How Big Data Is Revolutionizing Manufacturing

UM SI/HON/PHYS 365: Cyberscience, Fall 2014

The attached article provides insightful analysis of how big data and advanced analytics can streamline biopharmaceutical, chemical, and discrete manufacturing. One big use of analytics is the ability to increase yields and reduce costs. This analysis is done on collected operational and shop floor data. Ten other ways big data is revolutionizing manufacturing are listed below.

  • Increasing the accuracy, quality and yield of biopharmaceutical production
  • Accelerating the integration of IT, manufacturing and operational systems making the vision of Industrie 4.0 a reality.
  • Better forecasts of product demand and production (46%), understanding plant performance across multiple metrics (45%) and providing service and support to customers faster (39%) are the top three areas big data can improve manufacturing performance.

LNS Graphic

  • Integrating advanced analytics across the Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) framework to fuel continuous improvement.
  • Greater visibility into supplier quality levels, and greater accuracy in predicting supplier performance over time.
  • Measuring compliance…

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A Blueprint for Digital Partnerships: How Data Analysis Expands Your Customer Base

Foundational Theories in Big Data Strategy, Analytics and Product Management

Originally Published on Wired

Everyone and everything seems to be vying for customers’ attention these days.

Attracting and keeping the interest of existing customers — let alone acquiring new ones — is tricky for most enterprises. Modern consumers have many choices in how they spend their time, attention, and money. This makes it particularly important for businesses to be constantly on the look out for new ways to engage and retain their customers.

One powerful way to aid enterprises in this search is cross-enterprise “customer sharing” via symbiotic digital partnerships. Enterprises have long tried to upsell and cross sell new products and services to existing customers. But they can now extend these tactics through digital partnerships with other enterprises. The key difference is that the cross selling and upselling happens in a meticulous, targeted manner to the partner’s customers, through the use of big data analysis and targeted and personalized…

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How Data Analysis Drives the Customer Journey

Foundational Theories in Big Data Strategy, Analytics and Product Management

Originally Published on Wired

Driving down Highway 1 on the Big Sur coastline in Northern California, it’s easy to miss the signs that dot the roadside. After all, the stunning views of the Pacific crashing against the rocks can be a major distraction. The signage along this windy, treacherous stretch of road, however, is pretty important — neglecting to slow down to 15 MPH for that upcoming hairpin turn could spell trouble.

Careful planning and even science goes into figuring out where to place signs, whether they are for safety, navigation, or convenience. It takes a detailed understanding of the conditions and the driving experience to determine this. To help drivers plan, manage, and correct their journey trajectories, interstate highway signs follow a strict pattern in shape, color, size, location, and height, depending on the type of information being displayed.

Like the traffic engineers and transportation departments that navigate this…

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4 Big Data Essentials For Startups

DwellinCode

Data-driveninsights aren’t just for behemoth enterprises. Here’s what startups need to know before embarking on a big-data strategy.

Big-data products are generally targeted at large enterprises, and for good reason. They can be enormously expensive to initiate and operate, and therefore out of reach for the average startup or small business.

That’s changing, but fledgling firms need to answer some hard questions before embarking on a data-driven strategy. These include: Do I need a big data system? And what insights do I hope to gain from it?

At last week’s Techweek conference in Santa Monica, Calif., a two-day event connecting tech entrepreneurs with investors, Sean Anderson, manager of data services at cloud computing company Rackspace, offered some solid advice in his talk, “How Startups Can Leverage Big Data.” Anderson’s talk covered a wide range of data-related topics, but we’ve distilled a few takeaway points for companies debating the merits of…

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What is Hadoop

TuyenTruong@MUM

Imagine this scenario: You have 1GB of data that you need to process.
The data is stored in a relational database on your desktop computer and
this desktop computer has no problem handling this load.
Then your company starts growing very quickly, and that data grows to
10GB.
And then 100GB.
And you start to reach the limits of your current desktop computer.
So you scale-up by investing in a larger computer, and you are then OK
for a few more months.
When your data grows to 10TB, and then 100TB, you are quickly
approaching the limits of that computer.
Moreover, you are now asked to feed your application with unstructured
data coming from sources like Facebook, Twitter, RFID readers,
sensors, and so on.
Your management wants to derive information from both the relational
data and the unstructured data and wants this information as soon as
possible.
What should you…

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Data Lake Vs Data Warehouse

Namit's Blog

DataLakeIn my last blog, I wrote on Data Lake. The first comment on the Blog was to find out the difference between Data Lake and Data Warehouse. So in this blog, I will try to share some of my understanding on their difference:

Schema: In Data Warehouse (DW), schema is defined before data is stored. This is called “Schema on WRITE” or required data is identified and modeled in advance. But in Data Lake the schema is defined after the data is stored. This is called “Schema on READ”. So the data must be captured in code for each program accessing the data.

Cost (Storage and Processing) : Data Lake provides cheaper storage of large volumes of data and has potential to reduce the processing cost by bringing analytics near to data.

Data Access: The data lake gives business users immediate access to all data. They don’t…

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The 3Vs of Big Data – Volume, Velocity, and Variety

What's the Big Data Idea

Bringing Big Data to the People (Part 4 of 6)

Beyond Natural Selection – Variety

Data used to have to be carefully selected for processing both in quantity and quality.  Data was strictly formatted.  At first, its gatekeepers were men in lab coats and pocket protectors (and eventually morphed to the IT guys.)  

The original Computer lab

As data became more prolific, it became more personal through spreadsheets and databases that were possible on home computers via Lotus and Microsoft.  Anyone with a PC and cheap software could learn basic capabilities with a little effort. With a lot of effort, any PC could actually accomplish quite a bit with these tools (most users only utilize less than 10% of any MS product ability.)  Anyone who’s worked with a pivot table or even just got the “!” trying to use a spreadsheet understands the need to have the right format…

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2015 Trends: Rich Mobile Apps, Embedded Analytics and Wearable BI

Small Data Group

Gazetteer IoS App

Back in the summer I explored the concept of “wise” devices being proposed by Fitbit designer Gadi Amit and introduced the idea that small data will be the OS for these mobile and wearable devices.

Since then, my teams at Actuate have been exploring these ideas and accelerating our work around applying information design best practices for a new generation of rich mobile apps and embedded analytics. The goal: expand the boundaries and our understanding of what it means to assemble and display intelligence in context. We’ve also been teaming with our engineering group to look at new ways to demonstrate the rich APIs provided by the BIRT iHub to better access real-time device data, visualize it, and embed these packaged insights on “non-traditional” devices like smartwatches, tablets or even large-format displays.

Some initial results – including a very cool IoT-telematics demo that leverages the BIRT technology stack and open data standards…

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Big Data, Small Humanities

Amazing event!

MEDIA PRAXIS

I attended the Claremont Graduate University’s Big Data, Better World? conference and wanted to make a small comment about the role of the humanities (and Digital Humanities) at that event, and more broadly in academia and ever, perhaps where academia presses against, speaks to, corrects, augments, and influences (and is influenced by) industry.

The point is not really mine–I’m simply reporting here–it was eloquently expressed by all three professors on the Big Data and the Humanities panel, and then reflected and reemphasized through the vision of Jack Dangermond, founder and president of Environmental Systems Research Institute (ESRI), “a pioneer in spatial analysis methods but also one of the most influential people in GIS,” who gave the keynote address “Mapping a Better World.”

Dangermond’s vision is of a planetary nervous system of real-time and past data that is both produced by and available to many, and can be…

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What Google’s Eric Schmidt Doesn’t Understand

That’s big data, find the intelligence under number!

The Curtain - A Novel by Author Patrick Ord

Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?

– T.S. Eliot “The Rock” (1934)

Eric Schmidt

What Google’s Eric Schmidt Doesn’t Understand

Eric Schmidt is at it again, promoting his vision of the future.

It was only a couple of years ago that Schmidt stepped down as Google’s CEO – ostensibly because of blowback from such comments as:

“We don’t need you to type at all. We know where you are. We know where you’ve been. We can more or less know what you’re thinking about.” (Atlantic Monthly forum in October 2010).

and

“If you have something that you don’t want anyone to know, maybe you shouldn’t be doing it in the first place.” (December 2009 interview with CNBC’s Maria Bartiromo)

Presently, Google’s (still) Chairman is continuing to challenge our notions of privacy. In a CNNMoney interview by Logan Whiteside on October…

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