Knowledge Hub

A Data Glossary is a vital step towards mitigating the risk of creating a potential Data Swamp.

Data Glossary vs. Data Dictionary falls under the umbrella of Metadata Management.

• A Data Dictionary is a repository that houses the names, definitions, and attributes of data elements, providing critical contextual information about data.
• A Data Glossary, on the other hand, is a collection of terms that define the key characteristics of your data. These terms are organized in an easy-to-search format to facilitate quick understanding and access.

What Does a Data Glossary Contain?

A Data Glossary includes business terms that provide meaning and context. For instance, a table may have terms like region, sales, quantity, brand, and year. Anyone analysing this table will quickly understand that it relates to sales data, with quantities likely provided brand-wise at a regional level.

A comprehensive Data Glossary can refer to:

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.

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 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.

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 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.

We all have heard about the Data Driven Organization concept, so here is how it’s done. Data Democratization will make it happen.

‘Data Democratization is the foundation for Data Self-Service Environment.’

Data Democratization/ Data Self-Service is not a tool, nor it’s a technology. It’s about the working behavior of everyone within an organization with respect to data. Traditionally, whenever we need data, we go to IT and ask for some reports or dashboards. We normally tell them, this is our KPI, in other words, we tell them in simple English e.g., we need sales reports about a product from a particular region. Now, it’s IT’s job to find out where relevant data is residing, it’s their job to extract, process and prepare data for business users in the shape of a report or a dashboard. The worrying part is e.g., after IT takes 3 months to prepare a dashboard for Business User and when user says, please include 1 more column 😊, now the whole exercise starts from scratch. Data Democratization fills this gap.

Data Literacy is the key for all Data Initiatives.

We know the difference between Business Intelligence (BI) developers and Business Analysts (BA). In previous eras, BA used to talk to business, create KPI(s) and pass it to BI developers to create reports and dashboards. 90% of BI developers would not know what a report or a dashboard is for. They won’t know the impact of good and bad reports. They won’t know the ROI of information this report, or dashboard can produce due to which they don’t care whether BA has provided right KPI(s) or not. This era was not a Data Era, in that era Reports or Dashboards were good to have. Most of the decision making used to happen in reports extracted in excels 😊.

Now time has changed, we have entered the Analytics era as compared to previous era of Analysis. Difference between Analysis and Analytics is simple but tricky i.e., Analysis is about finding Whys’ and What’s whereas Analytics is about finding How’s from a dataset. In other words, in datasets, Analysis search Questions and Analytics propose Answers.

Data Driven Organization is a new Buzz word in market where every organization wants to tag this Key Word with their organization.

Traditionally, we use data to make decisions. Decisions at top level for organizational revenue generation planning or at division level like whether division of a particular product is performing well or is it achieving its planned ROI, is it generating profits for the company.

Above mentioned attributes or properties doesn’t make you Data Driven Organization, above are previous era organizational behavior. Now, the current era is all about Data, everything within an organization must follow data decisions. When we data decisions we are talking about Daily Chores. We are talking about how employees hiring, and firing is done, we are talking about how transfers are done, we are talking about how printer Ink or Papers are orders, we are talking about procurement works, we are talking about how hardware, software or infrastructure resources are provisioned or de-provision.