You're looking for data discovery software for your BI projects. What's the best option out there?

There are many data discovery software available in the market, each with its own features, strengths, and limitations. Some of the factors that you may want to consider when choosing a data discovery software are: ? The type and volume of data that you need to work with ? The level of complexity and customization that you require for your analysis ? The ease of use and learning curve of the software ? The integration and compatibility with other tools and platforms that you use ? The cost and scalability of the software ? The support and community that the software provides Sigma Domo Tableau Power BI

As of my last knowledge update in January 2022, several data discovery and business intelligence (BI) tools were popular in the industry. However, the landscape is continually evolving, and new tools may have emerged since then. Additionally, the "best" option depends on your specific requirements, budget, and preferences. Here are some well-regarded data discovery and BI tools that were recognized in 2022: Tableau: Power BI (Microsoft): QlikView/Qlik Sense: Looker (now part of Google Cloud): Domo: Sisense: MicroStrategy: Yellowfin: TIBCO Spotfire: Dundas BI: When choosing a data discovery tool, consider factors such as ease of use, scalability, integration capabilities, cost, and specific features relevant to your organization's needs.

Je suis une fervente utilisatrice et évangélisatrice de Power BI qui, à mon sens est une solution complète en elle-même. Non seulement elle permet à un utilisateur de se connecter à différentes sources de données et de les faire parler entre elles, mais elle permet également, au passage, de les transformer afin de pouvoir les exploiter convenablement.

Usually, data discovery software’s engine is based on a certain architecture (SQL, Java, etc), has a predetermined capability to connect to types of databases (SQL, flat files, web, etc), and is part of a cloud solution (Azure, AWS, IBM, Oracle, etc). Depending on your organization’s data management tools, one or another software will be easier to install and integrate with data. For example, Azure family seamlessly integrates SQL, Reporting Service, Analysis Service, Power BI, and other tools using shared computing, collaboration, API, and languages (Power Query, ODB, etc).

Data integration is a key component of data discovery software that allows for easy access to a variety of sources, including files, databases, online services, and cloud platforms. This feature makes it easier to combine and transform several data types into a single format, avoiding silos and guaranteeing accuracy and quality.

Data has become big featuring velocity, volume, variety, and veracity. To successfully explore it you need powerful tools allowing efficiently conduct exploratory data analysis. Commonly used tools are Python and R programming language with popular thematic libraries. Unfortunately, traditional spreadsheet tools such as MS Excel may not have the capacity and statistical packages such as SPSS or Stata may be limited in functionality or efficiency. Besides Python and R, there are GUI software such as Orange3. They are graphical and require less programming. You can select, drag-and-drop, build models, and change their parameters in an easy and intuitive way.

Data exploration is a crucial component of data discovery software, providing interactive and user-friendly interfaces for finding and comprehending data. This feature set includes the ability to browse, filter, sort, and search in addition to calculating, aggregating, and transforming data. It gives consumers the ability to see patterns, abnormalities, and outliers in the data.

I usually roll up data/tables using a SQL tool (Teradata, toad, snowflake etc.) and put them pivots in excel. Rigorous slicing and dicing of data with averages and sums ,counts especially checking missing data gives the analyst a very good mental picture of what he/she is supposed to dive into. Post a very good EDA , the path to data visualization becomes very clear and easy

Explore data with tools like Tableau, QlikView, Power BI, Looker, Domo, Sisense, TIBCO Spotfire, and Google Data Studio for various visualization and discovery needs.

?? Choose a tool that offers intuitive exploration capabilities. Features like drag-and-drop interfaces and natural language queries can significantly enhance user experience.

I find Power BI and Tableau as the most functional visualization software in designing and creating interactive online dashboards. They require some learning curve. These tools are used not only for a one-time presentation of your insights but also for ongoing real-time interactive dashboards helping your stakeholders monitor performance across the whole organization. These tools offer various visuals to convey your message more clearly, interactivity to allow the users to filter, drill down and slice, and a lot of online resources to learn best practices. Access to them requires costs for your organization, but it is justified by the value you get. For example, time saved and better decisions made.

find Power BI and Tableau as the most functional visualization software in designing and creating interactive online dashboards. They require some learning curve. These tools are used not only for a one-time presentation of your insights but also for ongoing real-time interactive dashboards helping your stakeholders monitor performance across the whole organization. These tools offer various visuals to convey your message more clearly, interactivity to allow the users to filter, drill down and slice, and a lot of online resources to learn best practices. Access to them requires costs for your organization, but it is justified by the value you get. For example, time saved and better decisions made.

Consider Tableau, QlikView, Power BI, Looker, Domo, and Sisense for data visualization based on your specific needs and preferences.

?? Prioritize software with robust visualization options. The ability to create interactive dashboards and reports is key for sharing insights across the organization.

Data visualization presents data graphically for easier understanding. It uses charts, graphs, and maps to reveal insights and patterns. Choose appropriate visualizations based on data type and purpose. Tools like Tableau, Power BI, Python libraries (Matplotlib, Seaborn), and JavaScript (D3.js) facilitate creation. Emphasize accuracy, clarity, and interactivity. Consider accessibility for all users. Effective visualization aids exploration, analysis, communication, and decision-making.

Data collaboration is an essential component of data discovery software that makes it easier to work together on data projects with stakeholders, clients, or coworkers.

Data discovery tools like Tableau, QlikView, and Power BI make it easy for teams to explore and share insights from different datasets, fostering collaboration.

?? Ensure the platform supports collaboration, allowing team members to share findings, annotate reports, and work together on dashboards in real-time.

Choose data discovery software for enhanced data security, offering sensitive data identification, real-time monitoring, access controls, encryption, policy enforcement, and user activity auditing.

For BI projects prioritizing data security, consider Tableau for its advanced encryption and detailed permissions, Qlik Sense with its granular access controls and secure data connections, and Microsoft Power BI, known for its comprehensive security features including row-level security and Azure Active Directory integration. Each offers unique capabilities to protect data integrity while enabling powerful data discovery and analytics, ensuring your BI initiatives are both insightful and secure. Selecting the right tool depends on your specific security requirements, data ecosystem, and analytical needs.

?? Opt for software with strong security measures. Data encryption, role-based access control, and compliance with data protection regulations are must-have features.

When selecting data discovery software, prioritize alignment with business goals, user-friendly interfaces for widespread adoption, scalability to accommodate growth, seamless integration with existing systems, connectivity to diverse data sources, robust visualization capabilities, stringent security features, collaborative functionalities, support from reputable vendors, consideration of total cost of ownership, thorough evaluation through trials, ample user training and support, flexibility for customization, acknowledgment of user feedback and community strength, and a clear commitment from vendors to ongoing innovation and future development.

Cloud services like AWS Glue Data Catalog and Azure Purview organize metadata for data assets, streamlining discovery. Tools like Collibra and Alation offer comprehensive data governance solutions, promoting collaboration and informed decisions. ETL/ELT platforms such as dbt and Airflow automate data processing, ensuring consistency and quality while simplifying documentation.

Acredito que a melhor op??o é aquela que mais se aproxima do parque tecnológico já utilizado pela empresa. Se a empresa tem base em Google, talvez seja mais fácil aderir ao produto Google correspondente. O mesmo pensamento se aplica para Microsoft ou AWS. Claro que essa n?o é uma regra, apenas uma observa??o que vale a pena considerar no momento da decis?o.

?? Evaluate your specific BI needs and prioritize them. Test potential software with real datasets to assess performance. User reviews and free trials are valuable resources for making an informed decision.

For BI projects that support AI and ML, consider software like Alteryx, for its ease of use in data prep and advanced analytics, KNIME, known for its open-source, modular data science workflows, and DataRobot, which offers automated machine learning to empower users to build and deploy predictive models quickly. These tools excel in integrating AI and ML capabilities, facilitating data discovery and preparation, and enabling users to easily apply machine learning algorithms to their data, making them highly suitable for projects requiring advanced analytics.

Based on my personal experience, I prioritize having all the features mentioned (data integration, data quality, data visualization, reporting, policy, data lineage) available within the data discovery tool. This comprehensive approach offers a complete view of the analytical landscape, enabling any data enthusiasts to utilize the tool effectively and reap its benefits. It streamlines the process, significantly reducing the need for numerous meetings and discovery sessions with subject matter experts, thereby shifting the focus from spending 80% of their time searching for datasets to only 20% on data discovery activities. Consequently, use the remaining 80% of time to conduct data analysis and generating insights that drive outcomes.