Opinion

For ease of business customers, we have to increasingly digitize customer journeys. Thereby there is vast data that is generated from customer channels and service touchpoints. This data is either information willingly provided by customers or vast data generated as logs from systems and their processing.

Progressively, in the world of IoT, we are seeing decisions made by machines. These decisions either churn-up insights on what’s better for customers or reducing their anxiety in their service-relationship. For the machine-models to generate actionable insights, diverse data of good quality, is required in real-time to be available.

Classic models for managing quality of data

In1960’s data was supposedly managed…


Formalizing Data Collection from customers and third parties

To date, the organizations have focussed on formalizing data consumption practices through distribution technology, access-based delivery mechanisms for analytics, and AI functions. However, with data protection laws and positive awareness across the world, firms have extended the formalization to data collection management. This in-fact is the first life-cycle stage of data.

1.1. Managing data quality at sources: Across the multitude of native and digital channels, harmonizing data quality rules will bring consistency in sourcing correct customer data. There will be an increase in the use of AI-based discovery of data rules that will make it much easier for data offices…


The enterprises are modernizing their data platforms, and associated tool-sets to serve the fast-needs of the data practitioners, including data scientists, data analysts, business intelligence and reporting analysts, and self-service embracing business and technology personnel.

However, as the tool-stack in most organizations is getting modernized, so is the variety of metadata generated. As the volume of data is increasing every day, thereupon the metadata associated with data is expanding as is the need to manage it.

Image for post
Image for post
A generalized data landscape. Courtesy: Tejasvi Addagada

The first thought that strikes us when we look at a data landscape and hear about a catalog, “It scans any database ranging from…


With digital transformation, we are stepping through a worm-hole that takes us to a different time-line while re-defining the way we are doing business and consuming services. Now, most of us prefer to embrace digital transactions and try out door-delivery of goods.

As we are increasingly moving towards distributed work environments — perhaps our homes — Firms will look at embracing distributed agile delivery practices for solutions, in Information Technology.

One such aspect is Continuous integration and continuous delivery where delivery of quality software at frequent intervals, is enabled through automated ways of detecting, pulling, building, and unit testing code.

Tejasvi Addagada

Tejasvi Addagada is a data strategist and consultant assisting fortune 500 firms. He helps to build and optimize data management and governance solutions.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store