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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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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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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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How Encanvas helped Overall Eesti become data-driven

How Encanvas helped Overall Eesti become data-driven

May 18, 2022 | Data Driven Decision Making, Data Management, Data-Driven Culture, Digital Data Fabric, Digital Documents, Digital Transformation, Digital Transformation Platform

Data driven businesspeople

The desire of most digital businesses is to make informed decisions driven by rich data analytics. In this article, we explore how digital documents are helping to achieve that.

Business challenges that drive change 

Tonis Haamer is one of the cleverest businesspeople I know. He runs a Overall Eesti, technology business in Estonia along with his brother, Mart. Still today, a big part of the business is office equipment, the company’s heritage. But the market for office printing is not what it was, and this caused Tonis to realize that the onward growth of the business demanded a rethink in how it is resourced.

Now, Overall Eesti is a very people-centric business. The team behind the brand is extremely hard working, extremely loyal. Shedding staff wasn’t a desirable go-forward plan. For this reason, Overall Eesti began its journey to become data driven, and one of the most advanced digital businesses.

What it means to be data driven 

I had the opportunity to interview Tonis on behalf of Canon Europe when I was running strategy around software solutions for the brand. I was looking to see how companies are adopting data insights, and Overall Eesti was an example.

Over a couple days, Tonis explained the objective behind the company’s data driven agenda was to make smarter decisions more often to answer new questions as they emerged, and to reinforce sub-optimal processes with new applications built using digital documents when it became obvious there was an opportunity to streamline.

Tonis explains, “To be data driven means being able to answer new strategic questions as they emerge. To do that means you have to harness your operational data that comes from ERP, service, CRM, HR, and other front-line business systems. It demands the ability to re-use this data for new purposes. And the challenge that brings with it is how to get the quality and integrity right. Once you’ve achieved that, the possibilities open up. But it is not a trivial task to create composable data.”

Data fabric 

Almost a decade ago, Overall Eesti became one of the first companies in the world to create a home-grown Digital Data Fabric they called ‘CLIO.’

As Tonis explains, “Preparing your data is one of the biggest technical challenges of creating a data driven approach to business. It’s not just about harvesting the data you already have. Almost inevitably there will need to be enrichment of data, and you suddenly realize how poor the quality of data is from systems that only use aspects of the databases designed to support their operation. Furthermore, we found that some of the key links between data-sets did not exist. We had to find ways of connecting records in one system with the next by using fuzzy logic matching to construct the relational ties that were missing.”

Data is a big challenge, but it’s not the only one 

As an early adopter of data fabric technology, the Overall management team are very familiar with the journey to overcome technical challenges, but Tonis is clear that data quality is only a foundational stone of a broader change agenda.

It starts with rewiring the culture of management towards the importance and use of data. This, and understanding what the strategic priorities are and what questions remain unanswered. You need these three qualities: clarity of purpose, data culture, and data integrity all in place before you start to see returns for your investment. For many businesses, the cost and complexity of that change has discouraged them from moving forward.

Final thoughts 

Digital documents, and the data fabric they reside on, offers the necessary blend of tooling for organizations to become data driven. These technology instruments are important, but—as this case example implies—overcoming the cultural, behavioral and strategic planning challenges may still yet be the greater obstacle to success for business leaders prepared to give the data driven business model a try.

Avoid data spaghetti with a no-code aPaaS

Avoid data spaghetti with a no-code aPaaS

Mar 10, 2022 | aPaaS, Application Platform, Data Driven Decision Making, Data Management, Database Software, Enterprise Data Management, HPaPaaS, No Code Development Platform, PaaS

No-code aPaaS will transform your data driven decisioning.  Here’s how.

Most enterprises today operate more than 80 SaaS apps, the consequence being ‘data sense’ is harder.  The good news is that you can use a no-code aPaaS platform to bridge across enterprise silos to tame data spaghetti and create a single version of the truth

The problem SaaS exacerbated that no-code aPaaS solves

The short-lived joy of SaaS

data spaghetti graphic

When the possibility of Software-as-a-Service (SaaS) solutions arrived into the market in the early 2000s — heralded in by the evolution of web platforms and cloud computing — they were game-changing for innovators in the tech industry.

Through cloud SaaS innovations, developers could bring their products to market faster (and eat much lower costs), focus on very tight niche solutions, offer products on a subscription, and give customers the opportunity to try them out immediately. Furthermore, technical support and endorsements could be supplied through the same online site that sold the products.

For buyers, SaaS was equally advantageous. No longer did they need to commit to a purchase before experiencing a product to see if it delivered value. The quality of products leaped up the scale, as providers HAD to deliver excellent quality, intuitive, and responsive applications.

The downside of SaaS is that it spreads your data across a wide number of data silos.  

 

SaaS has re-balanced IT selection decisions towards department leaders, but at a cost

Enterprise computing practitioners were somewhat less thrilled by SaaS. Before its arrival, the role of the Enterprise CTO was unquestioned. They were the gods of technology, and nobody could get anything done in IT without their blessing.

The idea that departmental managers could arrive at the IT desk and start demanding software products they hadn’t even seen before, and show them immediate advantages was hard to counter, and left many IT heads on the back foot, trying to defend the common sense of requiring testing, integration and further validation before any recommendation was adopted.

data analytics graphic

The rise of SaaS adoption levels in the enterprise has soared over two decades, as department heads have got ever more involved in selection decisions on the tools they, and their teams, want to use. The power to make decisions drifted from the center of the enterprise to the margins.

Few could argue that the quality of applications used in business has benefited from SaaS.  But at what cost?

Step into any large enterprise and you’ll encounter find the common problem of data held in various SaaS platforms with departmental managers pulling their hair out trying to gather it up to drive decisioning.

Nick Lawrie

Managing Director, NDMC Consulting

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Check out our easy read guides.  Each is crammed with facts and insights on the hottest topics in enterprise IT.

Guide to Digital Transformation

Guide to Hyper Automation

Guide to Digital Ideation

Guide to Customer Data Platforms

Guide to No-Code Platforms

Data spaghetti

Software-as-a-Service technology has played its part in making it close to impossible for real-time business decisioning to happen across the enterprise without an additional layer of ‘business intelligence and analytics’ technology being superimposed.

Even with the best data visualization and analytical tools, the problem of fragmented data silos pervades.

It’s not simply the case that SaaS tools separate usage activity into different places across the enterprise computing biosphere, using a myriad of separately authored apps results in every app using its own core data tables for common things that every organization needs to know about — such as people, departments, organizational hierarchies, policies, processes, suppliers and user groups. While some of these building blocks can be inherited from common directories, most are simply individually reproduced time and again by vendors.

Digital transformation drives data re-use

Growing demand from department leaders and executives for new apps and real-time data analytics has created a demand for data reuse. And it’s when these requests emerge that the problems of data integrity and quality emerge.

Install any new digital innovation into an enterprise, and it’s almost inevitable that existing data will want to be harnessed.

When this happens, time and again, one finds that the original data tables operating within SaaS applications are incomplete, unused, or irrelevant. Business Analysts find themselves scratching their heads trying to work out which bits of data to string together to build a reliable picture of the operating reality.

The state-of-the-art is a right state!

  • Enterprise IT leaders wondering if they will see the day when no department manager comes to the door demanding the next new SaaS thing.’
  • The very same Department managers being given a subscription to Microsoft PowerBI forcing them to spend time away from their customers and teams to clumsily play with rubbish data, wondering ‘Why do I have to spend half my life trying to find data, rather than having the chance to use it?’
  • CxO Executives still struggling to know what processes, policies, and customers actually exist.

Businesses want to be ‘digital’ but lack the quality and integrity of data to innovate.

It begs the question: Is there a better way? The answer is yes — and it’s been around for a while.

Ready to discover the new era of codeless software? Check out our products.

Secure&Live – A feature rich and data secure digital transformation platform.  Secure and Live is a codeless Enterprise applications Platform-as-a-Service (aPaaS), built to turn business models and strategies into apps.

GlueWare –  Enterprise iPaaS for bringing your data together, mashing it up, and bridging between your eCommerce store and back-office, streamlining processes for maximum results.

 

AppFabric – Build as many apps as you need using an agile codeless SDLC approach and change them as often as you like.

Live Wireframe – Design and publish ebooks, courseware and apps, then go live in two clicks!

CDP – Codeless Customer Data Platform to create a single view of your customer data. Use our data integration tools to harvest insights from across your enterprise and beyond.  

 

 

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Learn about the data safeguarding, features and modules of Encanvas that make it a leader in codeless enterprise aPaaS

Better decisioning starts with a no-code aPaaS to harness data

The rise of no-code application platforms

The concept of cloud Platform-as-a-Service solutions has been buzzing around for over a decade since the arrival of cloud computing. PaaS describes the layer of technology that sits between Software-as-a-Service and the mechanical end of cloud–hardware infrastructure, memory disks and the like. 

A no-code application PaaS is an environment for designing, deploying and operating tens — if not hundreds — of apps and software robots without needing to use code to design, deploy, and run them. 

No-code aPaaS introduces a new skill-set

Using no-code-aPaaS means that Business Analysts found within IT or Digital teams (not coders) author applications.  They do so working in consort with business stakeholders in what Gartner fashionably calls ‘fusion teams.’

Applications requirements go straight from the workshop whiteboard into a live wireframe that swiftly becomes a new application.

Examples of mobile and web desktop applications designed and deployed on no-code aPaaS 

encanvas surface screenshot example_ mobile2
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What makes no-code aPaaS different to what comes before is that at least 60% of the things you need to produce an Enterprise App come out of the box.  It means the only things business Analysts need to get right are the drag and drop rules, if-then logic and workflows of the application they are building that are unique to the requirement.  While No-Code applications development is fast, building apps on a No-Code Application Fabric is even faster.

Ian Tomlin

Encanvas

No-Code aPaaS is a win: win for both IT and the business

People used to argue you needed a two-speed IT capability to make digital business work.  That notion has thankfully gone away.  No-code aPaaS returns IT influence from the outer fringes of the enterprise to the center.

That’s an awkward conversation in today’s boardrooms, but it’s arguably a necessary one.

Organizations that want to harness data, become data-driven, keep data safe, eradicate self-authored apps and spreadsheets, achieve excellence in customer experience, serve up the best applications for their stakeholders, and cut costs.  History tells us that the best way to achieve it is to have a unified computing and data environment.

pros and cons image

PROs

The benefits of no-code aPaaS for ‘data bridging’

Data quality/integrity benefits include:

  1. Faster data gathering / aggregation using no-code interfaces instead of APIs etc.
  2. Use of integrated no-code Robotic Process Automation (RPA) and Information Flow Automation (IFA) tools to streamline data uploads from remote locations
  3. Safer for data – data is secured at source, during transport, and when uploaded.
  4. Improved data quality through use of artificial intelligence/fuzzy logic tools to cleanse, normalize, enrich and de-dupe data
  5. Easier to test, replicate and scale applications using the provided ‘cloud container’ technology

CONs

The downside (a few things to think about)

1. IT people used to coding will be resistant to change

2. Departmental heads may initially be resistant to the idea of sharing their data

3. Not every no-code aPaaS offers ALL the features you will need. So selecting the right platform with be important and isn’t always straightforward.

ian tomlin profile picture

About Ian Tomlin

Ian Tomlin is a management consultant and strategist specializing in helping organizational leadership teams to grow by telling their story, designing and orchestrating their business models, and making conversation with customers and communities. He serves on the management team of Encanvas and works as a virtual CMO and board adviser for tech companies in Europe, America and Canada. He can be contacted via his LinkedIn profile or follow him on Twitter.

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get in touch

About Encanvas

At Encanvas we have a passion for low-code / no-code (we like to say codeless) software development. We’ve been leading innovation in the enterprise rapid applications development (low-code / no-code / data mashups) industry since 2002.

Our enterprise digital transformation platform is used to design, deploy and run custom apps by uniquely blending application (aPaaS), integration (iPaaS), Robotic Process Automation (RPA), and data mashup codeless software tools.

Encanvas brings agility and innovation to businesses.  Used by data-driven organizations around the world, our platform evolves digitalization plans at the speed of light to maximize customer experience and minimize IT costs.  Accelerate time to value of new applications as part of your digital transformation or data engineering program.  Read the Encanvas Blog to learn more about what we do.

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