Tag Archives: Analytics

Best Kept Secrets for Successful Data Governance

data governance

Best Kept Secrets for a Successful Data Governance

If you’ve spent some time studying and practicing data governance, you would agree that data governance is a challenging yet rewarding endeavor.  Across industries, a growing number of organizations have put data governance programs in place so they can more effectively manage their data to drive the business value. But the reality is, data governance is a complex process, and most companies practicing data governance today are still at the early phase of this very long journey.  In fact, according to the result from over 240 completed data governance assessments on http://governyourdata.com/, a community website dedicated to everything data governance, the average score for data governance maturity is only 1.6 out of 5. It’s no surprise that data governance was a hot topic at last week’s Informatica World 2015.  Over a dozen presentations and panel discussions on data governance were delivered; practitioners across various industries shared their real-world stories on topics ranging from how to kick-start a data governance program, how to build business cases for data governance, frameworks and stewardship management, to the choice of technologies.  For me, the key takeaways are:

  1. Old but still true – To do data governance the right way, you must start small and focus on achieving tangible results. Leverage the small victories to advance to the next phase.
  1. Be prepared to fail more than once while building a data governance program. But don’t quit, because your data will not.
  1. Why doesn't it fit?!One-size doesn’t fit all when it comes to building a data governance framework, which is a challenge for organizations, as there is no magic formula that companies can immediately adopt. Should you build a centralized or federated data governance operation? Well, that really depends on what works within your existing environment.
    In fact, when asked “what’s the most challenging area for your data governance effort” in our recent survey conducted at Informatica World 2015, “Identify roles and responsibilities” got the most mentions. Basic principle? – Choose a framework that blends well with your company‘s culture.
  1. pptLet’s face it, data governance is not an IT project, nor is it about fixing data problems. It is a business function that calls for people, process and technology working together to obtain the most value from your data. Our seasoned practitioners recommend a systematic approach: Your first priority should be people gathering – identifying the right people with the right skills and most importantly, those who have a passion for data; next is figuring out the process. Things to consider include: What’s the requirement for data quality? What metrics and measurements should be used for examining the data; how to handle exceptions and remediate data issues? How to quickly identify and apply security measures to the various data sets?  Third priority is selecting the right technologies  to implement and facilitate those processes to transform the data so it can be used to help meet  business goals.
  1. Business & IT Collaboration“Engage your business early on” is another important tip from our customers who have achieved early success with their data governance program. A data governance program will not be sustainable without participation from the business. The reason is simple – the business owns the data, they are the consumers of the data and have specific requirements for the data they want to use. IT needs to work collaboratively with business to meet those requirements so the data is fit for use, and provides good value for the business.
  1. Scalability, flexibility and interoperability should be the key considerations when it comes to selecting data governance technologies. Your technology platform should be able to easily adapt to the new requirements arising from the changes in your data environment.  A Big Data project, for example, introduces new data types, increased data speed and volume. Your data management solution should be agile enough to address those new challenges with minimum disruption to your workflow.

Data governance is HOT! The well-attended sessions at Informatica World, as well as some of our previously hosted webinars is testimony of the enthusiasm among our customers, partners, and our own employees on this topic. It’s an exciting time for us at Informatica because we are in a great position to help companies build an effective data governance program. In fact, many of our customers have been relying on our industry-leading data management tools to support their data governance program, and have achieved results in many business areas such as meeting compliance requirements, improving customer centricity and enabling advanced analytics projects. To continue the dialogue and facilitate further learning, I’d like to invite you to an upcoming webinar on May 28, to hear some insightful, pragmatic tips and tricks for building a holistic data governance program from industry expert David Loshin, Principal at Knowledge Integrity, Inc,  and Informatica’s own data governance guru Rob Karel.

Get Ready!

Get Ready!

“Better data is everyone’s job” –  well said by Terri Mikol, director of Data Governance at University of Pittsburgh Medical Center.  For companies striving to leverage data to deliver business value, everyone within the company should treat data as a strategic asset and take on responsibilities for delivering clean, connected and safe data. Only then can your organization be considered truly “Data Ready”.

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The Emergence of the Analytical (Data Ready) CEO

OaklandI remember being an A’s fan during the Moneyball era. When my wife and I saw the movie, we kept asking each other do you remember going to this game or that game. Prior to the movie, neither of us knew what was taking place in the A’s back office. All we knew was it was hard not to want to be part of this team with such a low payroll, kids drumming at every game, and irrepressible will to win even though the odds were stacked against them.

CEO leadership is needed to push analytics thinking

CurveJust like what happened during Moneyball, I am increasing finding that analytics do not happen in a vacuum. Leadership is needed to push analytical thinking. There needs to be an orientation to analytics and this needs to come from the top for an enterprise analytics approach to take hold and to grow. Clearly, not all organizations need to have their chief analytics officer reporting directly to the CEO, but there needs to be bias to use data rather than gut feel. And this bias needs to be set at the top of the organization. Otherwise, organizations end up with enterprise fiefdoms of information.

Last week, I was sitting with two IT leaders for a major mutual fund company. I was talking to them about the importance of analytics. While both agreed with me, they said that some managers prefer to use their intuition and experience versus data. Imagine that—the most data centric type of enterprise has managers preferring intuition over data. However, CEOs are starting to act as change agents here. Marc Benioff says, “I think for every company, the revolution in data science will fundamentally change how we run our businesses. Our greatest challenge is making sense out of data. We need a new generation of executives to understand and lead through data”.

Brian Cornell is a great case study

TargetOne of the leaders of the analytics vanguard is Brian Cornell. “Analytics have been a central part of Cornell’s approach”. When he headed Sam’s Club, he used analytics to improve the unit’s customer-insight system. The results were so good that Wal-Mart moved all of their analytics teams under Cornell. According to Stuart Aitken, Cornell does not just look at the data. He goes beyond the data and asks hard questions of customers and those on his team. Cornell method involves taking the clues he gathers from customer conversations and using analytics to look for broader patterns that would reveal problems and opportunities.

Cornell’s emphasis on analytics was a key reason why Target board’s hired him. His record with analytics is amazing. This included his ability to use data to expand in house brands and reverse sales declines at each company that he worked for. At Sam’s Club, for example, he made it into the fastest growing division of Wal-Mart. Presently, Cornell is using data and analytics to look for areas where Target can reestablish it’s right to win.

Change moment for CEOs

shutterstock_227713873 - CopyWe are clearly at a change moment for CEOs. In the past, CEOs and their managers relied on backward facing reporting to drive forward facing performance. But today, timely data exists to drive forward facing performance—especially, if the analytics are placed on top of them to show connections and predict near term impacts. Whether it be for the front office or the back office, with great data—data which is trustworthy and timely—it is possible for CEOs and their leadership teams to be the captain of the ship. It is possible with great data and a willingness to dig into what the great data represents to see the business icebergs ahead and to take action not only corrective action, but as well, to make use of the opportunities and trends that they represent. Clearly, with great data, analytical CEOs and their teams can develop strategies that can be the basis for new approaches to winning businesses.

Parting remarks

So how can you become the analytical leader that your enterprise needs? To point you in the right direction, here are four practices that will fuel your strategic use of data. The linked research combines the latest research from the Economist Intelligence Unit and a global survey of IT professionals and C-level executives. From this research, the connection between the strategic use of data and financial performance will become absolutely clear.

Related links

Solution Briefs

Next Generation Analytics

Related Blogs

Big Data: The Emperor may have their Clothes on but…

Should We Still be calling it Big Data?

Analytics Stories: A Banking Case Study

Analytics Stories: A Pharmaceutical Case Study

Analytics Stories: An Educational Case Study

Analytics Stories: A Financial Services Case Study

Analytics Stories: A Healthcare Case Study

Major Oil Company Uses Analytics to Gain Business Advantage

Is it the CDO or CAO or Someone Else?

Should We Still be calling it Big Data?

What Should Come First: Business Processes or Analytics?

Should Analytics Be Focused on Small Questions Versus Big Questions?

Who Owns Enterprise Analytics and Data?

Competing on Analytics

Is Big Data Destined To Become Small And Vertical?

Big Data Why?

What is big data and why should your business care?

Author Twitter: @MylesSuer

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Are You Ready to Compete on Analytics?

Building a Data Competence for a Decision Ready Organization

There has been a lot of talk about “competing on analytics.”  And this year, for the third year in a row BI/Analytics is the top spending priority for CIOs according to Gartner.   Yet, the fact is that about half of all analytics projects do not deliver the expected results on time and on budget.  That doesn’t mean that the projects don’t show value eventually, but it’s harder and takes longer than most people think.

To compete on analytics is to establish a company goal to deliver actionable business insights faster and better than anybody in your industry – and possibly competitors who may be looking to jump industry boundaries as Google and Apple have already done several times.

This requires a competence in analytics and a competence in data management, which is the focus of this blog.  As an analytics manager at a healthcare company told me this week, “We suffer from beautiful reports built on crap data.”  Most companies do not yet established standard people, processes and technology for data management.  This is one of the last functional areas in most organizations where this is still true.  Sales, Marketing, and Finance standardized years ago.  It is only in the area of data management, which is shared by business and IT, that there is no real standardization. The result is unconnected silos of data,  long IT backlogs for data-related requests, and a process that is literally getting slower by the day as it gets overwhelmed by data volume and data complexity.

Analytics Use Cases and Data Requirements

It is worthwhile to think of the different broad use cases for analytics within an organization and what that means for data requirements.

Analytics & Innovation

  • Strategic Insights are the high level decisions a company must make. Better performing organizations are moving from “gut feel” to data-driven decision making.  The data for these large decisions needs to be as perfect as possible since the business costs of getting it wrong can be enormous.
  • Operational Insights require quick decisions to react to on-the-ground conditions. Here, the organization might be willing to sacrifice some data quality in order to deliver quick results. There is a speed versus expected benefit tradeoff to consider.
  • Analytics Innovation is the process of asking questions that were often never possible or economic to even ask before. Often, the first step is to see if there is any value in the question or hypothesis.  Here the data does not have to be perfect.  Often approximated data is “good enough” to test whether a question is worth pursuing further.  Some data scientists refer to this as “fail fast and move on quickly.”

The point here is that there is a tradeoff between speed of data delivery and the quality of the data that it is based on.  Managers do not want to be making decisions based on bad data, and analysts do not want to spend a high percentage of their time just defending the data.

The Need for Speed in Business Insight Delivery

We are moving from historical to predictive and proscriptive analytics.  Practically everybody has historical analysis, so while useful, it is not a market differentiator.  The biggest competitive payoff will come from the more advanced forms of analytics.  The need for speed as a market differentiator is built on the need provide service to customers in realtime and to make decisions faster than competitors.  The “half-life” of an analytics insight drops rapidly once competitors gain the same insight.

Analytics Speed Progression

Here are a couple of quick examples or predictive and proactive analytics:

  • Many retailers are looking to identify a customer coming in the door and have a dashboard in front of the customer service representative that will give them a full profile of the customer’s history, products owned, and positive/negative ratings about this product on social media.
  • In Sales, predictive analytics is being used today to recommend the “next best step” with a customer or what to upsell to that customer next and how to position it.
  • Beyond that, we are seeing and emerging class of applications and smart devices that will proactively recommend an action to users, without being asked, based on realtime conditions.

The data problems

The big problem is that the data internal to an organization was never designed to be discovered, access and shared across the organization. It is typically locked into a specific application and that application’s format requirements.  The new opportunity is the explosion of data external to the organization that can potentially enable questions that have never been possible to ask before.   The best insights and most differentiating insights will come from data sources across multiple disparate sources.  Often these sources are a mix of internal and external data.

Common data challenges for analytics:

  • The 2015 Analytics and BI survey by InformationWeek found that the #1 barrier to analytics is data quality. And this does not just mean that that data is in the right format. It must be complete, it must have business meaning and context, it must be fit for purpose, and if joined with another data set, it must be joined correctly.
  • The explosion of data volume and complexity.
  • More than 50% organizations use is coming from external sources (Gartner). This data is often less-structured, of unknown structure, and may have limited business context as to what the data means exactly.
  • The time-value of money. As mentioned earlier, the value of data and insights is eroding at increasing pace.
  • Data Discovery: Gartner estimates that the BI tool market is growing at 8% but says that the market could be growing much faster if issues around data discovery and data management were addressed.

Recommendations for the Decision Ready Organization

If you truly want to compete on analytics, you need to first create a competency center around data management.  Analytics is a great place to start.  First:

  • Break down the data & technology silos
  • Standardize on data management tools, processes, skills to the extent possible
  • Design so that all of your data is immediately discoverable, understandable, and shareable with any application or analytics project that might need it

Requiements

Pick industry-leading data management tools, or even better, tools that are integrated into a comprehensive data management platform.  Make sure that the platform:

  • Works with any data
  • Works with any BI tool
  • Works with any analytics storage technology
  • Supports all the analytics use cases: Strategic Decisions, Operational Decisions, and Innovation
  • Supports multiple delivery modes: business analyst self-service as well as the more traditional IT delivery of data managed by a formal data governance body.

The past focus on applications has resulted in hard-to-access data silos.  New technologies for analytics are causing some organizations to create new data silos in the search for speed for that particular project.  If your organization is serious about being a leader in analytics, it is time to put the focus required into leading-edge data management tools and practices to fuel insight delivery.

We are working with organizations such as EMC, and Fidelity that have done this.  You don’t have to do it all at once.  Start with your next important analytics projects.  Build it out the right way.  Then expand your competence to the next project.

For more information see:

“How to organize the data-ready enterprise”

“What it takes to deliver advanced analytics”

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Put Yourself Ahead of Digital Transformation Curve – Informatica World 2015

The rapid advancement we are seeing in social, mobile and other digital technologies have transformed the way of our life significantly. My commute to airport for business travel takes three taps on an app on my smart phone and a leading retailer I shop frequently, knows what I like, which product I have put on the cart using my iPad, which products I “liked” on their social channels so they can do real-time recommendations while I am at their physical store. Their employees are now armed with information that is integrated in ways that empower them be more customer-centric than ever before.

All these rapid changes just over a decade are bought to us by companies that pioneered the digital transformation and data is at the center of this revolution. These organizations gained competitive advantage from social, mobile, analytics, cloud, and internet of things technologies. In a world filled with data of different variety, volume and velocity, it’s more important for organizations to become data-ready. While the potential for insight in big data is massive, we need a new generation of Master Data Management to realize all the potential.

In the contrast of this rapid change fueled by data, growing number of companies are realizing that they now have a massive opportunity in front of them. At the centers of this digital transformation is master data that provides an opportunity for organizations to:

  • Better understand customers, their household and relationships so they can do effective cross-sell and up-sell
  • Identify customers interacting with company via different channels so they can push relevant offer to these customers in real time.
  • Offer better product recommendations to customers based on their purchasing and browsing behavior
  • Optimize the way they manage their inventory leading to significant cost savings
  • Manage supplier relationships more effectively so they can negotiate better rate
  • Provide superior patient care and cure harmful deceases at early stage by creating patient-centric solutions that connect health information from more and more sources
  • Master wellhead and other upstream exploration and production assets so they can do better crew allocation and production planning
  • Be compliant to complex and ever changing government regulations leading to significant savings in terms of fines and punishments

We will talk about all this and more at Informatica World 2015 which is happening next week in Las Vegas. Join us for the MDM Day on May 12 followed by Information Quality and Governance track sessions on May 13 and 14. Register now.

We have 37 sessions that cover Master Data Management, Omnichannel Commerce, Data Quality, Data as a Service and Big Data Relationship Management. You get a chance to learn from Informatica’s customers about their experience, best practices from our partners and our vision and roadmap straight from our product management team. We will also talk about master data fueled Total Customer Relationship and Total Supplier Relationship applications that leverage our industry leading multidomain MDM platform.

Informatica world 2015

Here is your guide to sessions that will be covered. I will see you there. If you want to say hello in person, reach out to me at @MDMGeek and follow @InfaMDM twitter handle for all the latest news. The hash tag for this event is #INFA15

~Prash
@MDMGeek
www.mdmgeek.com

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Healthcare at Informatica World 2015

Healthcare at Informatica World 2015

Informatica World 2015

If you follow me on LinkedIn than you already know that there is no place I would rather be than in front of a client – virtually or in person. There is simply nothing that energizes me more than gathering the insights from client advocates. With this said, it will be no surprise that Informatica World makes me giddy; like a kid in a candy store – over 1500 clients telling their stories and sharing valuable lessons learned.

For healthcare alone, over a dozen payer and provider organizations have volunteered to share their use cases, their stories and their lessons learned. The array of brands represented is second to none; i.e. Kaiser, UPMC, Cleveland Clinic and Humana.

Beyond sessions, clients ask for more opportunities to network with peers and get hands on with the next releases of products and we listen!

  • Healthcare cocktail reception Tuesday evening
  • Healthcare Industry breakfast Thursday morning
  • Hands on Labs with industry specific content
  • Partner technology showcase

A complete list of healthcare sessions + a few you hot topic sessions is below. I look forward to seeing you in Las Vegas next week!

Informatica World 2015 - Healthcare

Informatica World 2015 – Healthcare

 

 

 

 

 

 

 

 

 

 

 

 

 

Informatica World 2015 - Healthcare

Informatica World 2015 – Healthcare

 

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Succeeding with Analytics in a Big Data World

shutterstock_227687962 (1) - CopyBig data is receiving a lot of press these days including from this author.  While there continues to be constructive dialog regarding whether volume, velocity, or variety are the most important attributes of big data movement, one thing is clear. Constructed correctly, big data has the potential to transform businesses by increasing sales and operational efficiencies. More importantly, when big data is combined with predictive analytics, big data can improve customer experience, enable better targeting of potential customers, and improve the core business capabilities that are foundational to a business’s right to win.

The problem many in the vanguard have discovered is their big data projects are fraught with risk if they are not built upon a solid data management foundation.  During the Big Data Summit, you will learn directly for the vanguard of big data. How have they successfully transition from the traditional world of data management to a new world of big data analytics. Hear from market leading enterprises like Johnson and Johnson, Transamerica, Devon Energy, KPN, and Western Union. As well, hear from Tom Davenport, Distinguished Professor in Management and Information Technology at Babson College and the bestselling author of “Competing on Analytics” and “Big Data at Work”. Tom will share in particular his perspective from interviewing hundreds of companies about the successes and failures of their big data initiatives. Tom Davenport initially thought big data was just another example of technology hype. But his research on big data changed his mind. And finally hear from big data thought leaders including Cloudera, Hortonworks, Cognizant, and Capgemini. They are all here to share their stories on how to avoid common pitfalls and accelerate your analytical returns in a big data world.

To attend in person, please join us on Tuesday the 12th at 1:30 in Las Vegas at the Big Data Summit. If you cannot join us in person, I will be share live tweets and videos through twitter starting at 1:30 PST. Look for me at @MylesSuer on twitter to follow along.

Related Blogs

What is Big Data and why should your business care?
Big Data: Does the emperor have their clothes on?
Should We Still be calling it Big Data?
CIO explains the importance of Big Data to healthcare
Big Data implementations need a systems view and to put in place trustworthy data.
The state of predictive analytics
Analytics should be built upon Business Strategy
Analytics requires continuous improvement too?
When should you acquire a data scientist or two?
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”
Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study

Author Twitter: @MylesSuer

 

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Come to Informatica World 2015 and Advance Your Career

INFA15- Data Integration

Come to Informatica World 2015 and Advance Your Career

5 Reasons Data Integration Professionals Absolutely Must Not Miss This Informatica World.

If you are a Data Integration or Data Management professional, you really cannot afford to miss this event.  This year’s theme in the Data Integration track at Informatica World is all about customers.  Over 50 customers will be sharing their experiences and best practices for succeeding with for data integration projects such as analytics, big data, application consolidation and migration, and much more.

If you still need convincing, here are the five reasons:

  1. Big Data:A special Big Data Summit is part of the track.
  2. Immediate Value:over 50 customers will be sharing their experience and best practices. Things you can start doing now to improve your organization.
  3. Architecture for Business Transformation. An architecture track focused on practical approaches for using architecture to enable business transformation, with specific examples and real customer experiences.
  4. Hands on Labs:Everybody loves them. This year we have even more. Sign up early to make sure that you get your choice. They go fast!
  5. New “Meet the Experts” Sessions:These are small group meetings for business-level discussions around subjects like big data, analytics, application consolidation, and more.

This truly will be a one-stop shop for all things data integration at Informatica World.  The pace of both competition and technology change is accelerating.  Attend this event to stay on top of what is happening in the word of data integration and how leading companies and experts are using data for competitive advantage within their organizations.

To help start your planning, here is a listing of the Data Integration, Architecture, and Big Data Sessions this year.  I hope to see you there.

QUICK GUIDE

DATA INTEGRATION AND BIG DATA at INFORMATICA WORLD 2015

Breakout Sessions, Tuesday, May 13

Session Time Location
Accelerating Business Value Delivery with Informatica Platform  (Architect Track Keynote) 10:45am – 11:15am Gracia 6
How to Support Real Time Data Integration Projects with PowerCenter (Grant Thornton) 1:30pm – 2:30pm Gracia 2
Knowledgent 11:30am – 12:15pm Gracia 8
Putting Big Data to Work to Make Cancer History at MD Anderson Cancer Center (MD Anderson) 11:30am – 12:15pm Gracia 4
Modernize your Data Architecture for Speed, Efficiency, and Scalability 11:30am – 12:15pm Castellana 1
An Architectural Approach to Data as an Asset (Cisco) 11:30am – 12:15pm Gracia 6
Accenture 11:30am – 12:15pm Gracia 2
Architectures for Next-Generation Analytics 1:30pm – 2:30pm Gracia 6
Informatica Marketplace (Tamara Strifler) 1:30pm – 2:30pm Gracia 4
Informatica Big Data Ready Summit: Keynote Address (Anil Chakravarthy, EVP and Chief Product Officer) 1:40 – 2:25 Castellana 1
Big Data Keynote: Tom Davenport, Distinguished Professor in Management and Information Technology, Babson College 2:30 – 3:15 Castellana 1
How to Test and Monitor Your Critical Business Processes with PowerCenter (Discount Tire, AT&T) 2:40pm – 3:25pm Gracia 2
Enhancing Consumer Experiences with Informatica Data Integration Hub (Humana) 2:40pm – 3:25pm Gracia 4
Business Transformation:  The Case for Information Architecture (Cisco) 2:40pm – 3:25pm Gracia 6
Succeeding with Big Data and Avoiding Pitfalls (CapGemini, Cloudera, Cognizant, Hortonworks) 3:15 – 3:30
What’s New in B2B Data Exchange: Self-Service Integration of 3rd Party Partner Data (BMC Software) 3:35pm – 4:20pm Gracia 2
PowerCenter Developer:  Mapping Development Tips & Tricks 3:35pm – 4:20pm Gracia 4
Modernize Your Application Architecture and Boost Your Business Agility (Mototak Consulting) 3:35pm – 4:20pm Gracia 6
The Big Data Journey: Traditional BI to Next Gen Analytics (Johnson&Johnson, Transamerica, Devon Energy, KPN) 4:15 – 4:30 Castellana 1
L&T Infotech 4:30 – 5:30 Gracia 2
What’s New in PowerCenter, PowerCenter Express and PowerExchange? 4:30 – 5:30 Gracia 4
Next-Generation Analytics Architecture for the Year 2020 4:30 – 5:30 Gracia 6
Accelerate Big Data Projects with Informatica (Jeff Rydz) 4:35 – 5:20 Castellana 1
Big DataMichael J. Franklin, Professor of Computer Science, UC Berkeley 5:20 -5:30 Castellana 1
  • Informatica World Pavilion5:15 PM – 8:00 PM

Breakout Sessions, Wednesday, May 14

Session Time Location
How Mastercard is using a Data Hub to Broker Analytics Data Distribution (Mastercard) 2:00pm – 2:45pm Gracia 2
Cause: Business and IT Collaboration Effect: Cleveland Clinic Executive Dashboard (Cleveland Clinic) 2:00pm – 2:45pm Castellana 1
Application Consolidation & Migration Best Practices: Customer Panel (Discount Tire, Cisco, Verizon) 2:55pm – 3:55pm Gracia 2
Big Data Integration Pipelines at Cox Automotive (Cox Automotive) 2:55pm – 3:55pm Gracia 4
Performance Tuning for PowerCenter and Informatica Data Services 2:55pm – 3:55pm Gracia 6
US Bank and Cognizant 2:55pm – 3:55pm Castellana 1
Analytics architecture (Teradata, Hortonworks) 4:05pm – 4:50pm Gracia 4
A Case Study in Application Consolidation and Modernization—Migrating from Ab Initio to Informatica (Kaiser Permanente) 4:05pm – 4:50pm Castellana 1
Monetize Your Data With Hadoop and Agile Data Integration (AT&T) 4:05pm – 4:50pm Gracia 2
How to Enable Advanced Scaling and Metadata Management with PowerCenter (PayPal) 5:00pm – 5:45pm Castellana 1
How Verizon is consolidating 50+ legacy systems into a modern application architecture, optimizing Verizon’s enterprise sales and delivery process (Verizon) 5:00pm – 5:45pm Gracia 6
A guided tour to one of the most complex Informatica Installations worldwide (HP) 5:00pm – 5:45pm Gracia 2
Integration with Hadoop:  Best Practices for mapping development using Big Data Edition 5:00pm – 5:45pm Gracia 4

Meet The Experts Sessions, Wednesday, May 14

Session Time Location
Meet the Expert: App Consolidation – Driving Greater Business Agility and Reducing Costs Through Application Consolidation and Migration (Roger Nolan) 12:00pm – 12:50pm, 1:00pm – 1:50pm and 2:55pm – 3:55pm Castelena 2
Meet the Expert: Big Data – Delivering on the Promise of Big Data Analytics (John Haddad) 12:00pm – 12:50pm, 1:00pm – 1:50pm and 2:55pm – 3:55pm Castelena 2
Meet the Expert: Architect – Laying the Architectural Foundation for the Data-Driven Enterprise (David Lyle) 12:00pm – 12:50pm, 1:00pm – 1:50pm and 2:55pm – 3:55pm Castelena 2
  • Informatica World Pavilion11:45 PM – 2:00 PM

Breakout Sessions, Thursday, May 15

Session Time Location
Enterprise Architecture and Business Transformation Panel  (Cisco) 9:00am – 10:00am Gracia 6
The Data Lifecycle: From infancy through retirement, how Informatica can help (Mototak Consulting) 9:00am – 10:00am Gracia 4
How Allied Solutions Streamlined Customer Data Integration using B2B Data Exchange (Allied Solutions) 9:00am – 10:00am Gracia 2
How the State of Washington and Michigan State University are Delivering Integration as a Service (Michigan State University, Washington State Department of Enterprise Services) 9:00am – 10:00am Gracia 1
Real Time Big Data Streaming Analytics (PRA Group) 10:10am – 11:10am Gracia 1
Extending and Modernizing Enterprise Data Architectures (Philip Russom, TDWI) 10:10am – 11:10am Gracia 4
Best Practices for Saving Millions by Offloading ETL/ELT to Hadoop with Big Data Edition and Vibe Data Stream (Cisco) 10:10am – 11:10am Gracia 2
Retire Legacy Applications – Improve Your Bottom-Line While Managing Compliance (Cisco) 11:20am – 12:20pm Gracia 4
How a Data Hub Reduces Complexity, Cost and Risk for Data Integration Projects 11:20am – 12:20pm Gracia 1
Title? (Cap Gemini) 11:20am – 12:20pm Gracia 2
What’s New in PowerCenter, PowerCenter Express and PowerExchange? 2:30pm – 3:30pm Gracia 4
Title?  Keyur Desai 2:30pm – 3:30pm Gracia 2
How to run PowerCenter & Big Data Edition on AWS & connect Data as a Service (Customer) 2:30pm – 3:30pm Gracia 1
Accelerating Business with Near-Realtime Architectures 2:30pm – 3:30pm Gracia 6
  • Informatica World Pavillion12:30 PM – 3:30 PM

Hands-On Labs

Session Time Location
General Interest
PowerCenter 9.6.1 Upgrade 1 Table 01
PowerCenter 9.6.1 Upgrade 2 (repeat) Table 02
PowerCenter Advanced Edition – High Availability & Grid Mon 1:00, 3:00
Tue 7:30, 11:45, 2:40, 4:25
Wed 10:45, 12:45, 2:55, 5:00, 7:00
Thu 9:00, 11:20, 1:15
Fri 7:30, 9:30, 11:30
Table 03a
PowerCenter Advanced Edition – Metadata Manager & Business Glossary Mon 2:00, 4:00
Tue 10:45, 1:45, 3:35
Wed 7:30, 11:45, 2:00, 4:05, 6:00
Thu 7:30, 10:10, 12:15, 2:15
Fri 8:30, 10:30
Table 03b
Data Archive Mon 1:00, 3:00
Tue 7:30, 11:45, 2:40, 4:25
Wed 10:45, 12:45, 2:55, 5:00, 7:00
Thu 9:00, 11:20, 1:15
Fri 7:30, 9:30, 11:30
Table 06a
Test Data Management Mon 2:00, 4:00
Tue 10:45, 1:45, 3:35
Wed 7:30, 11:45, 2:00, 4:05, 6:00
Thu 7:30, 10:10, 12:15, 2:15
Fri 8:30, 10:30
Table 06b
Analytics- Related
PowerCenter Big Data Edition – Delivering on the Promise of Big Data Analytics All other times not taken by 11b. Table 11a
Elastic Analytics:  Big Data Edition in the Cloud Mon 4:00
Tue 11:45, 3:35
Wed 12:45, 5:00, 7:00
Thu  9:00;1:15;2:15
Fri 10:30
Table 11b
Greater Agility and Business-IT Collaboration using Data Virtualization Mon 1:00, 3:00
Tue 7:30, 11:45, 2:40, 4:25
Wed 10:45, 12:45, 2:55, 5:00, 7:00
Thu 9:00, 11:20, 1:15
Fri 7:30, 9:30, 11:30
Table 12a
Boosting your performance and productivity with Informatica Developer Mon 2:00, 4:00
Tue 10:45, 1:45, 3:35
Wed 7:30, 11:45, 2:00, 4:05, 6:00
Thu 7:30, 10:10, 12:15, 2:15
Fri 8:30, 10:30
Table 12b
Democratizing your Data through the Informatica Data Lake Table 13
Enabling Self-Service Analytics with Informatica Rev Table 14
Real-time Data Integration: PowerCenter Architecture & Implementation Considerations Monday 1pm
Tuesday 7:30am, 1:45pm
Wed 7:30, 2:00, 4:05
Thu 9am, 11:20am
Fri 8:30am
Table 15a
Real-time Data Integration: PowerExchange CDC on z/OS Monday 2pm
Tue 10:45, 2:40
Wed 10:45, 5pm
Thu 12:15pm
Fri 9:30am
Table 15b
Real-time Data Integration: PowerExchange CDC on i5/OS Monday 3pm
Tuesday 3:35pm
Wed 11:45am, 6pm
Thu 1:15pm
Fri 10:30am
Table 15c
Real-time Data Integration: PowerExchange CDC for Relational (Oracle, DB2, MS-SQL) Mon 4pm
Tue 11:45am, 4:25pm
Wed 12:45pm, 2:55pm, 7pm
Thu 7:30am, 10:10am, 2:15pm
Fri 7:30am, 11:30am
Table 15d
Healthcare Data Management and Modernization for Healthcare Providers Table 16
Data Management of Machine Data & Internet of Things Mon 1:00, 3:00
Tue 7:30, 11:45, 2:40, 4:25
Wed 10:45, 12:45, 2:55, 5:00, 7:00
Thu 9:00, 11:20, 1:15
Fri 7:30, 9:30, 11:30
Table 17a
Handling Complex Data Types with B2B Data Transformation Mon 2:00, 4:00
Tue 10:45, 1:45, 3:35
Wed 7:30, 11:45, 2:00, 4:05, 6:00
Thu 7:30, 10:10, 12:15, 2:15
Fri 8:30, 10:30
Table 17b
Application Consolidation & Migration Related
Simplifying Complex Data Integrations with Data Integration Hub Table 18
Implementing Trading Partner Integration with B2B Data Exchange Table 19
Operationalizing and Scaling your PowerCenter Environment Mon 1pm, 2pm
Tue 7:30, 10:45, 2:40, 3:35
Wed 10:45, 12:45, 5pm, 6pm, 7pm
Thu 7:30, 9am, 11:20, 1:15
Fri 7:30, 9:30, 11:30
Table 20a
Effective Operations management and Administration – What’s New M: 3:00 – 3:45pm
4:00 – 4:45pm
Tu: 11:45 – 12:30pm
1:45 – 2:30pm
4:25 – 5:15pm
W: 7:30 – 8:15am
11:45 – 12:30pm
2:55 – 3:40pm
4:05 – 4:50pm
Th: 10:10 – 10:55am
12:15 – 1:00pm
2:15 – 3:00pm
F:  8:30 – 9:15am
10:30 – 11:15am
Table 20b
Getting the Most out of your Data Integration & Data Quality Platform – Performance and Scalability Tips & Tricks Mon 1:00, 3:00
Tue 7:30, 11:45, 2:40, 4:25
Wed 10:45, 12:45, 2:55, 5:00, 7:00
Thu 9:00, 11:20, 1:15
Fri 7:30, 9:30, 11:30
Table 21a
Getting the Most out of your BigData Edition – Performance Best Practices Mon 2:00, 4:00
Tue 10:45, 1:45, 3:35
Wed 7:30, 11:45, 2:00, 4:05, 6:00
Thu 7:30, 10:10, 12:15, 2:15
Fri 8:30, 10:30
Table 21b
Modernizing and Consolidating Legacy and Application Data: Leveraging Data Services, Data Quality and Data Explorer Mon 1:00
Tue 10:45,  2:40, 4:25
Wed 10:45, 2:00, 2:55, 4:05, 7:00
Thu 11:20, 2:15 PM
Fri  9:30AM, 10:30AM
Table 22a
Connect to *: Connectivity to Long Tail of Next Generation Data Sources Mon 2:00, 3:00pm
Tue  7:30AM, 11:45, 1:45
Wed 7:30AM,  12:45pm,, 5:00pm
Thu 9:00am, 10:10am, 1:15pm
Fri 7:30am,8:30AM,
Table 22b
Modernizing and Consolidating Legacy and Application Data with PowerExchange Mainframe and CDC Mon 4:00PM
Tue 3:35
Wed 11:45, 6:00
Thu 7:30AM, 12:15, 2:15
Fri 11:30
Table 22c
Retire Legacy Applications and Optimize Application Performance with Informatica Data Archive Table 23
Protect Salesforce Sandboxes with Cloud Data Masking Tue 3:35, 4:25
Wed 6:00, 7:00
Thu 1:15, 2:15
Fri 7:30
Table 24a
Optimally Provision Test Data Sets with Test Data Management Mon: all times Monday
Tues: 7:30,10:45, 11:45, 1:45, 2:40
Wed: 7:30, 10:45, 11:45, 12:45, 2:00, 2:55, 4:05, 5:00
Thurs: 7:30, 9:00, 10:10, 11:20, 12:15
Fri: 8:30, 9:30, 10:30, 11:30
Table 24b
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How Do You Know if Your Business is Not Wasting Money on Big Data?

big-data

Smart Big Data Strategy

While CIOs are urged to rethink of backup strategies following warnings from leading analysts that companies are wasting billions on unnecessary storage, consultants and IT solution vendors are selling “Big Data” narratives to these CIOs as a storage optimization strategy.

 

What a CIO must do is ask:

Do you think a Backup Strategy is same as a Big Data strategy?

Is your MO – “I must invest in Big Data because my competitor is”?

Do you think Big Data and “data analysis” are synonyms?

Most companies invest very little in their storage technologies, while spending on server and network technologies primarily for backup. Further, the most common mistake businesses make is to fail to update their backup policies. It is not unusual for companies to be using backup policies that are years or even decades old, which do not discriminate between business-critical files and the personal music files of employees.

Web giants like Facebook and Yahoo generally aren’t dealing with Big Data. They run their own giant, in-house “clusters” – collections of powerful servers – for crunching data. But, it appears that those clusters are unnecessary for many of the tasks which they’re handed. In the case of Facebook, most of the jobs engineers ask their clusters to perform are in the “megabyte to gigabyte” range, which means they could easily be handled on a single computer – even a laptop.

The necessity of breaking problems into many small parts, and processing each on a large array of computers, characterizes classic Big Data problems like Google’s need to compute the rank of every single web page on the planet.

In, Nobody ever got fired for buying a cluster, Microsoft Research points out that a lot of the problems solved by engineers at even the most data-hungry firms don’t need to be run on clusters. Why is that a problem? It is because, there are vast classes of problems for which these clusters are relatively inefficient, or a very inappropriate, solution.

Here is an example of a post exhorting readers to “Incorporate Big Data Into Your Small Business” that is about a quantity of data that probably wouldn’t strain Google Docs, much less Excel on a single laptop. In other words, most businesses are in dealing with small data. It’s very important stuff but it has little connection to the big kind.

Let us lose the habit of putting “big” in front of data to make it sound important. After all, supersizing your data, just because you can, is going to cost you a lot more and may yield a lot less.

So what is it? Big Data, small Data, or Smart Data?

Gregor Mendel uncovered the secrets of genetic inheritance with just enough data to fill a notebook. The important thing is gathering the right data, not gathering some arbitrary quantity of it.

Twitter @bigdatabeat

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When Should You Acquire a Data Scientist or Two?

Data Science should change how your businesses are run

data scienceThe importance of data science is becoming more and more clear. Marc Benioff says, “I think for every company, the revolution in data science will fundamentally change how we run our business”. “There’s just a huge amount more data than ever before, our greatest challenge is making sense of that data”. He goes on to say that “we need a new generation of executives who understand how to manage and lead through data. And we also need a new generation of employees who are able to help us organize and structure our business around data”. Mark then says “when I look at the next set of technologies that we have to build at Salesforce, it is all data science based technology.” Ram Charan in his article in Fortune Magazine “says to thrive, companies—and the execs who run them, must transform into math machines” (The Algorithmic CEO, Fortune Magazine, March 2015, page 45).

With such powerful endorsements for data science, the question you may be asking is when should you hire a data scientist or two. The answer has multiple answers. I liken data science to any business research. You need to do your upfront homework for the data scientists you hire to be effective.

swotCreate a situation analysis before you start

You need to start by defining your problem—are you losing sales, finding it takes too long to manufacturer something, less profitable than you would like to be, and the list goes on. Next, you should create a situational analysis. You want to arm your data scientists with as much information as possible to define what you want them to solve or change. Make sure that you are as concrete as possible here. Data scientists struggle when the business people that they work with are vague. As well, it is important that you indicate what kinds of business changes will be considered if the model and data deliver this results or that result.

Next you need to catalog the data that you already have which is relevant to the business problem. Without relevant data there is little that the data scientist can do to help you. With relevant data sources in hand, you need to define the range of actions that you can possibly take once a model has been created.

Be realistic about what is required

With these things in hand, it may be time to hire some data scientists. As you start your process, you need to be realistic about the difficulty of getting a top flight data scientist. Many of my customers have complained about the difficulty competing with Google and other tech startups. As important, “there is a huge variance in the quality and ability of data scientists”. (Data Science for Business, Foster Provost, O’Reilly, page 321). Once you have hired someone, you need to keep in mind that effective data science requires business and data science collaboration. As well, please know that data scientist struggle when business people don’t appreciate the effort needed to get an appropriate training data set or model evaluation procedures.

proposalMake sure internal or external data scientists give you an effective proposal

Once Once your data scientists are in place, you should realize that a data scientist worth their salt will create a proposal back to you. As we have said, it is important that you know what kinds of things will happen if the model and data delivery this results or that result. Data scientist in turn will be able to narrow things down to a dollar impact.

Their proposal should start by sharing their understanding of the business and the data which is available. What business problems are they trying to solve? Next the data scientist may define things like whether supervised or unsupervised learning will be used. Next they should openly discuss what efforts will be involved in data preparation. They should tell you here about the values for the target variable (whose values will be predicted). They should describe next their modeling approach and whether more than one model is be evaluated and then how models will be compared and final model be selected. And finally, they should discuss how the model will be evaluated and deployed. Are there evaluation and setup metrics? Data scientists can dedicate time and resources in their proposal to determining what things are real versus expected drivers.

To make all this work, it can be a good idea for data scientist to talk in their proposal about likelihood because business people that have not been through a quantitative MBA do not understand or remember statistics. It is important as well that data scientist before they begin ask business people the so what questions if the situation analysis is inadequate.

analyticsLeading an internal analytics team

In some cases, analytical teams will be built internally. Where this occurs, it is really importantly that the analytic leader have good people skills. They need as well to be able to set expectations that people will be making decisions from data and analysis. This includes having the ability to push back when someone comes to them will a recommendation based on gut feel.

The leader needs to hire smart analysts. To keep them, they need a stimulating and supportive work environment. Tom Davenport says analysts are motivated by interesting and challenging work that allows them to utilize their highly specialize skills. Like millenials, money is nice for analysts but they are more motivated more by exciting work and having the opportunity to grow and stretch their skills. Please know that data scientists want to spend time refining analytical models rather than doing simple analyses and report generation. Most importantly they want to do important work that makes a meaningful contribution. To do this, they want to feel supported and valued but have autonomy at work. This includes the freedom to organize their work. At the same time, analysts like to work together. And they like to be surrounded by other smart and capable collogues. Make sure to treat your data scientists as a strategic resource. This means you need development plans, career plans, and performance management processes.

Parting remarks

As we have discussed, make sure to do your homework before contracting or hiring for data scientists. Once you have done your homework, if you are an analytic leader, make sure that you create a stimulating environment. Additionally, prove the value of analytics by signing up for results that demonstrate data modeling efficacy. To do this, look here for business problems that will lead to a big difference. And finally if you need an analytics leader to emulate, look no further than Brian Cornell, the new CEO of Target.

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Myles in Twitter: @MylesSuer

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