Perfecting Data Processing With Schemaless Solutions

Data processing systems are indispensable in any sizeable organization today and for many organizations such as Wal-Mart they provide a major competitive information advantage.  All data processing systems are primarily automated publishing systems and these provide the information that powers practical problem solving but so far they’re pretty poor publishing systems. Now a new schemaless approach to data processing based on No-SQL databases promises to prefect data processing by overcoming all the substantial publishing limitations intrinsic to the traditional data processing systems we use today. 

The data processing systems we use today all too often impede problem solving because they restrict practical information flows and one way to understand this is thinking of data processing in terms of automated data clerks. In this sense every data processing program is a virtual data clerk with the powers of an automated idiot savant and blessed with a genius for accurately answering a very narrow range of highly specialized questions. In this same sense a data processing packages is a team of these highly specialized clerks but these teams seldom work well together. In a perfect world every organization would have a complete set of virtual data clerks capable of cooperating to answer any interesting practical question both accurately and adequately but it’s still far from a perfect world.

Today most organizations rely on off-the-shelf commodity data processing packages and these are typically provided by the leading enterprise software companies. Most organizations build their portfolio of data processing systems by mixing and matching products from multiple vendors and the result is always a patchwork with both duplication of package coverage and dead zones not covered by any package. Secondary manual IT systems compensate for many the dead zones while shadow IT systems built with spreadsheets and other desktop software tools compensate for other dead zones. Duplication of IT coverage requires routine maintenance and synchronization of multiple duplicate information models and these repetitive tasks involve lots of extra effort and expense.

Data processing packages are either custom built or commodity products but custom solutions are increasingly rare today because they’re both risky and ruinously expensive. Commodity packages provide only a very limited range of operational flexibility so they’re mostly a Procrustean fit for practical organization dynamics rather than a personalized fit and this poor fit all too often constrains competiveness. Many kinds of organizational processes and workflows are based on the commoditized practice models built into data processing packages but any practice that doesn’t closely conform to these cookbook models is likely to create huge IT headaches so creativity must be discouraged and conformity must be enforced. In these sense the virtual data processing clerk is capable of only very limited training and this is problematic because rapid change is constant in every competitive organization.

Data processing technology is mostly mature now and this is clearly demonstrated by many major consolidations in the data processing industry over the course of the last dozen years. Commoditization is another consequence of technological maturity and that’s why all the major data processing vendors now offer very similar product lines with very similar product features. Some data processing packages are now moving to cloud platforms but cloud product offerings are not substantially different than those available for data center hosting. Every data processing package vendor paints a rosy picture of rapid progress that will soon overcome all the frustrating limitations of traditional data processing practice but nobody much listens anymore since there’s very little actual innovation.

Data warehousing techniques are a great systematic making teams of virtual data processing clerks work together in new ways and this is particularly useful in building big picture data models of organization dynamics. Real-time data warehousing systems are making some progress today and these can overcome some of the publishing limitations of data processing systems both by supporting a broader range of question answering and by providing interesting answers to questions of somewhat greater complexity.

Now imagine a world where every organization has complete seamless data processing support for of every sort of practical activity. This is a world were data processing systems are disposable rather than durable and new fully customized data processing systems are automatically generated on demand. This is a world of schemaless information systems with far smarter information modeling and unlimited smart question answering complexity. This is the future provided by No-SQL data processing systems and it’s a future that’s just getting started today. The most important thing about this future is that it finally provides all the automated practical information flows that any organization needs and in this way overcomes all the substantial publishing limitations of the data processing systems we use today. Success in the next generation of data processing systems can only be achieved by refocusing the institutional information technology world on publishing rather than programming and this is already beginning in the best pioneering No-SQL solution projects.

The major advantages of schemaless No-SQL solutions are intrinsic simplicity and smartness. These are advantages that result in radical life-cycle costs reductions as well as information flows with far higher practical problems solving value. Schemaless information models are intrinsically sharable at Internet scale and these provide far better search success that either the data or document searching done today.

The schemaless data design cycle is remarkably simple because information models are always strictly reused rather than reinvented. The first step is preparing a sample document that contains all the kinds of practical information to be modeled and the second step is outlining the content of this document with just the same methods we master in middle school. The result of these steps is a strong schemaless information model and for many purposes this alone is sufficient to build smart codeless data processing applications. In other cases small amounts of code may be required but this coding is easily accomplished by front end web designers using familiar best practice. 

How can the perfection of data processing possibly be this simple? The short answer is by eliminating all the extreme extra cost and complexity of routine information reinvention but this is heretical because it means abandoning all attempts to perfect schema-structured information modeling. The history of data modeling innovation since the 1960s can be seen as a running battle seeking to scale up schema-structured information modeling methods while struggling against the mounting costs and complexities of scale. Today this battle has finally been lost since it’s now absolutely clear that schema-structured information systems can’t possibly support mass-scale federated Internet information systems.

Schemaless information modeling is really just systematic structured document modeling with all the sharing advantages of document and all the searching advantages of data. Internet document sharing and searching is a huge Internet success story at vast scale and there’s no apparent limit to this simple scalability. The obvious way to achieve just this same success for Internet data is to exactly follow the lead of Internet document publishing and this is just where schemaless data processing is already headed. Document publishing always strictly reuses information and all schemaless data publishing can always be done in exactly the same way while preserving all the intrinsic power, precision, and portability provided by practical documents.

All traditional data processing systems have always been isolated, incommunicado information islands but in the modern Internet Age every new data processing solution should fully support freely open Internet information sharing and searching and this is a huge intrinsic advantage of schemaless data processing. Schemaless data processing solutions provide intrinsic support for plug-and-play dynamic information interoperability at Internet scale and this is a great way to easily build mass-integrated Internet data processing systems such as value chain automation systems that span whole industries. tec

Today the transition to schemaless information modeling is well underway in the Internet publishing world and this trend is driven by sheer necessity since the scale of Internet publishing increases relentlessly and only schemaless websites architectures are intrinsically saleable. Now the data processing world is just beginning to discover the big intrinsic benefits of schemaless information systems and these are the inevitable future of data processing because it’s the only future with a workable path to rapid progress and to eventual perfection.

SchemaLess.Net is the pioneering schemaless solution system provider offering lots of great schemaless information modeling software tools based on all the simple models, methods, and mindsets of mature practical information publishing practice. The SchemaLess.Net System solves all the routine publishing and editorial problems involved in building great schemaless data processing solutions that fully support scalable Internet information sharing and searching.

Editorial and Encoding Frameworks in IT

Every IT professional is an expert at using digital encoding frameworks such as the Structured Query Language (SQL) frameworks widely used in relational database engineering and these are schema-structured information modeling frameworks. Every practical IT model has both an encoding structure and an editorial structure but only the encoding structures are modeled explicitly using schemas while the editorial structures are entirely implicit and the only way to expose these structures is systematic editorial analysis. Even so these schemaless structures are always there somewhere and they’re always the sole source of all the practical meaning any IT model. Schemaless information modeling differs from schema modeling in that the editorial structures are explicit rather than encoding structures so new kinds of editorial frameworks are required for schemaless modeling success.

Microsoft’s .Net and Oracle’s Java are two of the more popular software encoding foundation frameworks today and both are huge with many hundreds of thousands of reusable encoding elements. A typical program uses only a few hundred of these basic building blocks and the mix varies widely depending on the sort of programming done. Every software professional must master at least one modern software encoding framework and this is done in much the same way that a construction contractor might master the range of products available at a major home improvement warehouse store.

All practical programs use exactly the same editorial foundation framework and this is just the same one that we all use when we write practical documents. Editors have been studying this foundation framework for a century and editorial analysts must master this editorial foundation in much the same way the software professionals must master an encoding foundation. One remarkable thing about the editorial foundation is that it has only about ten percent of the complexity of some major software encoding foundations. This is around fifty thousand familiar practical meaning building blocks but we routinely use only a few thousand of these and constantly use only a few hundred while toddlers get by with a few dozen.

Schemaless structured information modeling needs no explicit encoding framework and it uses exactly the same familiar editorial framework that we all use all the time in all of our practical pursuits so there’s nothing new to learn. Open Practical English is a modular foundation framework for schemaless publishing and it’s the first full master editorial framework of international practical English. In many ways it resembles the modular encoding element foundations used in software engineering. 

Editorial foundation frameworks are new and unfamiliar in the IT world today but they’re indispensable in schemaless modeling just as encoding foundation frameworks are indispensable in schema modeling. In each case the foundation framework solves lots of fundamental problems and make it easy to efficiently reuse modeling elements. Open Practical English solves all the fundamental problems of mass-scale schemaless sharing and searching of open Internet information models and these are editorial problems rather than encoding problems.

Building any kind of good foundation framework is a major undertaking requiring highly specialized expertise and lots of effort. Open Practical English development has been underway for more than a dozen years and so far more than thirty thousand hours of expert editorial effort has been expended. Someday there will be alternatives to Open Practical English but for the foreseeable future it’s the only choice and today it’s the choice that provides the sound, solid, stable foundation needed to do successful schemaless Internet publishing.

A Totally New IT Information Modeling Choice

Strong and schemaless verses soft schemas is the new choice in IT structured information modeling and strong means that IT solutions can now be just as smart as a pencil and paper for all practical publishing purposes. That’s ten to a hundred times smarter that the schema modeling typically done today and this huge improvement opens up all kinds of new opportunities practical information sharing and searching innovation. The strong and schemaless choice is the basis for all the exceptional advantages of the SchemaLess.Net System.

Everyone in IT knows that structured information modeling starts by choosing the best way to reinvent information using schemas and there are many ways to do this in data, knowledge, and software engineering. Even the terminologies and taxonomies used in keyword search indexing are actually schemas and all schemas always destroy most of the rich composition structure that gives documents their editorial strength of meaning. IT modeling professionals never mourn this loss of meaning because everyone’s gotten used to the notion that all IT information models are always editorially soft so no IT professional every got fired for misplacing some surplus meaning.

Now there’s a totally new structured information modeling choice that always preserves all the editorial strength of documents and it’s this strength that provides the searching and sharing power of practical information. Yet this new choice is really a very old choice because it been used for a century in the publishing world when editors want to understand and use all the intrinsic structural strength of practical information to maximize the power, precision, and portability of publication meaning. Editors get paid to maximize the practical meaning strength of publications and they sometimes use sophisticated editorial systems to make sure that meaning doesn't go missing.

The new schemaless structured information modeling choice is borrowed lock, stock, and barrel from the publishing world so it’s a choice that comes from imitation rather than innovation. Schemaless information modeling is done with plain old practical English in just the same way as document modeling is done so all the extreme extra effort and expense of schema modeling is eliminated. Everyone is already a strong schemaless information modeling expert because that what we always do with documents and everyday editorial skills are always sufficient.

If it ain’t broken don’t fix it. This simple gem of conventional wisdom is the best way to explain the huge advantage of schemaless over schemas. Documents are a prefect modeling medium for all practical purposes because they always lets us say all that we mean and mean all that we say in the simplest possible way. Schemas make it hard to say anything meaningful because they always begin by throwing away the structural perfection of practical information meaning and starting over with a whole new set of simplistic structures such as set-relational structures that never work very well. For decades computer science researchers have been trying to perfect the schema by inventing new structures that work better than those we use in documents but nearly no progress has been made and this is futile since you can’t hope to improve on perfection. Schemaless always uses the full intrinsic perfection of practical information structures found in documents to the best publishing advantage and is the best we can every hope to do as well as all that we ever really need to do for any practical purpose.

Editorial Engineering in Practical Software Production

Today software engineers are encoding engineers who configure complex code structures and mostly worry about the speed and scalability of solutions. Yet every practical program has an editorial structure as well as an encoding structure and all the practical value lies in the editorial structure but today these structures are seen as secondary. Encoding is still important in schemaless software engineering but editorial engineering is more important and this is a new development in software engineering.

We all use the same editorial systems to say all we mean and mean all that we say but schema-structured software solutions are limited to a very small editorial range. In schemaless software the editorial range is ten to a hundred times greater so systematic editorial engineering is required. There’s no reason to invent new tools and techniques for editorial engineering because these have long been mature in the publishing world and they come with a mature practice culture that starts familiar middle-school editorial skills.

Editorial engineering really is an empirical engineering discipline because it’s subject matter is the high technology of technical information. Technical information itself is a forty-thousand year old mass technology that well all work to improve all the time and it’s build on authorities, authentic reference models in physical and practical reality. Technical information is the only sort we actually understand in depth and this understanding starts with the classic elements of composition we learn in the seventh grade.

We’re not used to thinking of technical language as a technology in its own right, let alone a high technology, but like all high technologies it has a highly principled basis for practice firmly rooted in the testable applied sciences. Editorial analysts in the publishing world have, of necessity, been exploring practical English for a century by systematically taking it apart to see what makes it tick so today technical English is thoroughly understood today and that deep understanding provides a convenient starting point for editorial engineering in practical software production.

Today encoding style is seen as the most important thing in software engineering and style is always important but in practical software it’s editorial subject matter that pays the bills. Subject matter is the primary focus schemaless software engineering and this is how editorial engineers will produce new kinds of sophisticated software solutions by making the best possible use of the high technology of technical language. 

Schemaless Software is Smarter

Software still isn't very smart today despite decades of innovation effort and the reason is schemas. Schemaless software is a new alternative that provides an easy way to produce software that’s far smarter than the software we routinely use today. This isn’t science fiction superintelligent software, it’s much like the software we use today but without the smartness limits of the schema-structured information modeling done today. Lifting the limitations imposed by schemas can increase the smartness of software by a factor of ten routinely with the possibility of an occasional hundredfold improvement.  

Schemaless software may seem to be an oxymoron like “dry water” because nearly all software is designed with schemas today but in the early days of digital computing all software was schemaless. When schemas came along in the 1960s most programmers found them unnatural because the freestyle programming methods then used were far more flexible than the rigid formats imposed by schemas. The freestyle structures of early schemaless programming were chosen to fit the natural nomological structure of the information being modeled and this intuitive approach was ideal in the days before modern software engineering when programming was done by subject matter experts. 

Why is schemaless software smarter? It’s smarter because it supports a far greater range of practical meaning structures and these structures are the ultimate source of all software smarts. These are smarts that comes from far greater editorial range rather than exotic algorithms and it means that practical software can finally be just as smart as the information modeling we do with practical documents. 

Does anyone really want smarter software?  We’ve all gotten used to today’s simplistic software and we’ve developed survival skills for dealing with its many limitations but smarter software has the potential to provide far greater practical value that the software we routinely use today. This new value includes far greater ease of use as well as substantially greater practical sophistication.  Every IT manager knows that there’s always lots of demand for enterprise software solutions that can’t be built due to unmanageable complexity and many of these are good candidates for schemaless software solutions. In just the same way there’s long been lots of latent demand for sophisticated consumer software products that can now be provided as schemaless solutions.

Schemas got their start in the age of data processing as a way to build on the legacy of punch card data modeling but schemaless is ideal for the Internet age where the grand challenge is building smart syndicated information systems at Internet scale. Today many still believe that schemaless is a digital dinosaur from the early days of digital computing but technological tradeoffs change over time and now it’s clear that schemaless set for a big comeback. 

What is No-SQL?

Well, it’s not SQL, but that’s not a very good answer. The most interesting new No-SQL applications are about exploiting the advantages of less-schema and schemaless information modeling and this is where the true promise of No-SQL lies. SQL databases are ideal for conventional data processing where lots of regular, repetitive information must be modeled and managed but most practical information doesn’t fit this mold. Practical information is intrinsically messy and No-SQL databases can easily capture much more of the intrinsic messiness of routine practical meaning with far greater flexibility and fidelity. SQL database modeling always requires that information be reinvented in terms of set-relational schemas but No-SQL databases support a far broader range of options ranging from schemas to schemaless. Schemaless data modeling is stronger and smarter but above all far simpler since it reuses the native structures intrinsic to all practical meaning and reuse rather than reinvention routinely provides a cost and complexity improvement ranging from tenfold to a hundredfold.

Schemaless is a trend that has long been developing in the IT world due to the competitive advantages of simpler, smarter solutions. The strongly typed programming languages of the past have lost much ground to new scripting languages with softer dimensional and dynamic typing because these are often advantageous for writing simpler, smarter code that provides much more application value. In just the same way No-SQL databases provide a range of information modeling choices for coping with the specific challenges of a given solution so it’s no longer necessary to shoehorn the great diversity of practical information structures into one-size-fits-all SQL schemas.

There are many sorts of No-SQL databases including document, sparse column, key-value, and graph databases but no single sort is ideal and combinations of different sorts are increasingly common. Each sort provides a different way of moving beyond the limitations of highly regular schema-structured information modeling and the right choice depends on the native, natural schemaless structures of the information that needs to be modeled and managed.

SQL has a strong technical culture with lots of time-tested tools, techniques, and theories plus an extensive textbook, technical, and trade literature. So far No-SQL completely lacks this sort of mature culture and that’s bad for business because it’s a big barrier both to adoption and to advantageous application. IT managers are naturally weary of immature technologies and nobody ever got fired for using SQL so now a mature No-SQL technical culture is urgently required and the ideal solution is to simply borrow one that already exists. The SQL culture focuses on encoding techniques and reinventing information using set-relational structures but No-SQL requires a very different culture that focuses on editorial techniques and reusing information. Just this sort of editorial culture has been evolving for a century in the practical publishing world and it’s ideal for all aspects of No-SQL information modeling and management.

The SchemaLess.Net System is based entirely on the best tools, techniques, and traditions of this mature publishing culture where schemaless structured information modeling has been routine for a century. Both Total Recall search and Open Practical English are optimized for schemaless information modeling and both provide strong and smart yet simple solutions for sharing and searching all kinds of practical information at Internet scale. These are solutions that require only routine publishing skills rather than programming skills so everyone is already an expert.

The Schemaless.Net System conveniently unifies all the different sorts of No-SQL databases providing a single set of models, methods, and mindsets suitable to all. Open Practical English provides a single medium with modular modeling that can easily be partitioned across diverse sorts of No-SQL databases and Total Recall search supports mass federation of schemaless searching across diverse SQL and No-SQL databases.

What is No-SQL? It’s schemaless information management for the Internet age that provides many new kinds of competitive solution advantages due to superior publishing automation tools and techniques that are substantially simpler, stronger, and smarter than SQL.

Is Schemaless the Next Big Thing?

Schemaless structured information modeling is a growing success story in Internet IT and it’s just now beginning to show signs of life in institutional IT. Is schemaless The Next Big Thing? Is it a fundamental advance or just a fad? Is it a tendency, a trend, or a tsunami? Is it a movement, a mirage, or a market opportunity? These are important questions for investors as well as implementers and these questions will be considered here by means of comparative innovation analysis.

Schemaless is the only workable way to publish text documents so we’re all schemaless experts but schemas have dominated data publishing for decades. Schemaless can be used for data publishing as well as document publishing and now the big open question is whether and when schemaless publishing will displace schemas for structured information modeling.

Schemaless and schemas are two fundamentally different technologies for structured digital information modeling and in many ways they’re antithetical. The most important differences between these technologies involve publishing rather than programming and it’s these publishing issues that will determine which technology is dominant in future IT modeling practice. In many ways schemas are the superior technology from a best practice programming perspective but schemaless has many major publishing advantages including simplicity and scalability which will surely be decisive in the Internet Age.

The printing press displaced parchment, telephony displaced telegraphy, and television displaced radio. The history of technology clearly shows us that superior publishing technologies always displace prevailing ones and schemaless is far superior to schemaless as an Internet publishing technology.

Most IT modeling has long been done with schemas but schemaless modeling is a mature alternative that finally enables mass-scale Internet open data sharing and searching systems as well as disruptive new application architectures that overcome many longstanding limitations of enterprise IT systems. Schemaless is just now beginning to challenge the longstanding dominance of schemas and the accelerating shift to schemaless is being driven by a combination of rapidly mounting economic and engineering forces. The competitive landscape of the IT industry will be substantially altered as the shift to schemaless becomes a switch to schemaless and while this switch may not yet be imminent it’s clearly inevitable. The switch to schemaless is likely to start sometime in the next few years but it will surely begin well before the end of this decade.

Schemaless is a method of systematic structured digital information modeling that’s best understood as an alternative to schema-structured modeling. Most IT modeling done today uses regular, repetitive schema structures and these have been the basis for information modeling in data processing since the 1960s. Schemaless information modeling is older than schemas and computer scientists tell us that schemaless is obsolete yet it’s still widely used in specialized applications where schemas provide inadequate modeling strength or scalability such as design automation.

Schemaless information modeling is hardly new, we all do schemaless information modeling all the time since it’s been the sole basis for practical information modeling since the dawn of human civilization. All practical language modeling is schemaless and the native, natural, nomological meaning structures of practical information modeling have been well understood since at least the 19th century. The classical elements of composition including rigor, rationale, and rhetoric completely explain all the meaning structures of schemaless information modeling and we all routinely master these elements in middle school.

Schemaless structured information modeling is just a very minor variation on the familiar ways that we all routinely model information with documents and so has the potential for widespread popularization. Moreover schemaless information modeling is easy to search and share at Internet scale while this is not possible with schema-structured information which can only be modeled by expert data engineers. For these reasons schemaless is the ideal choice for the democratization Internet data which will make it easy for everyone to author and analyze all kinds of data thus transforming Internet publishing in much the same way as democratization of Internet documents built on innovations such as blogs and wikis.

Systematic schemaless information modeling has been widely practiced in the practical publishing world for at least a century as a tool for ensuring information quality, as a way of doing systematic translations, and as a way of building practical writing skills. Schemaless digital information modeling dates back to the earliest days of digital computing and it’s still prevalent in assembly language programming. Schemaless data modeling has been widely used in design automation and defense automation for more than thirty years where it provides far more modeling strength and scalability that alternative schema-structured methods. The digital circuits in your cell phone are designed with schemaless circuit models and schemaless design automation systems created most of the models used to make and maintain your car.

Schemas are an entirely artificial approach to information modeling that always requires lots of smarts as well as highly specialized skills. Schemas began in the 1960s as a convenient way to move punch card information models from automated tabulating equipment to the first generation of scalable mainframe computers. Schemas are best suited to traditional data processing models represented as highly regular rectangular tables and these are ideal for many high volume applications such as inventory management and financial account journals. Schemas always trivialize any information modeled by stripping away all the referential richness that gives documents their strong meaning but schemaless information models easily retain all this richness and so rivals the editorial modeling strength of documents.

Today were seeing a substantial shift from schemas to schemaless in Internet IT as well as a much smaller shift in institutional IT. In both cases this shift is a technological transition from the mature prior art of schemas but shifts of this sort never happen without good reason.  Style wars have always been endemic in all IT specialties and these are always feuds about coding fads and fashions but the shift to schemaless is driven by major strategic technology advantage so it’s very much like the shift from gaslight to electric light in the 19th century.

In the case of Internet IT the major advantage lies in website scalability and the shift to schemaless becomes an urgent necessity once the intrinsic limitations of schema-structured information modeling and management have been reached. In this way the shift to schemaless is much like the transition from piston engines to jet engines made in the commercial aviation industry starting in the 1960s.

The next big schemaless opportunity in the IT industry is freely open data sharing and searching systems. Planetary scale document sharing and searching has long been a huge success story based on the open publishing technologies of the World Wide Web. So far all attempts to duplicate this success for data have failed due mainly to the scalability limitations of schemas but schemaless data publishing easily overcomes these limitations. Unlimited schemaless data publishing scalability provides the Internet industry with opportunities to build on its growing schemaless experience to pioneer big public data search engines, to finally launch the long-delayed Internet of Things, and to combine the complementary capabilities of individual websites by means of mass federation systems.

In the case of institutional IT there’s not yet an urgent need to switch to schemaless information modeling but schemaless provides the only remaining workable path to progress past the dead end of today’s fully mature data processing practice. Enterprise software progress has long been stalled due to the maturity of schema-structured IT modeling and this explains the long-term trend towards consolidation and commoditization in the enterprise software industry. Here the shift to schemaless most resembles the transition from tubes to transistors in the electronics industry beginning in the early 1960s. Transistors initially gained a foothold by enabling product innovations beyond the mature capabilities of tubes and over time the gradual increase of major and minor transistor product innovations rendered tubes obsolete.

In just this same way schemaless enterprise software promises to provide huge economic and engineering advantages in enterprise IT all the way to eventual perfection but progress will be incremental and we’re just starting to climb the learning curve today. It’s not at all clear how long it will take to climb this curve because there are strong precedents supporting predictions ranging from a few years to a decade or more. In any event this curve will surely be climbed so schemaless enterprise software will eventually render all current enterprise software technologies obsolete so schemas are surely doomed to follow vacuum tubes into technological obsolescence.

Transistors were pioneered by Bell Labs for commercial purposes but their rapid technological development was mostly driven by strategic defense systems while initial commercialization was driven mainly by innovative consumer electronics and computer hardware products. Today there is no sponsored innovation for schemaless IT innovation in government, industry, or academia but at the same time there are no big breakthrough innovation challenges. Much important software innovation of all kinds now results from side projects and these occasionally become the basis for great pioneering products. Schemaless solutions are fairly simple to build and these will surely increase steadily over time as success builds on success while sanctioned solutions will steadily increase as institutional IT managers build a better understanding of schemaless success factors.

Schemaless enterprise IT involves very different sets of tradeoffs than the sort done with schemas. Enterprise software products are packages of modular programs based on fixed schema models that provide a very narrow range of configuration and each package is a closed world system that acts as an isolated island of information so sophisticated techniques such as data warehousing are required to build bridges among these islands making it possible to systematically share and search information across packages. Schema modeling projects are costly and complex requiring weeks or months of expert effort while equivalent schemaless modeling projects can be done by everyone in hours or days. Schemaless information is easy to search and share across diverse sources and topics and systems of schemaless solutions are easily federated eliminating the longstanding islands of information problem.

Catalytic factors sometimes rapidly drive new strategic technology innovations to dominance where there aren’t any big innovation barriers as in the case of schemaless enterprise software. Aggressive U.S. federal government support and sponsorship of World Wide Web projects during the 1990s is one example of just this sort of catalysis. In just this same way new schemaless solutions might rapidly displace established enterprise software solutions and disrupt the enterprise software industry given the right set of strong catalytic factors. Asian governments have often provided just these sorts of catalytic factors in order to capture complacent markets for their nascent technology industries and in just this way an rapidly industrializing nation such as Vietnam or India might easily capture a major share of the enterprise software market in the space of a very few years.

All things considered schemaless information modeling is best understood as a mature technology that must eventually displace schema-structured information modeling in both the Internet and institutional IT segments. In the Internet segment schemaless is now becoming standard practice for larger websites and it’s becoming a standard offering in cloud computing infrastructure stacks. Schemaless is a small but growing trend in institutional IT where it’s seen as an advantageous alternative for an increasing range of specialized applications. There is no schemaless enterprise software industry yet and there is nearly no impact on enterprise software vendors so far but this is an unstable situation so a relatively rapid transition to widespread deployment of schemaless enterprise software is a very real possibility.