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.
If you don’t have a Customer Data Integration solution in your business, then you’re missing out. Find out why.
About
Customer Data Integration (CDI) is the process of consolidating and then properly managing information about your customers from all available business sources, including all known contact details, financial data associated with that client, and information gathered through marketing activities.
Encanvas Customer Data Platform (CDP) incorporates advanced tooling for Customer Data Integration (CDI) as standard.
Encanvas CDP Model Architecture Illustration
The problem Customer Data Integration solves
Most businesses want to maximize their customer experience by leveraging data. To achieve that requires the formation of an aggregated data-mart to present a single version of the truth. Preventing this ambition is the incumbent approach to data management, forged over the years as departments have invested in software to support discrete processes. Companies will use a blend of software applications and third-party tools along with their core enterprise resource planning system to fulfil their processes. This results in a fragmented and unregulated environment for data.
As businesses face an ever more regulated operating environment and greater competition in their local markets owing to the impact of Internet and mobile phone use, executives can ill afford to ignore the importance of installing appropriate controls over how customer and financial data is used, who uses it and how they use it.
Creating a complete single-version-of-the-truth
An important business driver for integration services comes from the desire of executives to harvest data from across their enterprise in order to make informed decisions. The drive towards a data-driven culture demands that systems connect to one another. The integrity of data executives review, is a major sticking point.
A survey of 442 business executives around the world by Harvard Business Review found that corporate decision makers have major concerns about access to, availability of, and the quality of internal and outside data. The result is reduced confidence in their decision-making ability.
Moreover, nearly half of the global respondents said their lack of confidence stems from a lack of information or easy access to data. The findings are puzzling given the emergence of big data techniques, the proliferation of global networks and the sheer processing power contained even in mobile devices.
One reason for the disconnect between big data and decision making, the Harvard researchers found, is that “silos of data, typically imprisoned in customer, financial, or production systems, are frequently inaccessible by individuals outside the functional group.”
In this regard:
43% of survey respondents said important external or internal data was missing
42% said data was inaccurate or obsolete, and;
33% said they “couldn’t process information fast enough.
Technology building blocks
There are a series of discrete activities that need to be performed in order to bring customer data together from where it exists:
Data connectors and Application Programming Interfaces (APIs) – These are software intermediaries that allows two applications to talk to each other. In the case of a customer data integration, it’s likely that data with be harvested by four or more systems. This demands that the customer data platform being used is capable of integrating with a diverse range of data sources, file types, protocols and systems.
Voting systems – Whilst there may be more than one source for any data asset, not all data is of a common consistency. Voting systems are employed by customer data integration platforms as a mechanism to recommend the best source of data when more than one source exists.
Data validation and protection again malware – To ensure any data being uploaded is free from malware and possesses a reasonable level of data integrity, it’s important that Data Integration systems and processes validate files ‘at source’ before data goes anywhere. To do this files need to be interrogated by running through some validation scripts before they are processed. Any failed uploads should then be monitored and reported by the data integration systems tooling.
Data cleansing, normalization, deduping and quarantining – It’s highly unlikely that any sourced data will be of a high quality. To improve the quality of data, a series of data cleansing routines can be enacted on data files. These will commonly include:
Normalizing – Enacting a series of data transformation routines to standardize the format of common fields such as email and phone numbers etc.
De-duping – Where more than one record exists of the same customer from the same system due to duplicate records, then systems may be required to determine the latest record and delete older records, or merge them.
Quarantining – Similarly, it may be necessary to set-aside (‘quarantine’) records that are of poor quality and require manual review.
Data upload – As the term suggests, data uploading is about taking a transformed file and uploading it to the data mart. This can require mapping and concatenation of fields to mirror the data architecture of the data mart. Uploads have to be recorded and it must be possible to rollback data uploads for administrative purposes.
Data extraction and software robots – Sometimes it’s not possible to directly connect to a live data source. There may be technical challenges in doing so, but also there may be load-balancing issues and business continuity and security concerns. In some cases, we have seen examples where companies are unable to gain authority from IT service providers to source the data they own from computing systems they don’t. Where direct connection
Considerations
The currency of data
Not all data is accurate and complete all of the time. Take accounting information for example. Sometimes, invoicing lags behind sales. Taking a read of data at the wrong time in the accounting calendar can return unhelpful and potentially destructive information! It’s very important therefore, that architects of any data harvesting project possess a deep appreciation of the business they are serving.
Support for multi-threaded, multi-sourcing and linking
Data integration is difficult to get right and has many technical challenges. One of them is systems latency. Sometimes, unless sessions are maintained, connections to third-party systems can drop.
Multi-threading is in essence about the receiving system giving sourcing systems a ‘nudge’ every now and then to remind them that they’re still needed.
Multi-sourcing happens when a system harvests data from more than one system concurrently.
Multi-linking happens when specific rows of table data are plucked from the databases of sourcing systems. All three of these technologies are needed in Customer Data Integration projects.
About Encanvas
Encanvas is an enterprise software company that specializes in helping businesses to create above and beyond customer experiences.
From Low Code to Codeless
Better than code-lite and low-code, we created the first no code (codeless) enteprise application platform to release creative minds from the torture of having to code or script applications.
Use Encanvas in your software development lifecycle to remove the barrier between IT and the business. Coding and scripting is the biggest reason why software development has been traditionally unpredictable, costly and unable to produce best-fit software results. Encanvas uniquely automates coding and scripting. Our live wireframing approach means that business analysts can create the apps you need in workshops, working across the desk with users and stakeholders.
When it comes to creating apps to create a data culture and orchestrate your business model, there’s no simpler way to instal and operate your enterprise software platform than AppFabric. Every application you create on AppFabric adds yet more data to your single-version-of-the-truth data insights. That’s because, we’ve designed AppFabric to create awesome enterprise apps that use a common data management substrate, so you can architect and implement an enterprise master data management plan.
Encanvas supplies a private-cloud Customer Data Platform that equips businesses with the means to harvest their customer and commercial data from all sources, cleanse and organize it, and provide tooling to leverage its fullest value in a secure, regulated way. We provide a retrofittable solution that bridges across existing data repositories and cleanses and organizes data to present a useful data source. Then it goes on to make data available 24×7 in a regulated way to authorized internal stakeholders and third parties to ensure adherence to data protection and FCA regulatory standards.
Encanvas Secure and Live (‘Secure&Live’) is a High-Productivity application Platform-as-a-Service. It’s an enterprise applications software platform that equips businesses with the tools they need to design, deploy applications at low cost. It achieves this by removing coding and scripting tasks and the overheads of programming applications. Unlike its rivals, Encanvas Secure&Live is completely codeless (not just Low-Code), so it removes the barriers between IT and the business. Today, you just need to know that it’s the fastest (and safest) way to design, deploy and operate enterprise applications.
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.
Read this article to better understand what a Customer Data Platform is an why your business might need one.
About
Customer data is anything that identifies a customer, or indeed any associated data that results from customer interactions including purchases, transactions, and customer service communications.
A Customer Data Platform is an enterprise computer processing platform used to harvest, aggregate, cleanse, manage, process, analyze and output customer associated data. Data is pulled from multiple sources, cleaned and combined to create a single customer profile. This structured data is then made available to other marketing systems. Unlike a Customer Database, a Customer Data Platform extends its functionality to all aspects of the customer lifecycle. Normally it will include campaign management and the provisioning of multi-channel communications. Advanced systems will manage customer offers and promotions.
The CDP Institute defines a Customer Data Platform as “packaged software that creates a persistent, unified customer database that is accessible to other systems.” Basically it’s a prebuilt system that centralizes customer data from all sources and then makes this data available to other systems for marketing campaigns, customer service and all customer experience initiatives. Gartner defines CDPs as – ‘integrated customer databases managed by marketers that unify a company’s customer data from marketing, sales and service channels to enable customer modeling and drive customer experience.’ An encanvas, we see this as a narrow definition, given that Customer Data Platforms normally serve up insights to strategic teams and all departmental functions to shape processes and priorities.
Why you need one
Encanvas CDP Model Architecture Illustration
Every business understands the impact of outstanding customer experience. Think of businesses like Amazon, whose singular purpose is obsessive Customer Fanaticism; placing the customer at the heart of everything it does. Even shareholders were taken aback when its founder Jeff Bezos explained that he was prepared to forego short-term profitability in exchange for an unbeatable customer experience. And he’s proven to be correct.
Like Amazon, if you want to delight customers with personalized offers then you will need to be world-class at capturing and making sense of lots of data about the customers you serve. Modern marketing methods rely on data to drive decision making. It eliminates the guesswork of content marketing because marketers know what motivates customers and the characteristics that profile the best-fit audience for their products and services.
Without a Customer Data Platform, the ability to appreciate the value and characteristics of customer relationships is rarely that straight-forward. In practice, data is stored in silos, whether organizational or technological, and this makes it challenging for companies to deliver consistent customer experiences.
Creating a complete single-version-of-the-truth
An important business driver for integration services comes from the desire of executives to harvest data from across their enterprise in order to make informed decisions. The drive towards a data-driven culture demands that systems connect to one another. The integrity of data executives review, is a major sticking point.
A survey of 442 business executives around the world by Harvard Business Review found that corporate decision makers have major concerns about access to, availability of, and the quality of internal and outside data. The result is reduced confidence in their decision-making ability.
Moreover, nearly half of the global respondents said their lack of confidence stems from a lack of information or easy access to data. The findings are puzzling given the emergence of big data techniques, the proliferation of global networks and the sheer processing power contained even in mobile devices.
One reason for the disconnect between big data and decision making, the Harvard researchers found, is that “silos of data, typically imprisoned in customer, financial, or production systems, are frequently inaccessible by individuals outside the functional group.”
In this regard:
43% of survey respondents said important external or internal data was missing
42% said data was inaccurate or obsolete, and;
33% said they “couldn’t process information fast enough.
Business impact
Your customer data has huge business value because it helps to improve your ability to engage customers in conversation, on topics they care about. Then, your marketing team can use customer insights to segment markets and develop tailored offers to specific groups of customers; to deliver personalized experiences, one customer, at a time. This attention to detail (and preference) has become a ‘killer-app’ in many consumer markets where buyers have so much choice.
“Customer Data Platforms help companies solve a huge and growing problem: the need for unified, accessible customer data. Like most packaged software, a CDP reduces risk, deploys faster, costs less, and delivers a more powerful solution than custom-built alternatives…With careful planning, a CDP will provide the foundation your company needs in the years ahead to meet customer expectations for exceptional personalized experiences.” — David Raab, Customer Data Platform Institute
Business benefits
A step-change in customer experience thanks to the ability of the enterprise to personalize offerings to satisfy customer needs.
Smarter evidence-based decision making thanks to a single view of the customer and an increase in the paucity of customer insights.
Operational effectiveness, particularly in areas of customer profiling, targeting, offer development, product design, customer service delivery, and customer communications.
Improved business agility, as the enterprise is better able to appreciate what motivates customers and to identify any changes in demand or requirements.
Expansion opportunities, by identifying product and market segments demanded by customers that are being poorly served.
Obstacles – Data volume isn’t the issue, quality and value is
In most businesses today, marketing teams are awash with data. The problem is, much of the data is spread across operating systems and platforms that have no means to present a single view of the customer. Only recently have senior management teams come to recognize the strategic importance of customer data and its role in delivering a competitive advantage.
As data volumes grow, and is spread across your organization and its partner ecosystem, you lose the ability to leverage it and gain a competitive advantage. Organizations come unable to assemble unified customer data.
Data quality is a huge problem. When the currency or completeness of is poor, the usefulness of insights is tested.
It’s common for operational systems not to populate all of the tables and rows that exist in back-office systems when they don’t directly benefit the specific process they serve. This can result in many of the harvested data-sets to be incomplete, or of poor quality. In consequence, organizations are unable to apply unified customer data in delivery systems.
To create high-quality data requires data to be cleansed and graded when sourced from operational systems. This requires specialist computer tooling, often driven by artificial intelligence technologies, used to vote on the best quality sources and graduate data as it is being harvested. According to the CDP Institute, something like 43% of business-to-business companies lack the ability to extract data from source systems.
Departments all benefit from a single view of the customer, but rarely can they agree who owns it and are prepared to share data to achieve it. Projects can easily be derailed as the result of inadequate budgets, poor cooperation across organizations, and a general lack of time in marketing and technology departments to commit effort into what is essentially a change management project.
How Customer Data Platforms Help
Implementing a Customer Data Platform will gather data to create a unified data-mart that presents holistic landscape views of your customers and their interactions with your business – irrespective of where the data is held. The result is that all teams across the enterprise benefit from an improved understanding of what customers care about to segment more effectively, shape customer offers in more refined ways and fine-tune processes to deliver above and beyond (and personalized) customer experiences.
According to the most recent study by the CDP Institute, the top reasons that organizations buy CDP solutions are to:
Create a unified customer view by collecting data from all sources with powerful identity matching and management, and to deliver customer profiles to other systems in real-time (86%)
Improve predictive modeling and recommendations with full detailed access to data collected (59%)
Improve the orchestration of customer treatments across all channels (49%) Improve message selection and personalization (49%)
Reduce reliance on IT resources (40%)
Access otherwise-unavailable data (40%)
Improve data analysis and segmentation (39%)
Spend less time on data management (29%)
Improve message delivery (27%)
Faster response to changing data management needs (25%)
The ultimate litmus test of quality
For most organizations, the ultimate test of whether a Customer Data Platform is delivering optimal outcomes lies in its ability to do three things exceedingly well:
To deliver fine-grained and detailed observations on the make-up of customer segments and communities; to determine ‘what makes a customer?’
To articulate customer lifecycles and conversational paths.
To accurately account for the most profitable customers and prospects.
Putting ‘the customer’ at the heart of your data story
Most organizations today structure their data within the operating departments that manage processes. This only serves to fragment customer data. A Customer Data Platform requires a rethink in the way data is managed, to ensure that all facets of the customer world are incorporated into the customer data model not limited to:
What makes a customer?
What jobs do they do?
What do they care about?
Where do they go to seek advice?
What voices/sources do they trust?
Who (role or persona) has the problem?
What solution preferences do they have/are they likely to have?
What conscious and unconscious undesirables do they seek to resolve?
How do they prefer to be contacted?
How do we interact with them?
What is the revenue opportunity the customer represents?
What does a typical deal look like?
How much business do we do with them?
What have they bought in the past?
When are they most likely to review their requirements?
What else are they likely to want to buy?
What are the alternatives to what we can offer them?
How much are they prepared to pay for a solution?
What’s the next best alternative?
The enterprise roles with an interest in customer data insights
One of the challenges in putting together a Customer Data Strategy is the broad cross-section of stakeholders with a voice on what the end-game should look like. The key stakeholders will tend to be any of the following:
There are typically five main components of a Customer Data Platform architecture. Note, the terminology used here is formed around the Encanvas CDP but alternative solutions follow a similar path:
Customer Data Integration (CDI) platform – Information Flow Designer is a data movement. transformation and integration toolset that gathers data from the source operational systems and third-party data sources.
Clearing House (Database) – A clearinghouse records data uploads and checks for errors. It also acts as a cached memory of data assets that may not have arrived into the data warehouse yet but is being referenced by other tables (for example, perhaps rich asset or product data is recorded in a proposal but not on financial systems).
Data Warehouse – This is a repository of data that re-models data assets into a more consistent, coherent and usable form (adding relationships between data assets that may not previously have existed and presenting new views of data).
Analytical and Predictive Engine – This component is used to make suggestions on how to leverage data and turn it into actions. This component particularly needs to be very customization and may be tailored to each industry or purpose.
Customer Treatment and Conversational Engine – The final stage is to create empathetic and fine-grained targeted campaigns utilizing personalized messages.
The processes powered by a Customer Data Platform
Create marketing campaigns – enabling the formation of fine-grained marketing campaigns that benefit from relative date, geo-referencing, profile segmenting and other advanced query and search tools.
Publish financial and franchise reports – equipping accounting teams to rapidly formalize new reports for financial applications such as management accounts and shareholder updates, and for franchises using reporting groups and codes that are completely customizable.
Produce ad-hoc marketing reports – Providing the means for marketers to generate ad-hoc reports for marketing suppliers and partners.
Generate actionable insights for operational managers
Bring access to rich insights – including dashboards, charts, map-based views, and pivot-table style reports – to fully develop understanding of operational performance and business challenges.
Measure and managing customer loyalty – by profiling customers by levels of revenue, repetitive ordering, average order value – and a comprehensive story of their engagement and interactions throughout their relationship history is provided.
Manage customer complaints and suggestions – an invaluable resource for managers to learn from mistakes and improve overall customer service experience across the business.
Securely share data with third party suppliers and systems – whilst satisfying data protection regulations
Obstacles to creating a Customer Data Platform
Here are some of the common obstacles we’ve encountered through previous projects implementing Encanvas as a Customer Data Platform:
No reliable mechanism is thought to exist that can be employed to identify sources of data and harvest useful content
No data engine exists to organize and manage data
Concerns exist over data governance and threats to data loss when data is exported from systems brought together in a single place
It’s not known what data exists, or where it is
Data quality issues compromize the delivery or RoI of projects
No data relationships exist between important data sources (such as financial records, service records, transaction history records, etc.) because the enterprise operates fragmented IT systems that create silos of data.
Completeness of data insights matter
A single view of the customer is only as effective as it is comprehensive and actionable. In the first place, the number of data sources available to a CDP is critical to the richness of insight it offers. You will want to have a comprehensive and current data set, along with the broadest platform to make use of the insights. If a Customer Data Platform has access to website data alone, then gaps will exist in the knowledge of customer interactions, transactions, etc. – and it will be impossible to accurately judge profitability. When solutions only use data to personalize a customer’s website experience, there are crucial gaps in customer experience delivery that extends beyond it – such as customer service and support.
Defining and sizing the Market
Market analysts offer conflicting data on the size and potential of the CDP market.
Early-stage reporting from researchers at Raab Associates – attributed to having coined the category term in 2014 – made the prediction in 2017 that, after analyzing 27 CDP vendors that collectively generated more than $300 million in revenue – Customer Data Platforms (CDP) would reach $1 billion by 2019. According to a Marketsandmarkets.com survey, the customer data platform market is predicted to grow from $639.9 million in 2017 to reach $3,265.4 million by 2023 at a Compound Annual Growth Rate (CAGR) of 29.3% during the forecast period. The base year for the study is 2017 and the forecast period is 2018–2023. The customer data platform (CDP) is said to have a potential scope for growth in the years to come due to increasing adoption of the customer data platform for omni-channel customer experience and demand for real-time data availability. Researchandmarkets.com suggest major growth drivers for the market include an increasing demand for omni-channel experience and actionable insights by marketers and effective tracking of customers to understand their behavior for target marketing activities, and increasing pressure on CMOs to deliver personalized content spurring the demand for real-time data availability. Gartner, one of the generally most trusted enterprise IT analysts, have incorporated Customer Data Platforms into a broader category called Customer Experience and Relationship Management Software. They say Worldwide spending on customer experience and relationship management (CRM) software grew 15.6% to reach $48.2 billion in 2018, according to research from Gartner, Inc. CRM remains both the largest and the fastest growing enterprise application software category.
The cross-over between Customer Data Platforms and Data Science
Gartner sees much of the data science aspects of Customer Data Platforms integrated within its data science and machine learning (DSML) platform category. Gartner defines a DSML platform as a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner and open source). Its primary users are data science professionals. These include expert data scientists, citizen data scientists, data engineers and machine learning (ML) engineers/specialists. Players in this space include Alteryx, Anaconda, Databricks, Dataiku, DataRobot, Domino, Google, H20.ai. IBM, KNIME, MathWorks, Microsoft, RapidMiner, SAS, TIBCO and Altair.
To define the DSML market and score vendors, Gartner has determined fifteen critical capabilities.
Data access: How well does the product support data access across many types of data?
Data preparation: Does the product have a significant array of noncoding or coding data preparation features?
Data exploration and visualization: Does the product allow for a range of exploratory steps, including interactive visualization?
Automation and augmentation: Does the product facilitate the automation of feature generation, algorithm selection, hyperparameter tuning, and other key data science tasks?
User interface (UI): Does the product have a coherent “look and feel” and have an intuitive interface?
Machine learning (ML): How broad are the ML approaches that are easily accessible and shipped?
Flexibility, extensibility, and openness: How can various open-source libraries be integrated into the platform?
Performance and scalability: How can desktop, server and cloud deployments be controlled?
Delivery: How well does the platform support the ability to create APIs, or containers?
Model management: What capabilities does the platform provide to monitor and recalibrate hundreds or thousands of models?
Precanned solutions: Does the platform offer “precanned” solutions ?Collaboration: How can users of various skills work together on the same workflows and projects?
Coherence: How intuitive, consistent and well-integrated is the platform to support an entire data analytics pipeline? Based on these criteria, Encanvas would also sit within this enterprise software classification were we not so fanatical about customer experience!
Players in the CDP software market
In addition to Encanvas, the following vendors are active in the CDP market include Oracle, SAP, Salesforce, Adobe, Nice, SAS Institute, Tealium, Segment, Zaius, AgilOne, ActionIQ, BlueConic, Ascent360, Evergage, Lytics, mParticle, NGDATA, IgnitionOne, Signal, Usermind, Amperity, Reltio, Ensighten, Fospha, and SessionM.
About Encanvas
Encanvas is an enterprise software company that specializes in helping businesses to create above and beyond customer experiences.
From Low Code to Codeless
Better than code-lite and low-code, we created the first no-code (codeless) enterprise application platform to release creative minds from the torture of having to code or script applications.
Use Encanvas in your software development lifecycle to remove the barrier between IT and the business. Coding and scripting is the biggest reason why software development has been traditionally unpredictable, costly and unable to produce best-fit software results. Encanvas uniquely automates coding and scripting. Our live wireframing approach means that business analysts can create the apps you need in workshops, working across the desk with users and stakeholders.
When it comes to creating apps to create a data culture and orchestrate your business model, there’s no simpler way to install and operate your enterprise software platform than AppFabric. Every application you create on AppFabric adds yet more data to your single-version-of-the-truth data insights. That’s because, we’ve designed AppFabric to create awesome enterprise apps that use a common data management substrate, so you can architect and implement an enterprise master data management plan.
Encanvas supplies a private-cloud Customer Data Platform that equips businesses with the means to harvest their customer and commercial data from all sources, cleanse and organize it, and provide tooling to leverage its fullest value in a secure, regulated way. We provide a retrofittable solution that bridges across existing data repositories and cleanses and organizes data to present a useful data source. Then it goes on to make data available 24×7 in a regulated way to authorized internal stakeholders and third parties to ensure adherence to data protection and FCA regulatory standards.
Encanvas Secure and Live (‘Secure&Live’) is a High-Productivity application Platform-as-a-Service. It’s an enterprise applications software platform that equips businesses with the tools they need to design, deploy applications at low cost. It achieves this by removing coding and scripting tasks and the overheads of programming applications. Unlike its rivals, Encanvas Secure&Live is completely codeless (not just Low-Code), so it removes the barriers between IT and the business. Today, you just need to know that it’s the fastest (and safest) way to design, deploy and operate enterprise applications.
Mason Alexander is a senior consultant specializing in helping organizational leadership teams to grow by implementing enterprise software platforms that improve data visibility, process agility; and organizational learning – creating an enterprise that learns and adapts faster. He writes on subjects of change management, organizational design, rapid development applications software, and data science. He can be contacted via his LinkedIn profile.
Further Reading
Marketsandmarkets CDP market sizing research report Prenewswire reprint of market sizing report cmswire.com ‘Keep your eye on Customer Data Platforms’ article CDPinstitute website offering more details about Customer Data Platforms
