Knowledge Hub

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 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.

We all have heard about Mean and Median values but not Spread.

Before jumping into Spread, let's have a quick definition of Mean and Median which will assist in understanding Spread.

• Mean: The mean (informally, the “average “) is found by adding all the numbers together and dividing by the number of items in the set: (10 + 10 + 20 + 40 + 70) / 5 = 30.
• Median: The median is found by ordering the set from lowest to highest and finding the exact middle. The median is just the middle number: 20.
• Spread: The simple explanation of Spread is e.g., how variable the data is or how spread out the data values are in the dataset, or it is also referred to as a measure of dispersion etc., from the Mean or Median values.

Let’s try to understand with an example. Let’s say we have a dataset with the customers’ age from 20 to 60.

Breakdown

• 8 customers with Age [10]
• 15 customers with Age [20]
• 12 customers with Age [30]
• 7 customers with Age [40]
• 3 customers with Age [60]

DataOps follows the same principles just like DevOps/ DevSecOps but here it has few more elements related to Data domain.

DataOps focuses on data related aspects of operations. It makes sure data is properly integrated with source and target, its flowing-in using automated and best practices data pipelines, its highly secured, it’s of high quality etc.

DevOps doesn’t care whether its data project or mobile application project or any other technology project, but DataOps focuses on Data like where it is coming and where it will go, why it is coming and how it will be used, how much it costed and how much it will monetize etc.

As mentioned above, DataOps includes all the steps or processes involved in DevOps/ DevSecOps but at the same time it also introduces a few additional steps.

DataOps Process Flow

• Regular Integration
• Regular Development
• Regular Build
• Regular Testing
• Regular Deployment
• Regular Delivery/ Run
• Regular Monitoring and Feedback

Data Liberalization and Data Democratization are very confusing terms as both work together and most of the time mixed with each other.

‘Data Liberalization ideally is a prerequisite for Data Democratization.’

The aim for Data Democratization is to remove the fear factor about data. Allow everyone to ask analytical questions. To have data access availability for everyone. Allow everyone to raise questions about data and then suggest resolutions, if possible.

Data Liberalization is about providing data in such a way that it should be reusable by users. It can be in any formal i.e., via API, via BI tools, via DaaS etc.

When we say, ‘Aim is to have data access availability for everyone.’ doesn’t mean show everything, of course with access control. Data Liberalization completes the cycle to make sure whatever user is accessing, is USABLE for them rather a pile of junk data.

Data Driven Organization concept can’t be completed just by Data Democratization, Data Liberalization is also a very crucial component to achieve.

Monetization is about generating benefit/ revenue/ profit from anything.

‘Data Monetization is about generating revenue from Data.’

In this era, social media is a very dominant channel to interact with customers. Platforms like WhatsApp, Facebook, Twitter, TikTok, Instagram etc. all provide free services to their customers. Have you ever noticed, if you are not paying a single penny to them then how they are generating money and how they are running their business?

The response to the above is by Data Monetization. They sell the data that’s generated by their customers. Having said that, please note, there are regulations that they can’t share personal details of their customers with anyone. But that doesn’t mean they can’t sell how many people are viewing what, how many are buying, how many are spending how much on which product category, from which location they are logged in etc.

For example, on Amazon, customers can buy anything they like. Now at the back end, Amazon must be recording what you are buying and what you are selling. Based on this Information, they can very well analyze which product is more viewed on their portal or what is the buying trend of a particular set of products in a particular season. Like, in December, they can see the trend of products related to Christmas are in great demand. They can sell information like WHICH & HOW MANY PRODUCTS WILL BE IN DEMAND IN COMING DECEMBER based on last few years trend to the product owner to plan their production. This information is GOLD for product owners, they can plan their raw material, work force, timeline and many more activities.

This is a principle rather a process to manipulate data, but it holds a very significant value in terms of managing and securing data.

Most of us mix Critical Data Elements with elements or data points identified during Data Quality exercise. Though those are also critical but when we talk about ‘Critical Data Element’ in Data Strategy, we categorize those elements which can have negative impact on the organization.

Critical Data Element can be any regulatory requirements, any legal or compliance requirements, those data elements which can negatively impact the image of company in general public, those data elements which if are found inaccurate make stakeholders’ loss their confidence on data.

Personal Information of customers is one of the most critical data elements in most of the organization. Credit or Debit Card details in the Banking sector are critical. Location details in Telecommunication are critical etc.

Before we understand Data Due Diligence, let’s first have a quick refer what is Due Diligence.

Due diligence is an enquiry, inspect, or assessment executed to check evidence or specifics of a matter beneath attention.

For example, when we are buying something we first check e.g., it is made of which country, it’s a new or used item, it’s produced by which company, what’s the quality of the items etc. All these checks and balances come under due diligence process.

Wikipedia

‘Due diligence is the investigation or exercise of care that a reasonable business or person is normally expected to take before entering into an agreement or contract with another party or an act with a certain standard of care.’

There can be many sub processes involved based on domains, businesses, or solution requirements.

A merger and acquisitions (M&A) process is very old process where due diligence is a must, of course when we’re merging or buying a million-dollar company, we need to make sure our decision is correct.

Data Maturity is the key to becoming a Data Driven Organization. There are many factors to judge it.

‘Maturity refers to stability level of an entity or a process or a product.’

Maturity is not about how many stages of process has passed through, it’s about how stable the product is after implementation.

You must have heard people referring to some individual that he is a mature person so let him take his own decision. Same way, decision can be mature or immature which depend on the result from those decisions.

In this era where Data has become the most powerful input for any organization, well it has been since its start. But now technologies are so MATURE that insight from data analytics are taken very seriously and million-dollar or sometimes billion-dollar deals are taken based on Data Maturity.

Everyone remembers the acquisition of WhatsApp right? Multi-billion-dollar deal just based on how many users is using it and what is the potential growth on a simple mobile application.

Data Inventory is a very simple term but sometime get a little bit confused with Data Catalog and Data Assets etc.

Data Assets is about knowing which kind of data we have in our organization with monetary value.

Data Catalog is about knowing system details, databases, tables, and columns and then mapping those with business requirements.

Data Inventory is about consolidating information from Data Asset and Data Catalog with addition details like.

• where this data comes from
• who generated this data?
• when it was generated
• frequency of inserts, updates, and deletes.
• contents details
• security and privacy approaches
• metadata management approaches
• data quality standards
• transformation details
• business rules
• data models
• list of software
• name of all the applications
• licensing details
• data sources details
• target systems details.
• owners of source and target systems

We all maintain inventory of something or the other, either it’s at home or office. A simple example is Passwords Inventory, now a days there are many social media applications then Hotmail, Gmail, etc. We normally maintain all those passwords somewhere safe right so that is your Password Inventory.