Data Prism

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Overview

$5,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

2 - 9

Employees

2023

Year Founded

ABOUT DATA PRISM

Data Engineering & Analytics Solutions

At Dataprism, we provide innovative data engineering solutions that transform raw data into meaningful insights. Our skills encompass data extraction, pipeline creation, warehousing, and modeling. We incorporate AI, machine learning, and big data processing into scalable cloud systems. Our mission is to establish robust, future-ready data infrastructures that enable our clients to succeed.

Languages Spoken

English

HIGHLIGHTS

$5,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

2 - 9

Employees

2023

Year Founded

Services

Focus

Industries

Clients

Services Provided

50% BI & Big Data Consulting & SI

15% Custom AI Chatbot Development

35% Robotic Process Automation (RPA)

About the Team

OUR STORY

Our Narrative We are Data Prism, a data engineering team founded by Usman Ashraf, who has over five years of experience in automating data-driven systems. We create data pipelines, warehouses, AI/ML integrations, web scrapers, API integrations, and bots to transform raw data into trustworthy insights. Our diverse team of engineers has successfully completed projects for clients such as DoorDash, Uber Eats, Interactive Brokers, and various startups, receiving accolades from clients and the industry for our technical excellence and reliable performance.

Portfolio

Data Storytelling Analysis

Unknown

**Project Overview** Toast Inc. sought analytical assistance to identify significant trends within extensive restaurant data stored in spreadsheets. The aim was to convert raw operational data into clear, narrative-driven insights that could support data storytelling articles and industry analyses. **Challenges** The client encountered several analytical and operational obstacles, including: - A vast quantity of unprocessed restaurant data spread across multiple spreadsheets - Insights dispersed over more than 20 tabs - Difficulty in recognizing significant consumption trends - Time-consuming manual analysis - The need for organized findings appropriate for editorial narratives To address these issues, a comprehensive, multi-variable data analysis approach was necessary. **Solution** Data Prism undertook an extensive analytics project utilizing spreadsheets to transform raw data into useful insights. They employed advanced formulas, pivot tables, and cross-sheet analysis methods within both Excel and Google Sheets. The analysis integrated multi-variable datasets to uncover consumption trends, regional performance, and product demand patterns. Key insights, such as the best-selling menu items categorized by city and restaurant type, were extracted and organized for editorial purposes. **Results** - Transformed raw restaurant data into actionable insights - Identified top-performing menu items in various regions - Revealed consumption and demand trends - Provided structured findings for storytelling publications - Facilitated data-driven editorial and industry reports

Facebook Data Pipeline using ChatGPT

Unknown

**Project Overview** Knok’d was working on a real estate platform aimed at property rentals and sales. In order to rapidly increase the number of listings, the client sought an automated method to gather property information from social media, especially Facebook groups, and convert unstructured posts into standardized, ready-to-use listings for their platform. The goal was to create a data pipeline that could efficiently collect, clean, organize, and deliver high-quality real estate data in real-time. **Challenges** The property listings on social media were highly variable and challenging to utilize effectively. The client encountered several significant obstacles: - Property data was dispersed across numerous Facebook groups - Listings were largely unstructured and filled with text - Important information such as price, location, and property type was frequently inconsistent or omitted - Images and text needed to be stored and processed separately - Manual cleaning and formatting were not sustainable A powerful AI-enhanced transformation pipeline was necessary to standardize incoming data according to industry norms. **Solution** Data Prism created and implemented an AI-driven data pipeline for social media that automated the process of ingesting and structuring property listings. The system extracted data from selected Facebook groups using Python-based scraping scripts, storing text in PostgreSQL and images in Google Cloud Storage. The raw listings were refined and processed using ChatGPT via the OpenAI API, which employed engineered prompts to convert unstructured posts into standardized real estate formats. The structured data was then sent directly to the client’s platform for automatic listing population. **Results** - Successfully established a fully automated data ingestion pipeline for Facebook - Converted unstructured social media posts into listings ready for the platform - Minimized manual data cleaning and formatting tasks - Increased the speed at which listings became available on the client’s marketplace - Enabled a scalable approach to acquiring real estate inventory data

Marketing Data Collector

Unknown

**Project Overview** BABR needed a centralized system to monitor and assess the effectiveness of its organic marketing efforts across various platforms. The existing manual reporting process was labor-intensive and drained valuable time and resources. The goal was to automate data collection, integrate metrics from different channels, and provide continuously updated performance dashboards. **Challenges** The client encountered several operational hurdles: - Marketing data was dispersed across multiple platforms. - Generating reports manually involved three employees. - Reporting took up an entire workday. - There was a lack of centralized visibility into performance. - Marketing metrics weren’t automatically refreshed. A cost-efficient, automated reporting system was necessary to enhance analytics workflows. **Solution** Data Prism created a no-code pipeline for aggregating marketing data using Make, an automation platform. Connectors were set up for Facebook, LinkedIn, Google Analytics 4, and Capsule CRM. Marketing performance metrics were automatically pulled and organized into structured dashboards in Google Sheets. Data pipelines were scheduled for daily updates, providing real-time insights into campaign performance, engagement metrics, traffic sources, and lead pipeline status. The reporting framework categorized insights by platform, facilitating structured analysis of page performance, post engagement, traffic acquisition, conversion patterns, and CRM opportunity tracking. **Results** - Automated reporting of cross-platform marketing performance - Eliminated the need for a full day of manual reporting - Reduced the operational workload on three internal staff members - Delivered centralized marketing analytics dashboards - Enabled daily tracking and optimization of performance - Enhanced visibility into the lead pipeline and campaign success

32 more projects

Projects & Clients

Project Size & Rate

Minimum Project Size

$5,000+

Average Hourly Rate

$50 - $99/hr

Pricing Snapshot

Min Project Size

$5,000+

Avg Hourly Rate

$50 - $99/hr

Cost Rating

N/A

What Clients Have Said

"Data Prism delivers quality projects within budget and timeline expectations based on verified client feedback."

Based on verified reviews and internal benchmark data

PROJECT BUDGETS

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BI & Big Data Consulting & SI

Custom AI Chatbot Development

Robotic Process Automation (RPA)

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Portfolio

Data Storytelling Analysis

Unknown

Facebook Data Pipeline using ChatGPT

Unknown

Marketing Data Collector

Unknown

32 more projects