How To Succeed Without Data, In A Data Driven World – Chaka Booker

1.jpg

There are some words that inspire confidence when you use them. “Data” is one of those words. Throw “data-driven” in front of “decision-making” and you’ll suddenly find yourself more credible. If someone is sharing an idea, ask about “the data” and your IQ shoots up several points. I believe in data. I understand how data can identify trends, minimize risk and lead to better decisions. Data comforts me. But the fixation on data has a drawback. It leads to the belief that decisions made without data – aren’t as strong. Never mind that bad decisions, based on data, get made all the time……

Read more: https://www.forbes.com/sites/chakabooker/2018/10/06/how-to-make-decisions-without-data-in-a-data-driven-world/#5ba0f01e1d6e

 

 

 

Your kindly Donations would be so effective in order to fulfill our future research and endeavors – Thank you

Advertisements

What is The Difference Between Data Analysis and Data Science?

1.jpg

Following the current technological transformations within the economy, there has been an emergence of enormous career options, wherein, Data Science is the hottest. According to the Glassdoor, Data Science arose as the highest paid area. On the other hand, there is a significant field which has been gazing attention since years, i.e., Data Analysis. Both the Data Science and Data Analysis is often confused by the individuals. However, the terms are incredibly different in accordance with their job roles and the contribution they do to the businesses. But, are these the only factors which make these two distinct from each other? Well, to know more we need to take a look below:

Data Analysis Data Science:

Data Analysis is referred as the process of accumulating the data and then analyzing it to persuade the decision making for the business. The analysis is undertaken with a business goal and impact the strategies. Whereas, Data Science is a much broader concept where a set of tools and techniques are implied upon to extract the insights from the data. It involves several aspects of mathematics, statistics, scientific methods, etc. to drive the essential analysis of data

Skills:

The individuals misinterpret Data Analysis with Data Science, but the methodologies for both are diverse. The skill set for the two are distinct as well. The fundamental skills required for Data Analysis are Data Visualisation, HIVE, and PIG, Communication Skills, Mathematics, In-Depth understanding of R and python and Statistics. On the other hand, the Data Science embed the skills like – Machine Learning, Analytical Skills, Database Coding, SAS/R, understanding of Bayesian Networks and Hive

Techniques:

Though the areas – Data Analysis and Data Science, are often confused about being similar, but the methodology is different for both. The methods used in the two are diverse. The essential techniques used in Data Analysis are – Data Mining, Regression, Network Analysis, Simulation, Time Series Analysis, Genetic Algorithms and so on. While, the Data Science involves – Split Testing, categorizing the issues, cluster analysis and so on

Aim:

Just like the areas are different, so are their goals. The Data analysis is basically about answering the questions generated, for the betterment of the businesses. While the Data Science is concerned with shaping the questions followed by answering The Data science, as illustrated above, is a more profound concept

The era of the Artificial Intelligence and Machine Learning is shaping economy in a much more comprehensive aspect. The organizations are moving towards data-driven decision-making process. The data is becoming imperative in functioning and are not limited to the Information Technology organizations. It is soon taking over the industries like – Sports, Medicine, Hospitality, etc.

Such technological advancements have led to a rise in the job opportunities in the area of Data Science and Analysis. The merely significant facet which needs to be taken into consideration is the understanding of the difference between the two. The Big Data is the future which is expected to lay a considerable impact on the operations of both industries and routine life.

 

 

Your kindly Donations would be so effective in order to fulfill our future research and endeavors – Thank you
https://www.paypal.me/ahamidian

 

The World’s Most Valuable Resource Is No Longer Oil, But Data – The Economist

1.jpg

A NEW commodity spawns a lucrative, fast-growing industry, prompting antitrust regulators to step in to restrain those who control its flow. A century ago, the resource in question was oil. Now similar concerns are being raised by the giants that deal in data, the oil of the digital era.

These titans—Alphabet (Google’s parent company), Amazon, Apple, Facebook and Microsoft—look unstoppable. They are the five most valuable listed firms in the world. Their profits are surging: they collectively racked up over $25bn in net profit in the first quarter of 2017. Amazon captures half of all dollars spent online in America. Google and Facebook accounted for almost all the revenue growth in digital advertising in America last year.

Such dominance has prompted calls for the tech giants to be broken up, as Standard Oil was in the early 20th century. This newspaper has argued against such drastic action in the past. Size alone is not a crime. The giants’ success has benefited consumers. Few want to live without Google’s search engine, Amazon’s one-day delivery or Facebook’s newsfeed.

Nor do these firms raise the alarm when standard antitrust tests are applied. Far from gouging consumers, many of their services are free (users pay, in effect, by handing over yet more data). Take account of offline rivals, and their market shares look less worrying. And the emergence of upstarts like Snapchat suggests that new entrants can still make waves.

But there is cause for concern. Internet companies’ control of data gives them enormous power. Old ways of thinking about competition, devised in the era of oil, look outdated in what has come to be called the “data economy” (see Briefing). A new approach is needed.

Quantity has a quality all its own

What has changed? Smartphones and the internet have made data abundant, ubiquitous and far more valuable. Whether you are going for a run, watching TV or even just sitting in traffic, virtually every activity creates a digital trace—more raw material for the data distilleries. As devices from watches to cars connect to the internet, the volume is increasing:

some estimate that a self-driving car will generate 100 gigabytes per second. Meanwhile, artificial-intelligence (AI) techniques such as machine learning extract more value from data. Algorithms can predict when a customer is ready to buy, a jet-engine needs servicing or a person is at risk of a disease. Industrial giants such as GE and Siemens now sell themselves as data firms.

This abundance of data changes the nature of competition. Technology giants have always benefited from network effects: the more users Facebook signs up, the more attractive signing up becomes for others. With data there are extra network effects. By collecting more data, a firm has more scope to improve its products, which attracts more users, generating even more data, and so on.

The more data Tesla gathers from its self-driving cars, the better it can make them at driving themselves—part of the reason the firm, which sold only 25,000 cars in the first quarter, is now worth more than GM, which sold 2.3m. Vast pools of data can thus act as protective moats.

Access to data also protects companies from rivals in another way. The case for being sanguine about competition in the tech industry rests on the potential for incumbents to be blindsided by a startup in a garage or an unexpected technological shift. But both are less likely in the data age. The giants’ surveillance systems span the entire economy:

Google can see what people search for, Facebook what they share, Amazon what they buy. They own app stores and operating systems, and rent out computing power to startups. They have a “God’s eye view” of activities in their own markets and beyond. They can see when a new product or service gains traction, allowing them to copy it or simply buy the upstart before it becomes too great a threat.

Many think Facebook’s $22bn purchase in 2014 of WhatsApp, a messaging app with fewer than 60 employees, falls into this category of “shoot-out acquisitions” that eliminate potential rivals. By providing barriers to entry and early-warning systems, data can stifle competition.

Who ya gonna call, trustbusters?

The nature of data makes the antitrust remedies of the past less useful. Breaking up a firm like Google into five Googlets would not stop network effects from reasserting themselves: in time, one of them would become dominant again. A radical rethink is required—and as the outlines of a new approach start to become apparent, two ideas stand out.

The first is that antitrust authorities need to move from the industrial era into the 21st century. When considering a merger, for example, they have traditionally used size to determine when to intervene. They now need to take into account the extent of firms’ data assets when assessing the impact of deals.

The purchase price could also be a signal that an incumbent is buying a nascent threat. On these measures, Facebook’s willingness to pay so much for WhatsApp, which had no revenue to speak of, would have raised red flags. Trustbusters must also become more data-savvy in their analysis of market dynamics, for example by using simulations to hunt for algorithms colluding over prices or to determine how best to promote competition .

The second principle is to loosen the grip that providers of online services have over data and give more control to those who supply them. More transparency would help: companies could be forced to reveal to consumers what information they hold and how much money they make from it.

Governments could encourage the emergence of new services by opening up more of their own data vaults or managing crucial parts of the data economy as public infrastructure, as India does with its digital-identity system, Aadhaar. They could also mandate the sharing of certain kinds of data, with users’ consent—an approach Europe is taking in financial services by requiring banks to make customers’ data accessible to third parties.

Rebooting antitrust for the information age will not be easy. It will entail new risks: more data sharing, for instance, could threaten privacy. But if governments don’t want a data economy dominated by a few giants, they will need to act soon.

Your kindly Donations would be so effective in order to fulfill our future research and endeavors – Thank you
https://www.paypal.me/ahamidian

What is Virtual Pins Exchange Any Data Between Blynk App & Your Hardware – Pavel

Virtual pins are different than  Digital and Analog Input/Output (I/O) pins. They are physical pins on your microcontroller board where you connect sensors and actuators.

Blynk lets you control any hardware connected to Digital and Analog pins without having to write any additional code.

For example, if you need to turn On/Off LED connected to Digital pin, you don’t have to write any code:

  1. Just use BlynkBlink code for your hardware.
  2. In the Blynk app – add Button Widget and set it to pin D8
  3. That’s it! No additional code is required. Simply press Play in the app.

That was, easy, right? But what if you need more flexibility?

Virtual Pins

Virtual pins allow you to interface with any sensor, any library, any actuator.

Imagine that there are “virtual” pins that you can use

Think about Virtual Pins as a box where you can put any value, and everyone who has access to this box can see this value.

It’s a very powerful feature to display and send any data from your hardware to Blynk app.

☝️ Remember, that virtual pins have no physical properties.

There are two fundamental commands you need to know to use Virtual Pins:

To read data from Blynk app widgets

Use this block of code:

BLYNK_WRITE(V5) // V5 is the number of Virtual Pin  
{
  int pinValue = param.asInt();
}

Where param.asInt()  is the value from V5.

👉 Full article: How to control anything with Blynk

To send data from your hardware to Blynk app

Use this command Blynk.virtualWrite(V5) where V5 is the Virtual Pin you are using.

⚠️ WARNING:
Don’t place
Blynk.virtualWrite(V5) inside  void loop()

– Why?
– Read here

👉 Full article: How to display any sensor data with Blynk

Thinking About Becoming An Infopreneur – Michael Guta

1.jpg

You probably have heard “Information is Power,” and in today’s digital ecosystem it has become much easier to turn this information into a business. The new infographic by Kajabi looks to answer how this is the right time to turn your knowledge and expertise into a business as an infopreneur.

Titled, “Knowledge Commerce: How to Turn Your Skill Into a Thriving Business,” this infographic shows how the market is ready for infopreneurs. According to Kajabi, there is now a huge opportunity  for knowledge commerce.

And thanks to the confluence of current technology and knowledge these businesses can be easily and quickly established by anyone. As long as you have the knowledge and expertise, you can make yourself available to a global customer base looking to consume what you offer.

What is an Infopreneur?

When Harold Weitzen came up with the term infopreneur in the 1980s, the internet was crawling at kilobits per second and smartphones, social media and gigabit speeds were still years away.

Fast forward to 2018 and access to massive amounts of data is literally in the palm of your hand — provided you have a smartphone. This availability gives entrepreneurs the rare opportunity to provide information to a massive audience almost instantaneously — and this is where infopreneurs come in.

An infopreneurs are people who take the knowledge they’ve accumulated and turn it into a money-making enterprise by teaching, consulting, engaging in knowledge commerce or creating knowledge-based media.

The Knowledge Market

Kajabi is a company which provides a platform for creating online courses. And in the infographic, the company claims the market for knowledge commerce is growing.

The E-learning market is expected to reach $241 billion by 2022 and with so much potential, Kajabi says experts can use the many different resources available to them to start a business or provide a second income.

The company first recommends you figure out in what area your expertise lies. This could includes taking an aptitude test to help you find your subject area and clearly identify your field.

You then need to market your brand — in this case, yourself! The infographic suggests publishing content on social media, blogs, your website, or in podcast and on YouTube while managing your time efficiently. And as with any other business, you have to create a product in which your customers will be interested and make it available at a great price point.

As Kajabi points out, the market is big and the competition is fierce, but “It’s all about combining your unique knowledge with your unique personality and viewpoint.”

Become an Infopreneur

Take a look at the infographic below to get some pointers on how to become an infopreneur today.

Can You Become an Infopreneur? (INFOGRAPHIC)

Your kindly Donations would be so effective in order to fulfill our future research and endeavors – Thank you

The Insane Amounts of Data We’re Using Every Minute (Infographic) – Rose Leadem

1.jpeg

With all the tweets, iMessages, streamed songs and Amazon prime orders, did you ever wonder just how much data is actually being generated every minute? To find out, cloud-based operating system Domo analyzed data usage over the past year, and shared the results in its sixth Data Never Sleeps report. It dives into online consumer behavior, examining the amount of data being generated every minute across popular apps and platforms including Google, Instagram, Amazon, Netflix, Spotify and more.

By the looks of the research, things are only getting bigger. In 2012, there were approximately 2.2 billion active internet users. In 2017, active internet users reached 3.8 billion people — nearly 48 percent of the world’s population.

When it comes to social media, data usage is unsurprisingly high. Since last year, Snapchat alone saw a 294 percent increase in the amount of images shared per minute. Nearly 2.1 million snaps are shared every 60 seconds. On average, there are 473,400 tweets posted every minute, 49,380 Instagrams photos and 79,740 Tumblr posts.

So who’s behind all this social media madness? Americans upped their internet usage by 18 percent since 2017, however it’s not all going to Snapchat and Twitter. Much of it is going to video-streaming services such as Netflix and YouTube. Since last year, Netflix saw a whopping 40 percent increase in streaming hours, going from 69,444 hours to 97,222. And YouTube videos have reached 4.3 million views per minute. Even the peer-to-peer transactions app Venmo saw a major data jump, with 32 percent more transactions processed every minute compared to last year. Overall, Americans use 3.1 million GB of data every minute.

To learn more about our data usage of 2018, check out the infographic below.

Your kindly Donations would be so effective in order to fulfill our future research and endeavors – Thank you

What It Takes To Make IoT Implementation A Success – Robert Plant & Cherie Topham

2.jpg

Organizations around the globe understand the importance of IoT. In fact, in a recent Forbes Insights/Hitachi survey of more than 500 executives worldwide, over 90% said IoT will be important to the future of their business. What’s more, of all emerging technologies, executives said IoT would be the most critical, ranking it above others like artificial intelligence and robotics.

While executives acknowledge the importance of IoT, 49% remain in the early stages of planning or are only operating pilot programs. We spoke with John Magee, Hitachi Vantara’s vice president of product and solutions marketing, to get his perspective on this state of development and how organizations can make IoT a larger part of their strategy and operations going forward.

If an executive is looking to invest in IoT and understand the economics behind it, what does he or she need to know?

Most organizations are looking to IoT projects to either improve operational efficiency or drive new revenue streams. A lot of organizations are seeking to use the data they can get from IoT sensors and connectivity to provide better visibility and help them understand what’s going on in their operations. For product companies, they’re often looking to optimize how their products are being manufactured or used, and to offer new data-driven services with those products.

The goal for most of these companies is to transform the way they operate and the way they compete. For business leaders looking to take advantage of IoT, the most important thing is to begin with the business outcome goals first and then determine what data IoT can provide that can help deliver those outcomes. It’s the new data that delivers the business value. So that should be the starting point for any project. Then you can work back from there to the technology required to meet the objective.

For example, manufacturers might want to understand why quality issues are creeping into one of their manufacturing lines but not the other. Logistics companies may want to understand the location of parts and deliveries to optimize scheduling. Product companies may want to sell new value-added software services that help customers get more value from their products. Whatever the goal, by understanding what data you need to collect and who needs access to it, the technology requirements will fall into place more easily and you won’t over- or underspend for success.

When executives are thinking about what data is most important to achieving their desired outcomes, what do they need to know? How should they approach this?

IoT is essentially a rich source of new business data. Data that comes from machines and devices, and from the spaces and environments those machines operate in. In many situations, just having access to real-time data about what’s going on—in a manufacturing plant, on a remote oil rig or in a city train station—can be transformative. In most situations, though, some analysis of the data is going to be needed to gain the insights that lead to business value.

1.jpg

This is where technologies like big data analytics, machine learning and artificial intelligence come into play. Analytics is the key to not just understanding what is happening but also learning and getting smarter so that your IoT solutions can predict when a problem will occur or find the root cause of product quality issues that would have been unsolvable without analyzing the mountains of data that IoT can deliver.

The right way to think about IoT is as an extension of the business analytics that your organization is probably already doing in other areas. At the end of the day, IoT is a means to accessing and interpreting more data. And data management, data integration and data science are all key enabling technologies for IoT, just as they are for most other areas of business today.

One new twist on IoT data that differs from traditional business data is the idea of a “digital twin.” The digital twin is the software representation of a physical device, such as a pacemaker, an elevator or a dump truck. As data streams in from the physical device, it is collected and stored in the corresponding digital twin. The digital twin knows everything about that asset: where it was manufactured, how it has been operated, when it was last serviced.

By using software to analyze hundreds or even thousands of these digital twins, data scientists can build powerful analytic models that can optimize the corresponding physical assets. Organizations are using this approach to enhance asset uptime and performance, extend the useful life of critical assets and optimize maintenance and operations.

Once you’ve aggregated data into a single version of the truth and are drawing conclusions, how can companies best integrate that information into broader networks?

There’s a sort of stairway to value in many IoT scenarios. The first step of the stairway is the physical devices themselves. The second step is the operations around those devices. And the third step is the business processes and ecosystem around those operations.

Think of a manufacturing plant. If you use sensors on critical plant equipment, you can get data that can help you operate that equipment more effectively. If you collect enough data, you can even start to predict when it will fail so you can service it before that happens. So that’s the next step – using the data insights about the equipment into optimizing your maintenance and repair operations.

But that data can also be useful at the next step in the stairway, which is how your supply chain responds to requirements for parts or materials being delivered based on the performance of the equipment and operations in the factory. The more data you have, the more visibility you have, and the more opportunity to optimize every part of the operation. Sort of like air traffic control for the factory.

This stairway, or hierarchy, of value—from asset to operations to business process—is one we see play out in industry after industry.

When it comes to IoT, which is a complicated endeavor, research shows that it’s best not to go at it alone. What should executives be looking for in a partner when they’re considering making this transformation?

Working with a partner who understands your industry and has a methodology to help you think through your data strategy are the real enablers for success. IoT is a hot technology right now, and it is easy to get caught up in the hype and invest in the wrong areas. Working with an experienced partner who has a pragmatic approach that starts with understanding how IoT data and analytics will drive the desired business outcome is the key to success.

If everyone who reads our articles and like it , that would be favorable if you send us your donations…THANK YOU

8 Ingredients Every Piece of Shareable Content Has – Rob Steffens

1.jpg

In an ideal world, every piece of content you create would get shared. Here on the Web in 2018, though, things are a little bit … different.

With millions of websites already active and countless thousands of new content pieces going live every day, though, even the best content needs every advantage it can get to become truly shareable.

Luckily, the most successful shareable content all has certain traits in common. If you develop all of your content with these in mind, you’ll enjoy much more social engagement now and later.

Let’s take a gander at the seven top traits of the most shareable content:

1. A Compelling Headline

Your headline is the most important part of any piece of content – it determines whether users will click.

The best way to brainstorm here is to whip up a batch of ten headlines or so before you pick one. A/B testing the headlines on your posted content can also help.

2. Visuals and More Visuals

Experts claim that when it comes to shareable content, infographics win hands down: They’re shared about 3X more than other content types.

Even conventional blog posts benefit from eye-catching imagery, embedded video, and other non-text touches.

3. A Worthwhile Hook

When you clicked on this article, you knew what you were getting: Tips on shareable content.

Readers need to know at a glance how each piece of content will help them, so make it easy for them. Avoid clever headlines and long, meandering introductions.

4. Strong Organization

Most shareable content is very easy to scan, because, well, people don’t read on the Internet.

They want to be able to skip straight down to the most valuable information for them. List-based posts with bullet points or short paragraphs are the most effective here.

5. Readable Text

Content can be interesting without being Shakespeare. Generally speaking, you should keep things simple and use jargon only when you have to.

It doesn’t hurt to have a little variety in your sentences, but your meaning should always be obvious.

6. A Call to Action

Most people simply won’t take the next step – whatever it might be – unless it’s spelled out. Your content should always have a clear call to action.

To maximize the power of your shareable content, that CTA should focus on … you guessed it … sharing.

7. An Easy Way to Share

Hopefully, your prospects are logged into LinkedIn or Twitter all the time. Still, you should make sharing as easy as possible for them.

The fewer clicks, the better. There are many great ways to incorporate social sharing buttons into your site design – just be sure they’re not too intrusive.

8. Some Social Media Backing

Okay: Your shareable content doesn’t need to have a whole social campaign behind it. But it helps, since people are more likely to share content they encounter on their own social feed.

Sharing content to influencers the day of its release and a week later can supercharge your shares.

So, there you have it: 8 quick and dirty techniques for more shareable content.

Even if social sharing isn’t exactly the cornerstone of your inbound marketing campaign, it’s still worthwhile to consider it in your content planning. Even a marginal increase in social shares can add thousands of hits to your content every quarter.

Just like organic search traffic, this social traffic compounds over time to provide momentum and visibility to your future content marketing campaigns. Plus, social signals are growing in importance in SEO and other measures of website success.

If everyone who read the articles and like it, that would be favorable to have your donations – Thank you.