Knowledge Hub

A term very near to Data Security and Data Privacy. Data Sovereignty is about keeping the data secure within the defined boundaries, especially e.g., certain sets of data should not go cross border for certain sectors.

In Data Security and Data Privacy, we make sure data is only accessed by those who need it or who own it. Within the organization, we maintain an ACL (Access Control List) to record the list of all the users with their level of access they can have on certain data sets. For example, which level of access developers can have, which level of access database administrators can have, which level of access department’s manager can have, which level of access a Business User can have, which of access Cost Centre person can have etc. So, in a way what we are trying to elaborate here is, there can be different levels of access granted to different level of roles in an organization to access data.

Data Mesh is normally confused with Data Mashup (explained separately), but both are totally different.

Data Mesh is a modern approach to data architecture and governance that emphasizes decentralization and domain-driven ownership of data products. Unlike traditional centralized data systems, which rely on a monolithic team to manage and deliver data assets across an organization, Data Mesh shifts the focus towards empowering individual domains to become autonomous in managing their data products. This paradigm fosters scalability, improves data usability, and ensures alignment with specific business needs.

Understanding the Foundations of Data Mesh

At its core, Data Mesh revolves around four key principles:

1. Domain-Oriented Decentralization: The fundamental premise of Data Mesh is the distribution of data ownership to individual domains. A domain, in this context, refers to a specific business area or operational unit, such as marketing, sales, or customer service. Each domain is responsible for defining, managing, and maintaining its own data products. By doing so, Data Mesh reduces the bottlenecks and inefficiencies associated with centralized data teams.
2. Data as a Product (DAaP): Data Mesh introduces the concept of treating data as a product. This means that data products must be designed with the same rigor and customer-centricity as any software product. Each data product must:

Data Swamp is a term famous as nightmare for CDO.

We all are aware of Data Lake or many other datastores where structure, semi and unstructured data is stored in an organized manner with proper Master Data, Metadata and Data Lineage. On the other hand, if datastores is missing Master Data, Metadata and Data Lineage, it becomes Data Swamp.

Data Swamp is a datastore that is not organized and from which it's very hard to extract information, which has a lot of duplications, and which has a lot of data quality issues.

In other words, retrieving and transforming data is no longer possible when it becomes a Data Swamp.

Data Swamps can happen quickly and can create a serious problem for companies relying on this storage, extraction, and transformation system for analytics.

Any organization which is in the process of becoming a Data-Driven Organization must first focus on Data Governance, Metadata Management, Master Data Management, Data Lineage. Having a data-driven organization with no clues like what and where data is residing is a nightmare and a waste of time and effort.

It refers to the staggering amount and variety of data produced by businesses every day. This is largely due to the variety of enterprise software, mobile apps, storage systems, and data formats each company relies on.

We have data in the shape of Video, Audio, Email, PDF, JSON, XML, Text etc. In other words, data is scattered in different formats and unless all formats are not converted in a standardized format, extracting information will also be challenging or time-consuming.

Imagine that you need to complete your taxes, but all your relevant papers are in different drawers, hidden in closets, and stuffed under couch cushions. Now imagine that you have multiple copies of the forms in these places, and some are written in Greek, while others are written in English and Spanish. How will you do your taxes, or clean your house for that matter, when this is the state of things? Unfortunately, this is a problem that is starting to plague companies across the world. This is Data Sprawl.

Dormant Data and Dark Data are mostly confused with each other.

Dark Data

‘Data that is not utilized is Dark Data.’

Dark Data is data that is not in use due to lack of technology and is not a Data Asset. Dormant Data is the data, which is useable, but decision-makers don't use it, or it’s not required for analysis but that doesn't mean you can delete it or purge it, so we keep it in Tape drives, in Hadoop and nowadays Object Storage is also good choice.

Before the introduction of Big Data, the % age of Dark Data was around 90% as it included semi and unstructured datasets and sometimes structured as well. For example, JSON or XML files, Video or Audio files, Logs, Metadata etc., all these datasets were there but technologies were either not there or the affair to analyze those were very expensive. The only reason companies kept this kind of data was for Compliance or Audit purposes. Since the Big Data ecosystem has come onto the surface and hardware has become dead cheap so hosting and processing these datasets has become possible.

As we discussed in topic i.e., Dark Data, that it is utmost important for a CDO (Chief Data Officer) to minimize Dark Data as much as possible, in the organization. In other words, the more the Dark Data, the more the cost without any ROI. Time is not far when Dark Data Management will become the most essential and obvious KPI for CDO (Chief Data Officer).

Now the questions are.

• Is all Dark Data bad? NO.
• Is all Dark Data delete able or purge able? Yes and No.

‘Data Detritus means that data, which is no more required, which is of no use anymore etc.’

• Dark data is something which was and is, not in-use.
• Dormant data is something which is hardly in-use.
• Detritus data is something which was in-use but will not be in-use, anymore.

In other words, Detritus data was once valuable before it became Dormant, as compared to traditional Dark Data (explained separately) which was never in-use due to technologies barriers etc. Detritus data is normally not deleted or purged as it can still be required for Regulatory or Compliance parties.

Data Dividend is something everyone must know either IT Professional or End User.

We all know what a Dividend is in the Stock Market. It's when a user buys a Stock of a particular company and keeps it for a certain time, the company pays the user a certain amount in Quarterly or Annual basis. The reason is the company is keeping users' money for a particular duration. At the backend, the company is investing that money in its business to generate revenue. Then a certain percentage of that revenue is shared with the user in the shape of a Dividend.

This era is to generate customer data either via mobile applications or via social media portals like Facebook, Twitter, Instagram, Amazon, Alibaba, etc. Users use these applications and, in a way, give their DATA instead of MONEY like their buying/ selling behavior, their search trend, their family/ friends interactions, etc. to above-mentioned companies which then use that DATA for marketing & advertisement, for selling services, to production houses, to industries, to inventory houses, etc. In this cycle, all these companies are making Fortunes, and nothing has been shared with the ENTITY i.e., end-users who are the core participants for the data generation.

Data Assets can be anything that generates data for decision making which ultimately generates revenue for the organization.

We discussed Data Catalog, Data Dictionary and Data Glossary. Data Assets are the perfect Business Case for having the above-mentioned 3 Ds, as One must know what the data is all about and where it is residing. For Data-Driven companies, Data Assets are the most valuable artifacts which can be systems, websites, portals, mobile applications, output excel files, dashboards, reports, campaigns etc.

Facebook, Google, Twitter etc. are the best examples of which use its data as an Asset to run their businesses.

There are multiple ways to maintain Data Assets.

• Cutdown Cost of Data Management by deleting/ purging unnecessary data.
• Make max value out of it by selling it to Generate Revenue
• Manage Data Catalog
• Protection and Security Measures

‘In nutshell, Data Assets are those datasets which has ROI and are able to general revenue.’

Data Liabilities is not handled properly and can severely damage the value of Data Assets.

If Data Liabilities overcome Data Assets in an organization, it can create negative impact of data value.

In-contrast with Data Assets, if Data hosted in your databases, data warehouse, data marts, data lake, data vault, business vault etc. are not utilized properly with no proper ROI and at the same it can’t be used for revenue generation, nor for management decision making then it becomes a liability for your organization. On other words, you are paying for something and not getting anything.

Data Liabilities can also become the reason for Data Loss and failure to ensure proper handling of data usage, data sharing, data quality and data integrity.

One of the major KPI for any CDO should be to reduce data liabilities as much as possible.

Not to mention, hosting and processing data is not a cheap affair plus if management or users can’t utilize it then it’s not worth storing it.

Data Citizens is a very important role in Data field with respect to Data Science activities which still hasn’t been identified as a NORM, but time is not far when just like Data Analysts, Data Citizens will also become one of the MUST-TO-HAVE roles in every department.

‘Today, if 80%+- of Data Science projects are failing it's because of the absence of these Data Citizens roles.’

Data Analysts are functional consultants with skill sets like excel, databases using SQL, reports, and dashboards to dig and present data related to management KPI(s), later these analysis results are passed to ETL/ ELT developers or Data Engineers to implement data pipelines for Productionisation.

Data Citizens are also functional consultants with the machine learning skill sets to do initial data science analysis using DRAG & DROP tools to create, test and process machine learning models to run and predict KPI(s) for business and management, later these models are passed to Data Scientists to implement ML models for Productionisation.