According to the IDG/Foundry 2022 Annual CIO Survey, economic instability and uncertainty are the leading drivers of declining technology budgets. Despite the desire to slash budgets, data remains a key component of business success, especially amid economic uncertainty. According to Harvard Business Reviewdata-driven companies better financial performancemore likely surviveand more innovatory.[1]
So how do companies find this balance and create a cost-effective data stack that can deliver real value to the business? New surveys from Databricks, Fivetran and Foundry In a survey of more than 400 senior IT decision makers in data analytics/AI roles in large enterprises, 96% of respondents said integration issues negatively impact their business. However, many IT and business leaders are discovering that modernizing their data stack can overcome these integration hurdles and provide the foundation for a unified and cost-effective data architecture.
Build a performance and cost-effective data stack
The Databricks, Fivetran, and Foundry report outlines four investment priorities for data leaders.
1. Automated Data Movement. Data pipelines are critical to modern data infrastructure. Data pipelines ingest data from popular enterprise SaaS applications and operational and analytical workloads and move it to cloud-based destinations such as data lakehouses. As data grows in volume, variety and velocity, businesses need a fully managed, secure, and scalable data pipeline that can automatically adapt as schemas and APIs change, while continuously delivering high-quality, up-to-date data. Modernizing your analytics environment with an automated data movement solution reduces operational risk, ensures high performance, and simplifies ongoing data integration management.
2. A single system of insight. Data Lakehouse incorporates integrated tools that automate ELT to move data to a central location in near real time. By combining both structured and unstructured data and eliminating separate silos, a single system of insight like a data lakehouse allows data teams to address all data types and workloads. This unified approach of data lakehouses greatly simplifies data architecture and combines the best features of data warehouses and data lakes. It enables improved data management, security and governance in a single data architecture to increase efficiency and innovation. Finally, it supports all major data and AI workloads, making data more accessible for decision making.
A unified data architecture creates a data-driven organization that gets BI, analytics, and AI/ML insights all at the speed of a data warehouse—a critical differentiator for tomorrow’s successful businesses.
3. Designed for AI/ML from the ground up. AI/ML is gaining momentum as more than 80% of organizations are using (or seeking to use) (AI) to stay competitive. “AI remains a fundamental investment in digital transformation projects and programs,” says Carl W. Olofson, research vice president at IDC, who predicts that global AI spending will exceed $221 billion by 2025.[2] Despite these efforts, becoming a data-driven company powered by BI analytics and AI insights is proving out of reach for many organizations struggling with integration and complexity challenges. Data Lakehouse solves this problem by providing a single solution for all major data workloads, from streaming analytics to BI, data science and AI. We help data science and machine learning teams access, prepare, and explore data at scale.
4. Addressing data quality issues. Data quality tools (59%) stand out as the most important skill to modernize the data stack according to IT leaders in the survey. Why is data quality important? Traditionally, business intelligence (BI) systems have enabled querying structured data from data warehouses to gain insights. Meanwhile, data lakes contain unstructured data that is kept for AI and machine learning (ML) purposes. However, maintaining siled systems or trying to integrate them through complex workarounds is difficult and costly. In a data lakehouse, a metadata layer on top of open file formats increases data quality, and a query engine improves speed and performance. It meets the needs of both BI analytics and AI/ML workloads to ensure the accuracy, reliability, relevance, completeness and consistency of data.
According to a Databricks, Fivetran, and Foundry report, nearly two-thirds of IT leaders are using a data lakehouse, and more than four in five say they’re likely considering implementing one. At a time when cost pressures call into question open investments in data warehouses and data lakes, savvy IT leaders are responding by placing a high priority on data stack modernization.
Download Full Report Discover exclusive insights from IT leaders on data pain points, how to solve them, and the role they expect cloud and data lakehouses to play in modernizing the data stack.
[2] Source: IDC’s Guide to Worldwide Artificial Intelligence Spending, February 2022 V1.