Mid sized companies dont have sensors and machines spitting out big data, but they have lots of data from traditional sources like CRM, ERP and Billing software. How do you use this. Some techniques, examples and case studies from other industries to get your creatives flowing
How We Did It: The Case of the Retail TweetersTeradata
BSI : Teradata is a fast-paced drama about a team of data warehousing and analytic specialists trained to solve business problems by examining data.
To watch BSI: Teradata Episode 2, visit our YouTube channel: http://www.youtube.com/watch?v=pVb8Dkd2mck
For more information on BSI, visit http://www.facebook.com/bsiTeradata
How the Game is Changing: Big Data in RetailBill Bishop
At Brick Meets Click, we've been tracking retailing professionals' experiences and attitudes toward big data for two years now, and more than 100 professionals participated in the Oct. 2013 survey. The results confirm the increasingly important role big data is playing in "changing the game" of retailing.
Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.
MIT report: How data analytics and machine learning reap competitive advantage.Nicolas Valenzuela
How Analytics and Machine Learning Help Organizations Reap Competitive Advantage
Produced MIT Technology Review, in Partnership with Google Analytics 360 Suite
How We Did It: The Case of the Retail TweetersTeradata
BSI : Teradata is a fast-paced drama about a team of data warehousing and analytic specialists trained to solve business problems by examining data.
To watch BSI: Teradata Episode 2, visit our YouTube channel: http://www.youtube.com/watch?v=pVb8Dkd2mck
For more information on BSI, visit http://www.facebook.com/bsiTeradata
How the Game is Changing: Big Data in RetailBill Bishop
At Brick Meets Click, we've been tracking retailing professionals' experiences and attitudes toward big data for two years now, and more than 100 professionals participated in the Oct. 2013 survey. The results confirm the increasingly important role big data is playing in "changing the game" of retailing.
Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.
MIT report: How data analytics and machine learning reap competitive advantage.Nicolas Valenzuela
How Analytics and Machine Learning Help Organizations Reap Competitive Advantage
Produced MIT Technology Review, in Partnership with Google Analytics 360 Suite
How to Ruin your Business with Data Science & Machine Learning by Ingo MierswaData Con LA
Abstract:- Everyone talks about how machine learning will transform business forever and generate massive outcomes. However, it's surprisingly simple to draw completely wrong conclusions from statistical models, and correlation does not imply causation is just the tip of the iceberg. The trend of the democratization of data science further increases the risk for applying models in a wrong way. This session will discuss. How highly-correlated features can overshadow the patterns your machine learning model is supposed to find this leads to models which will perform worse in production than during model building. How incorrect cross-validation lead to over-optimistic estimations of your model accuracy, especially we will discuss the impact of data preprocessing on the accuracy of machine learning models. How feature engineering can lift simple models like linear regression to the accuracy of deep learning but comes with the advantages of understandability & robustness.
Learn how retailers can leverage their own Big Data. Go from data sources to increasing profits, margins and market share at a fraction of the time and cost.
This is a presentation in a meetup called "Business of Data Science". Data science is being leveraged extensively in the field of Banking and Financial Services and this presentation will give a brief and fundamental highlight to the evergreen field.
This whitepaper is geared to help
bank marketing professionals
understand the scope of marketing
analytics and also on how it can
contribute value to the various
factions of a bank’s marketing
activities.
Analytical Thinking is a fortnightly newsletter from the UK Business Analytics team.
The purpose of the newsletter is to raise awareness about why analytics is a hot topic at the moment, where is analytics being referenced in the press and in what ways are organisations using analytics.
Business Analytics (Operational Research) is part of the Digital Transformation team in Capgemini Consulting UK
TechConnectr's Big Data Connection. Digital Marketing KPIs, Targeting, Analy...Bob Samuels
This presentation was given at the Deep Dive Conference in November. 2013.
Big Data Applications... example, digital marketing, and targeting and optimization...
Feedback, and additional perspectives, is appreciated.
Thank you,
Bobby Samuels
TechConnectr.com
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Big Data in Industry
Many believe that Big Data is a new asset which will help companies catapult others to become the best in class.
What is it about Big Data that is so appealing across industries? Simply, data is intertwined into every sector and function in the global economy and much of modern economic activity would not be able to take place without data.
Big Data relates to large meres of data which can be brought together and then analyzed to inform decision making and discern patterns. The insights which Big Data brings, will become the basis of competition and growth for companies worldwide through further enhancing productivity as well as generating significant value for the global economy by increasing the quality of goods and services.
Previous trends in IT investment and innovation such as cloud adoption and the impact of this on competitiveness and productivity can be mirrored by Big Data which serves as a crucial way for large companies to outperform their competition. Across industries, time-honored competitors and new entrants to the market will use data-driven strategies to compete, innovate and seize value. The knowledge that big data brings informs the creation of new services and the design of future products. In fact, some companies are using Big Data to conduct controlled experiments to inform better management decisions.
http://www.extentia.com/service/big-data
www.extentia.com/contact-us
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
How to Ruin your Business with Data Science & Machine Learning by Ingo MierswaData Con LA
Abstract:- Everyone talks about how machine learning will transform business forever and generate massive outcomes. However, it's surprisingly simple to draw completely wrong conclusions from statistical models, and correlation does not imply causation is just the tip of the iceberg. The trend of the democratization of data science further increases the risk for applying models in a wrong way. This session will discuss. How highly-correlated features can overshadow the patterns your machine learning model is supposed to find this leads to models which will perform worse in production than during model building. How incorrect cross-validation lead to over-optimistic estimations of your model accuracy, especially we will discuss the impact of data preprocessing on the accuracy of machine learning models. How feature engineering can lift simple models like linear regression to the accuracy of deep learning but comes with the advantages of understandability & robustness.
Learn how retailers can leverage their own Big Data. Go from data sources to increasing profits, margins and market share at a fraction of the time and cost.
This is a presentation in a meetup called "Business of Data Science". Data science is being leveraged extensively in the field of Banking and Financial Services and this presentation will give a brief and fundamental highlight to the evergreen field.
This whitepaper is geared to help
bank marketing professionals
understand the scope of marketing
analytics and also on how it can
contribute value to the various
factions of a bank’s marketing
activities.
Analytical Thinking is a fortnightly newsletter from the UK Business Analytics team.
The purpose of the newsletter is to raise awareness about why analytics is a hot topic at the moment, where is analytics being referenced in the press and in what ways are organisations using analytics.
Business Analytics (Operational Research) is part of the Digital Transformation team in Capgemini Consulting UK
TechConnectr's Big Data Connection. Digital Marketing KPIs, Targeting, Analy...Bob Samuels
This presentation was given at the Deep Dive Conference in November. 2013.
Big Data Applications... example, digital marketing, and targeting and optimization...
Feedback, and additional perspectives, is appreciated.
Thank you,
Bobby Samuels
TechConnectr.com
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Big Data in Industry
Many believe that Big Data is a new asset which will help companies catapult others to become the best in class.
What is it about Big Data that is so appealing across industries? Simply, data is intertwined into every sector and function in the global economy and much of modern economic activity would not be able to take place without data.
Big Data relates to large meres of data which can be brought together and then analyzed to inform decision making and discern patterns. The insights which Big Data brings, will become the basis of competition and growth for companies worldwide through further enhancing productivity as well as generating significant value for the global economy by increasing the quality of goods and services.
Previous trends in IT investment and innovation such as cloud adoption and the impact of this on competitiveness and productivity can be mirrored by Big Data which serves as a crucial way for large companies to outperform their competition. Across industries, time-honored competitors and new entrants to the market will use data-driven strategies to compete, innovate and seize value. The knowledge that big data brings informs the creation of new services and the design of future products. In fact, some companies are using Big Data to conduct controlled experiments to inform better management decisions.
http://www.extentia.com/service/big-data
www.extentia.com/contact-us
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
PresentationThe capability of enormous information - or the new .pdfaradhana9856
Presentation
The capability of enormous information - or \"the new oil,\" as a few CIOs and industry
specialists have named it - appears as perpetual as it is subtle. Huge information battles are in
their early stages, with endeavors of all stripes making sense of how to utilize new, old,
unstructured and outer information to make a focused procedure.
Despite the fact that the standard procedures for get-together information and investigating its
value are as yet coming to fruition, organizations know they have to get in the diversion. They
are gathering and mining information on clients, workers, market flow, the climate, and so on,
with instruments going from conventional business insight (BI) frameworks to more trial ones,
for example, geospatial and constant versatile following innovations, online networking
investigation and NoSQL databases.
SearchCIO isn\'t remaining on the sidelines, either. Our Essential Guide on enormous
information incorporates a preliminary for beginning with information social affair and
investigation, true contextual investigations from the CIO and business viewpoints, tips on the
best way to beat hindrances experienced by the huge information pioneers, and expectations on
the following huge information boondocks and what it implies for aggressive methodology.
This aide on the development of huge information is a piece of SearchCIO\'s CIO Briefings
arrangement, which is intended to give IT pioneers vital administration and basic leadership
guidance on opportune themes.
The most effective method to Collect Big Data ?
1 year agoby Ayush1 Comment
The most effective method to Collect Big Data ? : Yes we knoe you would have various inquiries
in your psyche like Collection of Big Data, How organizations gather Big Data, how to gather
information for quantitative research so don\'t stress, in the event that you are here to scan for
these inquiries here then you are on the right website page as here we are going to give you a
complete article on Collection of Big Data techniques quickly.
Astounding Facts about Rise of Big Data Collectection
Consistently buyers make around 11.5 million installments by utilizing Paypal
Consistently, Walmart (chain of rebate retail chains) handles more than 1 million client
exchanges
510 remarks, 293000 status and 136000 overhauls are posted on Facebook consistently
Consistently, ~7000 tweets are made on Twitter
Simply picture the measure of information created if the above details are figured for 24 hours?
Whoa! That is huge.
The term \'Enormous Data\' is ordinarily connected with 4V\'s to be specific, Velocity, Volume,
Variety, Veracity. These 4V\'s appropriately speaks to the genuine way of Big Data. Each \"V\"
has a noteworthy part to play in the presence of Big Data. On the off chance that consolidated,
these 4V paints a wonderful clarification of Big Data which can be comprehended as \" Big Data
as an idea alludes to high speed gathering of information in expansive volumes which radia.
The presentation talk about how utilizing the big data can give marketers an edge over its competition, and win customers trust....credits google research
Big Data Done Right by Successful OrganizationsEuro IT Group
Euro IT Group can help you unlock the tones of information already flowing through your organization, analyze it, extract value and transform it into insight that drives growth and revenue. Furthermore, by going through one of our big data quick wins programs, you will be able to enjoy the benefits of big data extremely fast, test and validate big data technologies and make better strategic decisions for managing your overall company data; our quick win program enables you to enjoy quickly new insights and measurable results by putting at work your existing data streams and to test and validate Big Data Technologies that can complement your legacy BI / DWH infrastructure.
Penser Consulting answers the key questions:
- What is big data, and why does it matter?
- How can big data drive business decisions?
- How can you build data analytics capabilities in your organisation?
Leveraging big data to drive marketing innovationAndrew Leone
Summary of the book: "The Big Data-Driven Company." Contains insights into leveraging data to drive marketing innovation. To buy this book: http://amzn.to/1YTdtqY
How Big Data helps banks know their customers betterHEXANIKA
Enterprises today mine customer data to ensure maximum success by targeting their products and solutions to the right audience. Let us have a look at how Big Data and Customer Analytics are helping businesses use their customer data for maximum benefits.
Jonathan Lee, Managing Director, Brand Strategy, and Ken Allard, Managing Director, Business Strategy at HUGE, gave this presentation at "Ambidexterity 2," the VCU Brandcenter's Executive Education program for account planning on June 24th at the VCU Brandcenter in Richmond, VA.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
4. How it influences decision making
Big data has been used to confirm or refute conventional wisdoms held by an enterprise
5. SME’s, Big Data and Analytics
Doing it
Some Case studies and Tools
Who are we
Canada Post analytics
showed that the risk of
dog bites is higher with
home delivery compared
to community boxes
Companies will shift
their future investment
away from IT-developed
reporting solutions
toward business-user-led
analysis solutions.
6. Do you have big data
Characteristics of big data
7. Data sources; big and small
ERP CRM
RFID
Network Switches
Billing
CDR
M/c
Sensors
Social Media and Pancreatic Cancer
8. Evolution of the Dataperson
I know the answer to all my problems and
really don’t need any more data: I didn’t
need data analysis for this, I knew it
Using traditionally available data on a
largely post facto basis to solve known
problems and opportunities
Collect and analyze all possible data in
real time to take decisions in real time to
solve problems and realize opportunities
9. You are doing your data right now
You are Scenarios How did you do data analysis
A takeaway 1. A surge at 4 pm every day
2. Low sales of 3 items every
alternate day
1. Your boys told you, You saw it
when you were there, you
say the hourly billing data
2. No idea
A retailer 1. End of month sales is
declining
2. Per user basket size reducing
on a day to day basis
1. No Idea
2. ERP or Billing software or BI
Hospital 1. Nurse retention ratio is down
2. Department patients billing
down
1. No Idea
2. Doctor changed, new
hospital, from the daily
patient report
10. Can we start by ourselves
10
Math and OR
Expertise
Develop analytic algorithms
Visualization Expertise
Interpret data to present in
meaningful ways
Tools and Users
Reduce complexity by
using off the shelf tools,
work with expert users
Vertical /
Domain Expertise
Develop hypothesis,
identify business issues,
ask the right questions
Data Experts
Data architecture,
management,
governance, policy
Use data for decision
making
Apply information to solve
business issues
yes Take help Later
11. how to start: do what you have: LHF
Set up a Cross
Functional Team
Do a Data
Inventory
Validate Data for
analytics use
Problems to Data
mapping
1. Get functional
heads, send out a
mail to all, go
online, IA and
Business analyst
2. Throw in some
contrarians and a
professor
3. Give mandate of
using data and
generating
shocks
1. Start with each
Functional head
2. Get data source
and sample data
for each function
3. Ask IT to do an
inventory of data
sources not
being used or
owned
4. Already owned
external
databases
1. Select tools to
use: Excel and
Access, Ask IT for
statistical
modeling tools,
Go online,
Kaggle, Watson,
Own App tools,
Google
2. Check Volume,
Velocity, Variety
to qualify data
1. For each data
source check
what problem
might it give an
answer to
2. Get a list of
problems, pet
theories, black
holes from the
CFT and @All
mail
3. Map them to the
data source if
possible and
make Tool choice
12. how to start: do what you have: LHF
Generate Analytics Course Correction Feedback loop Start over
1. Use tool selected
and run the
analytics
2. Check if the
results can be
viewed as
Diagnostic
analytics
1. Makes changes
to process, staff,
resources,
infrastructure,
design to reflect
analytics results
1. Check results and
see if there are
positives.
2. Check analytic
design , check if
you are
comparing
apples to apples
1. Do it all over
again.
2. Add more and
more data to the
mix, draw new
correlations,
more real time
data, more
behavioral data
Once you have tasted the LHF….you probably
would have made more money, understand data
better: Time to invest in tools and staff
13. Data Inventory Example
Functions Sources Sources Sources
Sales CRM Billing Software After Sales
Service/Mfg ERP / PPC Quality system Machine data
Supply Chain Accounting DB Logistics Sys
HR Attendance Payroll Social Media
HRIS
Marketing Social Media
Measurement
tools
Google Analytics Twitter Analytics
Finance Accounting SW Bank Data
Back
14. Running a Sales analytic
Sales
Decline
Qty
Per user
Fewer /
Diff Prod
Fewer
Users
Fewer/
User
Price
Per
product
Discount
Margin
Product
Mix
Correlations
• Higher Price
• New Products
• New store
• Cust Complaint
• Sales Returns
Trend Analysis and Forecasting
accuracy
• User behavior over different
time periods, Day, Month,
Season, Year
Unstructured data mining
• Social Media mention
• Email campaign reception
Correlations
• Discount
• Margin
• Product Mix
Trend Analysis and Forecasting
accuracy
• User reaction to price and
Supplier behavior over
different time periods, Day,
Month, Season, Year
Unstructured data mining
• Social Media mention of price
• Email campaign reception
Back
15. SME’s, Big Data and Analytics
Doing it
Some Case studies and Tools
Who are we
Canada Post analytics
showed that the risk of
dog bites is higher with
home delivery compared
to community boxes
16. Some places to start: Sales
P / O P / O
Who is our most valuable customer Who doesn’t pay by time but still gets a
good deal
Who is the least value customer Who does most sales returns, which
product does most sales returns
Lost sales, missed sales leads, high
probability sales leads botched
Which sales agent leads to most sales
returns
Customers who don’t come back, who
are they, where are they from, was there
a warning sign: fewer visits, lower billing
What happens after a customer does a
sales return, a complaint,
Is sales responding to Social Media
mentions
Which area is showing decline in sales,
which is growing,
Is discount working to increase my sales,
which promotion lead to a secular
increase in demand, is there a particular
time at which discounts work, is there a
particular product on which it works
Which products seem to be price
inelastic, is there a good time to increase
prices, do other goods get impacted if
price increases [cannibalization]
17. theory of inventive problem solving
• Wolfram Alpha, an online personal analytics tool that helps people analyze their Facebook feeds and
displays their account activity in pie charts, graphs and maps. Wolfram Alpha will expand its personal
analytics tools to allow users to input and analyze a wide range of personal data including emails,
instant messages, tweets and health data.
• Google’s Field Trip, a customizable local discovery engine. When people approach something
interesting, it automatically informs them about the location. No click is required and it can even read
the information to them. Field Trip bases its recommendations on user inputs and lets users find the
cool, hidden and unique things and places wherever they are
• ValuTex, a mobile marketing service, which sends special offers to smartphones of shoppers who have
opted-in when they enter a “geo-fence,” . These offers can be customized to specific customers using
profiles maintained by the merchant
• Global hotel chains are exploring applications that, with guests’ permission, recognize them via their
cell phones when they pull into the parking lot. These apps let hotels automatically check in guests
and have their room key and paperwork – and perhaps their favorite beverage – waiting at the front
desk before they even walk through the door
• Scout Mob and Womply, which allow local merchants to personalize offerings for particular
consumers, increasing customer loyalty. These services let local merchants combine information on
purchases with social media data to provide a more complete picture of customer preferences
• QuickBooks Online, includes a Trends feature that anonymously aggregates customer data and allows
small businesses to see how their income and expenses stack up against similar businesses. A roofer in
Philadelphia grossing $250,000 annually can compare results with other roofers in the area or across
the country.
18. theory of inventive problem solving
• A company wanted to shift its operations from City A to City B. Most employees won’t tell you if they
don’t like the move and wait till they get another job and then leave you. That would mean business
disruption. We made an analytic by going through employee data to come up with a profile that would
be most likely to quit, a profile that might not quit if some parameters were taken care of.
• Mackenzie Hospital: 34 beds, partnership between Cisco, Blackberry, Thoughtwire and funded by
Ontario CoE that picks up data from the workflow and tries to solve the problem of locating people and
getting to them the right information.
• EagleView Technologies provides roofers and solar panel installers with precise measurements of roof
sizes and slopes based on aerial photographs. Local contractors use the images and measurements to
inspect roofs, estimate costs and identify potential customers without the need for costly site visits
• When they didn’t have loyalty-card data, small businesses were dominated by their owner-managers,
who made decisions on their past experiences and any consumer information they could get their hands
on. One firm, was asked by a retailer to produce a range of ready meals, simply looked at other products
on the market and tried to imitate them.
• a large bank wants to monitor Twitter and Facebook for entries mentioning life-changing events. The
theory is that postings about developments such as pregnancies, births, or marriages can become
marketing opportunities for the bank. But the bank also wants to understand whether negative
developments, such as announcements about an acrimonious divorce, will raise a flag that credit lines
need to be carefully monitored or frozen
19. theory of inventive problem solving
• Financial Engines, helps hundreds of thousands of people navigate the complexities of retirement
planning. Founded by Nobel prize-winning economist Bill Sharpe, the firm provides individuals with
sophisticated financial advice – previously available only to the world’s largest institutional investors.
Its foundation is cloud technologies, new ways to access large financial data sets and advanced data
analysis tools.
• Parchment, a startup that helps high school students choose and apply to college. By analyzing a large
database of student profiles such as grade point averages, SAT scores and acceptance data, Parchment
assesses a student’s likelihood of admission to a specific school. It then determines what the student
must do to improve acceptance chances. Parchment also plays matchmaker, pointing students toward
schools that match their profiles, helping them find a good fit.
• Factual, which offers a cloud-accessible database of 58 million businesses and places of interest in 50
countries, effectively creating an uber Yellow Pages with a truly global reach. Businesses will be able
to use this data to identify, target and market to business customers anywhere in the world as easily
as to those in their home towns.
• Startup Compass, which collects data from tens of thousands of startups and creates best practice
information, benchmarks and performance indicators that help entrepreneurs make better decisions.
This new, cloud-based service currently has 17,000 companies submitting data and using it to help run
their businesses. .
20. Resources and Tools
• IBM's Watson Analytics advanced and predictive business analytics doesn't require using complex data
mining and analysis systems, but automates the process instead. This self-service analytics solution
natural language" technology helps businesses identify problems, recognize patterns and gain
meaningful insights: free and freemium.
• Google Analytics, Free Web-traffic-monitoring tool, provides data about website visitors, using a
multitude of metrics and traffic sources. where traffic is coming from, how audiences engage and how
long visitors stay on a website (known as bounce rates)
• InsightSquared connects to popular business solutions — such as Salesforce, QuickBooks, ShoreTel Sky,
Zendesk : Use for pipeline forecasting, lead generation and tracking, profitability analysis, and activity
monitoring. It can also help businesses discover trends, strengths and weaknesses, sales team wins and
losses from CRM. 99USD pm.
• Canopy Labs, a customer analytics platform, uses customer behavior, sales trends and predictive
behavioral models to extract valuable information for future marketing campaigns and to help you
discover the most opportune product recommendations.
• Tranzlogic works with merchants and payment systems to extract and analyze proprietary data from
credit card purchases. This information can then be used to measure sales performance, evaluate
customers and customer segments, improve promotions and loyalty programs, launch more-effective
marketing campaigns. No tech smarts to get started — it is a turnkey program, meaning there is no
installation or programming required. Simply log in to access your merchant portal.
• SiSense, which allows small companies to draw information out of the transaction statistics being
collected on their e-commerce sites and in CRM databases Prism, is intended to be used by business
analyst (rather than IT experts) who are interested in running "self-serve analytics,“
• OneQube The program mines comments and conversations on social networks like Twitter to identify
the most relevant prospects, or Constant Contact offers big data-based benchmarks to help marketers
21. Resources and Tools
• ODX is a partnership between Communitech, the University of Waterloo, D2L
(Desire2Learn), CDMN (Canadian Digital Media Network), and OpenText; the initiative will
evolve over the next three years using FedDev Ontario’s Investing in Commercialization
Partnerships (ICP) grants for small business in Ontario, as well as non-profits and post-
secondary institutions. The Open Data Exchange (ODX) depends on the sharing,
distribution, and analysis of large datasets. The founding partners of ODX consist of for-
profit businesses, non-profits, and a post-secondary institution. The local community has
to sign up as a supplier of data to maximize the impact of ODX. Verticals: - Consumer
products; Education; Energy (Electricity/Oil and gas); Finance; Health care; and
Transportation
• 2001, the Government of Canada funded a not-for-profit corporation called Canada
Health Infoway. Its charge to facilitate health care transformation includes developing
health information standards, providing tools and services for technology vendors, and
working with the clinical community to enhance its value.
22. Resources and Tools
• Workforce Analytics Forum by the Canadianinstitute.com is a program developed
specifically for HR and workforce analytics professionals besides getting course credits
• Community Cloud: online collaboration and business process platform: Salesforce To
bring employees, customers, suppliers and distributors together. Targeted
Recommendations an addition to this, uses algorithms to analyze both structured and
unstructured data so the most relevant content is delivered to each community member.
All this is easy to do by using templates so you don’t need IT too much
• Twitter Analytics is an easy-to-use, free tool :For monthly account summaries, which
tweets generated largest engagement, identify top followers of your brand.
• Business analytics from Bell can help you in building an analytic engine and so also if you
are an Intuit Quickbook user. Intuit has collective data of more than 45 million customers
that ranges from individual purchases and spending habits to business inventories,
transactions, and trends. QuickBooks Online Trend’s goal is to help the mom and pop
store compete with Macy’s or Starbucks down the street.
• The Chang School offers several programs to provide proficiency and skills in big data and
advanced analytics. Professional Master's Program in Big Data at the SFU. Lassonde
School of Engineering will lead a program in Data Analytics and Visualization providing
interdisciplinary training in both computational analytics and perceptual design
methodologies
23. Points to Ponder
Data is the new Oil. Data is just like crude. It’s valuable, but if
unrefined it cannot really be used.
– Clive Humby, DunnHumby
We have for the first time an economy based on a key resource
[Information] that is not only renewable, but self-generating. Running out
of it is not a problem, but drowning in it or squandering it is.
– John Naisbitt
24. SME’s, Big Data and Analytics
Doing it
Some Case studies and Tools
Who are we
26. Leadership Leadership Leadership
Sales & Ops Sales & Ops
Virtual
Organization
Virtual Leadership
Support team
Sales & Ops
Support team
Consulting
Support
Start up /
Turnaround
Emerging /
Languishing
Mid sized / Large
Client Ubika
However SME’s normally have skills only in one or two functions which makes our integrated
multi-disciplinary approach the right fit for SME’s that want to hit the ground running
Solving problems / realizing opportunities almost
always require skills in multiple functions.
What do we do
27. Who are we
USA and Process Consulting
Maria Achilleoudes has a MSc from Columbia University and a BSc from
City University of New York and a Lean Six Sigma Master Black Belt . She
first worked with IBM’s Quality, then Marketing Division in New York. She
then joined Universal Bank, Cyprus before starting a consulting firm which
works on Cost reduction and Process Improvement jobs.
Canada and Strategy
George Antony has spent 60% of his time with the Big 4 in advisory and the
rest in the Industry heading finance for a start up where he raised equity
and debt, set up the finance department and managed procurement. In the
Big 4 he advised a variety of clients on Process improvement and strategy.
At Ubika he works with clients on Strategy, Training and Interim
Management. He also teaches entrepreneurship to rural entrepreneurs
george@ubika-hetu.com | +1 647 771 2017 | @togeorge