How can you integrate data for better business insights?

In any Organisation you have internal as well as external systems and each will have its own data storage/processing/access mechanism that is designed keeping that system in mind. With DWH you will be able to bring all of these systems into a single place to get a wider view with respect to the data. With DWH you will be able to build Analytical use cases that spans across the systems e.g. provisioning / maintenance / Fulfilment / Monitoring & Repair to solve wider business problems of increasing customer satisfaction, reducing churn , better operational efficiencies.

Adding my insights : Data Quality: Perform data cleansing to remove duplicates and handle missing values. Data Security: Implement measures to protect sensitive information. Relevance to Business Objectives: Prioritize data that directly contributes to key performance indicators (KPIs). Scalability: Design data integration processes to scale with the growing volume of data. Data Integration Tools: Choose appropriate tools based on the complexity of data sources and integration requirements. Documentation: Document data integration processes, transformations, and any business rules applied. Monitoring and Maintenance: Set up monitoring mechanisms to detect anomalies or issues in real-time.

A DW takes the pressure off your main ERP/Sales system It turns facts into data into information Much easier to review information frozen in time (like stock over time or debtors/creditors balances)

Some other advantages of DWH as I see it from my professional experience over the years. 1. Standard format and structure of data visualization from multiple systems. 2. One source of truth for the latest change set of data based activities across multiple systems. 3. Ability to view trending facts across multiple data points at products, customer and other variable elements. 4. Create customized and/or generic views of data for multiple consumers. I am sure there are many more advantages but the above ones makes a lot of value for business providing services to customer or just viewing their own data.

actually the hard part is making the data consistent and reliable across all systems and parts of the organization. For example you need to make sure that you any person is identifiable across and within all systems. all interactions with each individual are recorded in a manner which allows the organization to see the complete picture of each customer/prospect. It can be eye opening to see that most organizations see a person multiple ways in a single system, much less across the whole business.

The only add-on to the design of the data warehouse write up I’d consider adding is designing test cases for the data warehouse as part of the design. Testing (like quality) is not added at the end. It’s part of the design. Test cases should not be left until testing is to performed. Test cases should be part of the design’s artifacts. With test cases preparing to both functional and nonfunctional requirements. Based on the type of testing done (during design / development / deployment) harvesting from the test cases library into test plans keeps testing managed and above reproach.

Quality of the data model and, schema design, data types are super important for maintainability. Avoid denormalizing from day one. Denormalized data may have many duplicates and require a lot of memory to cache and process. It hurts the overall system.

Designing a data warehouse involves key steps: Business Requirements: Identify goals to guide data needs and usage. Choose Architecture: Opt for an architecture like centralized, dimensional, or Data Vault based on needs. Data Modeling: Pick a model like star or snowflake to organize entities and relationships. Data Sources: Locate where the essential data resides, such as databases or APIs. ETL Design: Plan extraction, transformation, and loading methods for data movement and storage. Security: Implement access controls and audit trails. Tuning: Optimize for performance and scalability. Testing: Validate data integrity and performance. Deployment: Roll out and maintain. It is crucial to align the warehouse with business objectives.

Bad data is always be present so always load into a staging area and work out how to deal with the outliers. Think about what your business will be in 10 years time and start building that Don't build in functional stages (eg don't start with just sales) think about the whole picture and start with all your master/dimensional tables before moving to the facts

The approach needs to be systematic. Always begin with a clear understanding of business objectives, the metrics to support them, and the identification of data sources for those metrics. Current architecture typically dictates extraction methods but modern tools/services have largely solved this; it all depends on how deep your pockets are and the political will to bend architecture models if needed. Efficiency and scalability are crucial here. Metadata management, data quality, data access controls should be in the mix. How will the final output appear and be interacted with in your analytics platform? You need flexibility to absorb change and growth in scope here. At the end: Docs, knowledge transfer, and maintenance processes.

Also - design cycle tools are necessary… They are put together over multiple iterations of studying and practicing. They are what you get when you get the right people. During design, tools to define and document business processes (as DWH solutions should be business process oriented rather than “report” oriented), business requirements, data structure requirements and data sourcing requirements - along with test cases for each. These could be: business process models, dimension maps, bus matrices, logical data models, physical data models and source to target maps. Technology agnostic design tools are not only necessary, but almost sufficient conditions for getting the design right, before attempting to deploy software tools.

La elección de una plataforma de almacenamiento debe estar siempre acompa?ada de un TCO (Total Cost of Ownership) adecuadamente realizado y favorable para la organización. Adicionalmente se deben considerar las regulaciones legales que rigen a la organización con respecto al almacenamiento de datos.

There are a tremendous number of data warehousing tools. From ETL software platforms like Informatica, to data governance, database administration, and schema design tools like 'Workbench', to cloud storage and computing platforms like Azure and AWS, to Semantic layers like data marts, to the visualization and data consumption tools like Excel, Google Analytics, Tableau, Microstrategy, and so many other Business Intelligence and Reporting platforms.

Work out what you have already - people and technology. Every tool will work and work well. Big $Spend <> Big $Value Just start already

We evaluate a data warehouse against bother functional and nonfunctional requirements. Functional requirements are those set out during the requirements specifications: specific business cases evaluated against test cases as per a test plan, defined much earlier during the definition stage. Nonfunctional requirements (also called quality attributes) are those that span wider than the specifics of this solution: accuracy, maintainability, performance, scalability and documentation. Yes, documentation is an important quality attribute of the solution. In my experience, the existence and quality of documentation is directly proportional to the fulfillment of all the other functional and nonfunctional requirements of the data warehouse.

There are many ways to evaluate a data warehouse, but the most common ways are often in-line with the same process a company may use for the age-old Build-versus-Buy decision. Will an EDW provide incremental utility value? Will it deliver process improvement? Greater speed to insights? Decreased development costs? and ultimately: insights that improve the business? These are all valuable considerations for evaluating a Data Warehouse.

Calidad y precisión de los datos: Los datos del almacén deben ser completos, correctos, coherentes y oportunos. Esto es importante para garantizar que los análisis y las decisiones que se toman con base en los datos sean precisos y confiables. Rendimiento y escalabilidad: El almacén de datos debe poder manejar el volumen y la variedad de sus datos, así como admitir la velocidad y la complejidad de sus consultas y análisis. Esto es importante para garantizar que los usuarios puedan acceder a los datos de forma rápida y sin problemas, incluso si los datos son grandes o complejos. Facilidad de uso y accesibilidad: El almacén de datos debe ser fácil de usar para los usuarios finales.

Evaluating data warehouse helps improve data and business insights. Steps to evaluate your data warehouse are: - Determine the scope of evaluation and determining the KPIs, such as data quality, performance, security etc - Finalize the evaluation criteria like completeness, accuracy etc - Query and analyze data - Based on analysis identify the areas of improvement, which can be data quality improvement, performance optimization etc - Develop an action plan and timeline for implementation of improvements.

Evaluating a data warehouse involves assessing various aspects to ensure it meets the needs of an organization. Performance Scalability Data Quality Data Integration Security Usability Maintenance and Support Cost Compatibility with Analytical Tools Business Alignment

Having Data model-centric approach in mind can help organize collaboration, transparency, equal access to all data stakeholders, setting up common standards and data governance practices. By visualizing your database with cloud data modeling tool that has intuitive user-friendly interface, you are able to identify relationships between objects, collaborate on specific items, create branching, centralize all metadata, work seamlessly on several cloud projects within your organization.

Well the new approach now is more a lakehouse concept and same, Bill Inmon wrote a book about it a year ago. The data is not just structured as in our organizations we have many unstructured or even semi-structured data. How to get the value of it for both ABI and DSML ? This is the concept of a data lakehouse.

I believe as many of my colleagues do that we have to keep updating the design and keep a model to share with interest parties. We also may need to create individual business lines their own data mart for the core DW or EDW.

The 3 best ways to improve a data warehouse are through 1) Implicit ETL and Data Architecture/Design, 2) Strong Database Administration via Data Definition Management, Data Governance Processes, and ultimately 'Master Data Management' or MDM., and finally 3) Robust QA and accuracy audits to ensure the information model is as correct, precise, and accurate as possible.

Improvements in a data warehouse can be made across various layers, including data integration, the semantic layer, and the data model. Let me share few insights refining the data integration layer (from my experience): Sourcing: Identify and streamline jobs that are unusually slow, especially with large tables, and consider incremental loading strategies Transformation: For complex transformations with large tables, determine which are most time consuming and seek optimization opportunities. Loading: If loading to the data warehouse is slow, explore parallel loading or ELT approaches to speed up the process. Always monitor job execution times and set alerts to identify and address performance issues promptly for continuous improvement.

Data Warehouses were originally designed for supporting analytical needs. Even today it has not deviated much from that mission. But probably now is the time to think beyond - can it support both analytics and apps? We have already moved towards Data Lake Houses. Which is a mix of multiple layers, with different granularities, different structures. So integrating an ODS in a SAAS product should very much be possible. And we should be able to use it as a transactional layer supporting Apps or ODS supporting analytics or MDM supporting governance interchangeably, as long as underlying infra is strong enough to support different needs. Thoughts?

Think iteratively by use cases. That will maximize the value of a DWH / Data Lake / Lake House and it will not seen a complete technical project but more a business oriented support.

Designing a data warehouse requires recognizing it as an evolving tool, continuously adjusted as demand grows. Key is selecting scalable technologies to keep data extraction-to-dashboard delivery times short. Avoid planning an all-encompassing data warehouse with lengthy wait times. Instead, focus initially on architecture that powers critical business process reports. Then, iteratively expand to include additional data and reports.

DWH is to provide people with good clean insight information - design for people imagination and clear eyes, full heart, can't lose. bad data is always be present so always load into a staging area and work out how to deal with the outliers. A dedicated timescale/calendar table designed for your business is a big timesaver and can be managed by non-tech people Always evolve and don't get stuck in the endless wondering, embrace Samsara and don't be afraid to burn what was done in the past and start again