Strategic Planning for Social Media in Libraries (2012)
Sarah K. Steiner
p. 1 definition of social media for libraries
six primarytypes exist: “collaborative projects, blogs, content communities, social networking sites,” and two types of virtual worlds: “virtual game worlds, which ask users to follow the rules of the game, and virtual social worlds, wherein users can behave without rules in almost any way they like” (Kaplan and Haenlein, 2010: 59)
it is not that I disagree with such definition, but i wish there was a “door” mentioning “flexibility” and “necessity to reassess” what social media is every year, 3 year, 5 years
p. 2 definition what is strategic planning
- identify the needs of your target audiences,
- identify the ways in which you can meet those needs, and
- identify ways to respond confidently and proactively to changesin those needs.
- Where the organization is
- Where the organization should go
- How the organization can get there (McNamara, 2011)
It must be:
- Based on data
- Regularly cared for
covers and confirms my notes to the SCSU library use of its social media:
p. 83 ask uncomfortable questions
in planning, we must be prepared to ask, critically consider and answer questions that make us uncomfortable (not only that I was not let to ask questions, I was ousted from any body that was making decisions regarding social media. I was openly opposed and rebuked for asking why 3 reference librarians will keep the passwords to the account for the library SM)
p. 83 Communicate
If your team communicates honestly and thoroughly, then positive feelings and advocates for your social media endeavors will grow. In the span of 6 months, I had to ask three times where are the notes of the social media committee kept and eventually i will receive an answer, which in it nebulous and apologetic form was practically not an answer.
p. 83 Don’t rush to conclusions
Satisficing often works, but it can also lead to conclusions that are less then optimal.
In the fall of 2013, I had to fight an overwhelming majority opposing my proposal that social media needs to include student representation, since SM is about dialog, not broadcasting (see page 86) and the current staff and faculty see SM as another form of broadcasting. In the span of six months, by the summer of 2014 library staff and faculty had fallen in the other extreme, letting one single student run all library SM. That student did/could not have understanding of the scope and goals of the library resulting in satisficing.
p. 84 aim for consensus, but don’t require it
Consensus was the leitmotiv of the dean; it failed in general, and it failed in SM.
p. 84 get an external reviewer
p. 84 value and celebrate small success
a strategic plan will be realized through a series of small actions, not one or two pivotal plots.
p. 84 create accountability
p. 86 maintain a consistent tone and brand
visual and tone based consistency.
This library DOES maintain consistency by posting Instagram pictures of people covering their faces with books, so part of their face compliments a face on cover of books. It is done by other libraries and it would have been cute and original if not overdone. If the SM activities of a library consist mostly of such activities then the “branding” part definitely is hurt. Yet, the faculty in this library vehemently adhere to “let’s see what other libraries are doing,” but does not understand that it needs further conceptualizing to figure out how to transform into “brand.”
p. 86 capitalize on the strengths of social media
“in many cases, business and libraries use SM exactly as they use their websites: to push content.
This has been the main criticism from the start: the three reference librarians holding the passwords to the SM account were using Facebook as a announcement board and kept dormant the other accounts. The resolution of the library faculty who was called to arbitrate the argument with these three librarian: “I don’t understand very well Facebook.” The interim dean, who, subsequently had to resolve this dispute: “I don’t use Facebook.”
p. 87 Metrics
Analyze and tweak plan
measuring success is about maximizing time and efforts, not about laying blame for shortcomings or failures.
this applies to daily tasks and responsibilities and shuffling time, but when the organization does not have a clear overarching goal and clear strategy how to achieve it, then issues must be raised up. which leads to:
p. 92 Plan for conversation
the inclusion of conversation. incorporate your patrons as primary content creators (not appointing just a single student worker to broadcast)
p. 92 use SM as an assessment or feedback tool
p. 93 plan to monitor your brand
if you decide to start watching these types of mentions, you’ll want to consider whether you’ll adopt a passive or an active role in responding to them.
Social media strategy 2013-2014
National Library Australia
10 Social Media Marketing Tips for Libraries,February 12, 2013
Social Media: Libraries Are Posting, but Is Anyone Listening?By May 7, 2013
Strategic Planning for Social Media in Libraries: The Case of Zimbabwe
Global E-Learning Market in Steep Decline, Report Says
By Richard Chang
a recent report released by Ambient Insight Research, a Washington state-based market research firm.
Revenues for self-paced e-learning in 2016 are heavily concentrated in two countries — the United States and China. The growth rate in the U.S. is at -5.3 percent, representing a $4.9 billion drop in revenues by 2021, while in China, the rate is at -8.8 percent, representing a $1.9 billion drop by 2021. The e-learning market in China has deteriorated rapidly in just the last 18 months, the report said.
- Of the 122 countries tracked by Ambient Insight, 15 have growth rates for self-paced e-learning over 15 percent during the next five years. These countries are heavily concentrated in Asia and Africa, with the two outliers being Slovakia and Lithuania.
- Eleven of the top 15 growth countries will generate less than $20 million by 2021. Of the top 15, Slovakia and Lithuania are anticipated to generate the highest revenues for self-paced products by 2021, at $55.4 million and $36.5 million, respectively.
- The growth rates are negative in every region except Africa, where the growth is flat at 0.9 percent. The steepest declines are in Asia and Latin America at -11.7 percent and -10.8 percent, respectively. The economic meltdowns in Brazil and Venezuela are major inhibitors in Latin America.
- There are 77 countries with flat-to-negative growth rates. The countries with the lowest growth rates are Yemen (-18.7 percent), Brazil (-19.8 percent), Qatar (-23.5 percent) and Venezuela (-26.8 percent).
Self-paced e-learning products include online courses, managed education services, managed training, e-books and learning management systems, according to the report. The author does not consider mobile and game-based learning, which are growing, to be in the self-paced e-learning category.
The news on the self-paced e-learning industry is so bad, Ambient Insight will no longer publish commercial syndicated reports on the industry, the firm says on its website and in the report.
more on elearning in this IMS blog
Learn data mining languages: R, Python and SQL
– Fantastic set of interactive tutorials for learning different languages. Their SQL tutorial is second to none. You’ll learn how to manipulate data in MySQL, SQL Server, Access, Oracle, Sybase, DB2 and other database systems.
– The best way to learn is to work towards a goal. That’s what this helpful blog series is all about. You’ll learn SQL from scratch by following along with a simple, but common, data analysis scenario.
– This course is recommended for the intermediate SQL-er who wants to brush up on his/her skills. It’s a series of 10 challenges coupled with forums and external videos to help you improve your SQL knowledge and understanding of the underlying principles.
– Created by Code School, this interactive online tutorial system is designed to step you through R for statistics and data modeling. As you work through their seven modules, you’ll earn badges to track your progress helping you to stay on track.
– If you’re a complete R novice, try Lead’s introduction to R. In their 1 hour 30 min course, they’ll cover installation, basic usage, common functions, data structures, and data types. They’ll even set you up with your own development environment in RStudio.
– Once you’ve mastered the basics of R, bookmark this page. It’s a fantastically comprehensive style guide to using R. We should all strive to write beautiful code, and this resource (based on Google’s R style guide) is your key to that ideal.
– Learn R in R – a radical idea certainly. But that’s exactly what Swirl does. They’ll interactively teach you how to program in R and do some basic data science at your own pace. Right in the R console.
Python for beginners
– The Python website actually has a pretty comprehensive and easy-to-follow set of tutorials. You can learn everything from installation to complex analyzes. It also gives you access to the Python community, who will be happy to answer your questions.
– A complete list of Python tutorials to take you from zero to Python hero. There are tutorials for beginners, intermediate and advanced learners.
Read all about it: data mining books
Data Jujitsu: The Art of Turning Data into Product
– This free book by DJ Patil gives you a brief introduction to the complexity of data problems and how to approach them. He gives nice, understandable examples that cover the most important thought processes of data mining. It’s a great book for beginners but still interesting to the data mining expert. Plus, it’s free!
Data Mining: Concepts and Techniques
– The third (and most recent) edition will give you an understanding of the theory and practice of discovering patterns in large data sets. Each chapter is a stand-alone guide to a particular topic, making it a good resource if you’re not into reading in sequence or you want to know about a particular topic.
Mining of Massive Datasets
– Based on the Stanford Computer Science course, this book is often sighted by data scientists as one of the most helpful resources around. It’s designed at the undergraduate level with no formal prerequisites. It’s the next best thing to actually going to Stanford!
Hadoop: The Definitive Guide
– As a data scientist, you will undoubtedly be asked about Hadoop. So you’d better know how it works. This comprehensive guide will teach you how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. Make sure you get the most recent addition to keep up with this fast-changing service.
Online learning: data mining webinars and courses
– Learn data mining from the comfort of your home with DataCamp’s online courses. They have free courses on R, Statistics, Data Manipulation, Dynamic Reporting, Large Data Sets and much more.
– Coursera brings you all the best University courses straight to your computer. Their online classes will teach you the fundamentals of interpreting data, performing analyzes and communicating insights. They have topics for beginners and advanced learners in Data Analysis, Machine Learning, Probability and Statistics and more.
– With a range of free and pay for data mining courses, you’re sure to find something you like on Udemy no matter your level. There are 395 in the area of data mining! All their courses are uploaded by other Udemy users meaning quality can fluctuate so make sure you read the reviews.
– These courses are handily organized into “Paths” based on the technology you want to learn. You can do everything from build a foundation in Git to take control of a data layer in SQL. Their engaging online videos will take you step-by-step through each lesson and their challenges will let you practice what you’ve learned in a controlled environment.
– Master a new skill or programming language with Udacity’s unique series of online courses and projects. Each class is developed by a Silicon Valley tech giant, so you know what your learning will be directly applicable to the real world.
– Learn from experts in web design, coding, business and more. The video tutorials from Treehouse will teach you the basics and their quizzes and coding challenges will ensure the information sticks. And their UI is pretty easy on the eyes.
Learn from the best: top data miners to follow
– Chief Data Scientist at MailChimp and author of Data Smart, John is worth a follow for his witty yet poignant tweets on data science.
– Author and Chief Data Scientist at The White House OSTP, DJ tweets everything you’ve ever wanted to know about data in politics.
– He’s Editor-in-Chief of FiveThirtyEight, a blog that uses data to analyze news stories in Politics, Sports, and Current Events.
– As the Chief Data Scientist at Baidu, Andrew is responsible for some of the most groundbreaking developments in Machine Learning and Data Science.
– He might know pretty much everything there is to know about Big Data.
– He’s the author of popular data science blog KDNuggets
, the leading newsletter on data mining and knowledge discovery.
– As the Co-founder of OKCupid, Christian has access to one of the most unique datasets on the planet and he uses it to give fascinating insight into human nature, love, and relationships
– He’s contributed to a number of data blogs and authored his own book on Applied Predictive Analytics. At the moment, Dean is Chief Data Scientist at SmarterHQ
Practice what you’ve learned: data mining competitions
– This is the ultimate data mining competition. The world’s biggest corporations offer big prizes for solving their toughest data problems.
– The best way to learn is to teach. Stackoverflow offers the perfect forum for you to prove your data mining know-how by answering fellow enthusiast’s questions.
– With a live leaderboard and interactive participation, TunedIT offers a great platform to flex your data mining muscles.
– You can find a number of nonprofit data mining challenges on DataDriven. All of your mining efforts will go towards a good cause.
– Another great site to answer questions on just about everything. There are plenty of curious data lovers on there asking for help with data mining and data science.
Meet your fellow data miner: social networks, groups and meetups
– As with many social media platforms, Facebook is a great place to meet and interact with people who have similar interests. There are a number of very active data mining groups you can join.
– If you’re looking for data mining experts in a particular field, look no further than LinkedIn. There are hundreds of data mining groups ranging from the generic to the hyper-specific. In short, there’s sure to be something for everyone.
– Want to meet your fellow data miners in person? Attend a meetup! Just search for data mining in your city and you’re sure to find an awesome group near you.
8 fantastic examples of data storytelling
8 fantastic examples of data storytelling
Data storytelling is the realization of great data visualization. We’re seeing data that’s been analyzed well and presented in a way that someone who’s never even heard of data science can get it.
Google’s Cole Nussbaumer provides a friendly reminder of what data storytelling actually is, it’s straightforward, strategic, elegant, and simple.
more on text and data mining in this IMS blog
The EU just told data mining startups to take their business elsewhere
By enabling the development and creation of big data for non-commercial use only, the European Commission has come up with a half-baked policy. Startups will be discouraged from mining in Europe and it will be impossible for companies to grow out of universities in the EU.
more on copyright and text and data mining in this IMS blog
How to see everything Google knows about you
more on privacy in this IMS blog