The Enterprise Guide to Integrating Multiple Data Sources | NextPhase.ai скачать в хорошем качестве

The Enterprise Guide to Integrating Multiple Data Sources | NextPhase.ai

https://nextphase.ai/ -- What exactly is data integration, and how can you apply it to your business? Business leaders rely on data to make decisions based on facts, current trends, and real-time information. The process of turning raw data from various sources into actionable business insights starts with data integration. This guide to integrating multiple data sources walks you through the fundamental principles. FULL ARTICLE: https://nextphase.ai/integrating-data... What is data integration? Data integration involves consolidating data from disparate sources into a central platform for data analytics. The data itself can reside virtually anywhere, ranging from a spreadsheet on a local computer to a massive cloud data warehouse. Data integration refers to how all enterprise information is pulled together into a single, unified view to inform business decision making. Why is data integration important? Data integration is an essential component of data analytics and provides decision-makers with real-time insights into business performance. Data integration gives your team consistent access to business intelligence without having to wait for stakeholder reports. With a unified view of information, you’ll be able to improve processes, create efficiencies, gain insights, and make smarter, faster, more informed decisions. How is data integration implemented? Data integration takes careful planning and a deep understanding of your business’s processes and systems. We recommend starting by setting clear objectives for the data integration initiative with milestones that can be tracked and measured. A detailed scope of work and documented technical requirements is essential to establish early on. The next step is to work with stakeholders and access all of the systems, platforms, and applications that currently store data as well as any data feeds that need to be incorporated. If IT infrastructure upgrades are required, make them before implementing your data integration program. Also, be sure to consider data security at each stage in the integration process. Once technical requirements are established, the data scientist will design a data integration framework that defines how data flows through the system and is processed for analytics. The final step in the process is to implement the plan. All stakeholders should be involved with testing the system and ensuring accuracy of the integrated data. Now is also the time to establish who will provide ongoing maintenance and support if any system adjustments are needed. How do you integrate data from different sources? Data integration consists of three fundamental steps, also known as the Extract, Transform, Load (ETL) process: Extract Data Data extraction tools are used to collect, process, and manage massive amounts of information. Data extraction breaks down into four common delivery types covered by Data Integration Solutions Review. Clean Or Transform Data Data cleaning identifies and corrects errors, duplicates, and inconsistencies in data sets. In many cases, data will also need to be transformed into a consistent format or altered in some way before it’s ready for integration. Load Data Loading refers to the process of incorporating the extracted data into the destination. If your business uses a cloud-native platform such as Google BigQuery, Snowflake, or Microsoft Azure, you can use what’s known as the Extract, Load Transform (ELT) process. ELT enables you to transform data as it is loading, helping save time and computing resources. FULL ARTICLE: https://nextphase.ai/integrating-data... Have questions? We help companies like yours every day. Email us at hello@nextphase.ai About NextPhase.ai NextPhase.ai is a data cloud services provider specializing in Snowflake, cloud data management and analytics technologies. We accelerate enterprise digital transformation initiatives by leveraging our innovative cloud data management technology, "NextPhase.ai DATAFLO” to optimize and rationalize disparate enterprise data into relevant insights. "DATAFLO" is designed to automate the lifecycle of data management transformation using AI and ML along with expeditious on-ramps to the Snowflake data cloud infrastructure. NextPhase.ai provides a range of technology consulting services for the Financial Services, Biotech and Technology industry sectors combining our platform-based services, seasoned talent, and industry proven methodology so our customers can harness more from their data. We are a Silicon Valley based company with global presence having delivered high value service engagements for numerous Global 2000 enterprises.