Category Archives: Cloud Data Management

Cloud Data Management

How Great Data in the Cloud Can Make for Greater Business Outcomes

Great Cloud Data Improves Business Outcomes

Great Cloud Data Improves Business Outcomes

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.

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Does Your Sales Team Have What They Need to Succeed in 2015?

Like me, you probably just returned from an inspiring Sales Kick Off 2015 event. You’ve invested in talented people. You’ve trained them with the skills and knowledge they need to identify, qualify, validate, negotiate and close deals. You’ve invested in world-class applications, like Salesforce Sales Cloud, to empower your sales team to sell more effectively. But does your sales team have what they need to succeed in 2015?

Gartner predicts that as early as next year, companies will compete primarily on the customer experiences they deliver. So, every customer interaction counts. Knowing your customers is key to delivering great sales experiences.

If you’re not fueling Salesforce Sales Cloud with clean, consistent and connected customer information, your sales team may be at a disadvantage.
If you’re not fueling Salesforce Sales Cloud with clean, consistent and connected customer information, your sales team may be at a disadvantage.

But, inaccurate, inconsistent and disconnected customer information may be holding your sales team back from delivering great sales experiences. If you’re not fueling Salesforce Sales Cloud (or another Sales Force Automation (SFA) application) with clean, consistent and connected customer information, your sales team may be at a disadvantage against the competition.

To successfully compete and deliver great sales experiences more efficiently, your sales team needs a complete picture of their customers. They don’t want to pull information from multiple applications and then reconcile it in spreadsheets. They want direct access to the Total Customer Relationship across channels, touch points and products within their Salesforce Sales Cloud.

Watch this short video comparing a day-in-the-life of two sales reps competing for the same business. One has access to the Total Customer Relationship in Salesforce Sales Cloud, the other does not. Watch now: Salesforce.com with Clean, Consistent and Connected Customer Information

Is your sales team spending time creating spreadsheets by pulling together customer information from multiple applications and then reconciling it to understand the Total Customer Relationship across channels, touch points and products? If so, how much is it costing your business? Or is your sales team engaging with customers without understanding the Total Customer Relationship? How much is that costing your business?

Many innovative sales leaders are gaining a competitive edge by better leveraging their customer data to empower their sales teams to deliver great sales experiences. They are fueling business and analytical applications, like Salesforce Sales Cloud, with clean, consistent and connected customer information.  They are arming their sales teams with direct access to richer customer profiles, which includes the Total Customer Relationship across channels, touch points and products.

What measurable results have these sales leaders acheived? Merrill Lynch boosted sales productivity by 15%, resulting in $50M in annual impact. A $60B manufacturing company improved cross-sell and up-sell success by 5%. Logitech increased across channels: online, in their retail partner’s stores and through distribution partners.

This year, I believe more sales leaders will focus on leveraging their customer information for competitive advantage. This will help them shift from sales automation to sales optimization. What do you think?

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Posted in 5 Sales Plays, Business Impact / Benefits, Business/IT Collaboration, CIO, Cloud, Cloud Computing, Cloud Data Integration, Cloud Data Management, Customer Acquisition & Retention, Data Integration, Data Quality, Enterprise Data Management, Intelligent Data Platform, Master Data Management, Operational Efficiency, SaaS, Total Customer Relationship | Tagged , , , , , , , , , , , , , , , | 1 Comment

Is Your Data Ready to Maximize Value from Your CRM Investments?

Is Your Data Ready to Maximize Value from Your CRM Investments?

Is Your Data Ready to Maximize Value from Your CRM Investments?

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.

Looking Ahead!

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:

Follow me on Twitter @DataisGR8

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Posted in Architects, Cloud, Cloud Application Integration, Cloud Computing, Cloud Data Integration, Cloud Data Management, CMO, Customer Acquisition & Retention, SaaS | Tagged , , , , , , , , , | Leave a comment

There are Three Kinds of Lies: Lies, Damned lies, and Data

Lies, Damned lies, and Data

Lies, Damned lies, and Data

The phrase Benjamin Disraeli used in the 19th century was: There are three kinds of lies: lies, damned lies, and statistics.

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.

Twitter @bigdatabeat

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Posted in Big Data, Cloud Data Management, Data Governance, Data Transformation, Data Warehousing, Hadoop | Tagged , , , , | Leave a comment

With the Winter 2015 Release, Informatica Cloud Advances Real Time and Batch Integration for Citizen Integrators Everywhere

Informatica Cloud Winter 2015 Release

Informatica Cloud Winter 2015 Release

For those who work in tech, or even have a passing interest in the latest computing trends, it was hard to miss the buzz coming out of Dreamforce and Amazon re:Invent. As a partner to both companies, engaged on a parallel path, Informatica Cloud is equally excited about these new developments. With the upcoming Winter 2015 release, we have three new platform enhancements that will take those capabilities even further.

The first of these is in the area of connectivity and brings a whole new set of features and capabilities to those who use our platform to connect with Salesforce, Amazon Redshift, NetSuite and SAP.

Starting with Amazon, the Winter 2015 release leverages the new Redshift Unload Command, giving any user the ability to securely perform bulk queries, and quickly scan and place multiple columns of data in the intended target, without the need for ODBC or JDBC connectors.  We are also ensuring the data is encrypted at rest on the S3 bucket while loading data into Redshift tables; this provides an additional layer of security around your data.

For SAP, we’ve added the ability to balance the load across all applications servers. With the new enhancement, we use a Type B connection to route our integration workflows through a SAP messaging server, which then connects with any available SAP application server. Now if an application server goes down, your integration workflows won’t go down with it. Instead, you’ll automatically be connected to the next available application server.

Additionally, we’ve expanded the capability of our SAP connector by adding support for ECC5. While our connector came out of the box with ECC6, ECC5 is still used by a number of our enterprise customers. The expanded support now provides them with the full coverage they and many other larger companies need.

Finally, for Salesforce, we’re updating to the newest versions of their APIs (Version 31) to ensure you have access to the latest features and capabilities. The upgrades are part of an aggressive roadmap strategy, which places updates of connectors to the latest APIs on our development schedule the instant they are announced.

The second major platform enhancement for the Winter 2015 release has to do with our Cloud Mapping Designer and is sure to please those familiar with PowerCenter. With the new release, PowerCenter users can perform secure hybrid data transformations – and sharpen their cloud data warehousing and data analytic skills – through a familiar mapping and design environment and interface.

Specifically, the new enhancement enables you to take a mapplet you’ve built in PowerCenter and bring it directly into the Cloud Mapping Designer, without any additional steps or manipulations. With the PowerCenter mapplets, you can perform multi-group transformations on objects, such as BAPIs. When you access the Mapplet via the Cloud Mapping Designer, the groupings are retained, enabling you to quickly visualize what you need, and navigate and map the fields.

Additional productivity enhancements to the Cloud Mapping Designer extend the lookup and sorting capabilities and give you the ability to upload or delete data automatically based on specific conditions you establish for each target. And with the new feature supporting fully parameterized, unconnected lookups, you’ll have increased flexibility in runtime to do your configurations.

The third and final major Winter release enhancement is to our Real Time capability. Most notable is the addition of three new features that improve the usability and functionality of the Process Designer.

The first of these is a new “Wait” step type. This new feature applies to both processes and guides and enables the user to add a time-based condition to an action within a service or process call step, and indicate how long to wait for a response before performing an action.

When used in combination with the Boundary timer event variation, the Wait step can be added to a service call step or sub-process step to interrupt the process or enable it to continue.

The second is a new select feature in the Process Designer which lets users create their own service connectors. Now when a user is presented with multiple process objects created when the XML or JSON is returned from a service, he or she can select the exact ones to include in the connector.

An additional Generate Process Objects feature automates the creation of objects, thus eliminating the tedious task of replicating hold service responses containing hierarchical XML and JSON data for large structures. These can now be conveniently auto generated when testing a Service Connector, saving integration developers a lot of time.

The final enhancement for the Process Designer makes it simpler to work with XML-based services. The new “Simplified XML” feature for the “Get From” field treats attributes as children, removing the namespaces and making sibling elements into an object list. Now if a user only needs part of the returned XML, they just have to indicate the starting point for the simplified XML.

While those conclude the major enhancements, additional improvements include:

  • A JMS Enqueue step is now available to submit an XML or JSON message to a JMS Queue or Topic accessible via the a secure agent.
  • Dequeuing (queue and topics) of XML or JSON request payloads is now fully supported.
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Posted in B2B Data Exchange, Big Data, Cloud, Cloud Application Integration, Cloud Data Integration, Cloud Data Management | Tagged , , , | Leave a comment

Remembering Big Data Gravity – PART 2

I ended my previous blog wondering if awareness of Data Gravity should change our behavior. While Data Gravity adds Value to Big Data, I find that the application of the Value is under explained.

Exponential growth of data has naturally led us to want to categorize it into facts, relationships, entities, etc. This sounds very elementary. While this happens so quickly in our subconscious minds as humans, it takes significant effort to teach this to a machine.

A friend tweeted this to me last week: I paddled out today, now I look like a lobster. Since this tweet, Twitter has inundated my friend and me with promotions from Red Lobster. It is because the machine deconstructed the tweet: paddled <PROPULSION>, today <TIME>, like <PREFERENCE> and lobster <CRUSTACEANS>. While putting these together, the machine decided that the keyword was lobster. You and I both know that my friend was not talking about lobsters.

You may think that this maybe just a funny edge case. You can confuse any computer system if you try hard enough, right? Unfortunately, this isn’t an edge case. 140 characters has not just changed people’s tweets, it has changed how people talk on the web. More and more information is communicated in smaller and smaller amounts of language, and this trend is only going to continue.

When will the machine understand that “I look like a lobster” means I am sunburned?

I believe the reason that there are not hundreds of companies exploiting machine-learning techniques to generate a truly semantic web, is the lack of weighted edges in publicly available ontologies. Keep reading, it will all make sense in about 5 sentences. Lobster and Sunscreen are 7 hops away from each other in dbPedia – way too many to draw any correlation between the two. For that matter, any article in Wikipedia is connected to any other article within about 14 hops, and that’s the extreme. Completed unrelated concepts are often just a few hops from each other.

But by analyzing massive amounts of both written and spoken English text from articles, books, social media, and television, it is possible for a machine to automatically draw a correlation and create a weighted edge between the Lobsters and Sunscreen nodes that effectively short circuits the 7 hops necessary. Many organizations are dumping massive amounts of facts without weights into our repositories of total human knowledge because they are naïvely attempting to categorize everything without realizing that the repositories of human knowledge need to mimic how humans use knowledge.

For example – if you hear the name Babe Ruth, what is the first thing that pops to mind? Roman Catholics from Maryland born in the 1800s or Famous Baseball Player?

data gravityIf you look in Wikipedia today, he is categorized under 28 categories in Wikipedia, each of them with the same level of attachment. 1895 births | 1948 deaths | American League All-Stars | American League batting champions | American League ERA champions | American League home run champions | American League RBI champions | American people of German descent | American Roman Catholics | Babe Ruth | Baltimore Orioles (IL) players | Baseball players from Maryland | Boston Braves players | Boston Red Sox players | Brooklyn Dodgers coaches | Burials at Gate of Heaven Cemetery | Cancer deaths in New York | Deaths from esophageal cancer | Major League Baseball first base coaches | Major League Baseball left fielders | Major League Baseball pitchers | Major League Baseball players with retired numbers | Major League Baseball right fielders | National Baseball Hall of Fame inductees | New York Yankees players | Providence Grays (minor league) players | Sportspeople from Baltimore | Maryland | Vaudeville performers.

Now imagine how confused a machine would get when the distance of unweighted edges between nodes is used as a scoring mechanism for relevancy.

If I were to design an algorithm that uses weighted edges (on a scale of 1-5, with 5 being the highest), the same search would yield a much more obvious result.

data gravity1895 births [2]| 1948 deaths [2]| American League All-Stars [4]| American League batting champions [4]| American League ERA champions [4]| American League home run champions [4]| American League RBI champions [4]| American people of German descent [2]| American Roman Catholics [2]| Babe Ruth [5]| Baltimore Orioles (IL) players [4]| Baseball players from Maryland [3]| Boston Braves players [4]| Boston Red Sox players [5]| Brooklyn Dodgers coaches [4]| Burials at Gate of Heaven Cemetery [2]| Cancer deaths in New York [2]| Deaths from esophageal cancer [1]| Major League Baseball first base coaches [4]| Major League Baseball left fielders [3]| Major League Baseball pitchers [5]| Major League Baseball players with retired numbers [4]| Major League Baseball right fielders [3]| National Baseball Hall of Fame inductees [5]| New York Yankees players [5]| Providence Grays (minor league) players [3]| Sportspeople from Baltimore [1]| Maryland [1]| Vaudeville performers [1].

Now the machine starts to think more like a human. The above example forces us to ask ourselves the relevancy a.k.a. Value of the response. This is where I think Data Gravity’s becomes relevant.

You can contact me on twitter @bigdatabeat with your comments.

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Posted in Architects, Big Data, Cloud, Cloud Data Management, Data Aggregation, Data Archiving, Data Governance, General, Hadoop | Tagged , , , , , , | Leave a comment

Making the Hybrid Cloud Work for Public Sector

Making the Hybrid Cloud Work for Public Sector

Hybrid Cloud and Public Sector

If you’ve been working in the government sector for any amount of time, you had to see the advent of the “hybrid cloud” coming. Like all new technologies, when first introduced, “the cloud” was the answer to all your IT woes. It is cheaper, more reliable, infinitely scalable, instantly adaptable, and so on. But, as time has gone by and many of you have dipped your toes in the water, the reality is beginning to surface, and challenges are beginning to appear. Sure, moving email to the cloud was a great first step, and it certainly gave most agencies the ability to show progress in leveraging the cloud. Yes, archiving data to the cloud is also a good use case and is showing progress. But, what’s next? There are plenty of new SaaS offerings popping up, and purpose-built to solve various public sector challenges, and yes, they are generally decent applications. Yet, would it be fair to suggest new challenges are arising as your agency begins to adopt new cloud solutions? In particular, has the advent of specialized applications for government made your overall IT portfolio simpler or more complex? Government has always struggled with a vast array of siloed systems and isn’t the cloud creating yet more challenges in this regard? Well, maybe. Let’s take a look.

What I love about the cloud is it has something of value to offer practically any government organization, regardless of size, maturity, point of view, approach. Even for the most conservative IT shops, there are use cases that just plain make sense. And with the growing availability of FEDRAMP certified offerings, it’s becoming easier to procure. But, thinking realistically, for reasons of law, budget, time, architecture, we know the cloud will not be the solution for every public sector problem. Some applications, some data will never leave your agency’s premises. And here in lies the new complexity. You have applications and data on-prem. You have applications and data in the cloud. And you have business requirements that require these apps to work together, to share data.

So, now that you have a hybrid environment, what can you do about? Let’s face it, we can talk about technology, architecture and approaches all day long, but, it always comes down to this, what should be done with the data. You need answers to questions such as; Is it safe? Is it accessible? It is reliable? How do I know if the integrity has been compromised? What about the quality? How error-prone is the data? How complete is the data? How do we manage it across this new hybrid landscape? How can I get data from a public cloud application to my on-prem data warehouse? How can I leverage the flexibility of public IaaS to build a new application that will need access to data that is also required for an on-prem legacy application?

I know many government IT professional are wrestling with these questions and seeking solutions. So, here’s an interesting thought. Most of these questions are not exactly new, they are just taking on the added context of the cloud. Prior to the cloud, many agencies discovered answers in the form of a data integration platform. The platform is used to ensure every application, every user has access to the data they need to perform their mission or job. I think of it this way. The platform is a “standardized” abstraction layer that ensures all your data gets to where it needs to be, when it needs to be there, in the form it needs to be in. There are hundreds of government IT shops using such an approach.

Here’s the good news. This approach to integrating data can be extended to include the cloud.  Imagine placing “agents” in all the places where your data needs to live, the agents capable of communicating with each other to integrate, alter or move data. Now add to this the idea of a cloud-based remote control that allows you to control all the functions of the agents. Using such a platform now enables your agency to tie on-prem systems to cloud systems, minimizing the effect of having multiple silos of information. Now government workers and warfighters will have the ability to more quickly get complete, accurate data, regardless of where it originates and citizens will benefit from more effectively delivered services.

How would such an approach change your ideas on how to leverage the cloud for your agency? If you live near the Washington, DC area, you may wish to drop in on the Government Cloud Computing and Data Center Conference & Expo. One of my colleagues, Ronen Schwartz will be discussing this topic. For those not in the vicinity, you can learn more here.

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3 Ways to Simplify SAP Connectivity Protocols

Simplify SAP Connectivity Protocols

Simplify SAP Connectivity Protocols

SAP’s Jam social platform has generated a great deal of buzz since its release last May – and for good reason. As detailed by Alan Lepofsky in his coverage for ZDNet, the new Jam reboot included the kind of things, such as out-of-the-box integration, workflow templates and simplified developer tools, that make both IT and business users very happy.

However, the complexity of SAP’s Business Suite (or ECC as it’s called) does not easily lend itself to integration with other applications. The code underlying it is built on a proprietary language called ABAP – a combination of COBOL and Open SQL with some object-oriented features– that requires specialized knowledge and skill not easily found outside of the SAP ecosystem. Up until now, the typical integration project required the involvement of a specialized SAP consultant to develop custom ABAP code or map complex BAPI/IDoc structures as well as a BASIS administrator to transport the ABAP code from development to QA to production. The result was expensive and manually intensive. Integration projects took a few months or longer to complete and were not agile enough to handle ongoing requirements or even field changes.

Today, Informatica Cloud offers business a more innovative approach to SAP data extraction – ultimately, promoting agile development and enabling rapid deployment with the following three important features.

Automatically Generating ABAP Code

At the core of Informatica’s solution is the Cloud Connector for SAP. While the face of the Connector is a simple, wizard-based, drag-and-drop interface, under the hood it uses a Remote Function Call (RFC) to dynamically generate ABAP code (based on user choices) to connect with SAP and access the data through the application layer.

Drag and Drop Design Palette

The wizard guides the user through the steps necessary to extract the data from SAP and send it to any application where it is needed. Using Informatica Cloud’s drag-and-drop design palette, one can simply choose SAP – like any other application endpoint – and select what is needed to connect to the target, without ever having write specialized code.

Because of the dynamically generated ABAP code, SaaS application administrators trying to connect to SAP don’t have to deal with the SAP transports (and the lengthy development cycle) for each extract, reducing the time from months to weeks for an individual project, as well as the load on the BASIS administrators. The increased agility enables end users to respond to business demands by acquiring related data extracts, field and feature changes and/or additions – in near real time. Informatica also reduces the load on both the admin and the server – even further by eliminating the need for transports and sending the data in packets, and by running the extracts in the background. And since no data is staged or buffered on the SAP server, there is never a risk of compromising the system’s or online users’ performance.

Speedy Development Through Vibe Integration Packages

Informatica’s solution also includes a technology bundle to speed up development time and reduce the user’s learning curve. The bundle, or Informatica Vibe integration package, consists of downloadable templates that help the user to understand and use the complex SAP interfaces. Future roadmap releases will contain resources for additional SAS endpoints.

In his review mentioned above in the opening, Lepofsky notes the importance of integration and the partner ecosystem to the Jam platform. The same can be said of the SAP Business Suite and the specialized LOB cloud apps that orbit it. Without ready and real-time access to SAP’s data, even the most feature-rich app is of little use to anyone. With Informatica’s Cloud Connector for SAP, business users, like Informatica customer Addivant, now have a simple and efficient way to solve the most pressing problems presented by SAP to cloud app integration.

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