Give Me My Data by Don Juzwishin

Objectives

  • Identify 4 forces that have empowered patients
  • Why they emerged
  • Why we need to pay attention to them
  • What will be policy and health delivery implications

Coiera–4 rules for reinventing health care

  • Technical systems have social consequences
  • Social systems have technical consequences
  • We don’t design technology, we design social technical systems; and
  • To understand sociotechnical systems, we must understand how people and technologies interact

Implications for the future

  • For patients
  • For researchers
  • For policy makers
  • For health care delivery

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When Machines Decide – Improving Manufactuing Productivity with Data Analytics by Vasu Netrakanti

In an automated manufacturing environment, computers can help machines decide.

Third Industrial Revolution:

Digitisation of Manufacturing

(The Economist)

  • Perspective of optimization solutions provider
  • Predictive analytics  – Decision support
  • Integrated with plant infrastructure
  • Real-time decisions: routing (what next), selection (which order) …

Challenges

  • Reliable, accessible, timely data
  • Complement or supplement existing business processes?

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Preparing for Analytics – Extracting Meaningful Data from Physical Processes by Mark Hambliin



To move toward formal analytics, manufacturers must convert their physical transactions to an electronic form

Contents:

  • The different types of manufacturers and their different data requirements
  • Obtaining data in low-tech environments
  • How this data can be used to generate competitive advantages

Value of Analytics in Discrete

  • Proper analysis often results in:
  • Reduced expediting costs
  • Higher margins and fewer bad receivables
  • Reduced office overhead
  • Improved customer service
  • Lower inventory levels
  • Improved shop productivity
  • Improved asset utilization
  • Improved quality

Conclusion

  • Analytics can significantly improve operational efficiency and profitability
  • The first step in being able to properly analyse data is to collect the data
  • Alberta discrete manufacturers have a number of options to improve data collection

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Approaching Real-time Business Intelligence – Trading at the Speed of Light by Sean McClure –

Defining “Real-Time”

Three types of latency:
  • Data latency: time taken to collect and store the data;
  • Analysis latency: time taken to analyze the data and turn it into actionable information; and
  • Action latency: the time taken to react to the information and take action.

Approaching “zero” latency

  • Real-time business intelligence technologies are designed to reduce all three latencies as close to zero as possible;
  • Traditional BI only seeks to reduce data latency.

 Summary

  • Introducing Excellerate
  • Real-Time BI and HFT
  • Information at High Frequency
  • Strategies at High Frequency
  • Developing and Deploying Models
  • Executing and Monitoring Real-Time Systems

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Perfect timing by Mike Kouritzin

The only

  1. Constant is change –Heraclitus of Ephesus (535 BC -475 BC)
  2. Market consistency is volatility
  3. Sure non-winning strategy is buy and hold
  4. Way to win is to throw your money around

Volatility is Good

  • Markets become irrational -Mispricing
  • Computer Trading –Faster Decisions not Better
  • Goal: Be Prepared –Fast and Smart

 

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Seekers Media Kit- Best of Analytics by Jim Barr

Analytics is the way to monitor the temperature of an online property

WHAT’S UR ONE THING

Apple = innovation
Disney = magic
Volvo = safety
Tim Horton’s = convenience
What is your thing and answer that question every day.
In social media the more you sell – the less you sell. Future of marketing is about getting people psyched and using the analytics to ensure that this is happening.
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Growing Importance of Data in Retail by John Putters –

There has to be a balance between privacy and collecting meaningful data, which will always be the challenge.

Why is this important?

  • Broadcast advertising is increasingly shrinking as companies harness the ability to better define customers.
  • Technology can accomplish this, and is doing so increasingly.
  • Example, Facebook. Why is Facebook so valuable? Because of the information it collects from its users, then leverages that for advertising.
  • Mobile apps have raised the bar further, enabling retail to track people through GPS, and even customize ads based on shopping history.

Why is that data important?

  • It’s not just about customer service and giving the people what they want.
  • It is also about creating better efficiencies.
  • Washroom example: Not only risk mitigation but also, resources and supplies used versus traffic.
  • Customers can now produce reports that provide an overall efficiency rating on the bathroom.
  • So savings are found both in liability, but also in being more efficient in cleaning and maintaining.

Conclusions

  • Data collection will become increasingly important in defining customers.
  • Data, a least for our customers, is a powerful tool for becoming more responsive to customers and more efficient.
  • The challenge is collecting pertinent data without compromising privacy.
  • Visionstate has benefited greatly from this trend toward bigger data, and continues to develop products with an emphasis on this.

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Building Analytics Capacity in the HR Community by Mike Annett

Change Management at a Community Level Starts where the people are.

Overview

  • Context: Human Resource Business Partner
  • Issue: Change Management at a Community Level
    • Change Management Steps
    • Community Development and Engagement Principles
  • Actions: Capacity Development Practices and Reinforcements

 

Conclusion

  • Project Alignment
  • Why: Strong HR Community
  • How: Simple, Transparent, and Community-Oriented Change Practices.
  • What: Formal & Informal Training, Enabling Resources
  • Next Steps
    • Continue Data Dictionary
    • Continue Development Steps
    • Assess Progress

 

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Projecting the Education Workforce by Mark Bevan

Business intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insight and decision-making. -Forrrester

for us this means . . .

Communicating the education sector workforce story so that “non-technical people” can make strategic decisions informed by accurate data.

So what have we learned…

  • Garbage in…garbage out
  • Humans need to validate the data
  • Stories must be driven by, and be meaningful to the business user
  • Business rules do matter
  • Data stories must be user-friendly and intuitive to read
  • We are learning our way forward…

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The Three R’s Are Old School – Now It Is All About Volume, Velocity & Variety by Peter Guest

The world is changing and becoming more instrumented, interconnected and intelligent. The resulting explosion of information creates a need for a new kind of intelligence to help build a smarter planet!

 

 

 Why Business Analytics Matter

The Need for Analytics is Pervasive Across Business and Industry

  • The healthcare industry spends $250 -$300 billion on healthcare fraud, per year. In the US alone this is a $650 million per day problem.
  • One rogue trader at a leading global financial services firm created $2 billion worth of losses, almost bankrupting the company.
  • $93 billion in total sales is missed each year because retailers don’t have the right products in stock to meet customer demand.
  • 5 billion global subscribers in the telco industry are demanding unique and personalized offerings that match their individual lifestyles.

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