Aspire Systems Case Studies Data Lake Implementation for a Big5 Consulting Firm

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	Data Lake Implementation for a Big5 Consulting Firm Aspire Systems

Data Lake Implementation for a Big5 Consulting Firm

Aspire Systems

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Education Equipment & Machinery
Use Cases	Movement Prediction Predictive Maintenance
Services	Data Science Services System Integration
Challenge	The customer, a global network of professional services firm providing audit tax and advisory services, was looking to digitize their business operations. They were in need of a partner to do a technical proof of concept on Cortana analysis suite (CAS’s) data lake set of offerings. The customer's needs included bringing in data from multiple cloud tenants into a single location, running descriptive and predictive analytics on those data, establishing a regular schedule as well as data organization for the above, and the need for one consolidated version of data points. Read More
About Customer	The customer is a part of a global network of professional services firm providing audit tax and advisory services. They are part of the Big5 consulting firms and have operations carried out in Australia. As part of their digital transformation journey, they were looking for a partner to help them with a technical proof of concept on Cortana analysis suite (CAS’s) data lake set of offerings. Their needs were centered around data consolidation, analytics, scheduling, and data organization. Read More
Customer Name	Not disclosed Read More
Solution	Aspire Systems conducted a detailed analysis and brought in the data from different sources into Data Lake Store using Data Factory. They used U-SQL for Descriptive, and Azure Machine Learning (ML) for Predictive Analytics, combining the power of Declarative(SQL) as well as Imperative(C#) programming. Azure Data Factory was used to create multiple Data pipelines for data orchestration and scheduling. Cortana analysis suite (CAS) provided an integrated set of components, and Azure ML provided an extensive and extensible set of algorithms, reducing the need to look for an external source. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Education

Equipment & Machinery

Use Cases

Movement Prediction

Predictive Maintenance

Services

Data Science Services

System Integration

Challenge

The customer, a global network of professional services firm providing audit tax and advisory services, was looking to digitize their business operations. They were in need of a partner to do a technical proof of concept on Cortana analysis suite (CAS’s) data lake set of offerings. The customer's needs included bringing in data from multiple cloud tenants into a single location, running descriptive and predictive analytics on those data, establishing a regular schedule as well as data organization for the above, and the need for one consolidated version of data points.

About Customer

The customer is a part of a global network of professional services firm providing audit tax and advisory services. They are part of the Big5 consulting firms and have operations carried out in Australia. As part of their digital transformation journey, they were looking for a partner to help them with a technical proof of concept on Cortana analysis suite (CAS’s) data lake set of offerings. Their needs were centered around data consolidation, analytics, scheduling, and data organization.

Customer Name

Not disclosed

Solution

Aspire Systems conducted a detailed analysis and brought in the data from different sources into Data Lake Store using Data Factory. They used U-SQL for Descriptive, and Azure Machine Learning (ML) for Predictive Analytics, combining the power of Declarative(SQL) as well as Imperative(C#) programming. Azure Data Factory was used to create multiple Data pipelines for data orchestration and scheduling. Cortana analysis suite (CAS) provided an integrated set of components, and Azure ML provided an extensive and extensible set of algorithms, reducing the need to look for an external source.

Impact #1	The implementation of the Data Lake has resulted in a seamless to and fro data flow into the data lake. The solution has improved data security and scalability, and has allowed for better integration with BI tools. The data orchestration has brought on more agility in handling data. The solution has also prepared the customer to handle challenges in the future in terms of data & analytics. Overall, the solution became a natural extension of the customer’s existing investments.

Impact #1

The implementation of the Data Lake has resulted in a seamless to and fro data flow into the data lake. The solution has improved data security and scalability, and has allowed for better integration with BI tools. The data orchestration has brought on more agility in handling data. The solution has also prepared the customer to handle challenges in the future in terms of data & analytics. Overall, the solution became a natural extension of the customer’s existing investments.

Benefit #1	Data from multiple sources can now be brought into the Data Lake, irrespective of the data size.
Benefit #2	The customer is now able to run both Descriptive and Predictive analytics on the data.
Benefit #3	Improved data security and scalability.

Benefit #1

Data from multiple sources can now be brought into the Data Lake, irrespective of the data size.

Benefit #2

The customer is now able to run both Descriptive and Predictive analytics on the data.

Benefit #3

Improved data security and scalability.

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Overview

Data Lake Implementation for a Big5 Consulting Firm

Operational Impact

Quantitative Benefit