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ICYDK: My new book may be of interest to some members.   Kindle edition: https://www.amazon.com/Creating-Value-Social-Media-Analytics-ebook/dp/B07CLNC5PZ ; Paperback: https://www.amazon.com/Creating-Value-Social-Media-Analytics/dp/1977543979 Companion site: https://analytics-book.com/     Preface Often termed as the ‘new gold,’ the vast amount of social media data can be employed to identify which customer behavior and actions create more value. Nevertheless, many brands find it extremely hard to define what the value of social media is and how to capture and create value with social media data.
In Creating Value with Social Media Analytics, we draw on developments in social media analytics theories and tools to develop a comprehensive social media value creation framework that allows readers to define, align, capture, and sustain value through social media data. The book offers concepts, strategies, tools, tutorials, and case studies that brands need to align, extract, and analyze a variety of social media data, including text, actions, networks, multimedia, apps, hyperlinks, search engines, and location data. By the end of this book, the readers will have mastered the theories, concepts, strategies, techniques, and tools necessary to extract business value from big social media that help increase brand loyalty, generate leads, drive traffic, and ultimately make sound business decisions. Here is how the book is organized. 

Chapter 1: Creating Value with Social Media Analytics
Chapter 2: Understanding Social Media
Chapter 3: Understanding Social Media Analytics
Chapter 4: Analytics-Business Alignment
Chapter 5: Capturing Value with Network Analytics
Chapter 6: Capturing Value with Text Analytics
Chapter 7: Capturing Value with Actions Analytics
Chapter 8: Capturing Value with Search Engine Analytics
Chapter 9: Capturing Value with Location Analytics
Chapter 10: Capturing Value with Hyperlinks Analytics
Chapter 11: Capturing Value with Mobile Analytics
Chapter 12: Capturing Value with Multimedia Analytics
Chapter 13: Social Media Analytics Capabilities
Chapter 14: Social Media Security, Privacy, & Ethics

Praises for the book
“Gohar F. Khan has a flair for simplifying the complexity of social media analytics. Creating Value with Social Media Analytics is a beautifully delineated roadmap to creating and capturing business value through social media. It provides the theories, tools, and creates a roadmap to leveraging social media data for business intelligence purposes. Real world analytics cases and tutorials combined with a comprehensive companion site make this an excellent textbook for both graduate and undergraduate students.”
—Robin Saunders, Director of the Communications and Information Management Graduate Programs, Bay Path University. 

“Creating Value with Social Media Analytics offers a comprehensive framework to define, align, capture, and sustain business value through social media data. The book is theoretically grounded and practical, making it an excellent resource for social media analytics courses.”
—Haya Ajjan, Director & Associate Professor, Elon Center for Organizational Analytics, Elon University. 

“Gohar Khan is a pioneer in the emerging domain of social media analytics. This latest text is a must-read for business leaders, managers, and academicians, as it provides a clear and concise understanding of business value creation through social media data from a social lens.”
—Laeeq Khan, Director, Social Media Analytics Research Team, Ohio University.

“Whether you are coming from a business, research, science or art background, Creating Value with Social Media Analytics is a brilliant induction resource for those entering the social media analytics industry. The insightful case studies and carefully crafted tutorials are the perfect supplements to help digest the key concepts introduced in each chapter.”
—Jared Wong, Social Media Data Analyst, Digivizer

“Creating Value with Social Media Analytics is one of the most comprehensive books on social media analytics that I have come across recently.”
—Bobby Swar, Assistant Professor, Concordia University of Edmonton, Canada. 
Thank you, Khan https://goo.gl/Kmmiri #DataScience #Cloud

ICYDK: We have been profiling a large number of APIs as part of an effort to populate the Streamdata.io API Gallery. We are creating OpenAPI definitions for each of the APIs we add, which provides us with a pretty comprehensive view of the surface area of each API. After completing an OpenAPI, it is pretty easy to assess the overall maturity of an API platform, and identify how far along in their API journey they are. Something that often translates pretty quickly to whether or not they are ready for, and able to put to use, our streaming API services.

One sign of a mature API platform is the presence of webhooks and the documentation around the types of events that are occurring across the platform to which the webhooks will respond. These are the valuable, meaningful events that are occurring amidst all the noise that API consumers and providers want to be notified of. The API providers who have begun the work to identify and cater to these events are further along in their API journey and have a much more mature way of looking at the activity that occurs via their operations. You can take a look at a few of the API rockstars out there today if you want an example of this in action. https://goo.gl/Xi2KMG #DataIntegration #ML

The word Data and Data Science have taken the business world by storm. Nowadays, improving business productivity and performance greatly depends on collection and analyzing data. Businesses have been processing data for ages but the introduction of Internet of Things (IoT) has been a game changer. Data collected through IoT is analyzed using different techniques as compared to that collected traditionally. Furthermore, Data Scientists require more sophisticated skills for analyzing IoT data.

The Hype about Internet of Things

IoT has definitely caused a lot of hype across the business industries. It is also a rapidly advancing field so keeping up with the latest trends is a must for many businesses. The roots of IoT are spreading far and wide, so it is quite easy to come across the term. But is not just another complex technology that businesses are using to outsmart each other. If utilized the right way, it can produce an unparalleled intelligence for a business. And as usual, this depends on how businesses process the excessive inflow of data from IoT software and hardware.

Traditional Data and IoT-generated Data

A business has multiple sources of generating data but IoT changes the value of the data. In comparison to traditionally collected data, one generated through IoT is in real time. This is immensely useful for industries that can flourish given a constant stream of freshly generated data. Another simple way to differentiate between traditional and IoT-generated data is to say that the latter is dynamic. Traditional data does not change. For example, if a device is used to select a city from a specific country, the list of the cities is going to remain the same. On the other hand, if data is being rendered from a device that belongs to an IoT network, it’s going to have an IP address that delivers the information to other devices with IP addresses. This information may be collected by a server that is accessible to different companies. So let’s say one of these devices is your smartphone and is collecting information on your online shopping habits. So if the server is accessed by an online store, they will have your information.

Now that you understand how IoT is being used to collect data, it is obvious that there is an excessive amount of data being collected at any given time. It also makes the data complex. Furthermore, the way IoT-generated data is stored and processed also varies.

Difference between Traditional and IoT Data Science

Traditional Data Science has been supporting businesses on static data and it will be wrong to undermine its importance. However, the business world has become highly competitive and it is only going to intensify in those terms. For this purpose, newer and smarter technologies are needed. Therefore, businesses are now finding it necessary to invest in IoT Data Science.

In traditional Data Science, the analytics are static and restricted in use. The information that is received may not be updated so the results achieved after processing may not be smart or usable. On the other hand, since IoT data is being received in real-time, the analytics complement the latest market patterns. This allows making these analytics more actionable and intelligent as compared to traditional ones.

However, it is not so easy to process such complex information. There are many sensor sources within an IoT network. It becomes important to differentiate between the multiple sensor points and external components that may be responsible for adding to the data points. Also, as more technology layers are added or integrated with IoT, it becomes more difficult to structure and process the multitudes of incoming data. So yes, Data Scientists do need to up their skill in order to comprehend IoT-generated data.

How is IoT Changing the Face of Data Science?

As the popularity of IoT increases, a surge of data lies in the future. It is bound to the change the way we have viewed Data Science for some time now. The boom in data is not only going to require better infrastructure but smarter Data Scientists. We will need an infrastructure that can reliably process a constant stream of complex data. And it will be a serious waste of time and money if we can’t use this surge of data to make more and better sense out of it. Therefore, this explosion of data should be an opportunity for data scientists.

By taking advantage of Data Science for IoT, we are not only helping the business industries in terms of better performance and profit. In fact, we can create a better world. With smarter analytics, we can resolve multiple problems faced globally.

IoT Data Science for Changing the World

Data Science for IoT can help overcome some global challenges. It can help generate more accurate decisions. This means smarter solutions for the consumers around the world. IoT also creates the concern of privacy for people but if you add security technologies like Blockchain to IoT, then you can realize many benefits. With such combined technologies, it will become easier to protect patents and eliminate piracy. IoT Data Science also allow integrating artificial intelligence. This means that processing of data will become easier as devices will be able to self-learn about identifying patterns. The opportunities that can be exploited using IoT Data Science are aplenty.

IoT Data Science Challenges

The above discussion shows a picture of a very bright future. But it is not as easy to realize to make these opportunities happen. There are multiple challenges that arise from IoT Data Science. For example, how to make machines communicate important data if they belong to different manufacturers. The integrity of the data is a question too. Who will own this data and who will be or not be allowed to access the data? The frequency of data sharing needs to be managed as data nature varies. Then there is the ever-present challenge of customer and device security.

In conclusion, IoT Data Science are bound to change the future but not without hurdles.

  https://goo.gl/nwZiQv #DataScience #Cloud