The Imitation ERP


The Imitation Game was applied to computer science by Alan Turing and became known as the Turing Test. (If you are not familiar with Alan Turing, check out the book “Alan Turing: The Enigma” or the movie “The Imitation Game.”) The original objective of the game was for a machine to successfully imitate a human being under a written interrogation. This was not to prove that a machine could think, just that the machine could be taught to imitate human conversation. Of course, the machines took a huge step forward in understanding and imitating human language when IBM’s Watson defeated the best human experts in Jeopardy. But imagine the Imitation Game applied to ERP: could the ERP make business decisions that effectively imitate management decisions, leaving humans to approve the decisions and handle the exceptions? If the management decisions made by the ERP – how to allocate scarce inventory, how to schedule jobs in the plant, which orders should ship first – were indistinguishable from the decisions made by management, could we say that the ERP had successfully imitated management?

Sound farfetched? I’m not so sure. I’ve been bringing my analytical skills current with online classes from Coursera (if you’re a programming geek like me, check out this Machine Learning class), and can report that the combination of algorithm, data, and computing power available today has the potential to completely transform what we think of as ERP. The field of Machine Learning offers solutions that could learn from past management behavior and accurately predict future decisions. And many of these approaches will start appearing in ERP solutions in the near future.

For example, a manufacturer that cannot keep up with demand faces a problem of allocating scarce inventory among hundreds of product-hungry retailers. Every day, someone must decide who gets product and who doesn’t. Conventional solutions attempt to implement naïve rules that must be known in advance (e.g. all ‘high priority’ customers get inventory first), but inevitably managers spend hours first gaming the system (after all, which customers should be ‘high priority’?), then more hours each week manipulating the simplistic recommendations to try to achieve an appropriate balance. These decisions just aren’t as clear cut as we would like.

We face decisions that are not so clear cut in our everyday lives, like choosing what songs you want to play from your favorite music streaming service. Your service pays attention to your decisions about what to listen to, assembles a playlist of recommended songs, and allows you to select or ignore each one (using those decisions to refine the next playlist it will present). You don’t have to select anything in advance, the machine learning algorithms that assemble the playlist can work solely based on your past decisions. Any other information you can add (your age, gender, favorite band, etc.) only improves the results.

But back to our management dilemma: who gets the scarce inventory? A machine learning solution focuses on capturing the decisions made by management to predict future decisions – much the same way the music service focuses on capturing decisions about which songs you play in order to predict what you want to hear. Using a machine learning solution allows the ERP to imitate the manager’s decision criteria, and then allows the managers to focus on approving those decisions and managing the exceptions.

The Machine Learning field has developed technology that can leverage enormous quantities of data about decisions to truly imitate the decisions made by the experts – without the experts ever defining even the most basic decision tree (let alone one that would yield all of the same decisions). Machine Learning systems can be “trained” with historical decision data, develop decision models that accurately reflect the criteria used by the experts (even when they don’t know their own criteria), and accurately predict the decisions those experts will use next week when they allocate scarce inventory again. Some algorithms hold the promise of learning on-the-fly the changing preferences of the experts, allowing the allocation recommendations to change with the business climate.

If you’re still skeptical, remember that we use systems driven by machine learning all around us. From the traffic routing systems in your GPS to facial recognition systems, from Siri to the suggestions presented in the Google search box, every day you are interacting with systems leveraging machine learning. Expect your ERP to start leveraging Machine Learning techniques in the near future, and see if you can tell whether the decisions recorded in your ERP were made by management or the system.

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Is Business Software really an Expense?


There’s an accounting answer to whether software applications – especially enterprise software applications, should be expensed or capitalized – though it seems to change from time to time. The introduction of cloud-based (and cloud-priced) solutions have muddied the accounting waters again, such that a $4M software implementation project must be expensed if the customer is implementing a cloud application, but can be capitalized if implementing the same software on premise. Personally, I’d argue that there is something wrong when you need $4M to implement a software application – though many of you that would consider the same $4M implementation expense a bargain.

I asked the question because the software that runs your business today seems to be considered a cost of doing business (i.e. expensed), where a dozen years ago we still ‘invested’ in ERP and similar systems. So what’s the difference? Back in the 1990s and early 2000s, organizations invested millions into ERP solutions to reduce the labor expense associated with processing information. The reduced work and error rates associated with ERP solutions – especially compared with manual approaches or non-integrated alternatives – drove significant savings for most businesses. The impact of this technology can be seen clearly in the productivity metrics captured by the government, and analyzed by the experts. US productivity continued to climb at increasing rates from the early 1990s (when the first integrated ERPs started to be adopted) through the early 2000s. Then productivity starts a long decline – even going negative in two of the last five years. I’ll argue that the revolutionary changes in administration and information processing related to the introduction of integrated ERP solutions was a major contributor to this productivity growth – and that the lack of any really new innovation in ERP solutions since that time has been a major contributor to the decline.

At this point, I’m ready to take on anyone who wants to argue that the enterprise application market has been a hotbed of innovation over the last decade. Cloud is a huge impact on the enterprise software marketplace just like everywhere else, but running substantially the same software on somebody else’s computer is no way to drive business productivity. And “pay-as-you-go” pricing models, as great as they are for accountants, do more for cash flow than productivity. Is any investment money going to firms dedicated to improving business productivity? A look at a current list of Unicorns (private companies valued at over $1B) shows new technologies driving disruptive business models aimed at consumers (Uber, Airbnb, Theranos, etc.), not technologies that might improve the productivity of most American businesses the way ERP did 25 years ago. University of Chicago economics professor Chad Syverson summed up the decline succinctly: “The second [productivity] wave hasn’t come yet. We have to figure out new ways to use technology and reconfigure the way other things are done.”

The second wave of productivity will arrive when we figure out how to dramatically reduce the time and effort workers spend processing information even more than the traditional ERP has. How? The next wave of ERP will leverage automation, integration, and machine learning to create a self-optimizing information processing machine that can manage the information related to the business. If we can create a car that drives itself, we can create an ERP that runs and adapts itself. A solution that did that would be worth the investment!

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When the Information Robots take over

A fully automated information system can flawlessly process more information at greater levels of detail, freeing up people time for better, faster decisions. As information systems move to fully automated ‘hands free’ operations, many of the information processing jobs that are the least productive – for example, data entry – will finally become obsolete as full automation is deployed. One study reported by The Telegraph forecasts that more than 40% of Administrative jobs will be replaced by automation in the coming years.

For some, an organization running ‘hands free’ enterprise applications is as hard to imagine today as it would have been to imagine an assembly line with no workers in Henry Ford’s day. In manufacturing, low-skilled labor and single-purpose machines gave way to a system of configurable robots that almost take raw materials in at one end of the plant and push unique finished products out the other. The few people still working in the plant use new skills to monitor production, verify quality, resolve issues, and re-configure the robots when required. Fully automated information systems will cause similar changes in the work people do with information.

What kinds of new information systems jobs will arise due to the implementation of fully automated information systems? Once these systems are in place, we see four major job categories that interact with the system: Management, Process Specialists, Information Auditors, and Communications Specialists.

Management continues to be responsible for what business policies to implement in the system (e.g. how much inventory should we carry?), and for review and approval of critical processes such as disbursement of funds. Management will leverage data captured by the system and appropriate analytical tools to make decisions about optimal policies.

Process Specialists will be the skilled jobs that involve configuring information robots to implement management policies, monitoring their performance, and managing any exceptions that arise.

Information Auditors are responsible for inspecting the results of the information processes, insuring that the system accurately and completely implements the business policies determined by management.

Communications Specialists perform the critical role of liaison between various stakeholders (customers, vendors, employees, management) and the information system. Their job is to facilitate better communications, both serving as the information concierge for the stakeholders, and helping capture stakeholders’ feedback to improve information processes.

These new roles will replace the traditional enterprise application functions. Once the information robots are configured by the Process Specialists to implement the policies set by Management, fully automated systems can gather, process, and verify data – pausing long enough for management to approve results at key steps – without further human intervention. Information Auditors will spot check results to insure the system is processing effectively. Questions and suggestions will be funneled through Communications Specialists to insure all stakeholders are satisfied with both the policies set by Management and the execution of those policies by the information robots. The only question that remains is what to do about all of the empty cubicles…

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Automation through Bots

You can define a bot as “a software application that runs automated tasks over the Internet. Typically, bots perform tasks that are both simple and structurally repetitive, at a much higher rate than would be possible for a human alone.” Bots automate routine tasks in software applications you likely use every day.

Search engines deliver functionality we rely upon every day. Search engines use bots to crawl the web and create indexes – simple and structurally repetitive tasks ideally suited for bots. By using bots, search engines get more data, get it faster, and have fewer errors than would have been possible if humans did all of the work.

Social media sites use lots of different bots to enhance communications and networking. The next time you share an article via social media, a bot will grab the title, the first line of the story, and a related image, and share it with your friends. If you had to grab these pieces of information separately (and had my keyboard skills), you’d think twice about sharing. But with bots automating the task, it becomes fast, easy, and error-free.

Last year, for the first time ever, bots outnumbered people on the web. More Internet tasks are being completed by bots than by people. If you’re looking to increase productivity – getting more done with the same number of people – bots seem like a successful strategy. So why not apply bots to the enterprise applications that run your business?

End-to-end automation can handle more data volumes faster and with fewer errors than people-driven processes. A payroll process that starts with approved time transactions should be able to use bots to aggregate pay, apply deductions and garnishments, compute taxes and withholdings, and allocate expenses according to rules defined by the organization. People are tasked with reviewing and approving the results, then the bots handle the distribution of funds and paperwork, including the filing of taxes, communication with benefit providers, and more. Each of the tasks performed by a bot is both simple and structurally repetitive – better performed by a bot than by people. People can then focus on tasks better performed by people than bots.

At HarrisData, we’ve developed AppsInHD as our enterprise platform of the future with a strong focus on end-to-end automation through bots. In all of our enterprise applications – HRIS, CRM, and ERP – productivity will soar as bots take on more of the workload.

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I, Unicorn

I have spectacular new technology. Should I become a unicorn?

First, I should figure out what becoming a “unicorn” involves. To the VCs, a unicorn refers to any tech startup company that reaches a $1 billion dollar market value. To become one apparently means spending $1.27 for each $1.00 in revenue, the path that unicorn Workday took. This trend isn’t isolated to Workday, and it isn’t new. To become a unicorn apparently means you get to spend a small fortune marketing your product in search of the magic revenue hockey stick your angel investor, venture capitalist, and/or investment bankers seek. As long as business looks good (and by business, I don’t mean the traditional definitions like profitability or cash flow), your masters can (and will) flip your company to the new ownership after a brief and frenetic run.

Noting that the presentation of unicorns is as real as the face of a model on a magazine cover, software entrepreneur D H Hansson (creator of Ruby on Rails) instead tried to start something useful. His vision is of a software company that serves customers instead of capturing them, strives to compete in the marketplace instead of dominating it, and has a vision to put a dent in the universe instead of trying to own the whole thing (though he expresses himself with a bit more colorful language). So he built some cool technology and created a startup technology firm – one with the kind of staying power that comes from loyal customers, reasonable profits, and positive cash flow.

After reading Mr. Hannson’s take, I took a long look into the mirror to see whether I thought HarrisData should become a unicorn. We’ve got a new product line in AppsinHD™ that meets all of the criteria for the kind of disruptive technology that fuels the ambitions of unicorns. A few million (or billion) in advertising funding and we’d be buying our way up the revenue hockey stick. But, unlike other startups, we’ve been serving our customers for over 40 years, and already understand the value of long term relationships with customers, partners, and employees. We owe it to them to not squander the opportunity on short-term lottery that is the current state of startup funding.

Thankfully, my reflection showed no horn growing from the center of my forehead.

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How to Triple Your Productivity without Hiring Additional Staff and Similar Claims

Improved productivity is a given of marketing claims in technology. The marketers rarely make clear what the basis is for the claimed improvement. Saying you are three times as productive calculating taxes with a computer than with a pencil is a true claim – but not a relevant claim to someone investing in a new software application to replace the current application. To get a big productivity improvement, the process needs to change, comparable to moving from manual calculation to computers and software in the first instance.


A quick review of productivity growth shows real productivity gains in the 90s, but little gain since. How did IT contribute in these time frames?

Growth Contribution of Computer Software

1959-73 1973-90 1990-95 1995-98
0.03 0.07 0.15 0.19

Federal Reserve, Table 3

The 70s and 80s were known as the “Computer Productivity Paradox.” In the 80s, first time automation (such as MRP) was widely adopted via departmental systems. Green screen applications optimized data entry for rapid calculation. Online entry / correction replaced pure batch entry, edit, process, and report cycles. Departmental stovepipe systems required re-keying of data from other departments to function. Despite the exploding investment in IT, this remained a labor intensive process and a brake on productivity growth.

The 70s and 80s also were known for the rise of Japan, Inc. where the US hemorrhaged manufacturing jobs as uncompetitive with the quality and cost of the Japanese. Exemplified by Taiichi Ohno and the Kanban system, this represented a change in the way a manufacturing plant and supply chain were organized. The traditional approach could not compete. Thus business leaders in the US needed radical change to what they were doing.

“Only in the late 1990s, after a major investment technology boom, were there enough information technology inputs to have a substantial impact on growth and labor productivity at the aggregate level” – NY Fed

In the 90s, tight integration across functional areas (ERP) made it so information was only typed once on the friendlier graphical (GUI) screens. Productivity grew faster as information passed instantly from department to department without re-keying. In the second half of the 90s deployment and productivity accelerated further as the Y2K scramble to handle the rollover to year “00” led to every system being replaced or modified with the newer integrated ERP.

The 90s also featured a massive reorganization of US business whether “reengineering,” “just-in-time,” or “lean”… the way US firms were managed changed dramatically. The technology boom coincided with radical change to how business operated leading to a jump in productivity. As late 90s new Y2K compliant systems came into production implementing tight integration over lean supply chains. The productivity surge carried through to 2004.

“Productivity has been in a low growth regime since late 2004” NY Fed

Productivity stalled since. (The exception in 2009 is described as “cyclical and transitory in nature” by the Fed). Innovation continued – smart phones and tablets provide even friendlier browser and mobile interfaces. Investment has intensified, especially in the “cloud.” Why then a reprise of the Computer Productivity Paradox? The applications are still the same underneath, supporting the same business processes and work flows. Restoring productivity requires change beyond the cosmetic effects of mobile user interfaces or cloud data centers of the past decade.

Change since 2004 is faster for consumers than industry with consumers adopting new Internet of Things gadgets. The consumer world is a democratic ecosystem of interconnection with self-registration on one side and approval / acceptance on the other. For example a consumer searches the web and clicks on a product to purchase, send the contents of the shopping cart to the vendor. The vendor responds with an order complete with pricing, and asks for payment information. The consumer responds with a credit card or payment service. An email confirmation is sent. The result is a secure transaction where all connections are created in real time based on shared information.

This contrasts the tightly integrated world of SOAP protocols on the industrial internet. Under SOAP a requestor sends a command on a tight connection. Each connection must be engineered and tested for proper formatting and syntax prior to connection. This is a very different baseline from the consumer example above where Web Services technology enables the loose connection for exchanging information until the transaction is complete.

As the Internet of Things expands from consumer gadgets to industrial equipment, the assumption changes to one where all devices are connected (loosely). Automation requires connectivity for productivity to expand to the ecosystem and past the supply chain. Less rigid but broader connection suits this environment (too many things coming too quickly for tight integration – but the data must flow). Back office work changes dramatically – data entry and keying is replaced by data flow from the Internet of Things and end consumers alike. Software bots can handle the gathering and processing of this data. People handle the remainder: quality assurance of the data as it flows through the organization – correcting issues as alerted one at a time, analyzing and drilling into the data sets, then approving and communicating the results. The organization increases productivity by changing work from keying centric to managing centric.

Want a productivity boost? You need to change what you are doing and use technology which supports that change. Embrace the Internet of Things as a reason to change. Deploy modern IT which eliminates keying as the featured activity and embraces loose connectivity. Restructure by assuring the quality of the data flowing through your organization. The needed software can be found here: AppsInHD.

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The Future is Now

A couple years ago the future of enterprise software could be described as bookmark, link and search. Today more attributes of the future are clear – and more important: available. Let’s explore the vision and the product which delivers it.

The vision begins with the user as a consumer, willing to do normal internet consumer activities like searching for data or a form, following links on a page to get to the underlying details, and bookmarking useful pages for return visits. This vision is one of user self-service – completely detached from traditional engineered keystrokes and defined work flows of traditional ERP systems. The challenge for programmers is to allow users freedom to do their jobs, not the old challenge for users to work around static computer programs to do their jobs in a dynamic world. Today that vision extends beyond consumer inspired web navigation with alerts and notifications to include the ability to drill down into real time dynamic data, plus total control and security of your application data.

The growing Internet of Things (IOT) provides additional challenges to any enterprise application vision. What do you do when the user is a Thing rather than a person? Things send certain kinds of data: status, location, and increasingly automation and actionable data. These arrive as discrete events in real time, not as a pre-bundled batch. They may either process normally behind the scenes (with a notification to a human as desired), or present an issue (requiring an alert for human intervention). The conversation will be based in Web Services protocols (instead of an inflexible SOAP spec or traditional user interface) to allow rapid addition of new Things to the enterprise. Thus an extended vision gives equality to Things as users.

To deliver on this updated vision, the underlying ERP application must be built in a different way from the traditional system optimization of client server approaches. In the past, the user organized work to minimize demands on the hardware. Today the focus is on the user, not on the back end system. The resource to be optimized is the user’s time. This means a new elastic approach to the system behind the cloud is required. Provide resources to the user as needed, let the flow of work drive the system. Deploy a web services architecture that is as nimble as the web, with loosely coupled application services conversing through web APIs across private or public networks with complete elasticity. This requires rebuilding the applications using web standards (Json, Jquery, RESTful APIs) to deploy business capabilities. Expected cloud benefits such as capacity on demand and the ability to customize deployments via simple mashups are easily delivered.

The HarrisData AppsInHD Payroll delivers this vision. The enterprise user is free to do her job by dealing with issues naturally, as they arise. She is no longer chained to the keyboard entering and editing data for processing. Time cards may be created by Things (in this case timeclocks). The system highlights potential problems and creates alerts for the user (perhaps someone is sick and has no time card). Alerts may be addressed one at a time, the batch processing cycle no longer constrains and controls work flows. The user then drills into summary data comparing this pay period to the prior to ensure the system found all the issues. Meanwhile, an array of service and automation bots securely manage work in the background while providing her with rapid responses. The payroll is completed to the human rhythm and pace of the work.

Thus time changes and improves on the vision, while the AppsInHD approach allows HarrisData to deliver products that meet the test of the future today.

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You Can’t See What’s Behind the Cloud

Cloud: 3. a dim or obscure area in something otherwise clear or transparent.

The entire information technology industry is moving to the cloud. What, if anything, have they done differently once they arrived? It can be hard to tell whether vendors do the same old thing or something new in the cloud. You can’t see what they are doing behind the cloud – the cloud obscures all behind the user interface, and that fact makes it a magical and happy place for vendors.

Vendors, enterprise transaction system vendors (e.g, ERP, HRIS, CRM) in particular, see huge risks and opportunities in moving to the cloud. Each vendor makes its own choices in how to avoid risks and reap opportunities. Each must decide how to respond to the difference between cloud infrastructure and the familiar on premises infrastructure then adapt their applications. The biggest changes are from branded infrastructure stacks frequently designed for client-server computing to the commodity managed services which run the consumer web. While it is possible to run a traditional on premises branded stack behind the cloud, it is desirable to run a web-centric commodity stack such as LAMP and adapt the internal application architecture to the web’s more freewheeling ways. The application could then be delivered on premises as well where appropriate, without the user paying brand premiums for raw processing power.

Does the cloud take the known problems of on premises deployment and obscure them? Or is better technology used to reduce or eliminate the problems? HarrisData lifts the curtain on AppsInHD design considerations so you can find out.

HarrisData is a provider of on premises ERP software for over 40 years. The first step to delivering a cloud option was to define what a cloud ERP should be. The cloud ERP should resemble successful products of the web – with a tablet ready interface navigated through bookmarks, links and searches. Any function in the ERP should readily mashup with anything else on the web including new possibilities from the Internet of Things. The enterprise customer should be able to adapt or modify the cloud ERP. All enterprise data should be secure and separate from others. The customer is free to focus on their business while HarrisData ensures the technology will not intrude upon or limit them.

Next define an application architecture to enable the capabilities of the vision. The temptation to wallpaper our traditional application (stick a mobile app in front of it) and hide it behind the cloud is great. It is a fast path to get the marketing cachet of cloud without spending too much on research and development. In effect this approach moves the complexity and cost of an on premises application behind the cloud. The result remains too rigid to take advantage of everything else the cloud has to offer.

Instead, HarrisData created a web services architecture that is as nimble as the web, with loosely coupled application services conversing through web APIs across private or public networks with complete elasticity. This requires rebuilding the applications using web standards (Json, Jquery, RESTful APIs) to deploy business capabilities. Expected cloud benefits such as capacity on demand and the ability to customize deployments via simple mashups are easily delivered.

Then select technology that best supports the vision, while retaining the ability to continue using the technology customers have in place today. HarrisData chose the LAMP stack, an open source infrastructure which runs on both Intel and IBM Power hardware (including a Turbo-LAMP version optimized for IBM Power). This choice frees HarrisData and its customers from vendor lock in while extending the value of current investments in IBM Power based servers. It is also the most common infrastructure on the web.

Behind the cloud, HarrisData reinvented the infrastructure of its ERP applications. It was not the most traveled path to the cloud, but it does offer the greatest value to our customers.

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Cloud Vendors Spend Too Much on Sales and Marketing

Look anywhere, and it seems the cloud is the answer you were looking for. How much of this is real, how much venture capital hype? Phil Wainewright interviewed Zoho CEO Sridhar Vembu and found evidence to support the hype part.

I believe we are in a bubble, for sure. We are in a bubble and this bubble is in many ways even bigger than the dot-com bubble in 2000.

Vembu believes cloud vendors are spending too much on marketing to achieve the high growth demanded by venture capital investors, and shortchanging customers by doing so.

If you look at the way they’re expanding, they’re spending $5, $6, $8, perhaps $10 on sales and marketing for every dollar they are spending on R&D.

While true for some, it is not necessarily true for all cloud vendors. [First a quick observation. Spending on R&D may translate into better product and more capability delivered to customers, but this is not always the case. Computer Associates CEO Charles Wang claimed that when a project ran behind schedule, the answer was to reduce staff. Effective and expensive R&D are not synonymous. -ed.]

Consider Workday, a very successful cloud vendor featuring high growth and well respected product. A scan of Workday’s latest (March 25) 10-K reveals R&D spending growing faster than Sales and Marketing spending. Workday spends twice as much on R&D as on Sales. Yet Workday still operates at a loss.

Workday Results

2015 2014 2013
Total Revenue 788 469 277
Total Expense 1003 622 391
Expense/Revenue 1.27 1.32 1.41

Clearly Workday spends too much on something to maintain it’s high growth rate, spending $1.27 per dollar of revenue. While the trend is toward parity, at nearly $1 billion in sales Workday is well past the point of making it up in volume. They contribute to the bubble by continuing to buy market share rather than focus on profits for their investors. Workday spends too much on Sales and Marketing.

HarrisData is far removed from the Silicon Valley hustle. We appreciate a high growth hockey stick graph as much as any firm. However we choose to achieve that growth the traditional way — by growing profits at the same time. This requires a simple discipline: spend less than one dollar for each dollar of revenue. Indeed, two hockey sticks (one for revenue, one for profit) are better than one.

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Cloud Inside

I’ve just written a Go Faster plan that will allow HarrisData to re-engineer our entire enterprise application portfolio – including HRIS, ERP, and CRM applications – into next-generation web applications in a matter of months

Why risk a complete re-engineering of what already works? Not so long ago, PC World named the original Sony Walkman (with the cassette tapes) as the greatest gadget of the previous 50 years. Yet Steve Jobs and Apple created the iPod (#2 on the same list) about 20 years later to replace what already worked. A comparison of the two shows both how similar they were, and how different in terms of scale and flexibility. The Walkman offered a theoretically unlimited song catalog (by switching cassettes that held about 36 songs each), had replaceable batteries, and played Beatles music from the beginning.  But even without the phone or the apps, the iPod offered the ability to store and play thousands of songs in a smaller, lighter device  – revolutionizing portable music. With virtually limitless access to Internet-based music catalogs (for a fee), these portable devices and their peers have transformed the entire music industry in a little over a decade.

Enterprise applications are due for a dramatic increase in scale and flexibility as well. They need to leverage the power of the Internet technologies that run the world’s biggest web sites in order to manage the massive amount of enterprise data that needs to be captured and mined to improve business performance. However, faced with the prospect of re-engineering their entire software stack and replacing millions of lines of java and/or C code, many vendors are unwilling to take the next daring step. To take that step, its time to put the cloud inside the application.

What does it mean to put the cloud inside the application? It means that enterprise applications need to be built to leverage what works on the cloud from the inside out. Vendors that are trying to keep up with the web are scrambling to add SOAP and REST APIs to their existing products. They believe that the massive, tightly integrated applications they developed 20 years ago need only be connected to the world of the web to gain the (marketing) advantages of being ‘on the web.’ The approach is not unlike the efforts to use ‘screen-scrapers’ to make applications look like Windows applications over a decade ago. This saves them from having to re-examine and re-write millions of lines of java and/or C code, but prevents them from taking the big step forward. Breaking the enterprise application into independent web services and loosely coupling them through resource identifiers enables the next generation of enterprise applications to scale and grow in ways unimaginable to the prior generation – in the same way the iPod seemed magical to those who owned a Walkman, even though it accomplished fundamentally the same task.

AppsInHD™ delivers this kind of loosely-coupled set of enterprise web services that are woven into applications by the resources that are defined to them.  AppsInHD fundamentally uses REST and its emphasis on resources (“pieces of information”) to create a network of services which work together. The AppsInHD network is defined in a way that allows new resources to participate in the network of services simply by defining the resource and any unique behaviors. To develop new applications, we design the resources (“pieces of information”) unique to the application and any actions unique to the resource, and deploy the application through AppsInHD. The Go Faster plan now focuses the work on application design, with coding, documentation, and testing phases focused and minimized to the unique facets of the resources in the target application. Watch carefully and see how fast HarrisData rolls out applications based on AppsInHD. 

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