human resources, which is why most hotels have automated wake-up call systems (or most people just use their smart phone’s alarm).

Though it seems obvious, Omada ran a randomized controlled trial to validate this hypothesis. We found an automated email performs just as well as a human coach in reminding a participant to weigh-in on our connected scale. Given that reminders are quick and easy, outsourcing this to AI is an effective move.

Act on Your Goal (Again):

For an automatic habit to form, the action must be repeated. My alarm goes off on Friday, but I am so sleep-deprived and tired that I decide to hit the snooze button. I text my trainer to cancel our session last minute, and fail to go for the third scheduled workout that week. Thus, I unfortunately fall short of my initial goal.

Reflect on the Goal:

Here we cross back into the goal loop by reflecting on the week and how well I’ve executed against my goal. This step is missing or glossed over in most versions of habit loops that you’ll see in books, but is critical if you want to continually iterate and improve upon your goals. In a previous article, I argued that reflection is the Keystone Habit when it comes to personal development, and articulated a concrete system to achieve your goals. Without reflection, we are doomed to repeat mistakes, and as I always tell my patients:

“a mistake repeated more than once is a decision.”
My trainer calls me over the weekend to reflect on our first week together. I share that I used my initial motivation to successfully go to the gym on Monday. I then used an alarm to remind me to go again on Wednesday, and was motivated by remembering the sense of accomplishment I felt after the first workout.

However, by the time Friday rolled around, we identified I was sore from starting a new exercise routine and the accumulated sleep deprivation from a long work week hindered my physical recovery and sapped my motivation. Here we see the power of human intelligence. While there were many factors that contributed to my skipping the gym on Friday, a quick conversation with my trainer identified the two major ones: muscle soreness and lowered motivation from sleep deprivation. An AI system has a much harder time identifying and isolating variables, especially psychological ones.

Plan the Goal (Again):

Based on the information gathered, my trainer and I work together to plan a revised goal for next week. He recommends I go to the gym next Monday and Wednesday at 7:30AM, but knowing I’m likely to be sleep-deprived and less motivated on Friday, recommends I go at 6PM just to warm up and stretch (thus reinforcing the habit of going regularly, while making the workout easy enough for my level of motivation). If that doesn’t work, we will switch the third weekly workout to Saturdays when I’m feeling more recovered and motivated.

Human intelligence shines at iterating on goals. Good trainers and therapists accurately identify individual strengths and barriers and leverage them to creatively optimize goals towards success. People’s physical and psychological states also vary tremendously on a day-to-day basis. Humans are better at picking up on these through our intuitive ability to read body language and facial expressions. Once a new goal is planned, the behavior change loop is executed repeatedly until the action becomes an automatic habit.

The Future of Augmented Intelligence

While AI has tremendous promise (and I advise AI startups in Silicon Valley because I believe they will be transformative), it’s currently better suited for specific intelligence, rather than general intelligence. When it comes to behavior change, AI is a clinically effective and cost-effective tool for habit formation by automating reminders for behaviors and providing variable rewards. But when it comes to goal formation, human intelligence (particularly that of an effective manager, trainer, or psychologist) is currently better leveraged to help people with goal planning and reflection to continually improve.

As a result, I believe the immediate future holds tremendous promise for hybrid systems, what I call “Augmented Intelligence,” which best combines human and artificial intelligence to change human behavior. Rather than replacing human coaching altogether, AI can support coaches by automating the easier tasks of reminding and rewarding habits. Thus freeing time to focus on what’s difficult and meaningful: helping others find and achieve their dreams.

source :- https://goo.gl/ZFxhI3







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03/04/2017

Era of Big data – Revolutionary

The massive amount of big data from source generates in hour by hour.

Enterprise has learned to harvest big data to earn higher profit offer better services and gain a deeper understanding their target clientele.

The basically huge amount of data generates on a day to day basis volume of data not relevant as what organization do with data.

Analyze big data can lead to insight that improves strategic business decision marketing.

Big data – valuable

Harvesting big data from any source enable reduction of price, time etc.

Big data with high energy analytics
-    Identify reason for failure
-    Generating voucher – point of sale based

Example
-    Automotive industry
-    Entertainment
-    Social media

Type of big data
-    Structure – refine – volume
-    Under structure – large volume – under values

Four variable – big data
-    Volume
-    Variety – Data source, Mass data append, speed of collection
-    Velocity – very high flow of data
-    Veracity – incompatibility

Some suggested Big data technology
-    MapReduce
-    Hadoop
-    Hive

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29/03/2017

A Corporate Story for Every ever person

Every day, a small 🐜 Ant arrives at work very early and starts work immediately.

🐜 She produces a lot and she was happy.

The Chief, 🐯 a Tiger, was surprised to see that 🐜 the Ant was working without supervision.

🐯 He thought if the 🐜 Ant can produce so much without supervision, wouldn’t she produce even more if she had a supervisor!

So 🐯 he recruited a 🐝 Bee who had extensive experience as πŸ‘· supervisor and who was famous for πŸ“ writing excellent reports.

The 🐝 Bee’s first decision was to set up a πŸ”” clocking in attendance system.

🐝 He also needed a secretary to help him write and type his reports and he recruited a πŸ‡Rabbit , who managed the archives and monitored ☎ all phone calls.

🐯 The Tiger was delighted with the 🐝 Bee's reports and asked him to produce πŸ“Š graphs to describe production rates and to analyse πŸ“‰ trends, so that he could use them for πŸ“ presentations at Board‘s meetings.

So the 🐝 Bee had to buy a πŸ’» new computer and a Laser printer and recruited a 🐈 Cat to manage the 🌐 IT department.

The🐜 Ant, who had once been so productive and relaxed, hated this new plethora of paperwork and meetings which used up most of her time…!

🐯 The Tiger came to the conclusion that it was high time to nominate a person in charge of the department where the 🐜 Ant worked.

The position was given to the πŸ’ Monkey, whose first decision was to buy an Air Conditioner and an ergonomic πŸ’Ί chair for his office.

The new person in charge, πŸ’ the Monkey, also needed a πŸ’» computer and a personal assistant , who he brought from his previous department, to help him prepare a πŸ“‘ Work and πŸ“ƒ 'Budget Control Strategic Optimisation Plan' …

The Department where the 🐜 Ant works is now a sad place, where nobody laughs anymore and everybody has become upset...

It was at that time that the 🐝 Bee convinced the boss, 🐯 the Tiger; of the absolute necessity to start a climatic study of the environment .

Having reviewed the charges for running the 🐜 Ant’s department , the 🐯 Tiger found out that the Production was much less than before.

So he recruited the 🐀 Owl, a prestigious and renowned consultant to carry out an audit and suggest solutions.

🐀 The Owl spent three months in the department and came up with an enormous report, in several volumes, that concluded...

“ The Department is overstaffed ...”

Guess who the 🐯 Tiger fires first?

Of course, 🐜 the Ant.........,

"....because she showed lack of motivation and had a negative attitude. "

"The Characters in this fable are fictitious; any resemblance to real people or facts within the Corporation is pure coincidence only…" i am just sharing... I am not the Author.



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