The New Front-End Landscape

March 16 2021

The New Front-End Landscape

This is post 4 of 4 in the series “The New Front-End Landscape”

Data is (and is not) Like Oil
Are You a Data-Intensive Application? [Yes]
Building Non-Linear Applications
The New Front-End Landscape

Data-intensive applications are everywhere. Companies are building these products to sell, and increasingly, more companies are building them internally to leverage their own internal data, AI/ML initiatives, sensors, and more.

We have seen countless companies sitting on exabytes of data but not having the ability to turn oil into gold. You constantly hear about companies that are never able to leverage their customer data, create a new offering for their customers, or even internal departments being stymied by their internal IT’s resources and bandwidth to take on new projects. Often this is because the bottleneck for creating such rich and non-linear workflows has been the creation of a usable front-end.

When we look out at the front-end development landscape we can decompose the world into 2 axes… how fast a team can get value/build a product and how open is the stack.

And in this space, something very exciting is happening at Kleeen Software – at the intersection of an open-stack and fast time to value.

Manual

Manual Development:
Open Stack + Long Time to Value

This is the current way companies build software today: Humans – lots of talented humans. Human designers make mockups and wireframes. Human developers convert those designs into code. This is a process that takes months to execute. And it isn’t just creating the MVP; every new feature and function you build must be designed, maintained, updated, and enhanced. And while some companies or IT teams forgo design, that can actually cost more in the long (and short) run. Even with component libraries, visualization libraries and advances in JavaScript frameworks, manual UI development is a long, erratic process, often focusing on front-end infrastructure and common workflows rather than on domain or product-specific features that can “wow” and win-over your customers.

But while the time-to-value is long, the control is quite high. You and your team can customize everything. The front-end stack is yours to control… and so is your middleware, your backend, your DB, your hosting, authentication and more. Anything you ask your engineering team to do is technically “possible,” but it all comes at a cost. That is the freedom that has kept manual development the go-to solution for building data-intensive SaaS applications.

Low-Code:
Closed Stack + Fast Time to Value

Part of the Low-Code “revolution” over the past few years has been the adoption of WYSIWYG tools – where product creators can drag and drop UI elements into pages. However, if you do not know the best practices to create good UI/UX, these tools quickly let you build bad UI/UX. Moreover, every link, every action, every table must be manually created – and the creator using these tools frequently ends up having to write code almost immediately to connect data needs to the backend provided.

And speaking of back-ends, most of these solutions are black-box monoliths – locking you into their stack. You are now limited to the database, data structure and infrastructure of those platforms. Your data must be stored on their systems, exposing you to any security risk that the vendor has – and you do NOT get your front-end code. These front-ends run on their cloud.

Because these tools are so limited in their front-end flexibility, each of the platforms in this space tend to focus on a specific type of application – the majority are data-entry and form-based workflows.

“most low-code platforms are in their own development silos, separated from other development tools with their own IDEs…Even the biggest and best low-code vendors admit that just using low-code isn’t going to power your business” – TONY GRAHAM

All these limitations have created questions — can low-code really rise to the expectation and promise advertised? They have found a new niche, but that is what it is – a niche – not a solution to power your software development needs. In that niche, you can move very fast – but a low-code platform is a black-box solution.

Embedded BI Tools:
Closed Stack + Slow Time to Value

When BI and Dashboarding tools were first created, they were never intended to deliver a product to end customers – they were data visualization tools. And they were fantastic. Load your data in, do aggregation and “basic analytics”, and visualize your data as a dashboard or a report. Now users that were not DB experts could slice their data and garner insights – once your DB architect sets up your BI tool, loads your data into their proprietary system, and teaches you how to use the BI tool itself.

While powerful for the right use case, this isn’t usually the quickest of solutions to deploy. Also, end-users found that BI-tools have limited utility for empowering knowledge workers to take action. The visualization of data is there to provide context which is meant to help an end-user to reach conclusions on actions to take, but these knowledge workers must switch between multiple applications to get insights and take actions to get their job done. This swivel-chair approach slows down end-users and increases friction. As a result, no one was fully satisfied with BI tools, but they were the best solution available.

As more and more companies started building their own data-intensive solutions for customers or internally for their employees, BI tools wanted to capture a piece of the pie too and started advertising “Embedded BI” – the idea of embedding a BI tool’s visualization or dashboard in your application. But let’s think about that. Now you still have to build all your product’s infrastructure, you still need to build the pages, dashboards and support all the rich features you need to ship a product – but you can save time by using a BI tool to make a visualization. That can be useful, but there is still a lot of manual development work to create everything that is not those BI visualizations – you must expose all your data to the BI tool’s proprietary database/stack/cloud, and the embedded BI components are not truly integrated with your shipping app. You can’t cross-link and connect/style those elements to the rest of your product.

In short, embedded BI takes a huge amount of time, requires much manual work, adds unnecessary complexity to your architecture, and still locks you into a closed stack with data-duplication/privacy issues.

Kleeen Software:
Open Stack + Quick Time to Value

Kleeen Software has taken a wholly different approach. Our solution is not about enabling customers to build workflow wizards or forms for their end-users; rather it empowers customers to build products/solutions which the end-user can use to investigate data a little differently every time. This is perfect for unstructured and non-linearly structured tasks – where there exists a clearly defined structure and relationship to the underlying data.

To create a product with Kleeen, you input WHAT you want your customers to do and WHY – not drag and drop. And as a result, it isn’t just “low code,” Kleeen is No Code to generate the application front-end. Kleeen’s AI will construct workflows, design UI, and generate production code ready for you to integrate to your APIs and ship.

Moreover, instead of previewing with a mockup, picture, or drag-and-drop layout – Kleeen builds you an instant prototype – the real production UI code (running on Kleeen’s cloud) but with simulated data. Test and validate before you spend months building out a data-lake or analytics. This is true WYSIWYG because this is the real UI with all interactions there to experience live. Thus you can design and build out a new product in a few hours, iterate and have a full front-end integrated with your stack in days, not months.

Kleeen Software is not just about BI and data visualizations. We create the UI and the UX, allowing you to create a “Single Pane of Glass” to merge insights and context from those data visualizations with CRUD operations, actionable insights, and buttons that can connect to complex back-end business logic or even change data values to move a company forward. In short, we allow end-users to analyze data, make their decisions, and take action, regardless of where the data resides.

The secret here is transparency. Kleeen Software generates front-end code and checks it into your Git repository as a pull request. You can see, review and approve any and all coding changes. Nothing is closed. Because at the end of the day, your team is responsible for your product – which means you are responsible for the code. And Kleeen Software knows that too.

In addition, every API needed by the generated front-end has a corresponding hook providing state and context from the UI and providing you the specification for the incoming data from whatever backend technology you wish to hook up. In the end, Kleeen is just a developer on your team — an extremely highly productive one.

Furthermore, Kleeen allows you to drop in custom modals, widgets, pages, authentication systems, even styling. As a result, if you like your BI tool, drop in embedded-BI. If you need to create something custom that is domain specific, product specific, or a dashboard with “sizzle,” Kleeen doesn’t stop you at all. Want to restyle your product to conform with your brand, go ahead and drop in your custom CSS. Kleeen doesn’t box you in – it takes the most common workflows and front-end infrastructure off your plate with a true no-code solution, freeing up time, people and money to focus on what makes your product great. And next time you have a new feature, want to make an update, or respond to a customer – it turns into a resourcing decision not an “if” decision.

Cover Photo by Robert Lukeman on Unsplash

March 9 2021

Building Non-Linear Applications

Josh Hailpern, CPO Engineering, Product

This is post 3 of 4 in the series “The New Front-End Landscape”

Data is (and is not) Like Oil
Are You a Data-Intensive Application? [Yes]
Building Non-Linear Applications
The New Front-End Landscape

Data-intensive applications are an essential part of the B2B landscape. They power more and more vital parts of the enterprise world. Marketing, Sales, IT, Engineering, and HR are all increasingly data-dependent segments in a business – as evidenced by how many products are being funded and built to target these data-hungry users. And while companies like Snowflake have created fantastic data solutions, the insights provided by the data/AI/ML are only valuable when exposed to end users in an intuitive way that enables them to investigate, understand and take actions in response to their changing data landscapes.

In short, humans need a front-end for these data-intensive applications. And the expectation for usability is growing, not shrinking.

But these data-intensive apps are not prescriptive – they are definitely not cookie-cutter or one size fits all. You cannot build a linear workflow or wizard to help users investigate and remediate their data – at least not for a broad range of data originating from different sources and being used by different kinds of end users in different businesses. End users will use data products a little differently every time; following different connections, making different decisions – all based on the data they see. These investigations/explorations are unstructured (or at least non-linearly structured) tasks. And as such, the UI and UX for these products must also support unstructured data exploration that could start at multiple places, traverse multiple different connections, and conclude at multiple endpoints.

Creating this type of application is a daunting task. One that involves configuration, CRUD (create, read, update, delete) operations, actions, data visualization (of course), and most importantly, a deeply interconnected set of pages and views that can respond to the changing needs of the end user. For most complicated data, using templates does not work as these applications must reflect specific concepts and the interconnected relationship of those concepts to give the users any chance of finding the kind of patterns they need to create or test their hypotheses.

Allowing a product to deal with this level of interconnectedness is the key to supporting the common categories of workflow a data-intensive product needs; monitor/browse – investigate/drill-in – remediate/take-action. The resulting complexity is also the most common frustration point. How many times have you seen something in a product and wanted to drill down?

“Oh this expense report from Steve looks high – let me see what items he is expensing?”

Only to find out it isn’t a link

“Damn, ok – let me write down this expense report name, then i’ll navigate to the report management page… and I’ll filter and find that report, ok – wait for that to load. Ok…”

And now you need to drill in again, and again and again… but your context is lost. You can’t even rely on your browser history because it is cluttered with all these intermediate pages. Every one of those links must be hand-coded. Every visualization of any relationship that helps you make such a connection must be manually connected by an engineer.

This is a constant failing of these data-intensive applications. A new way of building data-intensive applications is needed – one where creating connections is not just easy but automatic. A process by which generating most grids/tables, common visualizations, enabling actions/CRUD-operations is simple (and maybe even generated/facilitated automatically). A platform that can facilitate and generate the front-end infrastructure that takes forever to build (theming, internationalization, UI access control, navigation, etc) freeing up resources to create domain-specific and product-specific UI and UX. A solution that allows you to respond to customers and update your UI/UX in hours, allowing your talented staff to focus on the roadmap and adding “sizzle” and innovation to your product.

So what does this mean for developing and maintaining these data-intensive applications?

Cover Photo by Rodrigo Soares on Unsplash

March 2 2021

Are You a Data-Intensive Application? [Yes]

Josh Hailpern, CPO Management, Product

This is post 2 of 4 in the series “The New Front-End Landscape”

Data is (and is not) Like Oil
Are You a Data-Intensive Application? [Yes]
Building Non-Linear Applications
The New Front-End Landscape

This is a data-intensive world. Almost every product’s success depends on data. Data that end-users need to understand, to drill-in into, to make sense of, and to take action on.

IoT Management

Consider an IoT management system: It brings together data from an automotive plant, agricultural center, or smart-city infrastructure. There are thousands of sensors, terabytes of data, and rich AI/analytics capabilities that help make sense of this information. But the end-users of the management system need to find insights, drill into related data, identify possible new patterns, and then take action based on their investigations – be it to create a repair ticket for an automotive part defect, change watering cycles on a crop, or adjust electric/water distribution to respond to changing demands.

People Management

Consider an application to help recruiters (and their customers) sift through huge amounts of job candidates: End-users need to see trends, find outliers and distributions, drill into specific former employers or job-levels, filter and refine data by skills – create hypotheses, test them, and eventually take action on specific candidates.

Networking

Consider a company, providing wireless and wired internet to hundreds of employees (or servers in data centers) in anywhere from one to hundreds of locations: Not only do the IT analysts need to manage the internet infrastructure (finding performance issues, identifying the root cause and updating/adjusting the network in response), but they also need to track hacking attempts (using both known and unknown access points), find security threats as they happen, and then quarantine impacted users, applications and devices.

All of these examples (and more) are products or services that are predicated on data. Data empowers AI and machine learning to find patterns and make connections, but humans still need to understand that data, those patterns, and the insights provided by AI. Humans need to take action on that data and those insights, to remediate problems and impact the top and bottom line for their organizations. Humans still need to configure, update, and maintain the data and infrastructure providing that data.

What is perhaps most important is that while there is a clear structure to the abstract data and concepts of the product, it is ultimately humans that must determine if the specific relationships of that data on screen are useful. Are there valid relationships between the insights, information and analytics? A complex network can overwhelm a user, but in the end, a human must traverse it. Only through an exploration and investigation – from the problem, to root cause, to taking a remediation action – can this data provide real value to the end user and their organization.

But how do we build these Data-Intensive applications? Continue reading in our next article.

Cover Photo by Marcelo Quinan on Unsplash

February 23 2021

Data is (and is not) Like Oil

Josh Hailpern, CPO Product

This is post 1 of 4 in the series “The New Front-End Landscape”

Data is (and is not) Like Oil
Are You a Data-Intensive Application? [Yes]
Building Non-Linear Applications
The New Front-End Landscape

There is a saying, “Data is the new oil.” When it is raw and unrefined it has little or no value, but after it is refined, exported, and transformed it is worth lots of money. And like unrefined oil, raw data can be turned into many products, each with their own consequences for our businesses and our economy. Loss of data through data breaches (or like oil through spills) has huge ramifications. Hence only proper management and good governance can ensure that raw data will provide lasting value.

There is a key difference between the oil and data. Once oil is transformed into petrol or plastics, it is used up. You can’t use the same gallon of crude oil over and over to solve new problems (and yes we are ignoring recycling here) – but you can, and do, re-use raw data – it is truly the ultimate renewable resource!

Consider web-traffic for a website or maybe even behavior tracking in a product. That data can be examined in multiple ways. An AI system could provide insights about your visitor paths, entry points, and more. And as an owner/manager of that service, you will take different actions depending on those insights and the path into that data you traverse. Maybe after investigating and drilling in, you discover users convert if they come from a certain Keyword search and you would want to increase your SEO… but that same data, examined differently – traversed in a different path – would also lead you to understand that certain countries or time-zones are visiting your new material, adjusting when you push updates. And of course, looking at current vs historical trends is vital. Data keeps coming back.

The data has real value only when it supports an end user in their achievement of a goal. The data, the relationship between one piece of data and another, and even the data’s structure – that is what an end user needs to explore, to traverse, to combine, and to analyze. And unlike oil, that same data can and will be used and reused.

So what do we call these types of data-centric applications? This is not a form or data entry application. The focus here is on exploring, investigating, and taking action based on data. It isn’t a simple business intelligence (BI) or dashboarding solution that reports data in preconfigured ways.

The answer is that they are data-intensive applications.

Continue reading in our next article: Are You a Data-Intensive Application? [Yes]

Cover Photo by John Mark Arnold on Unsplash

The New Front-End Landscape