Point-of-Sale (POS) systems are continuing to evolve. What was once only a "processing and recording of sales" mechanism (such as a cash register), POS implementation is now a considerable competitive advantage in a retailer's strategy. As a result, retailers are always looking to get more long-term ROI out of their POS systems. They are determining how this interaction opportunity with a customer can support other parts of a company's multichannel sales strategy as well.
The operation of these systems is rapidly moving from a counter-based cash register to the retailer's floor, as credit card processing hardware increasingly supports this model. Mobile handheld devices, tablets and other consumer devices performing the actual sales transaction are now commonplace within a store, no longer tying personnel to the cash register. As a result, customers are now often asked to enter their own information via the on-the-floor transaction device, and an email address now more than ever is part of the collected customer information. Primarily, this is because customers increasingly prefer more efficient and environment-friendly e-receipts. Better yet, collecting an email address can then provide an opportunity for future communications via email for the retailer, and can result in increased ongoing customer engagement.
The value of collecting an email address can be considerable. Email-oriented and Web-based marketing are outpacing traditional advertising and direct mail methods, and multi-channel sales strategies are as critical to success as ever. Even with retailers where traditional methods of POS still make the most sense via a cash register, collecting email addresses are equally important for customer retention success.
However, there are a couple of challenges to collecting this kind of customer data on the floor. First, data entry can sometimes be a little tougher with these mobile devices. If the customer enters their own data, which is now often the case, the chances of collecting incorrect or invalid email addresses either by accident or otherwise can go up considerably. Even if the email address is collected properly, 30-40% of people change their email address every year. Simple typos or not, these issues can not only prevent e-mail receipts from being delivered, but a large incidence of invalid emails being entered can result in future marketing communications going into spam folders if Internet Service Providers (ISPs) detect enough email bounces and failed deliveries coming from the same sending source.
Real-time email validation that utilize an instantaneous out-to-the-Cloud check to ensure email deliverability can substantially reduce typos and otherwise bad email addresses from getting into the system in the first place, right there on the floor at the point of data capture. Utilizing this kind of technology of email address validation can reduce email bounces and failures by 90% or more. It can be an effective tool in ensuring the highest possible levels of data integrity when capturing customer details.
StrikeIron's Email Verification Solution is cloud-based; allowing it to determine in real-time if an email address is valid and deliverable before sending a message. It is used in many production POS systems today to ensure as often as possible that correct email addresses are obtained when collecting customer information.
The cloud-based, real-time approach is an important one, as StrikeIron is constantly evolving its algorithms in the background without requiring customers to update their POS integration in any way. Our team of email verification experts is constantly tweaking, enhancing, and otherwise modifying the algorithms that make these real-time checks as accurate as possible on an ongoing basis without any effort from the customer leveraging the technology.
Email Verification is only one easy-to-integrate API product available from StrikeIron. Others include Phone Number Validation, Address Verification, Do Not Call List checking, SMS Text Messaging and several more. For more information, please contact info@strikeiron.com.
We will be demonstrating our real-time lead enhancement, data quality, and SMS mobile marketing solutions this week at LeadsCon East in New York City.
The focus of the LeadsCon event is lead generation, online direct response and customer acquisition. Each of these marketing categories represents a prime application of StrikeIron's solutions that greatly increase the value of a lead through real-time data validation and data enhancement, making StrikeIron an ideal partner for companies that are taking on these kinds of initiatives.
For example, StrikeIron's real-time email verification solution, delivered via easy-to-integrate APIs in the Cloud, ensures that an email address is valid, live, and can accept messages. This helps to maintain clean, current, email addresses in customer and prospect databases, as sending email to disabled email addresses can put companies on spam lists and severely hamper marketing communications efforts.
Other solutions that will be demonstrated include address verification (North American and Global), phone validation, reverse phone and address append, email append, and our SMS text messaging solution, enabling organizations to communicate and engage with customers on mobile devices anytime and anywhere in the world.
All of these solutions are delivered through IronCloud, our award-winning Cloud-based data delivery platform, making it very easy to integrate any or all of them into Web sites, applications, business processes, or anything else that has the ability to consume a SOAP or REST-based Web service.
If you would like to meet with a StrikeIron representative at LeadsCon East to explore these solutions, please visit http://offers.strikeiron.com/leadscon
As organizations move applications to the Cloud where it makes sense to do so, they should recognize that this is an ideal opportunity to improve the value of the underlying data assets that feed these applications. After all, any system, Cloud or otherwise, is only as good as the data within it.
A "move to the Cloud" provides a unique opportunity to both ensure existing data is of the highest possible quality and to also
install mechanisms to govern that all future data that enters the system is accurate, current, and complete. This is especially ideal if data is also being moved from an existing internal database to a Database-as-a-service (DBAAS) product like SQL Azure or Amazon RDS, or a to a database that will be running on top of a Cloud service such as MySQL or SQL Server on Amazon, Microsoft Azure, Rackspace, or any other Cloud platform.
As data is moving from its source database, where it currently exists, into its target Cloud database, you can take advantage of this ideal time to:
- Ensure all physical addresses are valid, accurate, current and complete
- Ensure all email addresses are live, working email addresses that have not been disabled or changed (otherwise, you could find yourself on spam lists simply by trying to contact your customers)
- Ensure all telephone numbers are valid, accurate, and current
- Ensure all data fields are consistent in content and individual data elements are non-ambiguous, making data analysis and the emerging field of data science much more effective
- Fill in all missing data where possible
- Eliminate duplicate contact and customer records
- Incorporate any other data-specific business rules and requirements that make sense for your organization
Also, the wise organization puts real-time data quality and data enhancement mechanisms in place at the points of data collection, such as a data entry form or within a Web-to-lead process, to ensure that all new data coming into a system is of the highest possible quality. This also prevents degradation of data over time, so the same set of issues do not occur again a short time later. Otherwise, this will lead to more cleansing efforts and cost downstream.
A significant part of the success of any Cloud initiative revolves around cleansing existing data during migration, getting real-time data quality mechanisms in place, and establishing an ongoing data management plan with metrics and goals for going forward. Don't let rare application migration opportunities such as this go to waste.
Many of StrikeIron's direct customers integrate our various API-delivered data services into applications, Web sites, and business processes entirely on their own, usually with a single line of code or two - a testament to how easy this is to do. These product offerings available on the Cloud can be integrated into anything that can consume a SOAP or REST-based Web service (which is just about anything).
However, StrikeIron has also developed technology integration partnerships with many of today’s top software and Internet solutions platforms, solutions which are all enhanced by integrating Data-as-a-Service capabilities from StrikeIron.
Having these capabilities, such as real-time address verification, email verification, sales tax rates, foreign currency rates, SMS text messaging, and phone verification, pre-integrated into various other platforms that are already in use by large customers every day can be a very compelling solution. It is a win-win-win scenario for our customers, partners, and our technology.
One such partner is Informatica. Informatica has integrated several StrikeIron services for the purposes of contact data validation within its Informatica Cloud platform, as data validation is a very important step in the integration of data between various platforms. These services can be used via the Informatica Cloud StrikeIron plug-in, or as directly integrated within the Informatica Cloud platform per our most recent partnership. In the latter case, some of our services are available for use simply by checking a box directly within Informatica's Cloud application. This makes it very easy to have high quality, validated data arriving at a target destination, having been cleansed as an intermediate step while in transit from its source. You can view a recorded Webinar here.
"Big Data" is all the rage these days, and the Big Data marketing umbrella seems to be rapidly expanding as a result. The term is getting slapped on all kinds of product marketing narratives, including many kinds of data-oriented analysis product, or in products where data exists in any kind of volume (much of which is an evolution of data warehousing concepts needing some newness). So of course the usual market confusion is present as with any hot industry term.
As for me, I like to think conceptually of Big Data as referring to datasets that are so large, they tend to fall outside the scalability and performance afforded in traditional table-driven SQL-based data management approaches, and instead need a different way of thinking about and handling the tremendous amount of potential information that exists within these data entities.
The term Big Data emerged as many Web-scale companies such as Facebook, Twitter, Google, Amazon, and others started stretching the limits of traditional databases with their sheer data volumes and performance requirements, and began to realize they needed a data management approach more finely-tuned to their massive data requirements.
As a result, technology such as Hadoop, Cassandra, BigData, Dynamo, and others began to appear to assist in addressing these requirements. Analytics solutions focused on these massive data volumes have also begun to appear, as well as storage and performance alternatives slated as ideal for Big Data. There is also a new class of operational metrics solutions that help to generate these volumes of data, including both software and hardware instrumentation.
However, one concept seems to be often missing from these excited conversations: data quality. While it is true that much of big data goes well beyond structured data, much of it is still data, and data always has the potential to be unusable or flat out wrong. This omission of course creates opportunity for the astute and innovative. Many of the traditional data stewardship approaches are still applicable and necessary and need to be implemented with Big Data characteristics in mind. Customer data quality, profiling, data standardization, consistency prior to analysis and integration, rules-based testing, and even non-technology oriented quality initiatives (data completeness incentives for example) need to be part of any Big Data strategy for anyone hoping to have any sort of success.
So as you embark on the path of massive data volumes, be sure that a data quality strategy exists as part of the larger Big Data strategy, and keep your eye out for what happens in this space as its still in its formative period. After all, the last thing any organization wants is Big Bad Data.
There are many different kinds of batch data cleansing processes that can be performed against large databases of existing customer information. Standardizing inconsistent data, removing duplicate records, validating columns against up-to-date reference data, filling in missing data, and appending new data to existing data are all examples of customer data processing that can help improve the value of internal data assets.
When data assets undergo these kinds of processes their value increases and they enable business intelligence applications to be more useful, operations to be more efficient, and customer communication efforts to be more effective. These are worthwhile endeavors indeed.
However, it can often be a considerable effort to do large, after-the-fact database cleanup jobs - not to mention the considerable costs and complexity associated with offline data processing. Also, batch jobs are rarely a one-time effort, as the same problems begin to appear soon after a mass cleansing, and then begin to build to troublesome levels again, putting the data stewards of the organization back to square one.
An alternative can be to leverage
real-time data quality mechanisms at the point of data collection. This means validating data, filling in missing data, appending data, standardizing data, and comparing it to existing data for duplicates in real-time,
before it ever gets into the database. This can eliminate or dramatically reduce the cost and effort associated with downstream batch cleanup processes, enabling the benefits of clean, complete, accurate data to appear immediately across the organization. It also prevents the build up of these kinds of data quality issues over time.
Real-time data quality can be achieved by
integrating calls to data quality functions within business processes, Website data collection forms, customer-facing applications, call center applications where representatives speak with customers, and anywhere else that data is collected in real-time. Typically these programmatic calls are to Cloud-based APIs that are leveraging constantly refreshed reference data to ensure the highest possible data accuracy.
Here more than ever, an ounce of prevention is worth a pound of cure.
One of the exciting things about SOAP and REST-based Web services protocols is that they are text-based, providing for the platform independence necessary for broad machine-to-machine communication and open cloud computing models. In other words, describing data using a textual XML dialect allows iPhones to communicate with mainframes, as well as enabling Fortran-developed scientific instrumentation devices to be able communicate with Dell Server applications in the Cloud written in Java.
As long as both machines are aware of the "rules" of a given XML-dialect and how data is described, they can communicate and more importantly pass data back and forth to perform certain functions based on the resultant data. This is powerful and has really helped lay the groundwork for the success of the Cloud.
To demonstrate this concept, here is an example of an "Input" SOAP message to StrikeIron's Sales and Use Tax Basic service. Remember that XML is not meant to be human readable, but rather the implementation of a set of XML dialect rules. However, if you look closely then you can see the actual data elements that are passed within the XML message received by StrikeIron within our data centers by the calling entity:
Our application servers, which are always listening, receive the request, do some user authentication, and then perform the requested task and return the resultant data XML message below. It can then be used how ever necessary by the calling entity (to process an ecommerce transaction for example). Here is an example of the "Output" XML message:
This communication and data transaction has occurred entirely without human intervention. It takes place between machines that could be located anywhere on the globe, each completely oblivious to the hardware and software that comprise the other entity.
Fortunately, humans rarely if ever need to interact at the XML-level (sometimes it might be useful for debugging). Instead, the creation, sending, receiving, and interpretation of these XML messages are handled by the software development environments that one is working in, abstracting a developer or application user away from the XML-based data exchange.
This form of XML messaging is what makes companies like StrikeIron possible, opening up pre-built data processing, data validation, aggregated data sources, and other business functions available to the world. Regardless of what software and hardware environments a customer happens to be running, it's this approach that makes the ever-evolving "Great Data Highway" possible.
We are very excited to announce today that StrikeIron's IronCloud powers the DataFlux's Marketplace. DataFlux, a SAS subsidiary, is the leader in data quality, data integration, and MDM solutions.
Tony Fisher, president and CEO of DataFlux, said, “The ability to deliver data quality capabilities via the cloud keeps DataFlux ahead of the innovation curve. We are pleased to join forces with StrikeIron to provide cloud-based data quality solutions.”
The full release can be found at: http://www.prweb.com/releases/2012/5/prweb9457590.htm
For the Dataflux Marketplace release can be found at: http://www.businesswire.com/news/home/20120501006204/en/DataFlux-Marketplace-Cloud-based-Services-Improve-Customer-Data
So what are you waiting for, signup for a free trial and see what StrikeIron can do for your business:
Late last week, Amazon
released an update to its
DynamoDB service, a fully managed
NoSQL offering for efficiently handling extremely large amounts of data in Web-scale (generally meaning very high user volume) application environments. The DynamoDB offering was originally launched in beta back in January, so this is its first update since then.
The update is a "batch write/update" capability, enabling multiple data items to be written or updated in a single API call. The idea is to reduce Internet latency by minimizing trips back and forth to Amazon's various physical data storage entities from the calling application. According to Amazon, this was in response to developer forum feedback requests.
This update to help address what was already an initial key selling point of DynamoDB tells us that latency is still a significant challenge for cloud-based storage. After all, one of the key attributes of DynamoDB when first launched was speed and performance consistency, something that their NoSQL precursor to DynamoDB,
SimpleDB, was unable to deliver, at least according to some developers and users who claimed data retrieval response times ran unacceptably into the minutes. This also could have been a primary reason for SimpleDB's lower adoption rates. Amazon is well aware of these performance challenges, and hence the significance of its first DynamoDB update.
Another key tenant of DynamoDB is that it is a managed offering, meaning the details of data management requirements such as moving data from one distributed data store to another is completely abstracted away from the developer. This is great news, as complexity of cloud environments was proving to be too challenging for many developers trying to leverage cloud storage capabilities. The masses were scratching their heads as to how to overcome storage performance bottlenecks, attain replication, achieve response latency consistency, and perform other operations-related data management challenges when it was in their purview to do so. By the way, management complexity will likely still be a major challenge for other NoSQL vendors, and there are many "big data" startups offering products in this category, who do not offer the same level of abstraction that DynamoDB offers. It will be interesting to see if the launch of DynamoDB becomes a significant threat to many of these startups.
We learned this reduction of complexity lesson at
StrikeIron within our own niche offerings as well. We gained a much bigger uptake of our simpler, more granular Web services APIs, such as
email verification,
address verification, and other products such as
reverse address and telephone lookups as single, individual services, rather than complex services with many different methods and capabilities. This proved true even if the the more complex services provided more advanced power within a single API. In other words, simplified remote controls for television sets are probably still the best idea for maximum television adoption, as initial confusion and frustration tends to be inversely proportional to the adoption of any technology.
Another interesting point is that this is the fifth class of database product offerings in Amazon's portfolio. Along with DynamoDB, there is also still the aforementioned SimpleDB, a schemaless NoSQL offering for "smaller" datasets. There is also the original
S3 offering with a simple Web service based interface for storing, retrieving, and deleting data objects in a straightforward key/value pair format. Next, there is
Amazon RDS for managed, relational database capabilities that utilize traditional SQL for manipulating data and is more applicable for traditional applications. Finally, there are the various Amazon Machine Image (AMI) offerings on
EC2 (Oracle, MySQL, etc.) for those who don't want a managed relational database and would rather have complete control over their instances (and not have to utilize their own hardware) and the RDBMs that run on them.
This tells us that the world is far from one-size-fits-all cloud database management systems, and we can all expect to be operating in hybrid storage environments that will vary from application to application for quite some time to come. I suppose that's good news for those who make a living on the operations teams of information technology.
And along with each new database offering from Amazon also comes a different business model. In the case of DynamoDB for example, Amazon has introduced the concept of "read and write capacity units", where charges will be based on the combination of frequency of usage and physical data size. This demonstrates that the business models are still somewhat far from optimal, and will likely change again in the future. Clearly they are not yet quite right for the major vendors trying to figure it all out as business model adjustments in the Cloud are not just limited to Amazon.
In summary, following the Amazon database release timeline over the years yields some interesting information, namely that speed/latency, reduction of complexity, the likelihood of hybrid compute and storage environments for some time to come, and ever-changing cloud business models are the primary focus of cloud vendors responding to the needs of their users. And as any innovator knows, the challenges are where the opportunities are.
I had an opportunity to moderate a panel at the Data 2.0 Summit this week in San Francisco entitled "Why You Should Join the API Economy". There was a considerable amount of thought leadership on the panel, including Chris Moody, President of Gnip; Gaurav Dillon, CEO of SnapLogic; Chris Lippi, VP Products of Mashery; Peter Kirwan, Entrepreneur-in-Residence of Neustar; and Tim Milliron, Director of Engineering at Twilio.
We explored several topics including where success is occurring now within various API ecosystems (what is working), where money is actually being made with APIs, what some of the adoption challenges are moving forward, and how people can begin moving down an API path (both publishing APIs and finding relevant and valuable ones to consume) - all of these topics I plan to cover in future blog entries.
However, one area we explored that I thought was especially interesting is the adoption of API-centric business models within larger enterprises. Sure, high tech companies like Cisco and Salesforce have been utilizing APIs as significant parts of their business models for years. But where it is becoming especially interesting and demonstrates APIs moving into the mainstream is the traction of APIs and DAAS (data-as-a-service) in traditional vertical industries.
For example, many government entities are now opening up data channels to enable citizens to create innovative applications, such as San Francisco's open data portal, on the publishing side of data and APIs. Opening up this data to the masses can drive all sorts of innovation that bring benefits to entire communities.
On the consumption side, Mohawk Paper's (a company founded in the late 1800's) inspirational data integration case study that Gartner published was discussed as evidence of an enterprise pulling data together from multiple third parties to create a custom solution in the Cloud. One of these services is StrikeIron's real-time foreign exchange rate service API. And of course, among our 1800 customers there are several Fortune 500 companies that are leveraging our various API's and DaaS products at increasing rates, all evidence of expanding adoption in the enterprise.
As we see API-centric and DaaS-centric business models emerge that find traction in the enterprise in addition to all of the smaller entrepreneurial innovators and startups, we know we are getting closer and closer to mainstream adoption, which is where some of the biggest opportunities are yet to be realized.
