Dive Into the New Age of Accelerated Analytics
Case studies
Client: eCommerce company
Project: Complete Enterprise Data assessment
Outcome: 15% cost reduction achieved over 6 months
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Data Quality Improvement: Recommend improvements to the company's data quality practices, such as implementing data validation checks, improving data entry processes, and establishing data quality metrics.
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Data Governance Enhancement: Recommend the development of a comprehensive data governance framework, including the definition of data ownership, data stewardship, and data quality management processes.
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Data Integration Optimization: Recommend improvements to the company's data integration processes, such as implementing data standardization, establishing data integration best practices, and implementing data warehousing solutions.
Client: Regional management consulting firm
Project: Launch a new data practice
Outcome: The new product achieved a 20% enterprise review uplift
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Data Accessibility Expansion: Recommend expanding the accessibility of data to stakeholders, such as implementing data visualization and reporting tools, establishing data access policies, and providing data training and support.
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Data Analytics Maturity: Recommend the development of a data analytics strategy, including the selection of data analytics tools and techniques, the definition of data analytics best practices, and the implementation of data analytics training programs.
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Data Privacy and Security: Recommend improvements to the company's data privacy and security practices, such as implementing data encryption, establishing data backup and disaster recovery processes, and conducting regular data security audits.
Client: Traditional (brick and mortar) retail business
Project: Develop and complete strategic Enterprise Data RFP
Outcome: 2 internal resources were reallocated and 5% operation cost savings achieved
We played a significant role in achieving better results for the business:
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A better understanding of requirements: Efficient RFI and RFP writing helped to clarify the requirements of the project.
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Increased competition: The RFI and RFP attracted more competitive proposals from a wider range of potential vendors or partners. As a result, the business compared proposals more effectively and choose the best fit for their needs.
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Enhanced negotiation power: The business negotiated more effectively with vendors. This led to better pricing, terms, and conditions that were favorable to the business.
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Faster procurement: Streamlined the procurement process by reducing the need for additional clarifications or negotiations with vendors. This resulted in faster procurement times and quicker implementation of the desired solution.
Client: Private equity
Project: Complete Enterprise Data Due Diligence for Series D funding round
Outcome: Discovered significant data gaps consequently reducing the valuation of the company by 20%
A technology company was preparing to launch a new AI-powered product that would analyze customer data to provide personalized recommendations. The product was seen as a key growth opportunity for the company and was expected to generate significant revenue. However, during the data due diligence process, the team discovered several serious gaps in the customer data that was being used to develop the product.
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The data was not properly anonymized, meaning that customers' personal information, such as names and addresses, was not properly protected. This put the company at risk of serious regulatory consequences
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The data was not properly curated, leading to inconsistencies and errors. For example, the same customer was listed multiple times with different purchase histories, making it difficult for the AI system to provide accurate recommendations.
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The data was not properly secured, and there was a risk of data breaches, which could lead to serious security and privacy implications for the customers