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Pattern templates from issue 40
Fancy making something from our current issue?
Issue 40 has now hit the shelves and we have loads of fab projects that you can get your needle into! If you've spotted something you would like to have a go at, here are all the templates for you, at 100% sizing so you can get stuck in straight away! Just click on the thumbnail image to see the template and then right click it to save it to your computer, then print at 100%.
Travelling Doll Set:
Pom Pom Corsage:
Cool Shirt:
Retro Bag:


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Seattle Central Library Opening
Imagine opening a world-class library while hosting an unforgettable housewarming party.
TPN employees and more than 300 volunteers worked around the clock for the expected 30,000 guests.
Thousands of people moved through the building in effortless motion through the multi-media "discovery path".
The new sleek design and feel of the Seattle Public Library provided an unprecedented public appeal. "This is a true labor of love," says TPN CEO John Vadino.
Seattle's Public Library encourages growth and imagination. TPN captured this vision while producing an unforgettable event.

subset: commoncrawl


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There are two user-interface tools that normalize data; one for worksheet data and one for plotted data. The output from both is similar and both are documented in this topic. For specific information, consult the Reference pages for these two X-Functions:
Controls recalculation of analysis results
For more information, see: Recalculating Analysis Results
For help with range controls, see: Specifying Your Input Data
Enter a value. Use when Normalize Methods = Divided by a specified value.
Name a worksheet column or reference plot.
Used when Normalize Methods = Use Reference Column or Use Reference Plot:
For information on methods, see the Algorithm section, below.
Divide the worksheet column by the cell value.
Launches the Data Reader tool for picking a point on a curve.
X-value of point picked with the Data Reader.
Y-value of point picked with the Data Reader.
Row index number of point picked with the Data Reader.
Direct output results.
For help with the range controls, see: Output Results.
For algorithm specifics, see the documentation for the respective X-Functions in the Origin X-Function Help file:
Normalizing columns:
Normalizing curves:


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Big Data Analytics Use Cases
Are you data-flooded, data-driven, data informed? Are you outcome oriented, insight driven or hindsight driven?
Are you a firm where executives claim – "Data is our competitive advantage." Or sprout analogies like, "data is the new oil".
The challenge I found in most companies is not dearth of vision… everyone has a strategy and a 100,000 ft general view of the importance or value of data. Every executive can parrot the importance of data and being data-driven.
The challenge is the next step….so, how are you going to create new data products? How are you going to execute a data driven strategy? How are you going to monetize data assets? What are the right business use cases to focus on? How to map the use case to underlying models and data requirements? What platform is a good long-term bet? The devil is in these details.
Everyone is searching for new ways to turn data into $$$ (monetize data assets). Everyone is looking for new levers to extract value from data. But data ingesting and modeling is simply a means to an end. The end is not just more reports, dashboards, heatmaps, knowledge, or wisdom. The target is fact based decisions, guided machine learning and actions. Another target is arming users to do data discovery and insight generation without involving IT teams…so called User-Driven Business Intelligence.
In other words, what is the use case that shapes the context for "Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation." What are the right use cases for the emerging hybrid data ecosystem (with structured and unstructured data)?
Market Sizing – Business Analytics and Big Data
"Google, Facebook are really big data companies, not software companies. They collect data, process it and sell it back with value added extensions. They don't have better algorithms. They simply have more data." — Anonymous
The convergence of cloud, social, mobile and connected computing has sparked a data revolution. More than 90 percent of the world's data has been generated over the last two years . And with a projected 50 billion connected "things" by 2020 , the volume of data available is expected to grow exponentially. This proliferation of data has created a vast ocean of potential insights for companies, allowing them to know their customers in a whole new way.
Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data (Cloud, On-Premise, Hybrid IT) is challenging. It's a paradox of the information age. The glut of information that bombards us daily too frequently obscures true insight.
Help people uncover, see, understand and visualize data presents a broad and momentous market opportunity….call this user-driven discovery. Take for instance, Facebook (like Amazon.com) builds a custom Web page every time you visit. It pores over all the actions your friends have taken—their postings, photos, likes, the songs they listen to, the products they like—and determines in milliseconds which items you might wish to see, and in what order. Is this the future for every firm…..
The opportunity is simply getting bigger by the day. Every customer interaction is generating a growing trail of data ("data exhaust"). Every machine that services the customer is generating data. Every conversation, transaction, engagement, touchpoint location, offer, response is a potential digital bread-crumb of opportunity.
Now let's flip the context. A typical mobile user check their phone interface 150 times a day for updates. A Gen Y or Millenial user obviously much more than a Gen X user. The consumption patterns for information are changing continuously. Facebook style real-time updates which were revolutionary 5 years ago seem outdated in the mobile world. We live in an "attention deficit economy" where attention is the new basis for competition. The firms that create the evolving experience using data which can grab/hold your attention will attract marketing and ad $$.
As a result, the buzz and hype around data…small data, big data, machine data, social data, mobile data, wearables data….is relentless. As a result there are a lot of new initiatives and companies. I have been asked repeatedly by a lot of entrepreneurs and strategy teams about analytics market size and opportunity size. Product and services firms are also interested in opportunity sizing as they create new offerings in the data rich world.
I thought i would share a mashup of industry and market sizing data i have collected so far.
- How big is the overall market for Analytics, Big Data?
- How big is the market for Digital Customer Interaction or Engagement?
- How big is the market for Mobile and Social Intelligence?
- How big is the market for Wearables?
- What is growing fast, faster and fastest?
All good questions as services firms think about digital strategy, analytics and future state. You always want to be in the "hot" area… selling is easier, valuations are richer, revenue growth percentages exponential.
Enabling SoLoMoMe + Omni-channel Analytics
At the Analytics Executive Forum, I facilitated a session on Omni-channel analytics. It struck me how every leading consumer facing firm seems convinced that mobile is becoming the dominant B2C interaction channel. Mobile is the gateway to insight based marketing and the "always addressable customer"….
Insight-based interactions – The company knows who you are, what you prefer, and communicates with relevant, timely messages, using the power of analytical intelligence to detect patterns, decode strands of information and create meaningful offers and value.
The "always addressable customer." This is a consumer who fits the bill on three fronts simultaneously: (1)
- Owns and personally uses at least three connected devices; (2)
Goes online multiple times throughout the day; (3)
- Goes online from at least three different physical locations
The opposite of insight-based is "spray-and-pray" marketing – The company has very limited knowledge about who you are, forgets what you prefer, and tries to reach you with off-target communications that alienate you – based on fragmented data, poor data quality and inadequate integration, resulting in confusing, chaotic interactions. A good example: "I have 2 million frequent flyer miles with your airline and still do not get any recognition, respect or value from this loyalty."
As companies architect new insight based mobile use cases I suggest that they look at what is coming next. With IOS 7, Apple is delivering several new features – Passbook, Beacon.
Retailers, banks and other customer facing firms/brands better pay attention. 100+ million iPhones are automatically getting this feature with the new OS upgrade making this a mega-disruptor in the coveted target segment everyone is chasing. Read more
Mobile BI – Business KPIs and Dashboards "on-the-go"
Who doesn't want to achieve faster "time-to-information" and shorter "time-to-decision" for executives and managers with mobile BI? Who doesn't want to disseminate insights or KPIs to front-line employees, such as field sales representatives, line of business managers, and field service employees?
The question is not whether Mobile BI is a good idea but how to execute this program in a low-cost way? How to design and deploy eye-popping "wow" apps? How to support, maintain and enhance these apps which are constantly changing? What technology and infrastructure to put in for a national or global deployment? Who is going to fund all this plumbing – corporate, LoB or IT?
Business Analytics solutions for "always-on" 3/4G enabled mobile devices – iPads, iPhones, tablets, smart phones – are becoming prevalent as the form factor becomes appropriate for BI. We are increasingly seeing firms build state-of-the-art dashboard solutions for iPads. The "post-desktop" apps provide senior management with intuitive interactive access to the company's most important business KPIs and dealing with data overload.
Tablets, 4G Wireless and next gen displays (+gesture based, verbal interfaces) have enabled new productivity improvements and better ways to consume information, perform ad-hoc querying and scenario planning. Dashboard, heatmaps and scorecards on the iPad, iPhones and Androids are intuitive, attractive, powerful, available at any time and any place: a perfect mix for top managers, sales teams and even customers.
BI (and Information Management) is a natural fit for mobile devices. Managers, blue and white workers spend a majority of their time away from their desks. Most are traveling, walking about or driving from site to site. And it's these mobile workers who need the most up-to-date information. They need mobile BI to retrieve data to make on-the-spot decisions, monitor operational processes and review KPI, and work-in-process dashboards.
Apple iCloud Service – Lessons for "Big Data" and BI Architects
Apple with its iCloud offering is attacking the consumer facing digital content big data problem. Big Data is challenging on many fronts from the insights (e.g., analytics and query optimization), to the practical (e.g., horizontal scaling), to the mundane (e.g., backup and recovery).
On June 6th, 2011 Apple Inc. launched its new purpose built digital locker service called iCloud for its 225 million iTunes accounts that frees the end-user from the tyranny of the device. The iCloud service is a cloud offering that would allow users to store digital files such as photos, MP3 music, videos and documents in the cloud and access them from Internet-connected devices like iPhones, iPads, iPods, iMacs and others.
So, what's the big deal? They are addressing a classic BI data management problem: How to free up data trapped in "device and application jails" in a user-friendly way. The "scan and match" concept is quite applicable to large scale Enterprise Datawarehouses which suffer from data integrity issues as edge data capture and consumption devices proliferate.
Data ingestion, governance and management is a huge problem facing large organizations. As data volumes double every year, not having a basic data management strategy will become an Achilles heel. Most organizations unfortunately don't know what data assets they have, where these assets are, how they are organized and how well they are secured. Apple shows a neat way to address the Big Data problem in personal cloud management.


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Restore the performance of your soldering iron by replacing the tip. Replacement for S-TWP35 and S-TWLC100.
- ST Series Tips: Will fit the SP40L / SP40N series irons as well as the WP25, WP30, WP35, WLC100 series tools
- Precision ground copper tips are plated with iron, nickel and chromium
- ST1 - .062" / 1.58 mm wide Screwdriver tip
- Tinned with Lead Free alloy