You're starting a business intelligence project. How do you choose the right data analytics software?

Before you look for data analytics software, you need to have a clear idea on a few important things like , the best data transformation capabilities, prioritize solutions that strike a balance between robust customization with pro code and intuitive inbuilt analytical features. Look for platforms offering extensive flexibility for tailoring transformations while also providing built-in options for predictive analytics. This combination empowers users to fine-tune data processes to their specific needs while leveraging powerful analytical tools for insightful decision-making. This will definitely help us in long run.

Al elegir un software de inteligencia de negocios para tu proyecto, seguiría estos pasos: - Tama?o de la Empresa y Base de Datos: Para empresas peque?as o medianas, considerar utilizar hojas de cálculo para un inicio ágil. A medida que la empresa crece, migrar a una solución basada en datos estructurados y no estructurados. - Integración y Compatibilidad: Las herramientas elegidas se deben integrar fácilmente con las plataformas existentes en tu empresa y sean compatibles con los formatos de datos que manejas. - Automatización y Escalabilidad: Busca soluciones que permitan la automatización de procesos a medida que la empresa crezca. - Costos: La elección de herramientas dependerá de las necesidades y la infraestructura de tu empresa.

One thing I found helpful, Identify the specific analytics requirements of the business. Considering whether I need tools for data visualization, reporting, predictive analytics, or a combination. Align the software with key objectives.

Para uma melhor escolha do software, é importante saber o que você espera da ferramenta do ponto de vista técnico e funcional. Ex: Quais tipos de fontes de dados precisa ter integra??o? Como vai ser a distribui??o do resultado da análise? Precisa ter controle de acesso? Algumas dessas funcionalidades s?o entregues por padr?o mas cada software possui uma particularidade para se diferenciar.

Data Compatibility: Ensure the software can integrate with your current data sources and formats. Scalability: Select a tool that can grow with your business needs. User-Friendly Interface: Opt for software with an intuitive interface for ease of use. Advanced Analytical Features: Look for capabilities like predictive analytics and machine learning. Customization: The ability to customize reports and dashboards is crucial. Security Features: Strong data security measures are a must. Cost-Effectiveness: Balance advanced features with your budget. Support and Training: Consider the availability of customer support and training resources.

It's very important to decide the right data sources and the right reporting platform for your business requirements- a) Check the volume and complexity of the data you will be getting and accordingly decide the data source and at the same time check if it can be integrated with your existing data sources easily or not. b) Also the reporting platform which can be very cost effective and at the same time fulfills your reporting requirements. For eg- Power BI is a cost effective BI platform and at the same time it can be integrated with all other Microsoft services. Thank you!

Por regla general el software que uno quiera debe ser personalizable y altamente escalable para gestionar contactos, oportunidades de venta, comunicaciones transaccionales y campa?as de marketing. Por eso creo que primero debe hacer un buen y completo análisis de lo que uno quiere para su Negocio y como. Fundamental.

To correctly choose a data analysis platform, it is crucial to know the availability of native connectors from the manufacturer of the analysis platform in order to achieve an optimal relationship for the transfer, transformation and access to data. This is why it is necessary to turn to experts in these platforms before making a definitive choice of platform.

Hoje temos várias ferramentas que possibilitam a ingest?o de dados para um ETL de qualidade e após isso sim desenvolver as análises, porém mesmo com a diversidade de dados as empresas nem sempre est?o preparadas para trabalhar com novas ferramentas de B.i por esse motivo um dos principais pontos é “Avalie a fonte de dados pois depois de adquirido os softwares de Bi será muito difícil retroceder nos projetos” fica a dica!

Apart from the points discussed above, it is also a better option to identify the future analytical needs that can be explored in the future projects considering the high cost attached to subscribe or purchase any kind of analytical software. To identify the future needs, it is better to read more about the industry trends in the analytical domain. It is also a good option to ask the relevant internal stakeholders to understand their expectations from the analytics team in the future projects. With current and future needs identified for the organisation, it would make work easier for anyone to select the analytical software for the long run.

Every project, depending on its objectives and the sophistication of past data analysis and what type of analysis currently exists, may necessitate various analytical types. For instance, choosing a tool that only enables descriptive analysis for products that requiring complexity analyses in the future could incur substantial costs so it’s important to determine analytics needs at first.

While it is necessary to have the long-term projects to be carried out with these platforms we also need to know what data currently exists, the degree of cleanliness and accuracy as well as the strategy and data governance. This is why it is very useful to count on Qlik's offer since it proposes all the specified types of analysis (descriptive, diagnostic, predictive and prescriptive) as well as self-service reports/analysis for non-technical users, thus democratizing access to them.

One thing I have found helpful is analyzing common use cases or processes which fall under the umbrella of the project or large scale initiative. And then applying/running the tool on these use cases to see if the available functionality within the tool can meet the required needs of these use cases. This would help to see not only the suitability of the tool, but also it's power and performance.

Selecting a data analytics software that matches the intended type of analytics is crucial to ensure that the solution can efficiently achieve the project goals. Additionally, it is important to recognize that different projects may require different levels of analytical complexity and sophistication.

Ao iniciar um projeto de Business Intelligence, considere fatores como necessidades específicas, escalabilidade, facilidade de uso e integra??o com fontes de dados. Algumas op??es populares incluem Power BI, Tableau, QlikView e Looker. Avalie também o suporte a diferentes tipos de visualiza??es e a capacidade de processamento de grandes conjuntos de dados. Experimentar vers?es de teste pode ajudar na decis?o.

Choosing the right data analytics software for your project involves more than how well it integrates to your existing infrastructure and what kind of data you have. Scalability, flexibility, user-friendliness, security, advanced analytics, AI features, easy sharing and collaboration, total cost of ownership, and vendor reputation need to be considered too. By assessing these factors, you can make an informed decision for your business intelligence project. Weighing each of these factors by order of importance helps making an objective choice. If users value greatly an intuitive interface that allows them to customize and seamlessly interact with data, then that becomes your number one criteria.

Testing and validating data analytics software before committing to it is a wise move. This step is essential to ensure that the chosen software meets the specific requirements and expectations of the business intelligence project. To do this, organizations can request a demo, trial, or proof of concept to experience the software in action, assess its functionality, and evaluate its compatibility with project requirements, budget, and timeline. Furthermore, seeking feedback and references from other users who have similar project experiences with the software adds a valuable layer of insight. Learning from the firsthand experiences of others provides a more comprehensive understanding of how the software performs in real-world scenarios.

Validate, validate and validate. When you are done, re-read the previous sentence. For the new analysis platform it is necessary to be very clear that the ingestion, transformation, loading and visualization of the data and graphs are correct for the questions that need to be answered as well as to ensure that there will be no inconveniences caused by incorrectly entered errors.

Deve-se levar em conta o Total Cost Ownership (TCO) porque ele ajuda a identificar e avaliar todos os custos associados à implementa??o e manuten??o da ferramenta ao longo do tempo, indo além dos custos de aquisi??o inicial como despesas contínuas, como suporte técnico, atualiza??es, treinamento, tempo de inatividade, entre outros. Essa análise detalhada permite uma vis?o mais clara dos gastos envolvidos, incluindo custos operacionais, treinamento, suporte técnico, possíveis atualiza??es e integra??es.

un exemple concret pourrait être le développement d'un tableau de bord interactif qui regroupe les données clés de l'entreprise. Ce tableau de bord pourrait fournir des informations sur les ventes, les stocks, la satisfaction client, etc. afin d'aider les décideurs à prendre des décisions plus éclairées. Il pourrait également inclure des outils de visualisation tels que des graphiques, des tableaux croisés dynamiques ou des cartes géographiques pour faciliter la compréhension des données. En mettant en place un tel tableau de bord, l'entreprise pourrait suivre en temps réel ses performances et avoir une vision plus claire de ses activités, ce qui permettrait d'améliorer sa réactivité et sa compétitivité.

Important points in the choice that can speed up deliveries, visualize expansion possibilities and possible increments in the project that can add information to the business. The implementation of Data Quality for greater data accuracy and consequently generating better information. Take into account the team's familiarity, the software's learning curve, evaluating the cost-benefit of the tool. Well, there's no point in having expensive or cheap software if you don't have qualified professionals to get the project off the ground.

Something else to consider in the selection process are the applications surrounding the analytics platform such as native data integration platforms, continuously evolving graphs as well as improvements in the processing engines.