This article is the third in the series on Low Code/No Code. The first article provided an overview of the broad spectrum of Low Code/No Code platforms. The second article focused on intelligent Business Process Management Low Code/No Code.

Process and Data are two sides of the same coin: therefore in this article, we turn our attention to the next most important category - some would consider it even more critical than process-centric - of Low Code/No Code: Data-centric platforms and tools.

We are witnessing an explosion of data from many sources: transactional data in enterprises, data in social networking exchanges, data from connected devices - to name a few. Organizations of any size need management of data to run their businesses. They also need to gain insight from the data and take action to optimize their operations.

One of the tenants of Digital Transformation is to become a data-centric organization.

However, there are many choices, especially for data-centric Low Code/No Code platforms and tools.

Given the broad spectrum of data sources, how should enterprises leverage and prioritize the emerging and increasingly important area of Low Code/No Code platforms for data-centric applications?

There are four areas that are relevant to Data-centric Low Code/No Code: emerging Database/Spreadsheet Low Code/No Code platforms, Low Code/No Code tools for intelligent Database Management Systems, NoSQL Low Code/No Code platforms, and the Low Code/No Code tools for Citizen Data Scientists.

Database Low Code / No Code

We start with the simplest categories - Low Code/No Code platforms and database-centric tools. Many of these tools are easier to use spreadsheets - with some querying and forms generation capabilities. So the "database" is a collection of tables. The tool provides easy-to-use constructs to create tables, edit the tables, and rudimentary forms-processing capabilities for table records. A leading example is Airtable. Not surprisingly - as the name suggests - the platform is based on the Table paradigms - very similar to spreadsheets.

In addition to viewing and querying ("find") within Airtable, Citizen Developers can create forms and share those forms on a plathora of channels (Web, Facebook, Instagram, etc.) for participants in a business application to submit their data via the form linked to the Airtable database.

Airtable is often contrasted by a Google No Code spreadsheet acquisition - AppSheet. The platform also targets No Code Web and mobile application development using spreadsheet data from Google drive and other sources such as DropBox and Office 365. Appsheet also provides templates that illustrate the art of the possible application development with the platform.

Another leading example in this domain is Quickbase, which provides an elegant and easy-to-understand view of the relationships between the tables.

Like most similar platforms, Quickbase also provides elegant visual reports for its databases.

Other examples of No Code database-centric platforms include Kintone and Caspio.

These relatively simpler No Code data-centric platforms can often fit especially somewhat simpler Web or mobile applications. However, enterprises for either their internal or customer-facing applications need much more complex database management systems. As discussed in the next section, we are also seeing Low Code/No Code platforms and solutions.

Intelligent Database Management Systems

DBMSs that separated the data management from the application started to appear in the 1970s with navigational hierarchical and network models. In the 1980s we saw a significant evolution to relational databases that became quite popular, especially with the emergence of SQL as the de-facto query language for databases! The evolution of databases from relational included Object-Oriented Databases that combined Object-Oriented and Database capabilities for persistent storage of objects and Object-Relational Databases that combine the characteristics of both relational and object-oriented databases.

More recently – especially for handling large unstructured multi-media data in new digital applications - we saw the emergence of NoSQL to handle the demands of Big Data: large volume, variety, and velocity with heavy demands on scalability. This new generation of database focuses on the explosion of heterogeneous data and the storage and management of this data for innovative Internet applications (especially IoT). Still, by and large, most transactional data for mission-critical systems of record (which require transactional integrity) remains relational. All these trends culminate in intelligent DBMSs. The following illustrates the evolution of next-generation Intelligent Databases from How To Alleviate Digital Transformation Debt.

APEX from Oracle - Relational DBMS Leader

According to Oracle, APEX is "a low-code development platform that enables you to build scalable, secure enterprise apps, with world-class features, that can be deployed anywhere."

A study comparing the productivity of development leveraging APEX vs. conventional programming in JavaScript is impressive - 40X times faster with APEX:

NoSQL Low Code/No Code Database Platforms

In the past few years, we saw the emergence of NoSQL databases with a plethora of underlying paradigms and models such as column-based, key-value pair, document-centric, schema-free, and graph databases.

One example is KGBase, which allows anyone (aka Citizen Developers) to build knowledge graphs very easily: defining the nodes, their properties, and relationships. Through this powerful easy to use graph database platform, users can build sophisticated knowledge graphs and then drill or filter to comprehend interesting direct and indirect relationships - in other words, "knowledge:" Here is an example they provide for startup ecosystems:

Citizen Data Scientists

Data Science is an increasingly important discipline with especially data-centric organizations that have embarked upon Digital Transformation journeys. Data Science involves many disciplines. Organizations typically have in-house, outsourced or hybrid organizations that focus on the various activities and tasks for discovering and communicating business value from data: the Data Scientists.

Here are a couple of interesting examples of Low Code/No Code tools that can help business leaders perform Data Science tasks - or increase the productivity of the more specialized Data Scientists:

Automation of Data Preparation: This is the most crucial category, like cleaning and preparing the data constitutes more than 70% of the Data Scientists' effort. Tableau Prep provides easy to use data preparation that business users can themselves start to prepare and analyze the data.

Automating Machine Learning (AutoML): Machine Learning (ML) leverages Artificial Intelligence (AI) algorithms to discover patterns in the data. This is critical in the overall Data Science process. Now, when we shift to Citizen Data Scientists, it becomes crucial to Automate Machine Learning (AutoML) - vs. laborious and complex AI analysts' and Data Scientists' skilled efforts Several vendors are positioning their advanced AI automation tools as AutoML – this includes Google's Cloud AutoML and IBM Watson's AutoAI.

Recommendations

Here are my recommendations for the Intelligent Database Low Code/No Code space.

Platform Selection: The data-centric Low Code/No Code ecosystem is quite fragmented. The alignment of the tool or platform to the business need is an obvious recommendation but is critical for success. The aforementioned simple table modeling + forms with rudimentary querying (aka "find") capabilities could be a great fit and will probably be leveraged by organizations of all sizes. It is amazing how quickly - even more accessible than a spreadsheet - business leaders can create databases with robust capabilities. So that is a great place to start, and it does involve analysis based on reliability, scalability, and of course, price. Most provide a free trial version, so it will be worthwhile to try some before committing.
Of all the Low Code/No Code categories, this one is probably the easiest to understand and implement by any Citizen Developer (mostly business leaders) - who are used to spreadsheets and tables and searching for data.
NoSQL Low Code/No Code: The NoSQL area is less mature, but it will be worth investigating, especially in graph databases that are quite common in many domains. This is complementary to the spreadsheet/table No Code tools—the latter focus primarily on operational domains. Graph databases focus on knowledge - allowing the visualization and insight from non-obvious relationships. Many knowledge domains could benefit from No Code graph databases, such as org charts of organizations, CRM graphs, clinical trials, and many more.

Intelligent Database Management System Low Code/No Code: For large organizations, the emergence of Low Code/No Code productivity tools - such as Oracle's APEX mentioned above - provides opportunities to improve Database Administrator responsiveness and catalyze more flattened cultural changes. The powerful gatekeepers of enterprise data - namely, the Database Administrators sometimes exert too much control and block agility. For data-centric businesses to sustain a speed of innovation and change, the organization's culture needs to change - with empowerment and leverage of tools that can make a huge difference.
Emergence of the Citizen Data Scientist: This is the least mature area for data-centric Low Code/No Code - yet perhaps the most important. As discussed in Chapter 5 of How to Alleviate Digital Transformation Debt - and highlighted here and in a previous article - the Data-centric enterprise need to provide strategic as well as pragmatic tools for Citizen Data Scientists: from Data Lakes to Visualization to No Code Development to Machine Learning. Given its complexity, this will most likely be a strategic partnership between conventional data science technical roles, Data tools and business savvy Citizen Data Scientists for specific data science milestones.

Data-Centric Low Code/No Code Platforms and Solutions