Marketing to prospects (direct mail, telemarketing, and email) is expensive. Targeting incorrectly can hurt your brand, leaving prospects feeling spammed. Traditional techniques are not very sophisticated, resulting in low response rates which in turn leads to high cost-per-lead/acquisition numbers.

In the data-driven world, marketing technology companies like Numberly are increasingly dependent on their ability to deliver valuable insights from diverse, large datasets to maintain a competitive edge. The greatest return on marketing and advertising spend is achieved by delivering precisely targeted, relevant, and timely messages. To maintain its position as a global leader, Numberly faced the challenge of including, processing, and reconciling large and diverse datasets, such as static data from CRMs and streaming sensor data. They needed to make this data accessible for data science and analytics in near real-time.

The global information technology services company, which provides one of the largest e-commerce platforms in the world, was facing challenges due to the exponential growth in data volume resulting from increased internet transactions. The company needed to improve the relevance of information for discovery and required a semantic search engine to power the search function for all applications on its platform. This was crucial to understand user intent through search context and improve the relevance of results. The company also needed to create a modern architecture framework to enable better searches, replicate data, and perform experimental customization analytics. The challenge was to move data efficiently to allow effective searchability, a task the company was struggling with. The platform also needed to handle increased search traffic and data volumes expected in the future, while adhering to high security and compliance standards and avoiding high costs.

As Cox Automotive was formed, executives sought to implement an enterprise data strategy that would enable the company to deliver new customer experiences using rich automotive data from across its brands.When the 20 brands came together, it was not possible to bring all the data together in traditional environments such as Netezza or SQL Server.

ANZ Bank, a leading financial institution in Australia, was faced with the challenge of identifying and rectifying compliance gaps in its processes and products. The bank needed to analyze every single transaction over the past 20 years to uncover lapses, such as incorrect interest rates and fees charged. This was a daunting task as it involved sifting through approximately 10 PB of data. The challenge was compounded by the evolving regulatory landscape in Australia's banking sector, which had become more stringent since 2017. Banks were now required by law to design their products with their customers’ needs in mind and were held accountable for instances of non-compliance. ANZ Bank was committed to acting responsibly and wanted to rectify any mistakes made in the past to build customer trust.

Axis Bank, the third-largest private-sector bank in India, faced the challenge of adapting to the increasing demand for digital banking. With over 20 million customers and more than 4,500 branches, the bank needed to leverage big data and data analytics to boost profitability, manage risk, and improve operations. The shift towards digital banking services required Axis Bank to expedite its transformation to data-driven operations to meet changing customer needs. The bank needed to improve its understanding of the required data and the business problems it could solve. Simply collecting, storing, and managing data without a clear strategy or business objective would not create any real value. The bank also faced scalability challenges with its on-premises systems, which had difficulty integrating with cloud applications and could not facilitate ad-hoc analysis requirements for machine learning and artificial intelligence teams.