DISCOVER THE HIDDEN SECRETS IN YOUR DATA ANALYTICS

I am pleased to share my upcoming data analytics panel discussion at the University of California. On Tuesday, February 15th of, 2022, at 11 AM PST, we host a panel discussion?about “Discover the Hidden Secrets in your Data Analytics.”?The forum aims to go beyond abstract concepts and share practical & critical thinking skills, knowledge, and expertise.

EVENT DETAILS:

·???????Host: University of California, Davis

·???????Date: 02/15/2022

·???????Time: 11 AM PST

·???????Registration Link: https://ucdaviscpe.zoom.us/webinar/register/2416445433975/WN_9dEJ0c-dQrSUFl7OKNSadQ?

·???????Program Link: ?https://cpe.ucdavis.edu/subject-areas/data-analytics

THANKS TO THE UNIVERSITY OF CALIFORNIA TEAM!!

First, I wanted to thank the University of California team for their continued support in organizing this panel discussion to connect academy, industry, and technology. Thank you, Gina G Reed, John J Dolan, Frances Lessman, Jennifer L Kremer,?Crystal Marie Babowal, and Alex W Lowrie.

IS DATA NEW TO US??- NO.

IS ANALYTICS NEW TO US? - NO.

WHAT IS MISSING IN DATA ANALYTICS? - Let's take an example. I work very closely in the retail and consumer products food industry. Is chicken or turkey, or is beef new to the food industry in plain language? NO. But, the preparation of the food and food delivery is changing every day to meet the customer's expectations. So, the change is not in the food's core ingredients (ex: chicken is a chicken forever, and beef is a beef forever). Still, there is a notable change in food preparation and food delivery.?

Similarly, data remains, but data preparation and provisioning (making the data available) to business and other applications and analytics are changing. So, the change is not in the data itself in the modern digital and AI world. Instead, it is in data preparation and data provisioning.

LET'S DIVE DEEP:

I am sure we are all watching thousands of articles, videos, and learning resources on data analytics, and most of them talk about data literacy, and some talk about an engineering approach to data than a traditional approach and open standard data architecture such as data lakehouse, data lake, and data mesh.

DOES THE FOLLOWING DATA MODEL ARTIFACTS ARE NEW TO THE INDUSTRY?- ?NO. These data model artifacts are not new, and it has been in the industry for a while. SO, what is changed in data?

(a)ERD (Entity Relationship Diagram), (b)MDM (Multi-Dimensional Data Model), (c) Star Schema, (d) Extended Star Schema, (e) Snowflake Schema, (f) Tables, (g) Columns, (h) Dimensions, (i) Measures, (j) KPI's (key performance indicators), (k) Master Data Tables, (l) and Transaction Data Tables, (m) Fact Tables, (n) Cubes, (o) ODS (operational data sources), (p) DSO (datastore objects), (q) Different Data Types,?(r) Information Modeling, (s) and other data model artifacts.

ARE THE FOLLOWING DATA ROLES NEW TO THE INDUSTRY?- NO. These roles are not unique to the industry. Some recent roles are added to this list to support the open data standards and architecture extension to these roles. See below for the new data roles and the expectation for such new roles.

(a)Data Architect, (b)Data Modeler, (c)Database Administrator, (d)Data Analyst, (e)Data Steward, (f)Data Admin, (g)Database Developer, and other data management roles.

DOES THE DATA SECURITY ARTIFACTS NEW TO THE INDUSTRY? - NO. None of these data security artifacts are unique to Industry. Regardless of what data we use, how we use it, open standards, and the architecture we follow, we need the following access controls.

(a)"Role-Based Data Access" (what data users can and can't access dynamically), (b)Data Access Controls, (c)"Read Only View," (d)"Read & Write View," (e)"Read, Write and Delete," (f)Delete Controls and (g)Data Object Management Controls.

?SO, WHAT'S NEW IN DATA??

As we said, the changes in data are (1) data preparation and (2) data provisioning (making data available to business, applications, and analytics) with an open architecture to traditional data architecture and an engineering approach to traditional ETL (extract transform and load). Of course, both data preparation and data provisioning need a new strategy, modern approach, and roadmap to meet business goals.

WHY DO WE NEED AN OPEN STANDARD DATA ARCHITECTURE AND ENGINEERING APPROACH?

To keep it simple, we need open standards and modern data architecture ONLY for two new reasons (1) data preparation and (2) data provisioning (making data available) for business regardless of data source, data format, and data access frequency.

?NEW DATA ROLES:

Over some time, to support the data growth, data usage, open data standards, and architecture, the industry developed some of these new data roles, and these roles could be strategic, or they could be technical skill-based delivery roles. But the bottom line is that the data remains the same, and these new roles prepare the complex data sets and make them available for business.?

• ·???????Data Officier
• ·???????Data Scientist
• ·???????Data Engineer

NEW APPROACH TO DATA ANALYTICS: INTEROPERABLE DATA PLATFORM:

Suppose your goal is to leverage your existing data assets and develop a next-level data analytics business solution. You must learn to develop an interoperable data analytics platform. An interoperable data platform lets you connect your traditional platform (ex: data warehouse, business intelligence, and data marts) with the modern data platform (ex: data lakehouse, data lake, data mesh, data science).

In all of the client situations I am involved in, the clients expect to leverage their existing data warehouse and business intelligence platform rather than a greenfield data analytics platform.

MODERN OPEN STANDARD DATA AND ANALYTICS PLATFORM COMPONENTS:?

·???????Data Lakehouse

·???????Data Lake

·???????Data Pipeline

·???????Data Streaming

·???????Data Catalog

·???????Data Mesh

·???????Data Science

·???????Machine Learning

·???????Data Engineering - An engineering approach to make data available for business (not a traditional ETL approach)

Traditional Data and Analytics Components for ETL (extract transform and load): To quickly realize the measurable values of your data analytics, ensure these traditional data and analytics components work well with your modern data analytics architecture.

·???????Data Mapping

·???????Data Transformation

·???????Data Conversion

·???????Data Integration

·???????Data Profiling

·???????Data Enrichment

·???????Data Aggregation

·???????Data Injection

·???????Data Wrangler

·???????Full Data Load

·???????Delta Data Load

Traditional Data and Analytics Architectural Components for Data Source and Target: To quickly realize the measurable values of your data analytics, ensure these traditional data and analytics components work well with your modern data analytics architecture.

·???????Data Warehouse

·???????Data Mart

·???????Data Source

·???????Data Target

·???????Data Staging

·???????Source System

·???????Systems of Record

·???????Data Storage

·???????Data File

·???????EDI (Electronic Data Interchange)

?Traditional Data and Analytics Solution Components for “Data Governance”: To quickly realize the measurable values of your data analytics, ensure these traditional data and analytics components work well with your modern data analytics architecture.

·???????Master Data Management

·???????Data Governance

·???????Data Management

·???????Data Quality

·???????Data Owner

• ???"Role-Based Data Access" (what data users can and can't access)

Traditional Data and Analytics Solution Components for “Reporting and Analytics: To quickly realize the measurable values of your data analytics, ensure these traditional data and analytics components work well with your modern data analytics architecture.

·???????Data Visualization

·???????Business Intelligence

·???????Analytics & Repotting

·???????Data Analysis

·???????Data Analytics

PROVEN BUSINESS CASE: EFFECTIVE USE OF DATA ANALYTICS IN DIGITAL TRANSFORMATION:

https://www.dhirubhai.net/company/cadmv/

A few weeks back, I watched a video on LinkedIn demonstrating the digital transformation at DMV, California. After watching the video, I realized the effective use of data analytics is in the digital transformation for public services. The industry started recognizing the actual business values of data analytics in the business transformation - Thanks to the DMV leadership team Mr. STEVE GORDON and Mr. AJAY GUPTA.

?NEXT STEPS:?

Attend the panel discussions and discover hidden secrets in your data analytics, from strategy to execution to skill development.

?ABOUT JOTHI PERIASAMY:

I am not just an abstract person who lives on conceptual views and presentations. I worked with more than fifty plus (50+) global clients like PG, ExxonMobil, Colgate, Apple, etc. Recently, I developed a data lakehouse for an enterprise AI transformation. The lakehouse was set on AWS & Google Cloud (Multi-Cloud), and it stores and processes around 150 TB of TELCO data in a day.