DATA ENGINEERING

Innovate with Data you Trust

Why Data Engineering?

Data Engineering is the set of activities surrounding data preparation & discovery. These are prerequisites needed for Data Analysis, Data Science, & Additional Data Discovery. As an example, if Artificial Intelligence (our equivalent of Self-Actualization) is the top of our Hierarchy of Data Needs, then all other foundational needs like food, water, and shelter are required first.

Data Hierarchy of Needs

Data Hierarchy of Needs

Data Engineering v. Data Science

Data Engineering v. Data Science

What We Do?

Our Data Engineering practice focuses on the Business Processes that utilize data within your organization. Being well-versed in the entire “data value-chain” from the derivation & ingest of data through its integration or storage and training of models used by the business processes, business applications and any additional analytics needs.

As bridge builders & efficiency creators between the Business and IT departments, we bring together the needs of the business users and the capabilities of your IT. We stand for pragmatic and sustainable solutions focused on agnostic tool selections, best-of-breed solution mixes, & collaborative processes to facilitate adoption, making us the ideal partner in complex projects.

Data Pipeline

Data Pipeline

• Data Ingest
• Data Architecture
• Workflow Automation
• ETL
• Streaming/Complex Event Processing

Data Organization & Storage

Data Organization & Storage

• Data Modeling
• Database Design
• SQL/NoSQL
• Data Lakes
• Data Warehouse

Data Discovery

Data Discovery

• Machine Learning
• Deep Learning
• Data Mining
• Data Profiling
• Facilitate Data Science

Project Examples:

Cloud Data Warehouse

Client Challenges:
Data sources spread across global offices with little to no shared full data sets, mostly out of fear of data security, making consistent & consolidated reporting impossible

This meant:
Decision making was delayed waiting or manual consolidations to be completed and resulting in material inefficiencies, even pricing products at a loss

Axian’s Solution:
Created a consolidated cloud-based Data Warehouse utilizing Microsoft Azure Data Factory, Power Apps, Flow, SQL Server, SQL Data Warehouse

Results:
Immediate clarity and improved security of all reporting data, including Regional self-service capabilities and improved accuracy resulting in unfettered access to inventory analysis, costing/margin data, and improved insights into their supply chain

Ultimately, efficient decision-making and fewer losses

Machine Learning

Client Challenges:
Highly customized multi-tenant environment where customizations were client driven and not captured for reuse on future releases

This meant:
Customers extended/modified their own data model and customization value was lost, resulting in missed market opportunities

Axian’s Solution:
Utilized a CRISP methodology & Azure’s Machine Learning Studio to isolate, select, & train the appropriate Classification algorithm and model to identify the key Market Drivers & data model customizations.

Results:
Identified a set of Product Improvements solutions aligned to market needs without considerable investment into market
research, thus delivering the competitive advantage of detailed insights into your Customer’s needs

Ultimately, improved competitive positioning, increased sales, greater client retention

Strategy & On-Premise Data Warehouse

Client Challenges:
Legacy ERP facing systems needing replacement and numerous pockets of decentralized data stores throughout the organization which led to delays in insights as all debated which team had the most accurate data/report

This meant:
Decision Making was difficult because of the inability to utilize a centralized data store to create custom reporting with all requests for data extracts requiring IT, thus installing a bottleneck and multiple delays and missed opportunities

Axian’s Solution:
Implemented a Data Strategy & Technology Roadmap, tied all Technology/Data Solutions to the dependent/predecessor Business Priority and aligned all departments, created a new Enterprise Data Warehouse to augment the existing SAP BW data repository as the primary Reporting & Analytics Data Warehouse using Microsoft SQL Server & SAP Business Objects as the Reporting platform

Results:
Immediate impacts were realized around the capabilities of consolidated data stores, self-service reporting, & increased supply chain visibility including when
products would be delayed in shipping

Ultimately, increased customer loyalty, reduced costs, company growth

Partnerships:

AWS Partner Network

Microsoft Partner Network