Category Archives: Data Integration

More Evidence That Data Integration Is Clearly Strategic

Data Integration Is Clearly Strategic

Data Integration Is Strategic

A recent study from Epicor Software Corporation surveyed more than 300 IT and business decision-makers.  The study results highlighted the biggest challenges and opportunities facing Australian businesses. The independent research report “From Business Processes to Product Distribution” was based upon a survey of Australian organizations with more than 20 employees.

Key findings from the report include:

  • 65% of organizations cite data processing and integration as hampering distribution capability, with nearly half claiming their existing software and ERP is not suitable for distribution.
  • Nearly two-thirds of enterprises have some form of distribution process, involving products or services.
  • More than 80% of organizations have at least some problem with product or service distribution.
  • More than 50% of CIOs in organizations with distribution processes believe better distribution would increase revenue and optimize business processes, with a further 38% citing reduced operating costs.

The core findings: “With better data integration comes better automation and decision making.”

This report is one of many I’ve seen over the years that come to the same conclusion.  Most of those involved with the operations of the business don’t have access to key data points they need, thus they can’t automate tactical decisions, and also cannot “mine” the data, in terms of understanding the true state of the business.

The more businesses deal with building and moving products, the more data integration becomes an imperative value.  As stated in this survey, as well as others, the large majority cite “data processing and integration as hampering distribution capabilities.”

Of course, these issues goes well beyond Australia.  Most enterprises I’ve dealt with have some gap between the need to share key business data to support business processes, and decision support, and what current exists in terms of data integration capabilities.

The focus here is on the multiple values that data integration can bring.  This includes:

  • The ability to track everything as it moves from manufacturing, to inventory, to distribution, and beyond.  You to bind these to core business processes, such as automatic reordering of parts to make more products, to fill inventory.
  • The ability to see into the past, and to see into the future.  The emerging approaches to predictive analytics allow businesses to finally see into the future.  Also, to see what went truly right and truly wrong in the past.

While data integration technology has been around for decades, most businesses that both manufacture and distribute products have not taken full advantage of this technology.  The reasons range from perceptions around affordability, to the skills required to maintain the data integration flow.  However, the truth is that you really can’t afford to ignore data integration technology any longer.  It’s time to create and deploy a data integration strategy, using the right technology.

This survey is just an instance of a pattern.  Data integration was considered optional in the past.  With today’s emerging notions around the strategic use of data, clearly, it’s no longer an option.

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Posted in Data First, Data Integration, Data Integration Platform, Data Quality | Tagged , , , | Leave a comment

Ebola: Why Big Data Matters

Ebola: Why Big Data Matters

Ebola: Why Big Data Matters

The Ebola virus outbreak in West Africa has now claimed more than 4,000 lives and has entered the borders of the United States. While emergency response teams, hospitals, charities, and non-governmental organizations struggle to contain the virus, could big data analytics help?

A growing number of Data Scientists believe so.

If you recall the Cholera outbreak of Haiti in 2010 after the tragic earthquake, a joint research team from Karolinska Institute in Sweden and Columbia University in the US analyzed calling data from two million mobile phones on the Digicel Haiti network. This enabled the United Nations and other humanitarian agencies to understand population movements during the relief operations and during the subsequent cholera outbreak. They could allocate resources more efficiently and identify areas at increased risk of new cholera outbreaks.

Mobile phones, widely owned even in the poorest countries in Africa. Cell phones are also a rich source of data irrespective of which region where other reliable sources are sorely lacking. Senegal’s Orange Telecom provided Flowminder, a Swedish non-profit organization, with anonymized voice and text data from 150,000 mobile phones. Using this data, Flowminder drew up detailed maps of typical population movements in the region.

Today, authorities use this information to evaluate the best places to set up treatment centers, check-posts, and issue travel advisories in an attempt to contain the spread of the disease.

The first drawback is that this data is historic. Authorities really need to be able to map movements in real time especially since people’s movements tend to change during an epidemic.

The second drawback is, the scope of data provided by Orange Telecom is limited to a small region of West Africa.

Here is my recommendation to the Centers for Disease Control and Prevention (CDC):

  1. Increase the area for data collection to the entire region of Western Africa which covers over 2.1 million cell-phone subscribers.
  2. Collect mobile phone mast activity data to pinpoint where calls to helplines are mostly coming from, draw population heat maps, and population movement. A sharp increase in calls to a helpline is usually an early indicator of an outbreak.
  3. Overlay this data over censuses data to build up a richer picture.

The most positive impact we can have is to help emergency relief organizations and governments anticipate how a disease is likely to spread. Until now, they had to rely on anecdotal information, on-the-ground surveys, police, and hospital reports.

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Posted in B2B Data Exchange, Big Data, Business Impact / Benefits, Business/IT Collaboration, Data Governance, Data Integration | Tagged , , , , , , , | Leave a comment

Informatica Cloud Powers a New Era in Cloud Analytics with Salesforce Wave Analytics Cloud at Dreamforce 2014

We are halfway through Dreamforce and it’s been an eventful and awesome couple of days so far. The biggest launch by far was the announcement of Wave, the Salesforce Analytics Cloud, Salesforce’s new entry into Cloud analytics and business intelligence. Informatica has been the integration leader for enterprise analytics for 20 years, and our leadership continues with Cloud analytics, as our Informatica Cloud portfolio is the only solution that Completes Salesforce Analytics Cloud for Big Data, fully enabling companies to use Salesforce Analytics Cloud to understand their customers like never before. But don’t take our word for it, view the Analytics Cloud Keynote from Dreamforce 2014, and see Alex Dayon uniquely call out Informatica as their key integration partner during his keynote.

DIY Great Customer Data

DIY Great Customer Data

The Informatica Cloud Portfolio delivers a broad set of analytics-centric services for the Salesforce Analytics Cloud, including bulk and real time application integration, data integration, data preparation, test data management, data quality and master data management (MDM) services. The portfolio is designed for high volume data sets from transactional applications such as SAP, cloud applications like Workday and new data sources such as Hadoop, Microsoft Azure and Amazon Web Services.

We have a great booth in the Analytics Zone, Moscone West, 3rd floor, where you can see demos of Informatica Cloud for Salesforce Wave Analytics and get lots more details from product experts.

And, you don’t need to wait till Dreamforce is over to try out Informatica Cloud for Salesforce Analytics. The free trial of Informatica Cloud, including Springbok, for Salesforce Analytics Cloud is available now. Trial users have unlimited usage of Informatica Cloud capabilities for Salesforce Analytics Cloud for 60 days, free of charge.

Aside from new product launches, and tons of partner activities going on, we’ve also got some great customers speaking at DF. Today, we have a great session on “Get Closer to Your Customers Using Agile Data Management with Salesforce” with executive speakers from BT, Dolby and Travel Corporation explaining how they achieve customer insight with use cases ranging from integrating 9 Salesforce orgs into a single business dashboard to unifying 30+ acquired travel brands into a single customer view.

On Monday, we had Qualcomm and Warranty Group present how their companies have moved to the Cloud using Salesforce and Informatica Cloud to meet the agility needs of their businesses while simultaneously resolving the challenges of data scaling, organization complexity and evolving technology strategy to make it all happen.

Win $10k from Informatica!

Win $10k from Informatica!

Drop by our main booth in Moscone North, N1216 to see live demos showcasing solutions for Customer Centricity, Salesforce Data Lifecycle and Analytics Cloud. If you want a preview of our Informatica Cloud solutions for the Salesforce ecosystem, click here.

During Dreamforce, we also announced a significant milestone for Informatica Cloud, which now processes over 100 Billion transactions per month, on behalf of our 3,000+ joint customers with Salesforce.

Oh, and one more thing we announced at DF: the Informatica Cloud Data Wizard, our next-generation data loader for Salesforce, that delivers a beautifully simple user experience, natively inside Salesforce for non-technical business analysts and admins to easily bring external data into Salesforce with a one-touch UI, really!

For more information on how you can connect with Informatica at Dreamforce 2014, get all the details at informaticacloud.com/dreamforce

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Which Method of Controls Should You Use to Protect Sensitive Data in Databases and Enterprise Applications? Part II

Sensitive Data

Protecting Sensitive Data

To determine what is the appropriate sensitive data protection method to use, you should first answer the following questions regarding the requirements:

  • Do you need to protect data at rest (in storage), during transmission, and/or when accessed?
  • Do some privileged users still need the ability to view the original sensitive data or does sensitive data need to be obfuscated at all levels?
  • What is the granularity of controls that you need?
    • Datafile level
    • Table level
    • Row level
    • Field / column level
    • Cell level
    • Do you need to be able to control viewing vs. modification of sensitive data?
    • Do you need to maintain the original characteristics / format of the data (e.g. for testing, demo, development purposes)?
    • Is response time latency / performance of high importance for the application?  This can be the case for mission critical production applications that need to maintain response times in the order of seconds or sub-seconds.

In order to help you determine which method of control is appropriate for your requirements, the following table provides a comparison of the different methods and their characteristics.

data

A combination of protection method may be appropriate based on your requirements.  For example, to protect data in non-production environments, you may want to use persistent data masking to ensure that no one has access to the original production data, since they don’t need to.  This is especially true if your development and testing is outsourced to third parties.  In addition, persistent data masking allows you to maintain the original characteristics of the data to ensure test data quality.

In production environments, you may want to use a combination of encryption and dynamic data masking.  This is the case if you would like to ensure that all data at rest is protected against unauthorized users, yet you need to protect sensitive fields only for certain sets of authorized or privileged users, but the rest of your users should be able to view the data in the clear.

The best method or combination of methods will depend on each scenario and set of requirements for your environment and organization.  As with any technology and solution, there is no one size fits all.

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Posted in Data Integration, Data masking, Data Security, Data Services, Enterprise Data Management | Tagged , , , | Leave a comment

Which Method of Controls Should You Use to Protect Sensitive Data in Databases and Enterprise Applications? Part I

Sensitive Data

Protecting Sensitive Data

I’m often asked to share my thoughts about protecting sensitive data. The questions that typically come up include:

  • Which types of data should be protected?
  • Which data should be classified as “sensitive?”
  • Where is this sensitive data located?
  • Which groups of users should have access to this data?

Because these questions come up frequently, it seems ideal to share a few guidelines on this topic.

When protecting the confidentiality and integrity of data, the first level of defense is Authentication and access control. However, data with higher levels of sensitivity or confidentiality may require additional levels of protection, beyond regular authentication and authorization methods.

There are a number of control methods for securing sensitive data available in the market today, including:

  • Encryption
  • Persistent (Static) Data Masking
  • Dynamic Data Masking
  • Tokenization
  • Retention management and purging

Encryption is a cryptographic method of encoding data.  There are generally, two methods of encryption:  symmetric (using single secret key) and asymmetric (using public and private keys).  Although there are methods of deciphering encrypted information without possessing the key, a good encryption algorithm makes it very difficult to decode the encrypted data without knowledge of the key.  Key management is usually a key concern with this method of control.  Encryption is ideal for mass protection of data (e.g. an entire data file, table, partition, etc.) against unauthorized users.

Persistent or static data masking obfuscates data at rest in storage.  There is usually no way to retrieve the original data – the data is permanently masked.  There are multiple techniques for masking data, including: shuffling, substitution, aging, encryption, domain-specific masking (e.g. email address, IP address, credit card, etc.), dictionary lookup, randomization, etc.  Depending on the technique, there may be ways to perform reverse masking  - this should be used sparingly.  Persistent masking is ideal for cases where all users should not see the original sensitive data (e.g. for test / development environments) and field level data protection is required.

Dynamic data masking de-identifies data when it is accessed.  The original data is still stored in the database.  Dynamic data masking (DDM) acts as a proxy between the application and database and rewrites the user / application request against the database depending on whether the user has the privilege to view the data or not.  If the requested data is not sensitive or the user is a privileged user who has the permission to access the sensitive data, then the DDM proxy passes the request to the database without modification, and the result set is returned to the user in the clear.  If the data is sensitive and the user does not have the privilege to view the data, then the DDM proxy rewrites the request to include a masking function and passes the request to the database to execute.  The result is returned to the user with the sensitive data masked.  Dynamic data masking is ideal for protecting sensitive fields in production systems where application changes are difficult or disruptive to implement and performance / response time is of high importance.

Tokenization substitutes a sensitive data element with a non-sensitive data element or token.  The first generation tokenization system requires a token server and a database to store the original sensitive data.  The mapping from the clear text to the token makes it very difficult to reverse the token back to the original data without the token system.  The existence of a token server and database storing the original sensitive data renders the token server and mapping database as a potential point of security vulnerability, bottleneck for scalability, and single point of failure. Next generation tokenization systems have addressed these weaknesses.  However, tokenization does require changes to the application layer to tokenize and detokenize when the sensitive data is accessed.  Tokenization can be used in production systems to protect sensitive data at rest in the database store, when changes to the application layer can be made relatively easily to perform the tokenization / detokenization operations.

Retention management and purging is more of a data management method to ensure that data is retained only as long as necessary.  The best method of reducing data privacy risk is to eliminate the sensitive data.  Therefore, appropriate retention, archiving, and purging policies should be applied to reduce the privacy and legal risks of holding on to sensitive data for too long.  Retention management and purging is a data management best practices that should always be put to use.

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The Pros and Cons: Data Integration from the Bottom-Up and the Top-Down

Data Integration from the Bottom-Up and the Top-Down

Data Integration from the Bottom-Up and the Top-Down

What are the first steps of a data integration project?  Most are at a loss.  There are several ways to approach data integration, and your approach depends largely upon the size and complexity of your problem domain.

With that said, the basic approaches to consider are from the top-down, or the bottom-up.  You can be successful with either approach.  However, there are certain efficiencies you’ll gain with a specific choice, and it could significantly reduce the risk and cost.  Let’s explore the pros and cons of each approach.

Top-Down

Approaching data integration from the top-down means moving from the high level integration flows, down to the data semantics.  Thus, you an approach, perhaps even a tool-set (using requirements), and then define the flows that are decomposed down to the raw data.

The advantages of this approach include:

The ability to spend time defining the higher levels of abstraction without being limited by the underlying integration details.  This typically means that those charged with designing the integration flows are more concerned with how they have to deal with the underlying source and target, and this approach means that they don’t have to deal with that issue until later, as they break down the flows.

The disadvantages of this approach include:

The data integration architect does not consider the specific needs of the source or target systems, in many instances, and thus some rework around the higher level flows may have to occur later.  That causes inefficiencies, and could add risk and cost to the final design and implementation.

Bottom-Up

For the most part, this is the approach that most choose for data integration.  Indeed, I use this approach about 75 percent of the time.  The process is to start from the native data in the sources and targets, and work your way up to the integration flows.  This typically means that those charged with designing the integration flows are more concerned with the underlying data semantic mediation than the flows.

The advantages of this approach include:

It’s typically a more natural and traditional way of approaching data integration.  Called “data-driven” integration design in many circles, this initially deals with the details, so by the time you get up to the integration flows there are few surprises, and there’s not much rework to be done.  It’s a bit less risky and less expensive, in most cases.

The disadvantages of this approach include:

Starting with the details means that you could get so involved in the details that you miss the larger picture, and the end state of your architecture appears to be poorly planned, when all is said and done.  Of course, that depends on the types of data integration problems you’re looking to solve.

No matter which approach you leverage, with some planning and some strategic thinking, you’ll be fine.  However, there are different paths to the same destination, and some paths are longer and less efficient than others.  As you pick an approach, learn as you go, and adjust as needed.

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Posted in Big Data, Data Aggregation, Data Integration | Tagged , , , | Leave a comment

At Valspar Data Management is Key to Controlling Purchasing Costs

Steve Jenkins, Global IT Director at Valspar

Steve Jenkins is working to improve information management maturity at Valspar

Raw materials costs are the company’s single largest expense category,” said Steve Jenkins, Global IT Director at Valspar, at MDM Day in London. “Data management technology can help us improve business process efficiency, manage sourcing risk and reduce RFQ cycle times.”

Valspar is a $4 billion global manufacturing company, which produces a portfolio of leading paint and coating brands. At the end of 2013, the 200 year old company celebrated record sales and earnings. They also completed two acquisitions. Valspar now has 10,000 employees operating in 25 countries.

As is the case for many global companies, growth creates complexity. “Valspar has multiple business units with varying purchasing practices. We source raw materials from 1,000s of vendors around the globe,” shared Steve.

“We want to achieve economies of scale in purchasing to control spending,” Steve said as he shared Valspar’s improvement objectives. “We want to build stronger relationships with our preferred vendors. Also, we want to develop internal process efficiencies to realize additional savings.”

Poorly managed vendor and raw materials data was impacting Valspar’s buying power

Data management at Valspar

“We realized our buying power was limited by the age and quality of available vendor and raw materials data.”

The Valspar team, who sharply focuses on productivity, had an “Aha” moment. “We realized our buying power was limited by the age and quality of available vendor data and raw materials data,” revealed Steve. 

The core vendor data and raw materials data that should have been the same across multiple systems wasn’t. Data was often missing or wrong. This made it difficult to calculate the total spend on raw materials. It was also hard to calculate the total cost of expedited freight of raw materials. So, employees used a manual, time-consuming and error-prone process to consolidate vendor data and raw materials data for reporting.

These data issues were getting in the way of achieving their improvement objectives. Valspar needed a data management solution.

Valspar needed a single trusted source of vendor and raw materials data

Informatica MDM supports vendor and raw materials data management at Valspar

The team chose Informatica MDM as their enterprise hub for vendors and raw materials

The team chose Informatica MDM, master data management (MDM) technology. It will be their enterprise hub for vendors and raw materials. It will manage this data centrally on an ongoing basis. With Informatica MDM, Valspar will have a single trusted source of vendor and raw materials data.

Informatica PowerCenter will access data from multiple source systems. Informatica Data Quality will profile the data before it goes into the hub. Then, after Informatica MDM does it’s magic, PowerCenter will deliver clean, consistent, connected and enriched data to target systems.

Better vendor and raw materials data management results in cost savings

Valspar Chameleon Jon

Valspar will gain benefits by fueling applications with clean, consistent, connected and enriched data

Valspar expects to gain the following business benefits:

  • Streamline the RFQ process to accelerate raw materials cost savings
  • Reduce the total number of raw materials SKUs and vendors
  • Increase productivity of staff focused on pulling and maintaining data
  • Leverage consistent global data visibly to:
    • increase leverage during contract negotiations
    • improve acquisition due diligence reviews
    • facilitate process standardization and reporting

 

Valspar’s vision is to tranform data and information into a trusted organizational assets

“Mastering vendor and raw materials data is Phase 1 of our vision to transform data and information into trusted organizational assets,” shared Steve. In Phase 2 the Valspar team will master customer data so they have immediate access to the total purchases of key global customers. In Phase 3, Valspar’s team will turn their attention to product or finished goods data.

Steve ended his presentation with some advice. “First, include your business counterparts in the process as early as possible. They need to own and drive the business case as well as the approval process. Also, master only the vendor and raw materials attributes required to realize the business benefit.”

Total Supplier Information Management eBook

Click here to download the Total Supplier Information Management eBook

Want more? Download the Total Supplier Information Management eBook. It covers:

  • Why your fragmented supplier data is holding you back
  • The cost of supplier data chaos
  • The warning signs you need to be looking for
  • How you can achieve Total Supplier Information Management

 

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Posted in Business/IT Collaboration, Data Integration, Data Quality, Manufacturing, Master Data Management, Operational Efficiency, PowerCenter, Vertical | Tagged , , , , , , , , , , , , , , , , , , | Leave a comment

Scalable Enterprise Analytics: Informatica PowerCenter Data Quality and Oracle Exadata

In 2012, Forbes published an article predicting an upcoming problem.

The Need for Scalable Enterprise Analytics

Specifically, increased exploration in Big Data opportunities would place pressure on the typical corporate infrastructure. The generic hardware used to run most tech industry enterprise applications was not designed to handle real-time data processing. As a result, the explosion of mobile usages, and the proliferation of social networks, was increasing the strain on the system. Most companies now faced real-time processing requirements beyond what the traditional model was designed to handle.

In the past two years, the volume of data and speed of data growth has grown significantly. As a result, the problem has become more severe. It is now clear that these challenges can’t be overcome by simply doubling or tripling their IT spending on infrastructure sprawl. Today, enterprises seek consolidated solutions that offer scalability, performance and ease of administration. The present need is for scalable enterprise analytics.

A Clear Solution Is Available

Informatica PowerCenter and Data Quality is the market leading data integration and data quality platform. This platform has now been certified by Oracle as an optimal solution for both the Oracle Exadata Database Machine and the Oracle SuperCluster.

As the high-speed on-ramp for data into Oracle Exadata, PowerCenter and Data Quality deliver up-to five times faster performance on data load, query, profiling and cleansing tasks. Informatica’s data integration customers can now easily reuse data integration code, skills and resources to access and transform any data from any data source and load it into Exadata, with the highest throughput and scalability.

Customers adopting Oracle Exadata for high-volume, high-speed analytics can now be confident with Informatica PowerCenter and Data Quality. With these products, they can ingest, cleanse and transform all types of data into Exadata with the highest performance and scale required to maximize the value of their Exadata investment.

Proving the Value of Scalable Enterprise Analytics

In order to demonstrate the efficacy of their partnership, the two companies worked together on a Proof Of Value (POV) project. The goal is to prove that using PowerCenter with Exadata would improve both performance and scalability. The project involved PowerCenter and Data Quality 9.6.1 and x4-2 Exadata Machine. Oracle 11g was considered for both standard Oracle and Exadata versions.

The first test conducted a 1TB load test to Exadata and standard Oracle in a typical PowerCenter use case. The second test consisted of querying 1TB profiling warehouse database in Data Quality use case scenario. Performance data was collected for both tests. The scalability factor was also captured. A variant of the TPCH dataset was used to generate the test data. The results were significantly higher than prior Exabyte 1TB test. In particular:

  • The data query tests achieved 5x performance.
  • The data load tests achieved a 3x-5x speed increase.
  • Linear scalability was achieved with read/write tests on Exadata.

What Business Benefits Could You Expect?

Informatica PowerCenter and Data Quality, along-with Oracle Exadata, now provide the best-of-breed combination of software and hardware, optimized to deliver the highest possible total system performance. These comprehensive tools drive agile reporting and analytics, while empowering IT organizations to meet SLAs and quality goals like never before.

  1. Extend Oracle Exadata’s access to even more business critical data sources. Utilize optimized out-of-the-box Informatica connectivity to easily access hundreds of data sources, including all the major databases, on-premise and cloud applications, mainframe, social data and Hadoop.
  2. Get more data, more quickly into Oracle Exadata. Move higher volumes of trusted data quickly into Exadata to support timely reporting with up-to-date information (i.e. up to 5x performance improvement compared to Oracle database).
  3.  Centralize management and improve insight into large scale data warehouses. Deliver the necessary insights to stakeholders with intuitive data lineage and a collaborative business glossary. Contribute to high quality business analytics, in a timely manner across the enterprise.
  4. Instantly re-direct workloads and resources to Oracle Exadata without compromising performance. Leverage existing code and programming skills to execute high-performance data integration directly on Exadata by performing push down optimization.
  5. Roll-out data integration projects faster and more cost-effectively. Customers can now leverage thousands of Informatica certified developers to execute existing data integration and quality transformations directly on Oracle Exadata, without any additional coding.
  6. Efficiently scale-up and scale-out. Customers can now maximize performance and lower the costs of data integration and quality operations of any scale by performing Informatica workload and push down optimization on Oracle Exadata.
  7.  Save significant costs involved in administration and expansion. Customers can now easily and economically manage large-scale analytics data warehousing environments with a single point of administration and control, and consolidate a multitude of servers on one rack.
  8.  Reduce risk. Customers can now leverage Informatica’s data integration and quality platform to overcome the typical performance and scalability limitations seen in databases and data storage systems. This will help reduce quality-of-service risks as data volumes rise.

Conclusion

Oracle Exadata is a well-engineered system that offers customers out-of-box scalability and performance on demand.  Informatica PowerCenter and Data Quality are optimized to run on Exadata, offering customers business benefits that speed up data integration and data quality tasks like never before.  Informatica’s certified, optimized, and purpose-built solutions for Oracle can help you enable more timely and trustworthy reporting.  You can now benefit from Informatica’s optimized solutions for Oracle Exadata to make better business decisions by unlocking the full potential of the most current and complete enterprise data available. As shown in our test results, you can attain up to 5x performance by scaling Exadata. Informatica Data Quality customers can perform profiling 1TB datasets, which is unheard before. We urge you to deploy the combined solution to solve your data integration and quality problems today while achieving high speed business analytics in these days of big data exploration and Internet Of Things.

Note:

Listen to what Ash Kulkarni, SVP, at OOW14 has to say on how @InformaticaCORP PowerCenter and Data Quality certified by Oracle as optimized for Exadata can deliver up-to five times faster performance improvement on data load, query, profiling, cleansing and mastering tasks, for Exadata.

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Posted in Data Integration, Data Integration Platform, Data Quality, Data Services, Data Warehousing, Enterprise Data Management, PowerCenter, Vibe | Tagged | Leave a comment

Do We Really Need Another Information Framework?

Do We Really Need Another Information Framework?

The EIM Consortium is a group of nine companies that formed this year with the mission to:

Promote the adoption of Enterprise Information Management as a business function by establishing an open industry reference architecture in order to protect and optimize the business value derived from data assets.”

That sounds nice, but we do really need another framework for EIM or Data Governance? Yes we do, and here’s why. (more…)

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Posted in CIO, Data Governance, Data Integration, Enterprise Data Management, Governance, Risk and Compliance, Integration Competency Centers, Uncategorized | Tagged , , | Leave a comment