John Haddad

John Haddad

How Did Humanity Emerge from the Data Dark Ages?

A hundred years from now people will look back at this period of time and refer to it as the Data Dark Ages.  A time when the possibilities were endless but due to siloed data fiefdoms and polluted data sets the data science warlords and their minions experienced an insatiable hunger for data and rampant misinformation driving them to the brink of madness.   The minions spent endless hours in the dark dungeons preparing data from raw and untreated data sources for their data science overseers.  Solutions to the worlds’ most vexing problems were solvable if only the people had abundant access to clean and safe data to drive their analytic engines.

Legend held that a wizard in the land of Informatica possessed the magic of a virtual data machine called Vibe where a legion of data engineers built an intelligent data platform to provide a limitless supply of clean, safe, secure, and reliable data.  While many had tried to build their own data platforms only those who acquired the Informatica Intelligent Data Platform powered by Vibe were able to create true value and meaning from all types of data.

As word spread about Informatica Vibe and the Intelligent Data Platform data scientists and analysts sought its magic so they could have greater predictive power over the future.  The platform could feed any type of data of any volume into a data lake where Vibe, no matter the underlying technology, prepared and managed the data, and provisioned data to the masses hungry for actionable and reliable information.

An analytics renaissance soon emerged as more organizations adopted the Informatica Intelligent Data Platform where data was freely yet securely shared, integrated and cleansed at will, matched and correlated in real-time.  The data prep minions were set free and data scientists were able to spend the majority of their time discovering true value and meaning through big data analytics.  The pace of innovation accelerated and humanity enjoyed a new era of peace and prosperity.

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Data Warehouse Optimization: Not All Data is Created Equal

Data Warehouse Optimization (DWO) is becoming a popular term that describes how an organization optimizes their data storage and processing for cost and performance while data volumes continue to grow from an ever increasing variety of data sources.

Data warehouses are reaching their capacity much too quickly as the demand for more data and more types of data are forcing IT organizations into very costly upgrades.  Further compounding the problem is that many organizations don’t have a strategy for managing the lifecycle of their data.  It is not uncommon for much of the data in a data warehouse to be unused or infrequently used or that too much compute capacity is consumed by extract-load-transform (ELT) processing.  This is sometimes the result of business requests for one off business reports that are no longer used or staging raw data in the data warehouse.  A large global bank’s data warehouse was exploding with 200TB of data forcing them to consider an upgrade that would cost $20 million.  They discovered that much of the data was no longer being used and could be archived to lower cost storage thereby avoiding the upgrade and saving millions.  This same bank continues to retire data monthly resulting in on-going savings of $2-3 million annually.  A large healthcare insurance company discovered that fewer than 2% of their ELT scripts were consuming 65% of their data warehouse CPU capacity.  This company is now looking at Hadoop as a staging platform to offload the storage of raw data and ELT processing freeing up their data warehouse to support the hundreds of concurrent business users.  A global media & entertainment company saw their data increase by 20x per year and the associated costs increase 3x within 6 months as they on-boarded more data such as web clickstream data from thousands of web sites and in-game telemetry data.

In this era of big data, not all data is created equal with most raw data originating from machine log files, social media, or years of original transaction data considered to be of lower value – at least until it has been prepared and refined for analysis. This raw data should be staged in Hadoop to reduce storage and data preparation costs while the data warehouse capacity should be reserved for refined, curated and frequently used datasets.  Therefore, it’s time to consider optimizing your data warehouse environment to lower costs, increase capacity, optimize performance, and establish an infrastructure that can support growing data volumes from a variety of data sources.  Informatica has a complete solution available for data warehouse optimization.

The first step in the optimization process as illustrated in Figure 1 below is to identify inactive and infrequently used data and ELT performance bottlenecks in the data warehouse.  Step 2 is to offload the data and ELT processing identified in step 1 to Hadoop.  PowerCenter customers have the advantage of Vibe which allows them to map once and deploy anywhere so that ELT processing executed through PowerCenter pushdown capabilities can be converted to ETL processing on Hadoop as part of a simple configuration step during deployment.  Most raw data, such as original transaction data, log files (e.g. Internet clickstream), social media, sensor device, and machine data should be staged in Hadoop as noted in step 3.  Informatica provides near-universal connectivity to all types of data so that you can load data directly into Hadoop.  You can even replicate entire schemas and files into Hadoop, capture just the changes, and stream millions of transactions per second into Hadoop such as machine data.  The Informatica PowerCenter Big Data Edition makes every PowerCenter developer a Hadoop developer without having to learn Hadoop so that all ETL, data integration and data quality can be executed natively on Hadoop using readily available resource skills while increasing productivity up to 5x over hand-coding.  Informatica also provides data discovery and profiling tools on Hadoop to help data science teams collaborate and understand their data.  The final step is to move the resulting high value and frequently used data sets prepared and refined on Hadoop into the data warehouse that supports your enterprise BI and analytics applications.

To get started, Informatica has teamed up with Cloudera to deliver a reference architecture for data warehouse optimization so organizations can lower infrastructure and operational costs, optimize performance and scalability, and ensure enterprise-ready deployments that meet business SLA’s.  To learn more please join the webinar A Big Data Reference Architecture for Data Warehouse Optimization on Tuesday November 19 at 8:00am PST.


Figure 1:  Process steps for Data Warehouse Optimization

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Informatica + MongoDB is a powerful combo for Big Data applications

The power of big data means you can access and analyze all of your data.  Several of the worlds’ top companies and government agencies use MongoDB for applications today which means more and more data is being stored in MongoDB.  Informatica ensures that you can access and integrate all of this vital data with other enterprise data.

To make it easy for developers to get data into MongoDB, Informatica provides a visual development environment with near universal connectivity including MongoDB, pre-built parsers and transforms to ingest data into MongoDB.  With Informatica and MongoDB, developers waste no time accessing and preparing the data necessary to build their big data applications at scale.  Informatica can access all types of data from traditional relational databases, legacy mainframes, enterprise applications such as ERP and CRM, cloud applications, social data, machine data, and industry standards data.  Once the data is accessed you can integrate and transform it into the native MongoDB JSON document format.   For example, a large insurance company is moving massive amounts of policy information from a dozen relational data sources, transforming from relational to hierarchical JSON documents and populating MongoDB.

Informatica ensures that MongoDB does not become another data silo in your enterprise information management infrastructure.  Now with Informatica, companies can unlock the data in MongoDB for downstream analytics to improve decision making and business operations.  Using the Informatica PowerCenter Big Data Edition with the PowerExchange for MongoDB adapter you can access data in MongoDB, parse the JSON-based documents and then transform the data and combine it with other information for big data analytics all without having to write a single-line of code.  Informatica + MongoDB is a powerful combination that increases developer productivity up to 5x so you can build and deploy big data applications much faster.

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The Safe On-Ramp to Big Data

The hype around big data is certainly top of mind with executives at most companies today but what I am really seeing are companies finally making the connection between innovation and data. Data as a corporate asset is now getting the respect it deserves in terms of a business strategy to introduce new innovative products and services and improve business operations. The most advanced companies have C-level executives responsible for delivering top and bottom line results by managing their data assets to their maximum potential. The Chief Data Officer and Chief Analytics Officer own this responsibility and report directly to the CEO. (more…)

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How to Avoid the Big Data Trough of Disillusionment

Has big data entered the “trough of disillusionment?” That’s what I’ve heard recently. Like  many hyped up technology trends the trough can be deep and long as project failures accumulate, or for ‘hot’ trends that evolve and mature quickly the trough can be shallow and short, leading to broader and rapid adoption. Is the big data hype failing to deliver on its promise of increased revenue and competitive advantage for companies that leverage big data to introduce new products and services and improve business operations? Why is it that some big data projects fail to deliver on their promise? Svetlana Sicular, Research Director, Gartner points out in her blog Big Data is Falling into the Trough of Disillusionment that, “These [advanced client] organizations have fascinating ideas, but they are disappointed with a difficulty of figuring out reliable solutions.” There are several reasons why big data projects may fail to deliver on their promise: (more…)

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Building the Business Case for Big Data: Learn to Walk Before You Run

In a recent webinar, Mark Smith, CEO at Ventana Research and David Lyle, vice president, Product Strategy at Informatica discussed: “Building the Business Case and Establishing the Fundamentals for Big Data Projects.”  Mark pointed out that the second biggest barrier that impedes improving big data initiatives is that the “business case is not strong enough.” The first and third barriers respectively, were “lack of resources” and “no budget” which are also related to having a strong business case. In this context, Dave provided a simple formula from which to build the business case:

Return on Big Data = Value of Big Data / Cost of Big Data (more…)

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Let Your Data Scientists Be Scientists

With the rise in popularity of the elusive and expensive data scientist it’s very sad that once a data science team is assembled (at a very high recurring cost to the company I may add) that they spend most of their time doing work they weren’t really hired to do in the first place. That’s right! It turns out that data scientists spend only about 20% of their time doing real analysis – that is the work they were trained to do. How is the other 80% of their time spent? (more…)

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Balancing Opportunity and Risk in Big Data

Informatica conducted a recent big data survey of 600 IT and business professionals in North America, Europe and the Asia-Pacific region. The results found in the report entitled: Balancing Opportunity and Risk in Big Data – A Survey of Enterprise Priorities and Strategies for Harnessing Big Data show that big data has clearly moved beyond all the hype with nearly 70 percent of organizations of all sizes considering, planning, testing, or running big data projects.  While the majority of organizations are focused on big transaction data and analytics, the survey shows a strong interest in social media information, unstructured content, industry-specific data, and machine-generated information from sensors and devices. (more…)

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Informatica 9.1 Supports Best Practices For Agile Data Integration

Informatica supports Agile Data Integration for Agile BI with best practices that encourage good data governance, facilitate business-IT collaboration, promote reuse & flexibility through data virtualization, and enable rapid prototyping and test-driven development.  Organizations that want to successfully adopt Agile Data Integration should standardize on the following best practices and leverage Informatica 9.1 to streamline the data integration process, improve data governance, and provide a flexible data virtualization architecture.

1. The business and IT work efficiently and effectively to translate requirements and specifications into data services (more…)

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Agile Data Integration Maximizes Business Value In The Era Of Big Data

Adopting Agile may require a cultural shift and in the beginning can be disruptive to an organization.  However, as I mentioned in Part 1 of this blog series, Agile Data Integration holds the promise to increase chances of success, deliver projects faster, and reduce defects.  Applying Lean principles within your organization can help ease the transition to Agile Data IntegrationLean is a set of principles first explored in the context of data integration by John Schmidt and David Lyle in their book on Lean Integration.  First and foremost Lean recommends an organization focus on eliminating waste and optimizing the data integration process from the customers’ perspective.  Agile Data Integration maximizes the business value of projects (e.g. Agile BI, Data Warehousing, Big Data Analytics, Data Migration, etc.) because you can get it right the first time by delivering exactly what the business needs when they need it.  Break big projects into smaller more manageable deliverables so that you can incrementally deliver value to the business.  Agile Data Integration also recommends the following: (more…)

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