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Data Fabric: What Is It and Why Every Business Needs One

Data Fabric: What Is It and Why Every Business Needs One

Jan 17, 2023 | Business Intelligence, Data Driven Decision Making, Data Governance, Data Management, Data-Driven Culture, Database Software, Digital Data Fabric, Digital Transformation, Enterprise Data Management, Enterprise Software

What is a Data Fabric? 

…And Why Do You Need One?

Written by Ian C. Tomlin | 12th January 2024

Heard about data fabrics, data lakes and data mesh solutions? Wondering what all the fuss is about? Find out here.

Data access, the innovation imperative

24/7 eCommerce means that every business faces stiff competition from competitors located around the world. Brand experience has become the primary competitive weapon.  It means every business needs to be digital with decisions based on data, not conjecture.  Companies that don’t harness data and modern innovations like artificial intelligence, blockchain, bots, and 3D visualization, etc. face extinction.

 

Why do you need a data fabric?

The organization and exploitation of business data are central to the effectiveness of any business seeking to thrive in the digital era.  Before data can be fully harnessed, it needs to be harvested and blended into a common structural design, where every table and row has its place. This ‘layer’ of composable data is also known as a Data Fabric.  A data fabric is the foundation stone to achieve digital ambitions. It is the key to implementing digital transformation at scale–and specifically to the edge of the enterprise, where it is arguably most needed.

Unbundling data from data silos and disparate data sources

Data is commonly managed and stored by the IT systems that produce it. In a modern IT enterprise architecture, businesses will use a variety of SaaS apps, in addition to Cloud-Native applications, and platforms, like Google, Amazon, Office365, Facebook, LinkedIn, etc.  It means that data is fragmented across data silos, making it all but impossible for business users to access data when they need it.  The rising authority of departmental leaders to make their own IT decisions over the past decade has only served to increase data complexity, adding to the challenges of IT and Data Security leaders charged with protecting and leveraging data.  In most organizations, business systems and data systems are the same thing.

The need to re-think data management

Enterprise data delivery is broken; workers today lack access to the data they need to do their jobs, while executives lack insights to answer what-if questions and make data-driven decisions.  For data engineers, the first problem in any new project is to bring data together from its current source, cleanse, de-dupe, and normalize it, etc. before any serious work can begin to answer new questions, solve problems and build apps.  In most cases, spreadsheets become the only accessible tool to organize data into a useful format. Meanwhile, data governance capabilities remain woefully poor.  This has led to boardroom discussions on digital transformation to pivot towards data access, data sharing, and how to decouple data from the data silos to make it more reusable. Improving data consumption and data quality are now business-critical issues.

Fragmented data architectures slows down the time-to-value delivery of new projects.

Illustration of decoupled data architecture vs current state

Data fabric: what it is and what it looks like 

A data fabric is a conceptual data layer that spans your enterprise, releasing data from the applications and systems where data assets are found, to pre-process and organize it in such a way that it can be made useful for composing reports and be consumed by applications to facilitate machine-to-machine automation. 

A data fabric solution creates a unifying data umbrella layer across your enterprise. It means data is presented in a ready-to-use format. Its main elements include: 

1. Data Integration Tools to connect existing systems that hold data to the data ecosystem. 

2. Extract, Transform and Load Technology to harvest and integrate with data sources, with added tools to design Master Data repositories. 

3. Data Mashup Technology to pluck data from existing data sources into new structures and enrich with new data structures to meet new data demands. 

4. Monitoring, Validation, and Data Integrity Tooling to ensure that data pipelines are working correctly to present data from endpoints while preventing corrupted or high-risk data from being accepted into the data fabric. 

5. Analytical Tooling and Applications Development Software to consume data and present it for different uses. 

A data fabric enables a new standard in data management, presenting data of high integrity that data scientists can use to automate processes and answer questions. 

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Data Vault, Data Fabric, Data Lake, or Data Mesh—what’s the difference? 

The terminology used in the data industry can be confusing.  Here we try to unbundle the terms commonly used in data architectural discussions.

Data Vault

You create a Data Vault by designing a data taxonomy around core business intelligence ‘landscapes’ and the core data tables used to describe them.  Typically, these are the core records that identify your customers, suppliers, risks, opportunities, projects, products, etc. This creates a structured model of your critical data assets.  Then, data is normally ‘poured in’ to this by uploading it from its host system and transforming it into a unified single version of the truth.  Data Vaults are typically created using either relational databases or flat-file ‘big data’ systems.

Data Lake

A data lake is a centralized repository built to store, process, and secure large amounts of structured, semi-structured, and unstructured data, storing data in its native format. It is a very specific technological construct that relies on cloud computing and big data technologies to manage vast amounts of aggregated data, organized into a pre-planned taxonomy.  Back in the day, people would talk about data warehouses when thinking about a centralized repository, but cloud computing has changed the narrative.  Whilst Data Lake technology is extremely powerful, it isn’t always practical or affordable. 

Data Fabric

A data fabric is less about an IT platform (as per a data lake) and more about an outcome: it describes the data mart layer used to make data valuable to the enterprise autonomous and composable.  Your data fabric will contain business logic rules that dictate how data is shared, used, managed and consumed.

Data Mesh

A data mesh is more about philosophy and the recognition that businesses have previously seen value in owning data (and thereby trying to control and manage it internally to their business), whereas now, it’s increasingly recognized that harnessing third-party data and sharing it can yield more data asset value.   A data mesh exists therefore within, across, and beyond the enterprise as a federated data ecosystem. 

What are the benefits of a data fabric architecture? 

Strategic ambitions 

A key strategic element of a data fabric architecture is the separation of the data layer from the application layer, fostering greater re-usability of both. Businesses looking to adopt a data fabric approach site one or more of the following objectives. To: 

  • Liberate data from old, inflexible legacy core systems 
  • Transfer the ownership of data from IT to the business 
  • Bring data transparency to create a curious, learn fast/fail fast, data-driven decisioning culture 
  • Speed time to value of new projects and digital services 

Overcoming spreadsheet overload 

Overcoming the risks associated with the use of spreadsheets has become a widescale priority because of data security and privacy compliance. When data is used in spreadsheets, it becomes largely ‘invisible’ to those responsible for data governance.  

Additionally, spreadsheets are costly to produce and manage because of the manpower overheads used to drive them.  

Furthermore, spreadsheet files are prone to corruption and often contain formulas that only the creator understands, creating a single point of failure. 

For all these reasons, industrializing information management by replacing spreadsheets with a robust and resilient data fabric architecture makes sense.

Hyperautomation and digital transformation 

For organizations determined to reduce headcount and drive down back-office costs, removing the human in the loop is a prime driver for data fabrics. 

Becoming data driven 

While the importance of liberating data from data silos, spreadsheets, and increasing the pace of innovation are all good reasons for a data fabric architecture, there’s no question that most organizations will adopt it because it’s the only way for an enterprise to become truly data driven. 

Case stories and examples 

Ask anyone whether they feel well served by data and they will probably have their own horror story of having to use spreadsheets to harvest and manually crunch data to answer a question or solve a problem. Others will be using a spreadsheet as a quasi-business application because their IT team is too busy to find a robust alternative.  

While the ambition to create a data fabric that spans the enterprise is a desirable one, getting to it can be costly. There will always be a need to achieve quick wins along the way. It’s likely that early-stage solutions will be solving problems at a departmental or functional level.  

In this section, we’ve pulled together some examples of how data fabric architectures are being used to solve business problems. Applications for decoupling span the enterprise, although justifications for projects can originate at a departmental level, as illustrated by the examples below. 

Managing growth performance across a sales territory 

The sales division of a global electronics company responsible for the Middle Eastern, Eastern Europe, and African markets was being hampered by a shortfall in sales insights as the result of its widespread data silos. This meant the data-gathering process was time and effort intense.

Managers were presented with copious data but no actionable insights or recommendations for action. To resolve it, a data fabric was created across the regional sales platforms and ERP data repositories that could deliver timely actionable insights to stakeholders on demand. Read the full case story. 

Creating a Customer Data Platform (CDP) to focus operations toward profitable business 

The management team of a progressive Office Equipment and technology business in Europe identified the need to become ‘data driven. The sales leadership wanted to create a single view of its customers to focus sales efforts on the most profitable opportunities and automate delivery processes.  Read the full case story here. 

Scanning the market horizon, and matching resources to opportunities 

Power and Energy is a fast-changing market. In the professional services industry, becoming adept at surfacing new advisory opportunities–also knowing what advisory services to offer and how to resource them–is critical to success. Find out how one global advisory firm used a decoupling architecture to gain a competitive advantage. 

Why codeless PaaS technology is critical to forming a data fabric 

Codeless Platform-as-a-Service (PaaS) solutions take many forms. In the case of Encanvas, its codeless PaaS includes integration and application layers.  

Integration, software bots, and Extract, Transform and Load (ETL) componentry bring data together in a data fabric.  

Then, codeless software is used to design, publish and manage composed applications and machine-to-machine automations. 

Removing the need to program system interfaces, specify information flows using scripts, and write code (or script) to create, publish and manage apps substantially cuts the time and effort needed to implement an effective data fabric and application fabric: the two building blocks of a modern Composable Data Architecture. 

Missing building blocks of enterprise data management 

When organizational priorities for IT are dictated by departmental managers seeking to address operational challenges with point-specific solutions, this inevitably leads to more data silos and a whole series of missing pieces in data architectures. 

One of these is the vital framework of data bridges that link data elements together. It’s not uncommon, for example, to find an enterprise resource planning and customer relationship management solution using different fields to identify a customer. Equally, financial systems may not be correctly configured to identify product or service line profitability. When such data relationships are absent, it’s not possible to ask ‘what-if’ questions that combine multiple data sources, without first bringing data together into a new structure. 

Another missed opportunity is the ability of an enterprise to understand its digital DNA. This is the data that helps an organization define itself, such as the organizational structures, locations, people, processes, suppliers, systems, risks, and data elements that define an organization’s makeup and capability. 

For digital transformation to work you need data fabric architectures 

Businesspeople have been waiting for the day when they could ask what-if questions and access the data they need to do their jobs more efficiently. Businesses want to be data-driven, implement automation at scale, tie front-end and back-end systems together to serve customers better, and bring transparency to data processing. All these ambitions rely on access to useful data.  Consequently, conversations around data services have moved beyond slow-to-build data warehouses and crude point-specific solutions powered by spreadsheets. Enterprise IT has become less about systems and more about data asset value and reuse.   The good news is that codeless PaaS technology makes the implementation of a data fabric affordable as it takes far less time and effort to overcome the issue of data siloes.

Delivering a stand-out customer experience dictates the need for data virtualization

In most digital industries, competitiveness comes down to customer experience.  To achieve simpler customer journeys, more self-service, faster turn-arounds on orders and requests, to adapt faster to market needs, etc., all these outcomes rely on having faster access to more useful data.  Achieving data virtualization by releasing data from systems that hold it to ransom through the formation of a data fabric holds this promise. 

 

Data Lakes are not the answer to every problem 

Whilst the technology is certainly catching the headlines, it would be wrong to assume that harvesting everything into a data lake is a complete or fool proof solution.  There are many wheels to the data fabric wagon and getting data into a single ‘place’ may be a misguided outcome priority for some organizations.  Sometimes, there are quicker wins to be gained by delivering data fabrics across departments and solving a handful of high-priority projects.

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Decoupling Data Benefits for Digital Transformation

Decoupling Data Benefits for Digital Transformation

Jan 12, 2023 | Business Intelligence, Data Management, Digital Data Fabric, Digital Documents, Digital Transformation, Enterprise Software

Decoupling Data 

Benefits for Digital Transformation

Written by Ian C. Tomlin | 12th January 2024

Decoupling data:  What does it mean and why do it?  In this article, we explain the reason why so many CIOs have decoupled data architectures and data fabrics front-of-mind in their 2024 investment plans.

“All I want to know is…”

This is how most business intelligence conversations begin.

An employee or manager who wants to ask something new of their data because they are curious.
It does seem rather remarkable that here we are in 2023, and businesspeople still find themselves unable to answer the most fundamental questions about their customers, business, products, and growth performance.

I began my relationship with business intelligence and organizational change some 3 decades ago. In terms of outcomes, I can’t say it’s moved on much.

The problem of making data consumable for analysis has not gone away

Step into any major corporation and you will find humans employed solely to spend over 80% of their time making best use of spreadsheets to analyze data. Of this work, they are likely to spend at least half of their available time gathering, cleansing, normalizing and preparing data to make it useful FOR analysis.

Think too of the departmental and regional heads that spend over 20% of their time producing and presenting reports to more senior executives who could’ve asked their questions directly of the data, had they the means to do so.

You want your team to be curious, to explore new possibilities, understand market opportunity, customer wants, and separate the wood from the trees. But how do you equip them with the means to ask any question they have on their minds without first relenting to opening up a spreadsheet, entering or pasting data, merging columns, de-duping rows, and all the rest of it?

That’s what this article will answer.

AI bots need good data too!

The need for composable data is not peculiar to the subject of data analysis and business intelligence. For processes to be operated by software, those ‘digital agents’ and software applications must be fed with good data too.

Many of the tech industry headlines in 2022 focused on the growing role of artificial intelligence and its use in business to make decisions, supplement human skills, and to process larger amounts of data in shorter periods of time.

Companies want to bridge between their front-office and back-office with automations and software, not human-in-the-loop, hope for the best resourcing. Creating these automations requires data, data, data.

Is it any wonder why so many worthy digital transformation projects fail at the first hurdle, when the data they need to make decisions and action processes is scattered to the four winds?

Thinking data analyst illustration

Why your data is bundled in the first place

Traditionally, most business-critical enterprise data exists in systems of record, and for mature organizations, legacy systems. This data world was for decades, surrounded reassuringly by the protective sheath of a firewall.

Over the past decade, data supply and consumption have expanded exponentially beyond the enterprise boundary with more stakeholders wanting to share data and gain transparency over their services, more use of SaaS apps (built to service tasks independently of other systems), cloud services, and of social media and marketplace platforms like Facebook, LinkedIn, Amazon, Google, and WhatsApp.

In the end, your data is organized by software programs and services that generate the data. I can get a record of the purchase from Amazon and it will stay there forever. When my blood test is taken, it is stored by the provider of the test.

Every fragment of data gets stored into the system that created it.

The end result? Use over 50 SaaS apps and you end up with 50 unique data silos–organized in proprietary structural arrangements–that probably weren’t made for sharing.

The business need for digital decoupling

Every business today must be data-driven to survive. Digital business is generally always on, and customers want transparency in everything they do. The notion of real-time business is upon us; what Bill Gates called ‘business at the speed of light’.

The watchword in boardrooms is AGILITY; being able to switch on and switch off resources according to demands as they happen. All this presupposes people are making decisions at all levels of the enterprise based on facts, not intuition and gut feel.

Imagine a scene 12-months from today. You are sitting in your office, and you know you can ask Siri any question about your business, in the full expectation she has the data to answer your questions. Whilst the AI powered chatbot interfaces exist to achieve this today, the absence of a decoupled architecture means it won’t happen any time soon in most organizations.

While data is being held for ransom in the silos of third-party software vendors, the ability to ask any question you like remains a pipedream.

The way to solve this is to decouple data from the business systems that create and manage it; to form a departmental or enterprise-wide data fabric layer of pre-gathered, pre-cleansed, and fully composable data. This way, data can remain completely autonomous from systems, and multiple connected services can be then added to serve up data from this ‘clean’ repository as needed.

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The need for a decoupled data architecture

Data value is all about relationships and context.

Take for example a customer record from your CRM system. It means so much more when you can explore the financial data that exists on the same customer, or the service record that exists on your service management meeting. This customer record probably discloses further insights when you see what data exists about this customer on Google, Amazon and Facebook. And, if you can take the mobile phone data from call records and contact centre feeds, more so.

No system in its whole self can uncover all the secrets of the data it holds. To maximize your customer data, financial, data, training data, sales pipeline and performance data… you need to compare it to everything else in its biosphere.

Even when you create a system-specific reporting layer, it’s likely you will want to harvest contextual data from other third-party systems and present views for different stakeholder interest groups in different ways. The report tooling supplied by SaaS vendors is bluntly too primitive to service these needs, understandably because this requirement falls outside the scope of their own systems’ reporting capability.

Surprisingly, you would expect the problem of data integrity and organization only really exists when you look across multiple third-party systems. In fact, many organizations that operate software from THE SAME supplier can equally find their data repositories hold inconsistent data. This is because each operation will implement solutions in different ways.

Equally, the way teams use systems will vary according to local cultures and behavioral norms. This means a system designed in precisely the same format as another might still surface different data results (for instance, one team might use the ‘Customer’ field to identify a customer while another uses a Customer Code).

decoupled data architecture

Relentless departmental reporting requests create demands for multiple connected services to exist as a perpetual state. This is driving IT requirements for an enterprise-wide and autonomous data fabric layer. When ad-hoc solutions are created to serve discrete projects that evolve independently, the cost and risk to the business are amplified exponentially.

Composable data assets

Coined by Gartner, the term “composable enterprise” first appeared in 2021 and is widely used today to describe a modular approach to digital service delivery and software development. In other words, a plug-in-play application architecture whereby the various components can be easily configured and reconfigured.

A key element of this architecture is the separation of the data layer from the application layer, fostering greater re-usability of both.

A successful digital transformation requires decoupling the data layer from legacy IT so that companies aren’t forced to modernize their enterprise resource planning systems all at once—an expensive, time-consuming, and risky proposition. Companies that implement data and digital platforms—separating the data layer from legacy IT—can scale up new digital services faster, while upgrading their core IT. Source BCG

Critical decoupling architecture objectives

Organizations that adopt a data fabric and decoupled architecture will focus on the following priorities, to:

• Liberate data from old, inflexible legacy core systems
• Transfer the ownership of data from IT to the business
• Bring data transparency to create a curious, learn fast/fail fast, data-driven decisioning culture
• Speed time to value of new projects and digital services

Where we are today

Overlooking the obvious problem

In recent years, IT investment decisions have flowed down to departmental leaders who don’t see data quality or provisioning as a priority. Therefore, most organizational IT decision-makers have sought to overlook and avoid the need to invest in a data fabric that offers the ‘ready-to-use’ composable data needed to answer successive new what-if questions and power new systems and automation.

The obvious alternative (for department heads at least) is to employ more roles in data analytics and manually crank out data as and when it is needed.

These point-specific solutions inevitably lead to delays in projects and inefficiencies. Furthermore, every time a new requirement emerges for a different blend of data, it’s unclear whether the data relationships exist to combine data in the desired way.

In consequence, through this fragmentation of IT procurement and decisioning–and in some cases, the absence of a firm central hand to guide technology architectures–firms are supersizing their project risk, living with project delays, slowing their ability to answer new questions, and settling for a ‘business as usual’ cost to manually data crunching.

What a decoupling architecture looks like

There are common technology building blocks to decoupling architecture solutions:

Data harvesting and organization components

  • Infrastructure as a service and cloud-native provisioning to negate the use of poorly utilized in-house server infrastructure.
  • Data Mashups and Software Bots to augment data feed information flows using upload templates, watch folders, scheduled events, etc. to harvest data from existing systems and data sources.
  • Extract, Transform and Load (ETL) tooling, often powered by fuzzy logic and AI, to cleanse, normalize, enrich and organize data.
  • Database systems design and provisioning to create and organize relational and flat file databases to maximize data relationships and reuse.
  • Infrastructure Platform-as-a-Service and codeless data connectors to connect reporting systems to legacy systems and other data sources without having to code an interface.

Application components

  • Application Platform-as-a-Service (aPaaS) – to provision services in support of the design, deployment, and operation of software applications.
  • Application Fabric – A cloud platform to manage the publishing and organization of large numbers of discrete software applications used by digital workers to consume data.
  • Cloud infrastructure services – A cloud platform to administer cloud infrastructural deployments, data security, replication and scaling.
  • Cloud-native clustered deployments of secure private clouds at scale – A cloud platform service used to provision clustered private cloud deployments, thereby removing the need for administrators to log in to successive discrete sessions when supporting multiples of private clouds (something that often happens when businesses operate sales channels and supply chains).

Service delivery components

  • Integration with popular desktop and reporting tools
  • Reporting services to publish dashboards, charts, and reports
  • Information flow design tooling to create email/SMS alerts and notifications
  • Low-code/No-code/Codeless applications design and publishing services (to build apps needed to implement changes to processes resulting from
  • Digital documents to democratize data use and consumption
  • AI chatbot human interfaces, so digital workers can ask questions
two data analysts in the office illustration

Examples of decoupled architecture data use cases

Applications for decoupling span the enterprise, although justifications for projects can originate at a departmental level, as illustrated by the examples below.

Managing growth performance across a sales territory

The sales division of a global electronics company responsible for the Middle Eastern, Eastern Europe and African markets was being hampered by a shortfall in sales insights as the result of its widespread data silos.

This meant the data-gathering process was time and effort intense. Managers were presented with copious data but no actionable insights or recommendations for action.

To resolve it, a data fabric was created across the regional sales platforms and ERP data repositories that could deliver timely actionable insights to stakeholders on demand. Read the full case story.

Creating a Customer Data Platform (CDP) to focus operations toward profitable business

The management team of a progressive Office Equipment and technology business in Europe identified the need to become ‘data-driven. The sales leadership wanted to create a single view of its customers to focus sales efforts on the most profitable opportunities and automate delivery processes.  Read the full case story here

Scanning the market horizon, and matching resources to opportunities

Power and Energy is a fast-changing market. In the professional services industry, becoming adept at surfacing new advisory opportunities–also knowing what advisory services to offer and how to resource them–is critical to success. Find out how one global advisory firm used a decoupling architecture to gain a competitive advantage.

Final thoughts

Overlooking the obvious problem

1. Digital decoupling is a must-have for any business that wants to optimize its ability to be data-driven, foster a culture of curiosity, and answer new questions cost-effectively.

2. The success and time to value of digital transformations–and its substrates like hyper-automation, blockchain markets, customer self-service, etc. have become increasingly dependent on accessibility to a decoupled architecture that makes data composable through a coherent and useful data fabric. Trying to ignore or navigate around the data bundling problem to short-cut on delivery costs almost inevitably results in the reverse.

3. A decoupled architecture is simpler to achieve today thanks to advanced iPaaS/aPaaS codeless platforms like Encanvas that serve up all the necessary building blocks of data ETL, software bots, data mashup, data fabric, app fabric, fuzzy logic matching and bridging, digital document democratization and clustered private-cloud deployment.

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Digital Versus Digitized Documents

Digital Versus Digitized Documents

May 19, 2022 | Data Management, Digital Data Fabric, Digital Documents

Digital Versus Digitized Documents

How Documents Are Evolving to Support Digital Business

Written by Ian C. Tomlin | 12th January 2024

In this article, we uncover the big divide between old and new document technologies.

Documents, The Lifeblood of Business Administration

For a decade, business people have seen a move away from paper and documents in the office. It’s left many wondering how long businesses like theirs would continue to see multi-functional printers in the corner of the office.  In the past, documents have been the staple diet of business. Companies have consumed reams of paper to automate processes, report, record, share, and generally use information.

For information workers, documents are convenient, easy to use, and offer them a level of autonomy to ‘get things done’ that technology still woefully lacks.  There are certainly sound financial reasons NOT to use paper forms. Printing paper documents can be expensive, especially in full color. The advantages of digitized forms go beyond economies in printing. Not only are digital documents easier for computers to read, they don’t demand physical storage space compared to paper documents. Furthermore, there’s no need to pay a fortune to convert paper forms into a digital format later down the line.

One aspect of the digitized document that has held it back somewhat is the old guard of people familiar with reading, reporting, sharing, and storing paper documents. The adage ‘you can’t teach an old dog new tricks’ applies here. Many die hard printed document users don’t want to change their ways. They don’t like reading electronic documents from a screen.

Best Uses for Digitized Documents

There is a significant difference between a digitized and a digital document.  For most users, a digitized document is nothing more than a document structure with data melded into a file. It is an opportunity to capture, process, sign, view, and save documents in a format that computers understand.  Using Adobe’s proprietary Acrobat software—which isn’t cheap by the way—users can add a bit of interactivity into their digitized documents. Another feature of the full-fat version of PDF is that it allows data to be added to digitized documents in the form of basic fields.

This does make life easier for digital data capture and beats sending someone a Word document to complete. However, it’s not digital transformation by any means.

For marketers, one other stand-out feature of full-fat PDF should also be mentioned; the means to present PDF documents in full-screen mode with a black background. This looks so much cooler than the standard PDF view. Combine PDF with their party tools, like Flipsnack, and you can share electronic documents that work like a paper document with flipping pages (cool!).

New intelligent digital document formats—like CDF— don’t displace the role of their digitized predecessors. PDF will remain useful for signatures, archival, etc. for many years to come. Instead, they answer the new demands being placed on documents for the digital age. Which requires a wholly new digital file construct.

5 Things That Make Digital Documents Useful

New intelligent digital document formats—like CDF— don’t displace the role of their digitized predecessors. PDF will remain useful for signatures, archival, etc. for many years to come. Instead, they answer the new demands being placed on documents for the digital age. Which requires a wholly new digital file construct.

1. Autonomy of use

Like their hardcopy paper and digitized predecessors, this new document format can be used in a relatively autonomous way. That said, they are tethered to the Digital Clustered Cloud Space that manages them, and connected to a Digital Data Fabric that serves up composable data to users, so there’s no need to do quite so much of that tedious data crunching and cleansing work to make harvested data useful. 

The autonomy of use offered by digital documents is good news for information workers because they finally have the means to serve themselves with the information management and publishing tools they need to get their work done without constantly having to go to IT for more apps. It’s good news for IT teams because they can serve the long-tail of demand for apps across the business without having to lose control over enterprise architecture, data organization and data security to a citizen developer free-for-all.

2. Rich media, interactive document publishing

You can do a lot of things with digital documents. For one thing, they support rich media, so if you want to produce an elegant digital brochure for your website, or eBook, the world is your oyster. Once you’ve composed your eBook, to take it online, you can assign a URL, or post your page into an iFrame to embed it into your website. This makes digital documents extremely handy for marketers.  Another great feature of digital documents is that you can track user behaviors to see what content is interesting to your audience. Want to publish your PowerPoint presentation as an interactive document online? Yep, you can do that too!

3. Custom distance learning courseware with tracking

Those responsible for learning management across their enterprise will find digital documents handy too. Not only can you quickly turn PowerPoints into fully standalone interactive courses—complete with assessments, tests, and pre-qualifiers—you can record who has been trained. Connect courses into an eLearning system and you can offer learners the means to return to courses from the point where they left.

4. Mobile and desktop information management ‘apps’

Displacing the need to code, digital documents empower information workers to work with data on their terms, without having to build applications or suffer spreadsheets.

Data is served up in a composable format from a Digital Data Fabric, making it easier to work with—even when it’s been gathered from multiple data sources and systems. Use of no-code drag-and-drop design elements means that documents are easy to compose for business people who don’t have a black belt in computing.

5. Analytics and report publishing

Digital documents support plugins provided by IT teams, so that higher levels of sophistication can be added to the base features information workers use.

One example of how this can be applied comes in the form of interactive graphs, charts and dashboards. Digital documents make it easier to harvest data insights from across a series of data points, to then spin up a one page report for either temporary or permanent use.

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From Digitized to Digital Documents

PDF is arguably the best-known electronic document format. It comes in a variety of forms (notably 1b is the preferred option for digitized document archival), and it’s been around for over twenty years. 

PDF is most commonly used for converting unstructured documents (hard copy) into a digitized form that computers can read. More recently, PDF has seen a greater lease of life thanks to digital signatures.

That means, if you’ve ever scanned a document, you’ve probably used either an unstructured image or a PDF to present and share the information.

Setting The Optimal Balance Between It and The Business

While the notion of making information capture, processing, automation, analytics, and collaboration more accessible to information workers is desirable, the risk faced by IT leaders is to encourage a free-for-all of citizen apps.  This would only result in further complicating the data architecture of the business.   

This is where digital documents can deliver results. They are wedded to a Digital Data Fabric layer that serves up data in a composable form and is tethered to the Digital Cloud Space that manages the entire ecosystem. 

Using these two layers, IT leaders can re-enforce their strategic agenda, not dilute it by giving over control to willing but largely untrained users.

Where Digital Documents Have Come From

Digital documents are a new spin on documents. Like their analog predecessors, they share many of the same autonomous use characteristics.

One of the great things about documents is their familiarity with information workers and the fact that they democratize and simplify so many complex things that happen in a business.

Digital documents embrace this mantra but are a new digital construct for a digital era. 

Digital documents answer one of the biggest challenges facing IT teams in the fast-paced environment of digital transformations; namely, how to serve up the long-tail of applications that information workers need, particularly as organizations work hard to get innovation into the far reaches of the enterprise.

Comparing different digital document formats

PDF (Portable Document Format)

  • Digitized rich media document file
  • Contains data, design meta-data
  • Does not include if/then logic rules
  • Does not track user behaviours
  • Supports use on smartphones
  • Semi-autonomous—Requires PDF Adobe Acrobat installed to access full features of the file format, and PDF Reader app to read and use
  • Great for digital signatures and archival
  • Auto page numbering
  • Offers fullscreen presentation and basic features to turn PowerPoint presentations interactive

CDF (Canvas Document Format)

  • Smart digital rich media document file
  • Contains data, design meta-data and if/then logic rules
  • Supports use on smartphones
  • Tracks user behaviors
  • Semi-autonomous—Requires data fabric and presents as standard (secured or unsecured) HTML web page deployed to a dedicated URL or micro site
  • Auto page numbering (optional)
  • Supports rich media and can be fully interactive; great for distance learning course development, eBooks, and for capturing, processing, automating, analyzing, and sharing digital data and applications
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DIGITAL DOCUMENTS REMASTERED

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Micro-Portals • Forms • Reports • Training Dashboards • Charts • Maps • Tables Checklists • Onboarding • Risk Registers • Presentations • eBooks

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May 19, 2022 | Customer Data Platform, Data Management, Digital Documents, Digital Transformation, Software Development

Types of digital documents

Digital documents are a revolutionary tool enabling digital transformation for businesses. Discover some of the types of digital documents you can create using Encanvas.

Rich Media Content Experience

Content experience documents are all about engaging audiences in more impactful ways. A digital brochure (or eBook) combines rich media to produce persuasive, professionally crafted digital content. Rich media content—containing elements such as videos, testimonials, facts presented through interactive graphics, visualizations, etc.—increases stakeholder engagement, and improves customer experience, maximizing information consumption and use.

According to research into the opinions of 538 digital marketers conducted by Lemonlight in 2021, 81% plan to include video content in their marketing strategy over the next several years, while 94% said watching video content has helped them make a purchase decision at least once. Of those, 72% were swayed by a product video.

Distance Learning Courseware

Distance learning has transformed education. Digital courseware makes it faster and easier for courseware designers to design and publish courses. Furthermore, once published, digital courseware is a lot easier to update. One of its advantages comes from the ability to track learning journeys; sometimes, recording training and training results is essential for compliance.

Digital documents bring distance learning course development into the digital age. Take existing PowerPoint courseware and upgrade it to digital online courses in minutes. Use courses stand-alone or leverage an eLearning platform to manage them.

Web Forms

Web forms are another digital document example, as they’re used to capture data from customers and turn back-office processes into self-service experiences. Automating processes in this way not only improves customer experience by making more services on-demand but also increases customer engagement while reducing service costs.

Often, human-in-the-loop processes are re-engineered with the minimum amount of effort or fuss. Having captured data, workflow rules can be automated by software bots that also track and record customer interactions into Customer Data Platforms.

We’ve made forms integration with websites simpler too. Using Encanvas digital documents, web forms can be implemented as stand-alone solutions or be closely integrated with existing data repositories. Publish forms as secured permissions-based iFrames within existing websites, or link to public or private forms using dedicated URLs for direct access.

Digital Assistants

Software bots are great at recording transactions, making ‘micro-decisions’ across the enterprise, for harvesting and cleansing data, and moving it between locations. Many of the tedious tasks previously performed by humans and spreadsheet-powered data processing applications can be displaced by enterprise-grade IT solutions made possible by digital documents.

Spreadsheet Replacement and Micro Task Automation

While spreadsheets are versatile, they’re also labor-intensive. Give your knowledge workers more time to deliver value to customers and make a positive impact on innovation and business improvements by displacing spreadsheet apps with enterprise-grade digital document solutions. Our digital documents are powered by robots. That means, much of the heavy lifting that humans have previously done to capture, process, manage and analyze data can be discharged by software bots through automation.

Information Bridging GlueWare

Is your eCommerce front-end website fully automated with back-end systems? If so, then you’re ahead of the curve! Most organizations have a portion of their back-office data processing that goes ‘offline.’ This adds costs to service delivery costs while delaying customer requests and responses. Bottlenecks emerge and customer experience decays. The alternative is to use digital documents to bridge between systems and processes to create a ‘fully digital’ environment. The ability to leverage digital technologies to dramatically escalate time to value for digital transformations is increasingly being described as hyper-automation.

Data visualization

The possibilities to present and make sense of data using rich data visualization tools have grown dramatically thanks to innovations in cloud computing and big data tooling. Visualizations might come in the form of interactive maps used to track assets, parcels, people, vehicles or drones, or spatial charts and graphs that highlight key data attributes that would otherwise remain hidden to users.

Data processing

Arguably, the single biggest reason businesses invest in enterprise IT is to formalize and automate processes. Not so long ago, the way to do this was to invest in Systems of Record (SoR)—like Oracle, SAP, and Microsoft—that promised best practice data processing ‘templates’ that catered for the majority need and offered the assurety of robust and resilient data management and processing.

diagram of digital documents

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Deliver small and wide data with digital documents 

Deliver small and wide data with digital documents 

May 18, 2022 | Business Intelligence, Business Intelligence Software, Digital Documents

Deliver small and wide data with digital documents

Gartner Says 70% of Organizations Will Shift Their Focus From Big to Small and Wide Data

Written by Ian C. Tomlin | 16th December 2023

Gartner is speaking about small and wide data but what do they mean?  Read this article to get up-to-speed on how businesses are re-thinking their consumption of business data to create data-driven decisions with solutions like Encanvas’ digital documents.

Dashboards Aren’t Good For Business

A dashboard is a human interface that helps humans to understand data. At one time, the use of dashboards was all the rage in business. But not so much today.

Every dashboard requires a human to power it which costs time and money.

Dashboards have traditionally been designed for back-office users to make sense of data, interpret it, to then send out reports and make decisions. That doesn’t make sense either. Better instead to have automated, conversational, mobile, and dynamically generated insights customized to a user’s needs and delivered to their point of consumption. That way, data becomes actionable and reaches the people best placed to lever its value.

That’s where digital documents come in.

How digital documents create ‘small and wide’ data analytics

In this era of digital transformation, big data and composable applications, the digital document is king. It means that individual analytical experiences can be created at scale, and speed. The way Gartner describes this is is ‘small and wide’ data analytics

“Small and wide data, as opposed to big data, solves several problems for organizations dealing with increasingly complex questions on AI and challenges with scarce data use cases. Wide data — leveraging “X analytics” techniques — enables the analysis and synergy of a variety of small and varied (wide), unstructured and structured data sources to enhance contextual awareness and decisions. Small data, as the name implies, can use data models that require less data but still offer useful insights.”—Gartner

How digital documents create ‘small and wide’ data analytics

Digital documents take analytics to the edge.  Today, more data analytics technologies live outside of the traditional data center and cloud environments. This move from centralized data processing and analytics to edge technologies, like digital documents, reduces or eliminates latency for data-centric solutions and enables more real-time value.

Preparing data — the crucial role of data fabrics

Anyone that’s been involved in data analytics and producing dashboards and reports knows that getting the right data, at the right quality, and at the right time is the biggest challenge. Once these challenges have been overcome, presenting data these days is pretty straightforward. But getting the data stuff right is tremendously time-consuming and, unless automation are involved, they can mean late night for someone with a spreadsheet.  Thankfully, data fabrics underpin digital documents to establish a higher standard of data accessibility, integrity and quality. Rather than performing the heavy lifting of integration, extract, transform and load functions, digital documents only need to concentrate on shaping the end product, maybe a little blending of data from different tables and making it pretty—not much more.

Moving from dashboards to answers

In an era of artificial intelligence and machine-to-machine workflows, it doesn’t make much sense to build dashboards for people to look at when all they need is to know when change happens. Advanced digital document analytical solutions work with software bots (in the data fabric) to automate data alerts highlighting to humans when they need to examine data, rather than asking them to look at dashboards that yield limited value.

Answering new questions

One reason centralized data analytics fails lies in the fact that information workers these days are constantly curious, repeatedly asking new questions of data. Serving up all these queries in the form of dashboards and charts is an impossible task. The solution is to give information workers their own codeless tools to examine data and answer their own questions, while serving up high quality insights.

The only minor challenge is getting the balance right in this equation; I.e., ensuring information workers know enough about the data they’re looking at to appreciate its context of use. For example, when invoices aren’t billed until the end of the month, the only time during a month that some financial records will present a complete picture for decision makers is the minute after the last record is reconciled. Combining a digital data fabric with a composable solution like digital documents gives IT professionals the best possible opportunity to get this balance right for stakeholders.

The ambition of many business leaders in the digital age is to create a team of people in a business that are constantly curious, constantly questioning the norm and working out the difference of doing better things over doing things better.

Forging this new style of enterprise demands that information workers are given the tools to do the job. Access to information and information systems is key to this. But to democratize and de-skill IT comes with risks. Setting the right balance between IT and the business is the rump issue. Adopting a cloud native digital platform that offers a composable solution for information consumption, underpinned by a data fabric, may be a good way to achieve the results you seek.

Digital documents and analytics

Digital documents and analytics

May 18, 2022 | Business Intelligence, Business Intelligence Software, Digital Documents

Business digital data analysis

Digital business is driven by data. This article investigates how are digital documents transforming accessibility to the insights executives and information workers need?

Digital business has ramped up the need to insights

If you’re as old as I am you might remember the era of green sheet reports from the data center. Then, we went through a period of Harvard Graphics reports that did away with slides. Business intelligence promised to change everything, but was so slow and costly to roll-out that few implementations delivered on their promises. Then came the cloud and big data.

Even now, after decades of trying to get corporate reporting more useful, there is a huge gap between the centralized data analytics platforms that serve up insights, and the needs of decision makers and information workers.

Do you know what characteristics go into making a top 10 customer? How much profit you make by customer? A typical deal?

The nature of a digital age is that, behind every question is a curious mind with another new question. And the data needs to be fed in real time. Some systems track user behaviors while data is in transit, simply because decision makers trying to grow their businesses don’t have time to wait.

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DIGITAL DOCUMENTS REMASTERED

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Micro-Portals • Forms • Reports • Training Dashboards • Charts • Maps • Tables Checklists • Onboarding • Risk Registers • Presentations • eBooks

There are some truly excellent business intelligence tools on the market today. Each comes with its own blend of swishy 3D charts, smooth transitions and visualization tools.

For most people, the idea of being able to harness actionable data insights incentivises them to become a citizen developer and start experimenting with these tools to self-serve some results.

However, few people want to invest chunks of their week to perform reporting tasks if it could be done otherwise. Digital documents offer a simpler way to find answers to new questions, without having to become an expert in BI.

The role of a data fabric is key to data value

One of the features of a digital document architecture that makes it so valuable comes in the form of the digital data fabric this architecture resides on.

This is an umbrella of data harvesting, transformation and automation tooling—powered by software bots and AI—that brings data together from its various locations and re-blends it together so that digital document users can compose new solutions with it.

The data mashup capabilities of the digital document come into their own, once IT administrators have setup this powerful capability to forge a single view of data from across the enterprise.

Autonomy of digital documents is key to distributed insights

And this is where digital documents come in. Using digital documents, people enjoy the autonomy to harvest the actionable insights they need quickly, because the data fabric they reside on has already prepared data into a composable form.

There is no need to spend half a day designing a dashboard and the other half cleansing data to make it useful. Additionally, use of HyperDrive and it’s remarkable ability to consume any third-party data, DLL, COM+ object, or C# code without scripting means that business analysts can assist employees by filling any shortcomings in desktop features by adding tooling as needed.

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