Yearly Archives: 2012
Informatica 9.5 for Big Data Challenge #2: Social
Social networking is becoming inescapable. It has become mainstream faster than almost anyone could have predicted (other than perhaps Mark Zuckerberg.)
Full disclosure: I hardly ever use Facebook. Perhaps as a working mom with two young children, keeping up with former high school classmates is a luxury I can’t afford (or don’t want). But I use other types of social media extensively. I use LinkedIn for professional networking, recruiting, knowledge sharing and development. I use Twitter to communicate with customers, analysts and industry peers. I use several local online moms’ communities for advice on toddler tantrums, teething and preschools.
With more and more people interacting via social networks online, more and more social data becomes available to better understand behavior, sentiment and relationships. The data privacy issues are significant, but as they get sorted out over time, many businesses will have the opportunity to mine this data to improve their customer and market knowledge. The applicability is most obvious for consumer-oriented companies such as retail, entertainment, travel, hospitality and consumer packaged goods. But B2B buyers are also moving their professional activities, including vendor evaluations, into the social media realm, so it won’t be long before B2B companies also ramp up their social media efforts.
Of course, with the volume of social media activity that is generated on an hourly basis, the volumes can be overwhelming. And the data is almost entirely unstructured, making it very hard for traditional systems to process. Social data is one of the key elements driving the overall big data phenomenon, as new data processing platforms and paradigms are required to find the needle in the massive haystack of social network interactions.
That’s where Informatica 9.5 comes in. In addition to expanding pre-built connectivity to social data sources, Informatica 9.5 introduces a few critical pieces of functionality. First, natural language processing capabilities allow you to extract entities and meaning out of unstructured text that may be in a LinkedIn profile, or in a web posting. Second, social MDM enables you to extract information on customer preferences, profiles and relationships out of social networks such as Facebook (on an opt-in basis), and integrate it with existing customer master data to create a truly comprehensive customer profile. Third, in many cases IT groups are turning to the Hadoop stack to process social data, given its large volumes and highly unstructured nature. Informatica 9.5’s support for Hadoop, enabling both increased interoperability with traditional applications and a leap in development productivity for Hadoop, will be a big boost to social data processing.
There’s still a long way to go to bring social data into the mainstream enterprise, in part due to concerns over privacy and the potential “creepiness” factor of mining social data. But as those concerns are worked through, Informatica technology can ensure you can separate what data is useful from what is worthless, and fully utilize the relevant data to deliver new insight and value to the business.
Big Data, Big Problems: Leveraging Informatica 9.5 to Build an Effective Data Governance Strategy to Meet the Big Data Challenge
By: Chris Cingrani, Informatica DQ & MDM Practice Lead, Data Management Practice at SSG Ltd., www.ssglimited.com
Big data is something that I am continually asked about by clients, as the subject continues to gain significant press. While discussing this topic, I often address it from the angle that bigger data volumes will result in bigger data problems. Although this seems like a logical premise, the reality of what it really means to an organization and how to plan accordingly is what is often overlooked. Rather than solve the problem in this blog post, I want to focus on two key considerations from a data governance standpoint, as well as discuss why SSG sees Informatica 9.5 as a core component of a sound data governance strategy that can ensure an organizations’ business decision-making success. (more…)
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…)
Informatica 9.5 for Big Data Challenge #1: Cloud
Just five years ago, there was a perception held by many in our industry that the world of data for enterprises was simplifying. This was in large part due to the wave of consolidation among application vendors. With SAP and Oracle gobbling up the competition to build massive, monolithic application stacks, the story was that this consolidation would simplify data integration and data management.
Of course, the exact opposite happened. Applications did not consolidate (okay, there has been some consolidation, but not nearly as much as SAP and Oracle would have desired.) Rather, there has been a new wave of fragmentation due to the rapid mainstreaming of cloud applications. Cloud applications have proliferated quickly—sometimes without the knowledge or approval of corporate IT. This has led to the proliferation of new sources of transaction data, which are contributing to the rise of big data.
To be clear, it’s not about cloud replacing on-premise, it’s about the two co-existing together. That means managing a hybrid IT environment, in which Informatica has been investing since 2006.
Now Informatica 9.5 brings some major advances to the cloud. In particular, embeddable cloud integration services means that cloud applications no longer need to suffer from inaccurate or incomplete data, which in turn hobble user adoption. Informatica’s embedded services will ensure that cloud applications have all the relevant business data from on-premise and cloud systems, with high quality and in an integrated manner. This will further accelerate enterprise adoption of cloud applications because one of the biggest barriers to adoption—cloud data integration—has been taken down by Informatica 9.5.
A Return to Big Data
Quite a bit has happened on the topic of big data since my last post on Informatica Perspectives almost one and a half years ago. I have spent a career working with organizations on how to get control over their uncontrolled data growth and industry visionaries are promoting this brave new world of big data. (more…)
Data Prefetching and Caching
Treating Big Data Performance Woes with the Data Replication Cure Blog Series – Part 3
OK, so in our last two discussions, we looked at the memory bottleneck and how even in high performance environments, there are still going to be situations in which streaming the data to where it needs to be will introduce latencies that throttle processing speed. And I also noted that we needed some additional strategies to address that potential bottleneck, so here goes: (more…)
Who Wants to Clean Out the Fridge on Friday Night?
A while ago I wrote a piece about our company’s ‘green’ initiatives. Amongst the many programs we are adopting to be more environmentally conscious, one was to achieve the Energy Star Rating certification for our headquarters building in Redwood City, California.
Our facilities team worked hard to achieve the certification: we announced it, we shared the news with our community, issued a press release, patted ourselves on the back and then everyone went back to work. But clearly, it is a gift that keeps on giving … (more…)
Do You Know Where Your Existing Database Security Solutions Are Failing?
Recently, Oracle announced that its latest April critical patch update does not address the TNS Poison vulnerability uncovered by a researcher 4 years ago. In addition to this vulnerability from an attacker, organizations face data breaches from internal negligence and insiders. In a May 2012 survey by the Ponemon Institute, 50% say sensitive data contained in databases and applications has been compromised or stolen by malicious insiders such as privileged users. On top of that 68% find it difficult to restrict user access to sensitive information in IT and business environments.
While databases offer basic security features that can be programmed and configured to protect data, it may not be enough and may not scale with your growing organizations. The problem stems from the fact that application development and DBA teams need to have a solid understanding of database vendor specific offerings in order to ensure that the security feature has been properly set up and deployed. If your organization has a number of different databases (Oracle, DB2, Microsoft SQL Server) and that number is growing, it can be costly to maintain all the database specific solutions. Many Informatica customers have faced this problem and looked to Informatica to provide a complete, end-to-end solution that addresses database security on an enterprise-wide level.
Come talk to us at Informatica World and hear from our customers about how they’ve used Informatica to minimize the risk of breaches across a number of use cases including:
- Test data management
- Production support in off-shore projects
- Dynamically protecting PII or PHI data for research portals
- Dynamically protecting data in cross-border applications
At Informatica, you can meet us in our sessions on Thursday, May 17, at the Aria in Las Vegas:
10:10 – 11:10 – Ensuring Data Privacy for Warehouses and Applications with Informatica Data Masking in Room Juniper 3
11:20 – 12:20 – Protecting Sensitive Data Using Informatica’s Test Data Management Solution in Room Starvine 12
Also come to the Informatica Data Privacy booth and lab for in depth demonstrations and presentations of our data privacy solutions and customer deployments.
Top 10 Similarities Shared By Master Data Management (MDM) Customers
What do our master data management(MDM) customers have in common? Regardless of industry, use case, region, or the type of information they are mastering, I’ve identified 10 key factors they have in common.
Carved Wax Crayons designed by Pete Goldlust
1) Company size is $500M+
2) Experiencing rapid company growth, likely due to mergers and acquisitions
3) Facing a compelling event such as a new regulation (examples include WEEE in Manufacturing, Basel III in Financial Services, Sunshine Act in Pharmaceuticals) or a change in business dynamics that results in a drop in revenue or profit, triggering a renewed focus on efficiency and cost savings
4) Starting to view their data as a strategic asset and as a lever for strategic initiatives, but aren’t confident in the quality of their data yet (See Constellation Research’s Report on Customer Data: The Missing Link to Strategic Success by @rwang0)
5) Experiencing pain because business-critical data is fragmented, duplicated and inconsistent across the systems in the organization. Did you know that managers at companies without MDM spend 60% more time searching for data than managers at companies with MDM? (See Creating a Complete Customer View: Best Practices in Master Data Management by Aberdeen’s @peterostrow)
6) IT is ensuring business stakeholders are driving and funding the MDM initiative because it supports their strategic imperatives (typically the business stakeholder has a vision and MDM plays a foundational role enabling the “best version of the truth” to make that vision a reality)
7) Understanding the value of a multidomain MDM solution that can master more than one domain on a single platform to accommodate MDM expansion to different domains, departments and regions, rather than needing to buy another MDM system
8) Planning a go live in 6-9 months by using a flexible data model that adapts to their business and easily accommodates changes vs. needing to hand-code changes to a vendor-supplied data model
9) Centrally managing hierarchies of data about companies, contacts, products, vendors, assets and the managing relationships between data
10) Supporting data governance and “auditability” with workflow management that tracks the history of changes to master data overtime, and with a platform that includes MDM, data quality scorecards and data integration
Want to learn more about our multidomain MDM solution which supports a flexible, business model-driven MDM approach?
- Read Gartner Magic Quadrant for Master Data Management of Customer Data Solutions by @Gartner_inc @John_Radcliffe and Bill O’Kane which explains the two common MDM approaches
- If you are gathering MDM business requirements for an MDM solution, check out our popular Whitepaper: MDM Buyer’s Guide for IT Professionals, to access the top 10 critical MDM business requirements for a flexible solution to address current and future requirements.
- Want to understand the costs and benefits of the two common MDM approaches? Watch this TDWI Webinar: Is Your Approach to MDM Fixed or Flexible with @DavidLoshin author of the definitive book on MDM and @Ravi_Shankar_ Informatica’s MDM expert.
- Meet us in person and attend the Informatica World MDM Path, which includes 12 MDM sessions at including customer presentations from Conde Nast, Hyatt, Blue Cross Blue Shield of Michigan, Queensland Police and Printemps, a French retailer.
Are you seeing other similarities between companies that are using MDM? I welcome your comments below.
Some Thoughts about Data Proximity for Big Data Calculations – Part 2
Treating Big Data Performance Woes with the Data Replication Cure Blog Series – Part 2
In my last posting, I suggested that the primary bottleneck for performance computing of any type, including big data applications, is the latency associated with getting data from where it is to where it needs to be. If the presumptive big data analytics platform/programming model is Hadoop, (which is also often presumed to provide in-memory analytics), though, there are three key issues: (more…)
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