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Creating Big Data Success with the Collaboration of Business and IT Teams. By Edward Chenard. - Started big data at Best Buy, was working in big data at GE before it was called big data. - Set up one of the first hadoop clusters in Retail and the Midwest.

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Edward chenard

  • - and IT Teams Started big data at Best Buy, was working in big data at GE before it was called big data.

  • - Set up one of the first hadoop clusters in Retail and the Midwest.

  • - Won tax innovation credits for my work on big data

  • - Tekne finalist for big data innovation

  • - Set up big data, data science and data visualization teams

  • Managed teams as large as 300 with product portfolios of over $4B

  • I spend my time in cold places

Edward Chenard

[email protected]

Twitter: Echenard

Slideshare: Echenard



The reality of big data
The Reality of Big Data and IT Teams

  • As many as 3/4 of big data projects fail according to one Gartner study.

  • The third is that 39 percent of the failure of Big Data project is attributed to the fact the data is siloed and there’s not a lot of cooperation in gaining access to that data. Now that is the oldest problem in the history of IT. - Infochimps

  • 1. They focus on technology rather than business opportunities.

  • 2. They are unable to provide data access to subject matter experts.

  • 3. They fail to achieve enterprise adoption.

    Terradata's top three reasons why big data projects fail.

  • Lack of alignment. Business and IT groups are not aligned on the business problem they need to solve but instead are tackling it from a technology perspective. Lack of true commitment from business stakeholders also makes alignment harder to achieve. Peter Sheldon - Forrester Analyst


Big data at most companies
Big Data at Most Companies and IT Teams

IT

Business

Big Data


How a typical big data project takes place
How a typical big data project takes place and IT Teams

  • Someone hears about big data and then seeks funding.

  • Other teams want to own it. Months of fighting takes place over ownership.

  • The opportunity is either lost or the mission of the project gets altered.

  • Teams work in silos, poor communication takes place as teams spend more time playing CYA. Achievement: Project failure with cost over runs, deadlines missed and lack of focus.




What does big data really mean to business
What does Big Data Really Mean to Business and IT Teams

“The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it's going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.”

Hal Varian


Everything is and IT Teamsabout discovery


Why a focus on collaboration
Why a focus on collaboration? and IT Teams

  • Projects fail for simple reasons, lack of understanding the need for better collaboration and then knowing how to implement that collaboration, helps to ensure success.

  • Failure does not need to be an option

  • Big data is the future of how we live and work, but only if we get it right. Big data can be bigger than ecommerce in terms of impact on how we live.


Everyone discovers
Everyone Discovers and IT Teams


Data Discoverers and IT Teams

“The Data Discoverers looks a lot like you and me, but what’s different is their preoccupation with personal data.

They are relentlessly digital, they obsessively record everything about their personal lives, and they think that data is sexy. In fact, the bigger the data, the sexier it becomes.

Their lives - from a data perspective, at least - are perfectly groomed.”

data as a lifestyle


Data Discoverers and IT Teams

Data Discoverers are setting the trend in what will be common place in just a few short years.

More people will want to use their data and the consumerization of data and technology will continue.

As this trend goes, only organization that learn to merge the various disciplines of strategy, analytics and IT, will be successful

data as a lifestyle




Complex ecosystems
Complex ecosystems: the Age of Insight

multi-channel experiences

everyware environments

Service models

dynamic perspectives

Reactive data


Activity Centered the Age of InsightThinking


How different functions see the same issue
How Different Functions See the Same Issue the Age of Insight

“Understand the quality performance of a system so I can better determine if I need to replace it.”

- IT

“Understand a portfolio's exposures to assess portfolio-level investment mix.”

- Strategy Manager

“I need to understand the customer trends in the data so I can better create models.”

- Analyst


Identifying modes
Identifying Modes the Age of Insight

“Understand the quality performance of a system so I can better determine if I need to replace it.”

- IT

“Understand a portfolio's exposures to assess portfolio-level investment mix.”

- Strategy Manager

“Understand the customer trends in the data so I can better create models.”

- Analyst

Mode = ‘Comprehend’ (understand)


Comprehending the Age of Insight

‘To generate insight by understanding the nature or meaning of an item or data set’

e.g. “I need to analyze and understand consumer-customer-market trends to inform brand strategy & communications plan” – Director, Brand Image

Each Team has the same goal, to understand, what they may want to understand is often different but not exclusive or limit to the other team’s need to understand.


Identifying modes1
Identifying Modes the Age of Insight

“I needvisibilityinto the systems my colleagues are using in order to maximize the network ROI for the company.”

- IT

“I need to identify customers/marketers/dealers failing & at risk of de-branding based on performance problems.”

- Strategy

“I need to identify the best customer/consumer/region targets for our brand/products.”

- Analyst

Mode = ‘Explore’



9 distinct modes the Age of Insight

Locate

Verify

Monitor

Compare

Comprehend

Explore

Analyze

Evaluate

Synthesize


Where to Start the Age of Insight


The business value framework
The Business Value Framework the Age of Insight

Initiatives

Customer Acceptance

Business value

Customer Acceptance

Business value

Focus on Customers

Focus on Internals

Customer’s Wallet Share

Perceived Value

Ease of Data Collection

Ease of Implementation

Ease of Data Collection

Ease of Implementation

Value Perceived

Customer’s Wallet Share

Pre-recorded

Production Flexibility

Different Products

Customer Needs

More Products

Production Efficiency

Automated and prompted

Timeliness


How work gets structured
How work gets structured the Age of Insight

Clearly articulated vision for personalization and recommendations, precisely defined goals with how to measure. Defined scope of the product.

Market strategy, customer segmentation, prioritization, org focus, measurement and incentive systems

Production process, flexibility at scale, efficiency, relationship management, benchmarking, metrics, initiatives


Framing collaboration
Framing Collaboration the Age of Insight

Value (Shared): Show me the money!?!

  • Measurable Results

  • Multi-Channel Case Studies

Strategy: Where are you headed?

IT: What Tools and Why

  • Buy vs. Build

  • Open source options

  • Alignment with Analytical Infrastructure

  • Speed to Market

  • Privacy Considerations

Big Data Collaboration

  • MapReduce, Hadoop

  • Cassandra, The Cloud

  • Pig, Hive,

  • HDFS

Analyst: Who, How, Where?

  • Data Scientist vs. Statistician

  • Where to find talent?

  • Retain, Train

  • Offshore vs. Onshore

  • University involvement




Thank you
Thank You! the Age of Insight


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