Free Quiz
Write for Us
Learn Artificial Intelligence and Machine Learning
  • Artificial Intelligence
  • Data Science
    • Language R
    • Deep Learning
    • Tableau
  • Machine Learning
  • Python
  • Blockchain
  • Crypto
  • Big Data
  • NFT
  • Technology
  • Interview Questions
  • Others
    • News
    • Startups
    • Books
  • Artificial Intelligence
  • Data Science
    • Language R
    • Deep Learning
    • Tableau
  • Machine Learning
  • Python
  • Blockchain
  • Crypto
  • Big Data
  • NFT
  • Technology
  • Interview Questions
  • Others
    • News
    • Startups
    • Books
Learn Artificial Intelligence and Machine Learning
No Result
View All Result

Home » Boosting Business Intelligence Platforms with Data Version Control

Boosting Business Intelligence Platforms with Data Version Control

Tarun Khanna by Tarun Khanna
March 20, 2024
in Artificial Intelligence
Reading Time: 5 mins read
0
Business-Intelligence-Platforms
Share on FacebookShare on TwitterShare on LinkedInShare on WhatsApp

Table of Contents

Toggle
  • Introduction
    • Also Read:
    • What is Codex, OpenAI’s latest AI coding agent capable of multitasking?
    • Taiwan’s Computex to showcase AI advances, Nvidia’s Huang to take centre level
    • Improvements in ‘reasoning’ AI models can also slow down soon, analysis reveals
    • New study explores role of generative AI in the use of copyrighted material
  • What is a Business Intelligence Platform?
    • Data Management
    • Data Collection
    • Data Aggregation
    • Data Validation
  • What is Data Version Control?
  • Benefits of Data Version Control in Business Intelligence
    • Integrate the Most Successful Model
    • Utilize the New Product KPIs
    • Clear the Duplicate Data
  • Conclusion

Introduction

In today’s era of digital transformation, the significance of data is like that of oil in the previous eras. The increasing reliance of companies and organizations on data for decision-making highlights both advantages and disadvantages. Ensuring the reliability and consistency of analyzed data becomes crucial for making informed and precise data-driven decisions.

Data version control tools are pivotal in the evolving field of data science. As data undergoes manipulation and corrections, the need to create new versions arises. This inherent nature of data analytics requires a structured approach to data lifecycle management, facilitating seamless progress and easy maintenance of projects and architectures.

Also Read:

Google launches ‘implicit caching’ to make having access to its latest’s AI models less expensive

AI Fails the Social Test: New Study disclose Major Blind Spot

AI memory need propels SK Hynix to historic DRAM market leadership

Anthropic is releasing a new program to study AI ‘model welfare’

Understanding the intricacies and fundamental steps of data lifecycle management is imperative for creating and sustaining healthy data environments. Incorporating DVC tools adds sophistication, enhancing the efficiency and reliability of managing evolving datasets in the realm of data analytics.

What is a Business Intelligence Platform?

Building a business intelligence platform involves implementing a comprehensive set of essential steps within the domain of machine learning or data analytics projects. For optimal project management, data teams and comparable organizations should consistently incorporate these steps across all phases. Broadly, business intelligence platforms can be categorized into the following key steps:

Data Management

This phase concentrates on the comprehensive oversight and governance of data assets within the organizational framework. It encompasses crucial processes such as formulating data policies, standards, and procedures that regulate the entire lifecycle of data, from its inception to archival. Moreover, it entails the implementation of data cataloging, metadata management, and access control mechanisms to safeguard data, ensure compliance, and enhance usability throughout the organization.

Data Collection

This stage centers on the acquisition and ingestion of data from diverse sources into the data ecosystem. Key tasks encompass establishing data pipelines, connectors, and APIs to extract data from both internal and external sources. Moreover, it involves the creation and implementation of processes for data ingestion, transformation, and loading (ETL) to guarantee the efficient and reliable capture of data.

Data Aggregation

This critical phase centers on the consolidation and integration of data from various sources, aiming to form cohesive datasets for analytical and reporting purposes. Key tasks encompass designing data models, outlining aggregation processes, and establishing data warehouses or data lakes to efficiently store and organize the amalgamated data. Furthermore, it involves the implementation of mechanisms for data governance and metadata management, ensuring both the consistency and accessibility of the data.

Data Validation

This essential phase involves activities focused on guaranteeing the quality and integrity of data. It encompasses the design and implementation of validation processes and mechanisms aimed at verifying the accuracy, completeness, and consistency of data sources. This may entail the creation of automated tests, the establishment of validation rules, and the incorporation of data quality checks to identify and rectify anomalies or errors within the data.

Also Read: Beyond Imagination: Unveiling Generative AI

What is Data Version Control?

In the software development lifecycle, version control stands as a pivotal tool for meticulously tracking and overseeing each stage of a project. It provides teams with the capability to scrutinize and regulate modifications executed on the source code, thus ensuring that no changes slip through the cracks unnoticed.

Conversely, data version control entails the systematic storage of unique versions of a dataset, crafted or modified at different junctures in time. This robust mechanism guarantees that diverse iterations of the dataset are securely preserved, facilitating straightforward retrieval and comparison when necessary.

Benefits of Data Version Control in Business Intelligence

Several factors can lead to alterations in a dataset, and data specialists play a crucial role in enhancing the success rate of a project by testing machine learning models. This often involves undertaking significant data manipulations. Furthermore, datasets may undergo regular updates owing to the continual inflow of data from diverse sources. The preservation of older data versions becomes a valuable practice for organizations, offering the ability to replicate past environments as needed.

Source

Integrate the Most Successful Model

The fundamental objective of data science projects is to effectively address the specific business requirements of a company. In response to customer or product demands, data scientists take on the responsibility of crafting multiple machine-learning models. Consequently, there arises a need to integrate new datasets into the machine learning pipeline for each modeling endeavor.

Nevertheless, data experts must exercise caution to prevent the loss of access to the dataset that consistently produces the most optimal modeling outcomes. In navigating this challenge, a data version control system emerges as a crucial tool, playing a pivotal role in ensuring the attainment of this objective.

Utilize the New Product KPIs

In the contemporary digital landscape, the acknowledgment is widespread that companies utilizing data for informed decision-making and strategic formulation are more likely to prosper. Therefore, the preservation of historical data assumes paramount importance. Consider the case of an e-commerce company specializing in daily necessities. Every transaction conducted through their application contributes to the continuously evolving sales data.

As societal needs and preferences undergo transformations, the retention of all sales data becomes invaluable for extracting insights into emerging customer trends. This practice empowers businesses to discern appropriate strategies and campaigns that effectively cater to evolving customer demands. Ultimately, this data-driven approach furnishes companies with a novel business metric, offering a comprehensive gauge of their success and overall performance.

Clear the Duplicate Data

The key issue identified pertains to concerns about uniqueness in data quality. In the software development lifecycle, flawed backend architectural design often results in the generation of data with duplicate values. To address this, integrating an elimination function in the data pipeline is crucial for identifying and rectifying duplicates, preventing the spread of inaccurate insights to business teams. The detection and handling of duplicate records can be accomplished using the following SQL code snippet.

 

CREATE OR REPLACE TABLE sales_table AS
SELECT * EXCEPT(row_num)
FROM
(
SELECT *, ROW_NUMBER() OVER (PARTITION BY data_id ORDER BY data_time) AS row_num
FROM sales_table
) s
WHERE row_num = 1

Conclusion

In the current era of escalating data generation, data-driven companies must prioritize secure data storage practices. Cultivating a data-driven culture involves establishing robust management systems for storing, testing, monitoring, and analyzing data to extract valuable insights. For businesses managing large datasets, data version control is crucial. It allows tracking data evolution, reverting to previous versions if necessary, and enhancing the accuracy and efficiency of data management processes. Implementing data version control is key to maintaining data integrity and ensuring a systematic approach to handling substantial data volumes.

Tags: Business Intelligencebusiness intelligence platforms
ShareTweetShareSend
Previous Post

Beyond Imagination: Unveiling Generative AI

Next Post

Navigating the Impact of Artificial Intelligence on Job Markets

Tarun Khanna

Tarun Khanna

Founder DeepTech Bytes - Data Scientist | Author | IT Consultant
Tarun Khanna is a versatile and accomplished Data Scientist, with expertise in IT Consultancy as well as Specialization in Software Development and Digital Marketing Solutions.

Related Posts

Huawei readies new AI chip for mass shipment as China seeks Nvidia options, sources stated
Artificial Intelligence

Huawei readies new AI chip for mass shipment as China seeks Nvidia options, sources stated

April 23, 2025
ChatGPT search is developing quickly in Europe, OpenAI data suggests
Artificial Intelligence

ChatGPT search is developing quickly in Europe, OpenAI data suggests

April 22, 2025
As the trade war escalates, Hence launches an AI ‘advisor’ to help companies manage risk
Artificial Intelligence

As the trade war increase, Hence launches an AI ‘advisor’ to help enterprises manage risk

April 21, 2025
AI Breakthrough: Scientists Transform Everyday Transistor Into an Artificial Neuron
Artificial Intelligence

AI Breakthrough: Scientists Transform Everyday Transistor Into an Artificial Neuron

April 16, 2025
Next Post
Navigating the Impact of Artificial Intelligence on Job Markets

Navigating the Impact of Artificial Intelligence on Job Markets

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

+ 24 = 25

TRENDING

Optimizing Costs in Kubernetes Environments: A Comprehensive Guide

Kubernetes-Environments
by Tarun Khanna
January 30, 2024
0
ShareTweetShareSend

How to Exemplify Your Cardinal Skills with Deep Learning?

by Manika Sharma
February 21, 2021
0
ShareTweetShareSend

Data Science – Key to Bridging the Gap Between Tech And Business

Tech And Business
by Tarun Khanna
February 26, 2021
0
ShareTweetShareSend

Python for Data Analysis

Python for Data Analysis
by Tarun Khanna
March 24, 2021
0
ShareTweetShareSend

A Simple Guide to Blockchain: What, Why & How?

blockchain-guide
by Tarun Khanna
February 3, 2021
0
ShareTweetShareSend

Enormous Big Data Changing The Internet Experience For Average Consumers

Enormous Big Data Changing
by Tarun Khanna
February 23, 2021
0
ShareTweetShareSend

DeepTech Bytes

Deep Tech Bytes is a global standard digital zine that brings multiple facets of deep technology including Artificial Intelligence (AI), Machine Learning (ML), Data Science, Blockchain, Robotics,Python, Big Data, Deep Learning and more.
Deep Tech Bytes on Google News

Quick Links

  • Home
  • Affiliate Programs
  • About Us
  • Write For Us
  • Submit Startup Story
  • Advertise With Us
  • Terms of Service
  • Disclaimer
  • Cookies Policy
  • Privacy Policy
  • DMCA
  • Contact Us

Topics

  • Artificial Intelligence
  • Data Science
  • Python
  • Machine Learning
  • Deep Learning
  • Big Data
  • Blockchain
  • Tableau
  • Cryptocurrency
  • NFT
  • Technology
  • News
  • Startups
  • Books
  • Interview Questions

Connect

For PR Agencies & Content Writers:

connect@deeptechbytes.com

Facebook Twitter Linkedin Instagram
Listen on Apple Podcasts
Listen on Google Podcasts
Listen on Google Podcasts
Listen on Google Podcasts
DMCA.com Protection Status

© 2024 Designed by AK Network Solutions

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Artificial Intelligence
  • Data Science
    • Language R
    • Deep Learning
    • Tableau
  • Machine Learning
  • Python
  • Blockchain
  • Crypto
  • Big Data
  • NFT
  • Technology
  • Interview Questions
  • Others
    • News
    • Startups
    • Books

© 2023. Designed by AK Network Solutions