Tag Archives: cloud integration
The emergence of the business cloud is making the need for data ever more prevalent. Whatever your business, if your role is in the sales, marketing or service departments, chances are your productivity depends a great deal on the ability to move data quickly in and out of Salesforce and its ecosphere of applications.
With the in-built data transformation intelligence, the Data Wizard (click here to try the Beta version), changes the landscape of what traditional data loaders can do. The Data Wizard takes care of the following aspects, so that you don’t have to:
- Data Transformations: We built in over 300 standard data transformations so you don’t have to format the data before bringing it in (eg. combining first and last names into full names, adding numeric columns for totals, splitting address fields into its separate components).
- Built-in intelligence: We automate the mapping of data into Salesforce for a range of common use cases (eg., Automatically mapping matching fields, intelligently auto-generating date format conversions , concatenating multiple fields).
- App-to-app integration: We incorporated pre-built integration templates to encapsulate the logic required for integrating Salesforce with other applications (eg., single click update of customer addresses in a Cloud ERP application based on Account addresses in Salesforce) .
Unlike the other data loading apps out there, the Data Wizard doesn’t presuppose any technical ability on the part of the user. It was purpose-built to solve the needs of every type of user, from the Salesforce administrator to the business analyst.
Despite the simplicity the Data Wizard offers, it is built on the robust Informatica Cloud integration platform, providing the same reliability and performance that is key to the success of Informatica Cloud’s enterprise customers, who integrate over 5 billion rows of data per day. We invite you to try the Data Wizard for free, and contribute to the Beta process by providing us with your feedback.
Informatica’s Redshift connector is a state-of-the-art Bulk-Load type connector which allows users to perform all CRUD operations on Amazon Redshift. It makes use of AWS best practices to load data at high throughput in a safe and secure manner and is available on Informatica Cloud and PowerCenter.
Today we are excited to announce the support of Amazon’s newly launched custom JDBC and ODBC drivers for Redshift. Both the drivers are certified for Linux and Windows environments.
Informatica’s Redshift connector will package the JDBC 4.1 driver which further enhances our meta-data fetch capabilities for tables and views in Redshift. That improves our overall design-time responsiveness by over 25%. It also allows us to query multiple tables/views and retrieve the result-set using primary and foreign key relationships.
Amazon’s ODBC driver enhances our FULL Push Down Optimization capabilities on Redshift. Some of the key differentiating factors are support for the SYSDATE variable, functions such as ADD_TO_DATE(), ASCII(), CONCAT(), LENGTH(), TO_DATE(), VARIANCE() etc. which weren’t possible before.
Amazon’s ODBC driver is not pre-packaged but can be directly downloaded from Amazon’s S3 store.
Once installed, the user can change the default ODBC System DSN in ODBC Data Source Administrator.
Strata 2015 – Making Data Work for Everyone with Cloud Integration, Cloud Data Management and Cloud Machine Learning
Are you ready to answer “Yes” to the questions:
a) “Are you Cloud Ready?”
b) “Are you Machine Learning Ready?”
I meet with hundreds of Informatica Cloud customers and prospects every year. While they are investing in Cloud, and seeing the benefits, they also know that there is more innovation out there. They’re asking me, what’s next for Cloud? And specifically, what’s next for Informatica in regards to Cloud Data Integration and Cloud Data Management? I’ll share more about my response throughout this blog post.
The spotlight will be on Big Data and Cloud at the Strata + Hadoop World conference taking place in Silicon Valley from February 17-20 with the theme “Make Data Work”. I want to focus this blog post on two topics related to making data work and business insights:
- How existing cloud technologies, innovations and partnerships can help you get ready for the new era in cloud analytics.
- How you can make data work in new and advanced ways for every user in your company.
Today, Informatica is announcing the availability of its Cloud Integration Secure Agent on Microsoft Azure and Linux Virtual Machines as well as an Informatica Cloud Connector for Microsoft Azure Storage. Users of Azure data services such as Azure HDInsight, Azure Machine Learning and Azure Data Factory can make their data work with access to the broadest set of data sources including on-premises applications, databases, cloud applications and social data. Read more from Microsoft about their news at Strata, including their relationship with Informatica, here.
“Informatica, a leader in data integration, provides a key solution with its Cloud Integration Secure Agent on Azure,” said Joseph Sirosh, Corporate Vice President, Machine Learning, Microsoft. “Today’s companies are looking to gain a competitive advantage by deriving key business insights from their largest and most complex data sets. With this collaboration, Microsoft Azure and Informatica Cloud provide a comprehensive portfolio of data services that deliver a broad set of advanced cloud analytics use cases for businesses in every industry.”
Even more exciting is how quickly any user can deploy a broad spectrum of data services for cloud analytics projects. The fully-managed cloud service for building predictive analytics solutions from Azure and the wizard-based, self-service cloud integration and data management user experience of Informatica Cloud helps overcome the challenges most users have in making their data work effectively and efficiently for analytics use cases.
The new solution enables companies to bring in data from multiple sources for use in Azure data services including Azure HDInsight, Azure Machine Learning, Azure Data Factory and others – for advanced analytics.
The broad availability of Azure data services, and Azure Machine Learning in particular, is a game changer for startups and large enterprises. Startups can now access cloud-based advanced analytics with minimal cost and complexity and large businesses can use scalable cloud analytics and machine learning models to generate faster and more accurate insights from their Big Data sources.
Success in using machine learning requires not only great analytics models, but also an end-to-end cloud integration and data management capability that brings in a wide breadth of data sources, ensures that data quality and data views match the requirements for machine learning modeling, and an ease of use that facilitates speed of iteration while providing high-performance and scalable data processing.
For example, the Informatica Cloud solution on Azure is designed to deliver on these critical requirements in a complementary approach and support advanced analytics and machine learning use cases that provide customers with key business insights from their largest and most complex data sets.
Using the Informatica Cloud solution on Azure connector with Informatica Cloud Data Integration enables optimized read-write capabilities for data to blobs in Azure Storage. Customers can use Azure Storage objects as sources, lookups, and targets in data synchronization tasks and advanced mapping configuration tasks for efficient data management using Informatica’s industry leading cloud integration solution.
As Informatica fulfills the promise of “making great data ready to use” to our 5,500 customers globally, we continue to form strategic partnerships and develop next-generation solutions to stay one step ahead of the market with our Cloud offerings.
My goal in 2015 is to help each of our customers say that they are Cloud Ready! And collaborating with solutions such as Azure ensures that our joint customers are also Machine Learning Ready!
To learn more, try our free Informatica Cloud trial for Microsoft Azure data services.
It’s no secret that the explosion of software-as-a-service (SaaS) apps has revolutionized the way businesses operate. From humble beginnings, the titans of SaaS today include companies such as Salesforce.com, NetSuite, Marketo, and Workday that have gone public and attained multi-billion dollar valuations. The success of these SaaS leaders has had a domino effect in adjacent areas of the cloud – infrastructure, databases, and analytics.
Amazon Web Services (AWS), which originally had only six services in 2006 with the launch of Amazon EC2, now has over 30 ranging from storage, relational databases, data warehousing, Big Data, and more. Salesforce.com’s Wave platform, Tableau Software, and Qlik have made great advances in the cloud analytics arena, to give better visibility to line-of-business users. And as SaaS applications embrace new software design paradigms that extend their functionality, application performance monitoring (APM) analytics has emerged as a specialized field from vendors such as New Relic and AppDynamics.
So, how exactly did the growth of SaaS contribute to these adjacent sectors taking off?
The growth of SaaS coincided with the growth of powerful smartphones and tablets. Seeing this form factor as important to the end user, SaaS companies rushed to produce mobile apps that offered core functionality on their mobile device. Measuring adoption of these mobile apps was necessary to ensure that future releases met all the needs of the end user. Mobile apps contain a ton of information such as app responsiveness, features utilized, and data consumed. As always, there were several types of users, with some preferring a laptop form factor over a smartphone or tablet. With the ever increasing number of data points to measure within a SaaS app, the area of application performance monitoring analytics really took off.
Simultaneously, the growth of the SaaS titans cemented their reputation as not just applications for a certain line-of-business, but into full-fledged platforms. This growth emboldened a number of SaaS startups to develop apps that solved specialized or even vertical business problems in healthcare, warranty-and-repair, quote-to-cash, and banking. To get started quickly and scale rapidly, these startups leveraged AWS and its plethora of services.
The final sector that has taken off thanks to the growth of SaaS is the area of cloud analytics. SaaS grew by leaps and bounds because of its ease of use, and rapid deployment that could be achieved by business users. Cloud analytics aims to provide the same ease of use for business users when providing deep insights into data in an interactive manner.
In all these different sectors, what’s common is the fact that SaaS growth has created an uptick in the volume of data and the technologies that serve to make it easier to understand. During Informatica’s Data Mania event (March 4th, San Francisco) you’ll find several esteemed executives from Salesforce, Amazon, Adobe, Microsoft, Dun & Bradstreet, Qlik, Marketo, and AppDynamics talk about the importance of data in the world of SaaS.
A lot of the trends we are seeing in enterprise integration today are being driven by the adoption of cloud based technologies from IaaS, PaaS and SaaS. I just was reading this story about a recent survey on cloud adoption and thought that a lot of this sounds very similar to things that we have seen before in enterprise IT.
Why discuss this? What can we learn? A couple of competing quotes come to mind.
Those who forget the past are bound to repeat it. – Edmund Burke
We are doomed to repeat the past no matter what. – Kurt Vonnegut
While every enterprise has to deal with their own complexities there are several past technology adoption patterns that can be used to drive discussion and compare today’s issues in order to drive decisions in how a company designs and deploys their current enterprise cloud architecture. Flexibility in design should be a key goal in addition to satisfying current business and technical requirements. So, what are the big patterns we have seen in the last 25 years that have shaped the cloud integration discussion?
1. 90s: Migration and replacement at the solution or application level. A big trend of the 90s was replacing older home grown systems or main frame based solutions with new packaged software solutions. SAP really started a lot of this with ERP and then we saw the rise of additional solutions for CRM, SCM, HRM, etc.
This kept a lot of people that do data integration very busy. From my point of view this era was very focused on replacement of technologies and this drove a lot of focus on data migration. While there were some scenarios around data integration to leave solutions in place these tended to be more in the area of systems that required transactional integrity and high level of messaging or back office solutions. On the classic front office solutions enterprises in large numbers did rip & replace and migration to new solutions.
2. 00s: Embrace and extend existing solutions with web applications. The rise of the Internet Browser combined with a popular and powerful standard programming language in Java shaped and drove enterprise integration in this time period. In addition, due to many of the mistakes and issues that IT groups had in the 90s there appeared to be a very strong drive to extend existing investments and not do rip and replace. IT and businesses were trying to figure out how to add new solutions to what they had in place. A lot of enterprise integration, service bus and what we consider as classic application development and deployment solutions came to market and were put in place.
3. 00s: Adoption of new web application based packaged solutions. A big part of this trend was driven by .Net & Java becoming more or less the de-facto desired language of enterprise IT. Software vendors not on these platforms were for the most part forced to re-platform or lose customers. New software vendors in many ways had an advantage because enterprises were already looking at large data migration to upgrade the solutions they had in place. In either case IT shops were looking to be either a .Net or Java shop and it caused a lot of churn.
4. 00s: First generation cloud applications and platforms. The first adoption of cloud applications and platforms were driven by projects and specific company needs. From Salesforce.com being used just for sales management before it became a platform to Amazon being used as just a run-time to develop and deploy applications before it became a full scale platform and an every growing list of examples as every vendor wants to be the cloud platform of choice. The integration needs originally were often on the light side because so many enterprises treated it as an experiment at first or a one off for a specific set of users. This has changed a lot in the last 10 years as many companies repeated their on premise silo of data problems in the cloud as they usage went from one cloud app to 2, 5, +10, etc. In fact, if you strip away where a solution happens to be deployed (on prem or cloud) the reality is that if an enterprise had previously had a poorly planned on premise architecture and solution portfolio they probably have just as poorly planned cloud architecture solution and portfolio. Adding them together just leads to disjoint solutions that are hard to integrate, hard to maintain and hard to evolve. In other words the opposite of the being flexible goal.
5. 10s: Consolidation of technology and battle of the cloud platforms. It appears we are just getting started in the next great market consolidation and every enterprise IT group is going to need to decide their own criteria for how they balance current and future investments. Today we have Salesforce, Amazon, Google, Apple, SAP and a few others. In 10 years some of these will either not exist as they do today or be marginalized. No one can say which ones for sure and this is why prioritizing flexibility in terms or architecture for cloud adoption.
For me the main take aways from the past 25 years of technology adoption trends for anyone that thinks about enterprise and data integration would be the following.
a) It’s all starts and ends with data. Yes, applications, process, and people are important but it’s about the data.
b) Coarse grain and loosely coupled approaches to integration are the most flexible. (e.g. avoid point to point at all costs)
c) Design with the knowledge of what data is critical and what data might or should be accessible or movable
d) Identify data and applications that might have to stay where it is no matter what.(e.g. the main frame is never dying)
e) Make sure your integration and application groups have access to or include someone that understand security. While a lot of integration developers think they understand security it’s usually after the fact that you find out they really do not.
So, it’s possible to shape your cloud adoption and architecture future by at least understanding how past technology and solution adoption has shaped the present. For me it is important to remember it is all about the data and prioritizing flexibility as a technology requirement at least at the same level as features and functions. Good luck.
In 2014, Informatica Cloud focused a great deal of attention on the needs and challenges of the citizen integrator. These are the critical business users at the core of every company: The customer-facing sales rep at the front, as well as the tireless admin at the back. We all know and rely on these men and women. And up until very recently, they’ve been almost entirely reliant on IT for the integration tasks and processes needed to be successful at their jobs.
A lot of that has changed over the last year or so. In a succession of releases, we provided these business users with the tools to take matters into their hands. And with the assistance of key ecosystem partners, such as Salesforce, SAP, Amazon, Workday, NetSuite and the hundreds of application developers that orbit them, we’ve made great progress toward giving business users the self-sufficiency they need, and demand. But, beyond giving these users the tools to integrate and connect with their apps and information at will, what we’ve really done is give them the ability to focus their attention and efforts on their most valuable customers. By doing so, we have got to core of the real purpose and importance of the whole cloud project or enterprise: The customer relationship.
In a recent Fortune interview, Salesforce CEO and cloud evangelist Marc Benioff echoed that idea when he stated that “The CEO is now in charge of the customer relationship.” What he meant by that is companies now have the ability to tie all aspects of their marketing – website, customer service, email marketing, social, sales, etc. – into “one canonical file” with all the respective customer information. By organizing the enterprise around the customer this way, the company can then pivot all of their efforts toward the customer relationship, which is what is required if a business is going to have and sustain success as we move through the 2010s and beyond.
We are in complete agreement with Marc and think it wouldn’t be too much of a stretch to declare 2015 as the year of the customer relationship. In fact, helping companies and business users focus their attention toward the customer has been a core focus of ours for some time. For an example, you don’t have to look much further than the latest iteration of our real-time application integration capability.
In a short video demo that I recommend to everyone, my colleague Eric does a fantastic job of walking users through the real-time features available through the Informatica Cloud platform.
As the demo demonstrates, the real-time features let you build a workflow process application that interacts with data from cloud and on-premise sources right from the Salesforce user interface (UI). It’s quick and easy, thus allowing you to devote more time to your customers and less time on “plumbing.”
The workflows themselves are created with the help of a drag-and-drop process designer that enables the user to quickly create a new process and configure the parameters, inputs and outputs, and decision steps with the click of a few buttons.
Once the process guide is created, it displays as a window embedded right in the Salesforce UI. So if, for example, you’ve created an opportunity-to-order guide, you can follow a wizard-driven process that walks your users from new opportunity creation through to the order confirmation, and everything in between.
As users move through the process, they can interact in real time with data from any on-premise or cloud-based source they choose. In the example from the video, the user, Eric, chooses a likely prospect from a list of company contacts, and with a few keystrokes creates a new opportunity in Salesforce. In a further demonstration of the real-time capability, Eric performs a NetSuite query, logs a client call, escalates a case to customer service, pulls the latest price book information from an Oracle database, builds out the opportunity items, creates the order in SAP, and syncs it all back to Salesforce, all without leaving the wizard interface.
The capabilities available via Informatica Cloud’s application integration are a gigantic leap forward for business users and an evolutionary step toward pivoting the enterprise toward the customer. As 2015 takes hold we will see this become increasingly important as companies continue to invest in the cloud. This is especially true for those cloud applications, like the Salesforce Analytics, Marketing and Sales Clouds, that need immediate access to the latest and most reliable customer data to make them all work — and truly establish you as the CEO in charge of customer relationships.
The technology you use in your business can either help or hinder your business objectives.
In the past, slow and manual processes had an inhibiting effect on customer services and sales interactions, thus dragging down the bottom line.
Now, with cloud technology and customers interacting at record speeds, companies expect greater returns from each business outcome. What do I mean when I say business outcome?
Well according to Bluewolf’s State of Salesforce Report, you can split these into four categories: acquisition, expansion, retention and cost reduction.
With the right technology and planning, a business can speedily acquire more customers, expand to new markets, increase customer retention and ensure they are doing all of this efficiently and cost effectively. But what happens when the data or the way you’re interacting with these technologies grow unchecked, and/or becomes corrupted and unreliable.
With data being the new fuel for decision-making, you need to make sure it’s clean, safe and reliable.
With clean data, Salesforce customers, in the above-referenced Bluewolf survey, reported efficiency and productivity gains (66%), improved customer experience (34%), revenue growth (32%) and cost reduction (21%) in 2014.
It’s been said that it costs a business 10X more to acquire new customers than it does to retain existing ones. But, despite the additional cost, real continued growth requires the acquisition of new customers.
Gaining new customers, however, requires a great sales team who knows what and to whom they’re selling. With Salesforce, you have that information at your fingertips, and the chance to let your sales team be as good as they can possibly be.
And this is where having good data fits in and becomes critically important. Because, well, you can have great technology, but it’s only going to be as good as the data you’re feeding it.
The same “garbage in, garbage out” maxim holds true for practically any data-driven or –reliant business process or outcome, whether it’s attracting new customers or building a brand. And with the Salesforce Sales Cloud and Marketing Cloud you have the technology to both attract new customers and build great brands, but if you’re feeding your Clouds with inconsistent and fragmented data, you can’t trust that you’ve made the right investments or decisions in the right places.
The combination of good data and technology can help to answer so many of your critical business questions. How do I target my audience without knowledge of previous successes? What does my ideal customer look like? What did they buy? Why did they buy it?
For better or worse, but mainly better, answering those questions with just your intuition and/or experience is pretty much out of the question. Without the tool to look at, for example, past campaigns and sales, and combining this view to see who your real market is, you’ll never be fully effective.
The same is true for sales. Without the right Leads, and the ability to interact with these Leads effectively, i.e., having the right contact details, company, knowing there’s only one version of that record, can make the discovery process a long and painful one.
But customer acquisition isn’t the only place where data plays a vital role.
When expanding to new markets or upselling and cross selling to existing customers, it’s the data you collect and report on that will help inform where you should focus your efforts.
Knowing what existing relationships you can leverage can make the difference between proactively offering solutions to your customers and losing them to a competitor. With Salesforce’s Analytics Cloud, this visibility that used to take weeks and months to view can now be put together in a matter of minutes. But how do you make strategic decisions on what market to tap into or what relationships to leverage, if you can only see one or two regions? What if you could truly visualize how you interact with your customers? Or see beyond the hairball of interconnected business hierarchies and interactions to know definitively what subsidiary, household or distributor has what? Seeing the connections you have with your customers can help uncover the white space that you could tap into.
Naturally this entire process means nothing if you’re not actually retaining these customers. Again, this is another area that is fuelled by data. Knowing who your customers are, what issues they’re having and what they could want next could help ensure you are always providing your customer with the ultimate experience.
Last, but by no means least, there is cost reduction. Only by ensuring that all of this data is clean — and continuously cleansed — and your Cloud technologies are being fully utilized, can you then help ensure the maximum return on your Cloud investment.
Learn more about how Informatica Cloud can help you maximize your business outcomes through ensuring your data is trusted in the Cloud.
A friend of mine recently reached out to me about some advice on CRM solutions in the market. Though I have not worked for a CRM vendor, I’ve had both direct experience working for companies that implemented such solutions to my current role interacting with large and small organizations regarding their data requirements to support ongoing application investments across industries. As we spoke, memories started to surface when he and I had worked on implementing Salesforce.com (SFDC) many years ago. Memories that we wanted to forget but important to call out given his new situation.
We worked together for a large mortgage lending software vendor selling loan origination solutions to brokers and small lenders mainly through email and snail mail based marketing. He was responsible for Marketing Operations, and I ran Product Marketing. The company looked at Salesforce.com to help streamline our sales operations and improve how we marketed and serviced our customers. The existing CRM system was from the early 90’s and though it did what the company needed it to do, it was heavily customized, costly to operate, and served its life. It was time to upgrade, to help grow the business, improve business productivity, and enhance customer relationships.
After 90 days of rolling out SFDC, we ran into some old familiar problems across the business. Sales reps continued to struggle in knowing who was a current customer using our software, marketing managers could not create quality mailing lists for prospecting purposes, and call center reps were not able to tell if the person on the other end was a customer or prospect. Everyone wondered why this was happening given we adopted the best CRM solution in the market. You can imagine the heartburn and ulcers we all had after making such a huge investment in our new CRM solution. C-Level executives were questioning our decisions and blaming the applications. The truth was, the issues were not related to SFDC but the data that we had migrated into the system and the lack proper governance and a capable information architecture to support the required data management integration between systems that caused these significant headaches.
During the implementation phase, IT imported our entire customer database of 200K+ unique customer entities from the old system to SFDC. Unfortunately, the mortgage industry was very transient and on average there were roughly 55K licenses mortgage brokers and lenders in the market and because no one ever validated the accuracy of who was really a customer vs. someone who had ever bought out product, we had a serious data quality issues including:
- Trial users who purchased evaluation copies of our products that expired were tagged as current customers
- Duplicate records caused by manual data entry errors consisting of companies with similar but entered slightly differently with the same business address were tagged as unique customers
- Subsidiaries of parent companies in different parts of the country that were tagged again as a unique customer.
- Lastly, we imported the marketing contact database of prospects which were incorrectly accounted for as a customer in the new system
We also failed to integrate real-time purchasing data and information from our procurement systems for sales and support to handle customer requests. Instead of integrating that data in real-time with proper technology, IT had manually loaded these records at the end of the week via FTP resulting in incorrect billing information, statement processing, and a ton of complaints from customers through our call center. The price we paid for not paying attention to our data quality and integration requirements before we rolled out Salesforce.com was significant for a company of our size. For example:
- Marketing got hit pretty hard. Each quarter we mailed evaluation copies of new products to our customer database of 200K, each costing the company $12 per to produce and mail. Total cost = $2.4M annually. Because we had such bad data, we would get 60% of our mailings returned because of invalid addresses or wrong contact information. The cost of bad data to marketing = $1.44M annually.
- Next, Sales struggled miserably when trying to upgrade a customer by running cold call campaigns using the names in the database. As a result, sales productivity dropped by 40% and experienced over 35% sales turnover that year. Within a year of using SFDC, our head of sales got let go. Not good!
- Customer support used SFDC to service customers, our average all times were 40 min per service ticket. We had believed that was “business as usual” until we surveyed what reps were spending their time each day and over 50% said it was dealing with billing issues caused by bad contact information in the CRM system.
At the end of our conversation, this was my advice to my friend:
- Conduct a data quality audit of the systems that would interact with the CRM system. Audit how complete your critical master and reference data is including names, addresses, customer ID, etc.
- Do this before you invest in a new CRM system. You may find that much of the challenges faced with your existing applications may be caused by the data gaps vs. the legacy application.
- If they had a data governance program, involve them in the CRM initiative to ensure they understand what your requirements are and see how they can help.
- However, if you do decide to modernize, collaborate and involve your IT teams, especially between your Application Development teams and your Enterprise Architects to ensure all of the best options are considered to handle your data sharing and migration needs.
- Lastly, consult with your technology partners including your new CRM vendor, they may be working with solution providers to help address these data issues as you are probably not the only one in this situation.
CRM systems have come a long way in today’s Big Data and Cloud Era. Many firms are adopting more flexible solutions offered through the Cloud like Salesforce.com, Microsoft Dynamics, and others. Regardless of how old or new, on premise or in the cloud, companies invest in CRM not to just serve their sales teams or increase marketing conversion rates, but to improve your business relationship with your customers. Period! It’s about ensuring you have data in these systems that is trustworthy, complete, up to date, and actionable to improve customer service and help drive sales of new products and services to increase wallet share. So how to do you maximize your business potential from these critical business applications?
Whether you are adopting your first CRM solution or upgrading an existing one, keep in mind that Customer Relationship Management is a business strategy, not just a software purchase. It’s also about having a sound and capable data management and governance strategy supported by people, processes, and technology to ensure you can:
- Access and migrate data from old to new avoiding develop cost overruns and project delays.
- Identify, detect, and distribute transactional and reference data from existing systems into your front line business application in real-time!
- Manage data quality errors including duplicate records, invalid names and contact information due to proper data governance and proactive data quality monitoring and measurement during and after deployment
- Govern and share authoritative master records of customer, contact, product, and other master data between systems in a trusted manner.
Will your data be ready for your new CRM investments? To learn more:
- Download Salesforce Integration for Dummies
- Download a new Whitepaper on how to Maximize Integration ROI with a Hybrid Approach
- Consolidating Multiple Salesforce Orgs: A Best Practice Guide
- Sign up for a 30 Day Trial of Informatica Cloud Integration
Follow me on Twitter @DataisGR8
Not so long ago, Google created a Web site to figure out just how many people had influenza. How they did this was by tracking “flu-related search queries”, “location of the query,” and applied it to an estimation algorithm. According to the website, at the flu season’s peak in January, nearly 11 percent of the United States population may have influenza. This means that nearly 44 million of us will have had the flu or flu-like symptoms. In its weekly report the Centers for Disease Control and Prevention put this at 5.6%, which means that less than 23 million of us actually went to the doctor’s office to be tested for flu or to get a flu-shot.
Now, imagine if I were a drug manufacturer. There is a theory about what went wrong. The problems may be due to widespread media coverage of this year’s flu season. Then add social media, which helped news of the flu spread quicker than the virus itself. In other words, the algorithm is looking only at the numbers, not at the context of the search results.
In today’s digitally connected world, data is everywhere: in our phones, search queries, friendships, dating profiles, cars, food, and reading habits. Almost everything we touch is part of a larger data set. The people and companies that interpret the data may fail to apply background and outside conditions to the numbers they capture.
Now, while we build our big data repositories, we have to spend some time to explain how we collected the data and under what context.
It’s true. Data integration is a whole new game, compared to five years ago, or, in some organizations, five minutes ago. The right approaches to data integration continue to evolve around a few principal forces: First, the growth of cloud computing, as pointed out by Stafford. Second, the growing use of big data systems, and the emerging use of data as a strategic asset for the business.
These forces combine to drive us to the understanding that old approaches to data integration won’t provide the value that they once did. As someone who was a CTO of three different data integration companies, I’ve seen these patterns change over the time that I was building technology, and that change has accelerated in the last 7 years.
The core opportunities lie with the enterprise architect, and their ability to drive an understanding of the value of data integration, as well as drive change within their organization. After all, they, or the enterprises CTOs and CIOs (whomever makes decisions about technological approaches), are supposed to drive the organization in the right technical directions that will provide the best support for the business. While most enterprise architects follow the latest hype, such as cloud computing and big data, many have missed the underlying data integration strategies and technologies that will support these changes.
“The integration challenges of cloud adoption alone give architects and developers a once in a lifetime opportunity to retool their skillsets for a long-term, successful career, according to both analysts. With the right skills, they’ll be valued leaders as businesses transition from traditional application architectures, deployment methodologies and sourcing arrangements.”
The problem is that, while most agree that data integration is important, they typically don’t understand what it is, and the value it can bring. These days, many developers live in a world of instant updates. With emerging DevOps approaches and infrastructure, they really don’t get the need, or the mechanisms, required to share data between application or database silos. In many instances, they resort to coding interfaces between source and target systems. This leads to brittle and unreliable integration solutions, and thus hurts and does not help new cloud application and big data deployments.
The message is clear: Those charged with defining technology strategies within enterprises need to also focus on data integration approaches, methods, patterns, and technologies. Failing to do so means that the investments made in new and emerging technology, such as cloud computing and big data, will fail to provide the anticipated value. At the same time, enterprise architects need to be empowered to make such changes. Most enterprises are behind on this effort. Now it’s time to get to work.