Analytics

The evolving market landscape is driving an urgent need for a unified EPM solution, as finance teams face increasing pressures from several fronts. Rapid technological advancements, heightened competition, and the growing complexity of global markets have made financial agility and real-time decision-making critical to maintaining a competitive edge.

After being recognized by Gartner as the leading generative analytics experience for augmented analytics, ThoughtSpot’s SpotIQ just got an upgrade. As an integral part of ThoughtSpot’s core platform for nearly seven years, SpotIQ has unlocked the value of billions of rows of data for hundreds of customers. Even more inspiring are the customer testimonials highlighting how SpotIQ empowers business users to perform complex analytics and analyze key metrics—even on the go.

Oracle-driven finance teams today face increasingly complex challenges. In recent years, the finance function has had to adapt to become more flexible as they navigate market upheaval, global inflation, and rapid changes to technology. This year, an Oracle survey of CFOs reveals CFO’s top challenges include navigating the need to cut costs, retaining talent within the finance function, and focusing on more accurate forecasting.

When scaling data science and ML workloads, organizations frequently encounter challenges in building large, robust production ML pipelines. Common issues include redundant efforts between development and production teams, as well as inconsistencies between the features used in training and those in the serving stack, which can lead to decreased performance. Many teams turn to feature stores to create a centralized repository that maintains a consistent and up-to-date set of ML features.

Table of Contents SQL (Structured Query Language) is one of the most commonly used tools for transforming data within ETL (Extract, Transform, Load) processes. SQL transformations are essential for converting raw, extracted data in CSV, JSON, XML or any format into a clean, structured, and meaningful format before loading it into a target database or cloud data warehouse like BigQuery or Snowflake.

Table of Contents Python is one of the most popular programming languages used in data engineering, particularly in the ETL (Extract, Transform, Load) process. Its flexibility, extensive libraries, and ease of use make it a great choice for building and optimizing ETL pipelines.

Table of Contents Amazon Redshift has become one of the most popular data warehousing solutions due to its scalability, speed, and cost-effectiveness. As the data landscape continues to evolve, businesses are generating and data processing increasingly large datasets. Efficient analysis of these datasets is essential to making informed, data-driven decisions. Amazon Redshift allows companies to extract meaningful insights from vast amounts of structured and semi-structured data.

At a high level, bad data is data that doesn’t conform to what is expected. For example, an email address without the “@”, or a credit card expiry where the MM/YY format is swapped to YY/MM. “Bad” can also include malformed and corrupted data, such that it’s completely indecipherable and effectively garbage.

AI has entered many aspects of data integration, including data mapping. AI data mapping involves smart identification and mapping of data from one place to another. Sometimes, creating data pipelines manually can be important. The process might require complex transformations between the source and target schemas while setting up custom mappings.