Veja como você pode consolidar dados dispersos de vários bancos de dados ou fontes para análise.

Elluminate streamlines the consolidation of clinical trial data from various sources, such as EDC systems, patient registries, and external lab data, into a unified format. It connects to different databases to extract relevant clinical trial data and then transforms it by cleaning inconsistencies, handling missing values, and ensuring all data adheres to a predefined standard. This transformed data is integrated into a single, unified dataset, improving data quality and reducing the time and effort required for data management. Additionally, having a unified dataset enhances collaboration among researchers and CDMs, simplifies analysis and reporting, and ultimately leads to more efficient and effective clinical research outcomes.

Para consolidar sus datos, primero determine qué información es vital para su análisis, enfocándose en los KPIs que guían sus decisiones comerciales. Luego, identifique dónde se almacena cada dato, ya sea en la nube, bases de datos locales o hojas de cálculo. Este mapeo de fuentes y su relación con los KPIs asegura que ningún dato crucial se omita durante la consolidación. 🚀🗣️

The first step in the data consolidation effort is to Define Objectives and Requirements. This entails the following tasks: 1) Determine Business Objectives and KPi's. This is crucial for ensuring that appropriate data are gathered. 2) Define Data Requirements: Determine what data are needed from each source, including specific tables, fields, and records. 3) Identify Data Sources: List all the data sources need to consolidate (e.g., SQL databases, NoSQL databases, flat files, APIs, etc.). It has to be kept in mind that the process in this step has to be flexible enough to accommodate adjustments in business objectives and requirements.

To consolidate data from multiple databases or sources for analysis: 1. Identify data sources: List all databases and data sources. 2. Use ETL tools: Utilize tools like Apache NiFi, Talend, or Microsoft SSIS for extraction, transformation, and loading. 3. Centralize data: Load the cleaned and transformed data into a data warehouse or central repository. 4. Ensure data consistency: Standardize formats and clean the data to ensure consistency. 5. Automate updates: Set up automated processes for regular data updates and synchronization. This approach will help streamline data consolidation for effective analysis.

Per Okta's business at work report 2021, a software company has 158 applications on an average. This translates to 158 data silos. It is easy to create mountains of dashboards for reporting analysis. The question to ask is: Is this analysis "actionable"? The next step would then be to identify KPI's that are actionable.

Extract data from various sources like relational databases (e.g., MySQL, Oracle, Microsoft SQL Server), cloud storage platforms (e.g., Amazon S3, Microsoft Azure Blob Storage), and Software as a Service (SaaS) applications (e.g., Salesforce, marketing automation platforms). Tools like data integration platforms (ETL/ELT tools) can automate the process of extracting data from these disparate sources. If your data resides in Azure SQL Database or other Azure data sources, you can leverage Azure Data Factory for data extraction and orchestration. Azure Data Factory is a cloud-based ETL/ELT service that allows you to extract data from a variety of sources, including on-premises databases, cloud storage, and SaaS applications.

This is the "Raw Layer" where data is stored as-is in a format that is easy to consume for future data prep serve as an audit trail for how the data began. Avoid pre-filtering and pre-processing data to avoid tainting the"source of truth" before it is cleaned, prepped, and stored in the data warehouse. This is where you will check when an SME or auditor raises an issue with missing items or details in end solutions.

Data Extraction is the first step after data requirements and KPI's have been determined to start the data consolidation process. Although in a very small number of cases this step may be done manually, in the vast majority of cases automated tools need to be utilized. These tools are generally known as ETL (Extract, Transform, Load) tools. Some popular ETL tools include: Talend: Open-source data integration tool with robust ETL capabilities. Apache Nifi: Open-source tool for automating the flow of data between systems. Informatica: Enterprise data integration tool with powerful ETL capabilities. Microsoft SQL Server Integration Services (SSIS): Provides ETL functionality for SQL Server environments.

Consolidating scattered data from multiple sources involves extracting data using ETL tools, integrating it into a centralized location, performing necessary transformations, ensuring data quality, and automating processes for efficiency and reliability in analysis.

In this step it is most important to have better database and then take less interaction or any interference of foreign person for keeping the integrity of information. To use automated tools easier and faster the process of analysis and decision making.

This is the "Enrichment Layer" where additional features that are relevant to the data as well as defining the grain for the data set. Remove duplicates in the context of this grain, but also document that a different data set that uses this source at a different grain might include those removed rows. Once the data is prepped, store it in enriched flat tables. These tables will be the place to audit calculations and feature engineering as well as any data removed from the original data.

After the data is extracted it needs to undergo transformation to be usable. This involves: • Data Cleaning: Ensure data consistency and quality by handling missing values, duplicates, and errors. Some data cleansing tools are Trifacta, OpenRefine, DataCleaner, Talend Data Quality, IBM InfoSphere QualityStage. • Data Normalization: Standardize data formats (e.g., date formats, units of measurement) to ensure uniformity. Data normalization tools include Trifacta, OpenRefine, DataCleaner, Talend Data Quality, IBM InfoSphere QualityStage. • Data Mapping: Align and map data from different sources to a common schema or structure. Examples of data mapping tools are Informatica PowerCenter, Talend Data Integration, Apache NiFi, Microsoft SSIS.

- Ensure uniform data formats and naming conventions. - Validate data against known standards or reference datasets. - Fill missing values or discard incomplete records. - Remove duplicate entries.

This is the "Consolidation Layer" where multiple data sources are combined into a data warehouse that is optimized to read to answer any current and future business questions. This data warehouse will serve as the golden source for business questions and will ensure business conclusions reconcile with each other. Keep in mind, without this step, enterprise-scale business intelligence solutions may not reconcile and therefore their business conclusions will not be trustworthy. This later should include robust error handling and automated testing. From here, Start to create data dimensions that are standardized across the entire business and manage them centrally for guaranteed data quality and accessibility.

- Retrieve data from various sources, using SQL Queries for relational databases, API calls for web services and file reading for static files. - Clean and format the data for consistency. Handle missing values, outliers, and duplicates. Convert data types, standardize units, and normalize data. Integrate additional data if required. - Store the processed data into a consolidated database or data warehouse. You can use databases like PostgreSQL & MySQL and data warehouses like Amazon Redshift, Google BigQuery, Snowflake, etc.

This is the "Interpretation Layer" where data will be stored in fact tables that answer specific business questuons. Business Intelligence teams often are consumers of data rather than creators of data, and therefore need to curate a data warehouse that is optimized to read. I'd recommend using the Kimball Data Warehouse methodology to build what you need when you need it.There will rarely be a time where the data the BI team needs to use is only available in one location, so create dimensions that reflect the business needs instead of individual systems.

The consolidated data have to be stored and be ready for query and analyses. The storage can be in a centralized database such as a data warehouse and/or a data lake. Choosing between a data warehouse and a data lake depends on specific use cases, data types, and analytics requirements. In many modern data architectures, both are used together to leverage the strengths of each. • Data Warehouses are best for structured data and traditional BI use cases, optimized for complex queries and reporting. Examples: Amazon S3, ADLS, Google CS, IBM Db2. • Data Lakes are best for handling large volumes of varied data types, supporting advanced analytics, machine learning, and real-time data processing. Examples: Amazon S3, Azure DLS, Google CS..

Ensure there is an SDLC and review process for analysis conducted by anyone in the business, not just business intelligence teams. At this point analysis can be federated to everyone to analyze and improve their business. Responsibility and accountability must be clear to anyone making business decisions from their analysis, especially citizen developers. This is the phase where the business can start growing and innovating at scale! Remember, business intelligence alone has no inherent value; the value is directly proportional to the impact of the business problem it solves. Be accurate, Be thoughtful, Be safe.

With data ready for analyses various tools can be used to derive intelligence that guide business decisions. These tools offer a range of features tailored to different business needs, from simple data visualization to complex, enterprise-level analytics. Choosing the right BI tool depends on an organization's specific requirements, existing technology stack, and budget. The categories of tools and examples are: User-Friendly Visualization: Tableau, Microsoft Power BI, Domo. Data Discovery and Flexibility: Qlik Sense, Sisense. Enterprise-Level Solutions: SAP BusinessObjects, IBM Cognos Analytics, MicroStrategy, Oracle Analytics Cloud. Cloud Integration: Looker (Google Cloud), Domo, Oracle Analytics Cloud.

Raw data are just numbers with out intelligence, for ther thousand be constructive scene and mneasuring this in actions. Like a doctor the used value of exams to determine that better pills, we will do the same but whit compani's data, we construct the views and identify the better action to move on.

To consolidate scattered data from multiple databases or sources, utilize ETL (Extract, Transform, Load) tools to streamline data integration. Employ data warehousing solutions to centralize and standardize data for analysis. Implement data integration software like SQL, Apache Hadoop, or cloud-based platforms to efficiently merge diverse datasets, ensuring consistency and accuracy for comprehensive analysis.