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As we move from IaaS and PaaS to SaaS, it becomes very confusing segregating PaaS and SaaS, but please hold on to your horses’ 😊 we are going to nail it down in this topic.

To understand this example, pre-requisite topics are 1) ‘On-Promises vs Cloud’ and 2) IaaS and 3) PaaS.

In IaaS, we established that only Infrastructure/ Servers and Storage is provided by Cloud providers then in PaaS we establish along with Infrastructure/ Servers and Storage, Software(s) BUT EMPTY Software(s) are also made available along with rest of all the activities to be managed by the Cloud providers EXCEPT NO IMPLEMENTED SOLUTION.

In SaaS, there are pre-built or OOTB Solutions available in the Cloud. Please note, it’s not mandatory that all solutions are owned by Cloud providers, most of the solutions are created and maintained by the third parties. Third Parties use compute and storage from Cloud providers and build their own solutions and sell to customers. Now, customers just need to log-in into the Solution in Cloud as a Service that is why it’s called Software as A Service. A solution is owned and its management like installation/ upgradation, compute, storage, HA, DR etc., all are taken care of by the Solution Owner, again which can be by Cloud Providers or any Third Parties. Customers just need to subscribe to the SaaS service and start using the Solution.

Data As a Service (DaaS) is very near to SaaS service in Cloud. We know what SaaS is, it’s about pre-built or OOTB solutions available in the Cloud. Solution providers having their solutions in Cloud with API(s) enabled from where they allow customers to connect and pull data on demand who are then billed based on the data they pulled, this service is called DaaS.

For example, a company builds a solution on cloud where it pulls data from Facebook, Amazon, Ali Baba, etc., and using ML Modelling, predict customers buying behavior, customers surfing behavior, customers segmentation, products categorization, customers demographics etc., this analysis and analytics are then sold as DaaS to organizations in need for these details for marketing or sales planning etc.

The main difference between in SaaS and DaaS is, in SaaS, customer use whole vendor solution on Cloud e.g., customer logs in into vendor solution online and start using it whereas in DaaS service, customer can connect or subscribe to pull the data ONLY.

Please note, AAaaS has nothing to do with Cloud, it can be on On-Premises or On-Cloud. AAaaS is an environment setup for Data Scientists to play around with Machine Learning and Deep Learning Modelling.

Advance Analytics as a Service (AAaaS) is a new concept and still maturing, due to which it is still very confusing. Different companies are offering different approaches, so there is no one best approach.

Two approaches been followed.

1. AAaaS as PaaS: Modelling tools like Databricks, Azure AML etc., are available as pre-installed services (refer to topic: PaaS) where customers can provision these tools in few clicks and simply start developing their Machine Learning or Deep Learning Models to build their own solutions. Here, customers must have their own MLOps along with CI/CD.
2. AAaaS as SaaS: In this approach, Solution Providers hosts pre-built or OOTB Machine Learning and Deep Learning Models. These are offered to customers to consume only. Customers inject their own dataset for relevant data analysis and analytics. Here, customers must follow Solution Providers MLOps along with CI/CD.

This is a normal term for database modelers, but business users are not very familiar with it. Understand this and it will solve your 90% data problems. Don’t have good Data Model and you must deal with Chasm and Fan Traps.

Data Cardinality is about maintaining the level of details in a table. There are 2 types of Data Cardinality.

• High Cardinality: It is when there are no or less duplicates in a table. Tables with Pkey are mostly in Highest Cardinality mode.
• Low Cardinality: It is when there are high number of duplicates in a table. Tables with Fkey are mostly in Low Cardinality mode.

For example, Product table with details like Product ID, Product Name, and Product Manufactured Date etc. It can contain hundreds of products, but at the same time, a particular product cannot be repeated as it’s already in the table right. So, now we can say this table has High Cardinality. Referring to the same example if Product is there then one must sell it too. Sales of the products can be stored in a Sales table. Now, 1 type of product can be sold many times right. So, Sales tables can contain thousands of rows where hundreds of rows can be for 1 product means Product ID will be repeated in this Sales table so we can say this table is in Low Cardinality with respect to Products.

Almost every organization claims to recognize the importance of Data Governance, yet research and lived experience show that the majority still lack a functioning, enterprise-wide framework. Before the era of Big Data, governance was often ignored outside of highly regulated industries. But now, with a landscape filled with SQL and NoSQL databases, Data Warehouses, Data Lakes, Meshes, Lakehouses, Vaults, Hadoop clusters, Object Stores, and In-Memory platforms, governance is no longer optional, it is the operating system for how data is trusted and used.

The Purpose of Governance

Data Governance is not about bureaucracy or slowing down innovation. At its core, it is about balancing data as an asset with data as a liability. Done well, it creates clarity, consistency, and trust, the conditions for business value to emerge. Neglected, it leads directly to data swamps, failed projects, and regulatory exposure.

‘Data Quality is when we compare data against Business Rules.’

Organizations aspire to be data-driven, but the reality is that without quality, data becomes a liability rather than an asset. Decisions made on inaccurate, incomplete, or untimely data are not just ineffective, they can be damaging. Poor data quality has been repeatedly linked to regulatory fines, reputational loss, failed AI models, and massive operational costs. Simply put: bad data costs more than no data at all.

The Six Dimensions of Data Quality

• Accuracy – Data should reflect the real-world event or entity it represents. Accuracy depends on traceability through data lineage; without lineage, accuracy is always questionable.
• Completeness – Data must contain everything required to serve the business purpose. Completeness is not about “all data,” but about ensuring critical attributes exist to fulfill KPIs or regulatory needs.
• Consistency – Data values must not conflict across systems. If a customer’s birthdate appears in three formats across platforms, trust collapses.
• Validity – Data must adhere to business rules, constraints, and formats. For example, a date in DDMMYYYY or an amount rounded to four decimals.
• Uniqueness – Records must be free of duplication. Duplicate customer IDs or accounts lead to wasted marketing spend, skewed analytics, and customer frustration.
• Timeliness – Data must be available when promised. Missing overnight batches or delayed real-time feeds translate into broken business processes and lost opportunities.

This is a very confusing topic as both Categorization and Classification are used interchangeably.

Both Categorization and Classification are Metadata and are used for Data Governance as well.

Categorization is based on the attributes and properties of entities in dataset whereas Classification is based on the business requirements.

As you can refer to the image, data has been categorized in passport, health record, financial record, job posting etc., and at the same time these categories are classified as Medium Risk.

‘Both these terms fall under Supervised Learning in Data Science domain.’

For example, there are grandparents, parent, children, cousins, servants, gardener, machinic, car painter etc., all these falls under categories but there can be further classifications i.e., family members and non-family members.

Categories: Grandparents, Parent, Children, Cousins, Gardener, Machinic, Car painters

‘Data Segmentation is about dividing dataset into multiple sections based on some requirements.’

Segments are defined based on many parameters e.g., in a business, segments are defined by accessibility, actionable, profitable etc.

For example, when we go into a mall, there is a segment/section for Food Court where multiple categories of food are available with different classification like expensive or cheap food.

Steps to improve Data Segmentation

• Enrich your data to increase data quality.
• Plan and replan your data segmentation criteria(s)
• Improve real time segmentation.
• Design segment based on customer prioritization and feedback.
• Integrate with as many data elements.

In business, what you can do by having Data Segmentation

• You can create deals based on customer buying behavior.
• You can improve your products arrangement.
• You can plan your marketing strategy.
• You can analyze customer feedback.

Data Redaction is confused with data masking, neither it doesn’t work like SMD, but it works pretty much like DDM.

We know how data masking works i.e., it changes original data with random values so if data reaches into wrong hands, the identity of relevant person should not compromise.

Data Redaction also blacks out the data ON THE FLY which may or may not even make sense to the users using it.

There are four types of Data Redaction.

1. Random: Data is masked randomly
2. RedExp: Expressions are masked.
3. Partial: Some of the data is masked
4. Full: Everything is masked

We deal with Data Redaction on daily basis 😊. For example, when we do a Credit/ Debit Card transaction, a message comes on our mobiles. You must have noticed, except last few numbers all card numbers are always STARS i.e., ******* so for layperson those stars are Redacted values where partial data is masked.

Security and privacy are often mentioned in the same breath, but they serve distinct, and complementary, purposes. Both are essential pillars of responsible data management in the digital era.

Data Security is about protection. It ensures data remains confidential, accurate, and accessible only to those who are authorized. Security safeguards against unauthorized access, malicious attacks, accidental loss, and corruption of information. Techniques include access management, activity monitoring, encryption, masking, breach detection, and disaster recovery. In short, security preserves the confidentiality, integrity, and availability (CIA) of data.

Data Privacy is about rights and responsible use. It governs who can access what data, for what purpose, and under what conditions. Privacy extends beyond technical controls, covering contracts, policies, consent management, and compliance with laws such as the GDPR in Europe, the PDPA in Singapore and Malaysia, or the CCPA in the United States. Privacy ensures that individuals’ personally identifiable information (PII) is collected minimally, used ethically, and safeguarded throughout its lifecycle.