Data

Modernizing data storage systems using Data lakes

Leader in the chemicals industry needed to streamline data from multiple departments in their organization, so that they could process their high-volume of data and perform real-time analytics for understanding future business needs. 

Client: A global leader in specialty chemical and ingredients distributor based in the US. 

Problem: The client had been working on a legacy data storage platform, which made it challenging to store and analyze data. There were issues relating to data standards amongst the different departments within the enterprise, data duplication and missing data. Due to the legacy data storage platform and the vast amounts of data being collected by the client, they were not able to process the real-time data. The goal therefore was to modernize the data storage platform so that it was more flexible and agile than the traditional data management systems. 

VT Solution: We built an AWS data lake solution for the client so that all data collected throughout the organization can be put together. Improving data accuracy, reducing data redundancy, and forming a single source for all the client’s data needs while also allowing client to traceback to the original data sources. The solution is forward looking as it can be scaled to high data volumes in a cost-effective manner, while also featuring support for different types of analytics. 

These new capabilities allowed our client to generate effective insights by analyzing historical trends and utilizing machine learning models for predictive analytics to get recommendations and forecasts. In addition, it supported future analysis from within the client’s organization, by allowing in-house data analysts, data scientists, and business analysts to access the data using their choice of analytic tools. 

Key Points:

• Achieved the organization’s goal by designing and implementing a single standard data storage system, reducing data redundancy, and improving accuracy.  
• Provided a scalable solution that fits the clients need for growth and is personalized to their data requirements.
• Integrated analytical tools to allow easy access for in-house data professionals and analysts to work on the data with their preference of analytical and BI software.  

End-to-End Implementation of BI system

Luxury car maker required a Business Intelligence (BI) system to gain insights about their business processes and gain insights about customer needs and preferences, driving business growth and future opportunities. 

Client: A US-based luxury electric car automaker

Problem: The client had multiple cloud database systems capturing different aspects of the business and the needs and preferences of their customers. They required to bring all the cloud databases into a single data storage solution while also building Business Intelligence (BI) and analytics tools to allow for dashboard and visualization creation. The problem can be thought of has having three parts 

1. Gather all current data sources into a newly built one data storage system 
2. Build a foundation for business analytics for various internal departments, so that they can easily get actionable insights and enable data-driven decision making. 
3. Create enterprise level, interactive BI reports, and dashboards like customer usage reports, customer experience reports or such for the various different departments of the enterprise.

VT Solution: Since the client foresaw a 100x growth with a launch of a new product, one of the requirements was to have a flexible data storage system, that could be scaled rapidly. Therefore, we designed and implemented an AWS data lake to form the basis of data storage system. Bringing in source data from APIs, Legacy RDBMS, SAP, and vehicle files into AWS systems. Building Data Lake, creating data integration and ETL capacities to take raw data from data lake and creating a data warehouse. 

Finally, introducing semantic data models using Power BI to perform analytics, create BI reports and dashboards. Enabled self-service BI for client’s analytics team to utilize the data warehouse and its in-built analytical tools. 

Key Points:

• Achieved client’s goal by creating an end-to-end implementation of a BI system from the creation of a singular data source, data cleaning, integration, extraction to finally generating BI reports and visualizations
• Build data lake to form the singular source of raw data throughout the organization, automated data integration and ETL to create a data warehouse. 
• Built Analytics and BI reporting tools on top of the data storage sources, to allow generation of reports, visualizations, and dashboards for client. 

Pipeline creation for data streamlining

Client: A US-based manufacturer in the consumer goods and industrial manufacturing section

Problem: The client manufactures thousands of products with a diverse reach in the consumer goods section, while also producing important parts for other industries. With the huge number of products, the client produces high-volume of data. This data stream is extremely large, and it makes it hard for the client to process and analyze the data for real time insights. Even when the data is utilized by the client’s analysts, it needs to be processed and cleaned delaying the time it takes for them to go from data to actionable insights. The client wants a solution that can streamline the analytical process and improve overall efficiency for the analytical process.

VT’s solution: We created reliable pipelines for our client, that would perform the various steps required for data ingestion and data processing. Providing a processed, cleansed, and validated set of data, providing easy to operate on data, simplifying the analytical process. We also implemented a data storage solution that allowed for fast retrieval of the various kinds of metrics and data, enabling faster access to the processed data.

Key points:

• Achieved client’s business objectives by streamlining the analytical process, introducing automation, and providing easy to retrieve data for their analysts, while reducing time spent on the average analytical process.
• Provided an end-to-end implementation for the data engineering process, from automation through data pipelines to modern data storage solution to easily store and access data, handling large volume data streams to create easy to access processed data for analytics.