You need to start a new IT services project. Which data analytics tools should you consider?

What can also work is to formulate some hypotheses regarding the outcomes you are expecting. This is an easier way to decide what data and analysis you may need to do to evaluate the hypothesis and allows you to correct along the way. It is a cheaper way early on in the process.

Choosing data analytics tools involves evaluating project needs, budget constraints, data characteristics and team proficiency. Carefully consider scalability requirements and align with budget constraints and long-term objectives. Regularly reassess the technology stack to ensure adaptability and optimization. Toolset: 1. Data Visualization and BI: Tableau, PowerBI, QlikView/QlikSense 2. Programming Languages and Libraries: Python, R 3. Big Data Analytics: Apache Hadoop, Apache Spark 4. Predictive Analytics and Machine Learning: TensorFlow, PyTorch 5. Data Integration and ETL: Apache NiFi, Talend 6. Database and Query Languages: SQL, MongoDB 7. Collaboration and Project Management: Jira, Confluence, Slack

Well come what may, Microsoft Excel is still the omnipresent Analytics Tool, heavily used by the business analysts for any kind of analytics workloads, be it cross-tab analysis, simple graphs and charts or complex data visualization widgets. So that is a good starting point. But good friend MS Excel do have some pitfalls and hence you need to graduate to start using enterprise BI Tools, which are metadata driven so that they provide universal truths regarding your KPIs or actionable insights. Tools like MicroStrategy, PowerBI, Tableau, Qlik etc. One should look out for tools that have a robust schema layer, self-service data visualization capabilities, in-memory analytics to scale and above all connectivity to all kinds of data sources.

Se debe comenzar estableciendo el “para qué” del proyecto, es decir la finalidad o resultados esperados y así definir los Indicadores Claves de Negocio (KPIs), o en este caso, los Indicadores de salud del proyecto o del Proceso a implementar, de este modo, se podrán evaluar cuáles son los mejores métodos y herramientas para analizar estos indicadores. Se deberá tener en cuenta en dónde estarán almacenados los datos, en qué forma y con qué disponibilidad para finalmente definir las herramientas más óptimas a utilizar. Las opciones son casi infinitas, pero deberemos idealmente seleccionar la opción que mejor se adecue a nuestro caso particular, reduzca complejidad, sea escalable y maximice nuestra inversión.

Most of the time I would start with good, old-fashioned, spreadsheets (aka Excel); they provide agility as team/managers are becoming familiar with measures to be tracked, how best to calculate KPIs, any pitfalls in the data collection process, etc. VBA can (still) make wonders at this stage ;) Only after team/manager feel they enjoy a decent level of understanding of relevant metrics, would start using any of the top tools in the market (most likely Power BI and/or Tableau). And when it comes to "translating" spreadsheet calculations to actual reports, I would always prefer Power BI to any other tool (as you could easily turn Excel/VBA formulas to DAX).

Your insight into considering data sources and formats aligns with the critical aspect of data analytics tool selection. Acknowledging the diversity in data types and understanding the tools' capabilities and limitations is key to a successful implementation. Factors like volume, velocity, and variety of data are rightly emphasized, as they significantly impact the performance and scalability of analytics tools. Tailoring your choice based on these considerations ensures a more seamless integration with the specific data characteristics of your IT services project, enhancing the overall effectiveness of your analytics endeavors.

Efectivamente, es necesario conocer los tipos de datos, estructuras, volumen y la arquitectura tecnológica que los alojará, ya que estos son factores determinantes para poder seleccionar una plataforma que permita analizar esos datos. Adicionalmente, es importante pensar en nuestro escenario actual pero también en el escenario a futuro, al menos de los próximos 5 a?os, para garantizar que las plataformas a integrar cubran el corto y al menos el mediano plazo.

Considering the diversity of data sources and formats is crucial when selecting data analytics tools. It's important to understand the capabilities and limitations of these tools to ensure a successful implementation. Factors like volume, velocity, and variety of data play a significant role in the performance and scalability of analytics tools. By tailoring our choice based on these considerations, we can seamlessly integrate the tools with the specific data characteristics of our IT services project, enhancing the overall effectiveness of our analytics endeavors. Well said!

Evaluate the types of data sources you're dealing with - structured, unstructured, or semi-structured, along with their formats like CSV, JSON, XML, or databases. Ensure the chosen tools can handle your data's variety and volume effectively. Build for diversity and scale.

As the field of data analytics evolves, the range of available data analysis tools grows with it. If you’re considering a career in the field, you’ll want to know: Microsoft excel Pache R SAS Steamlit Python Tableau Data analytics tools are software and apps that help data analysts collect, clean, analyze, and visualize data. These tools are used to extract insights from data that can be used to make informed business decisions.

Given the analytics is to be performed over IT services, one another criteria to narrow down the data analytics’ tools’ selection could be the ease of integration of the tool with the IT services. For example, if your existing IT services heavily rely on cloud platforms like AWS, you might want to consider data analytics tools that have native integrations or robust compatibility with AWS services. This ensures smoother data flow and minimizes potential complications during implementation, enhancing overall efficiency.

Igualmente es importante realizar un benchmark en donde identifiquemos los costos de operar la tecnología que se pretende integrar, ya que en ocasiones existen pocos expertos en la tecnología seleccionada, aumentando el costo operativo y el mantenimiento de la misma. A mayor arquitectura de software estándar menor el riesgo de caer en un escenario de sobre costos.

Balancing the complexity of data analytics tools with the team's technical skills and available resources is crucial for efficient utilization. It's important to consider factors like coding requirements, user interface friendliness, and overall cost when making practical decisions that align with the business's capabilities and constraints. Opting for cloud-based tools with a low learning curve can be particularly advantageous, ensuring a cost-effective and agile approach to analytics that aligns with budgetary and time constraints. This strategic consideration enhances the feasibility and success of deploying analytics solutions within the business intelligence framework. Great insights! ??

Acknowledging the importance of technical skills and available resources in the context of data analytics tools is paramount. Balancing the tool's complexity with the team's skill set is crucial for efficient utilization. Considering factors like coding requirements, user interface friendliness, and overall cost aligns with making practical decisions based on the business's capabilities and constraints. Opting for cloud-based tools with a low learning curve can be particularly advantageous, ensuring a cost-effective and agile approach to analytics that aligns with budgetary and time constraints. This strategic consideration enhances the feasibility and success of deploying analytics solutions within the business intelligence framework.

Assess your team's technical expertise and the resources available for implementing, maintaining, and supporting the analytics tools. Choose tools that match your team's skills and fit within your resource constraints. You need to look at your resource skills and evaluate for both pre and post implementations ownership.

Regulatory requirements are key requirement which every industry needs to adhere to in this increasingly digital world specially when it comes to handling data.If not adhered, it can lead to heavy fines and penalties and impact the reputation of the company.

Prioritizing data security and compliance is crucial in today's landscape. Understanding and adhering to industry-specific regulations like GDPR or HIPAA demonstrates a commitment to ethical data practices. It's essential to ensure that data analytics tools have robust features for access control and prevention of unauthorized use to safeguard sensitive information. By addressing security and compliance requirements, we not only mitigate risks associated with data breaches but also establish trust and accountability in the analytics process. It's a cornerstone for responsible and effective data analytics strategies.

Assegurar a seguran?a de dados em um projeto é fundamental. Implemente criptografia robusta para prote??o durante a transmiss?o e armazenamento. Adote autentica??o multifatorial e controle rigoroso de acesso para prevenir acessos n?o autorizados. Monitore continuamente atividades suspeitas e mantenha backups regulares para recupera??o em caso de incidentes. Cumpra regulamenta??es de privacidade e realize auditorias periódicas para garantir conformidade. Eduque a equipe sobre práticas seguras e mantenha-se atualizado sobre amea?as cibernéticas. Essas medidas fortalecem a prote??o dos dados, preservando a integridade e a confidencialidade.

Una parte crítica del proyecto, es que, desde que se realiza el lobbyng, es decir, desde las primera etapas de planificación del proyecto, se incluya a las áreas regulatorias, como Privacidad de la Información, Seguridad de la Información, Seguridad Sistemas y las áreas legales para garantizar que la tecnología que se va a adquirir cumpla con los estándares necesarios.

Absolutely, data security and compliance are critical considerations when choosing a data analytics tool. Ensure that the tool aligns with relevant laws and regulations such as GDPR or HIPAA, especially if handling sensitive information. Look for features like access controls, encryption, and audit trails to safeguard data against unauthorized access, misuse, or loss. Tools like Microsoft Azure or Amazon Web Services often provide robust security features and compliance certifications to meet various regulatory requirements.

The overview of popular data analytics tools, including Tableau, Power BI, Python, R, and SAS, provides a diverse range of options for IT services projects. Each tool comes with unique strengths—Tableau's user-friendly interface and community support, Power BI's integration with Microsoft products, Python and R's programming capabilities, and SAS's comprehensive suite for various data tasks. Evaluating the specific features and benefits of these tools allows for a tailored selection based on project requirements, ensuring an optimal fit for data analysis and visualization needs within the IT services context.

En mi caso particular que brindo servicios de Diagnostico de KPI y Procesos, utilizo Complexus herramienta que combina técnicas del EDA con IA. (complexus.mx). Su foco es analizar todas las combinaciones de factores que determinan un indicador y presentar anomalias que representan problemas y oportunidades.

Selecting a data analytics tool requires weighing features and benefits tailored to your project. Tableau is user-friendly with strong visualization and a supportive community. Power BI offers seamless Microsoft integration and is cost-effective for Microsoft users. Python is versatile with libraries for analysis and machine learning, ideal for custom tasks. R provides comprehensive statistical tools and is favored in academia. SAS is known for advanced analytics and is widely used in stringent industries. Consider scalability, integration with existing systems, cost, the team's skill level, and the availability of support and community resources when making your choice.

A escolha das ferramentas deve considerar cuidadosamente as fontes de dados disponíveis, incluindo sua natureza (estruturados, semi-estruturados ou n?o estruturados) assim como as exigências de latência. O contexto atual e a infraestrutura da empresa também s?o cruciais, especialmente ao avaliar as op??es em nuvem, onde integra??es nativas podem simplificar a implementa??o. O or?amento e o tempo disponíveis também influenciam na decis?o, tornando ferramentas SAAS com interfaces amigáveis e baixa curva de aprendizado preferíveis em situa??es de recursos e tempo limitados.

Given the focus on services I would recommend process mining tooling like Disco (by Fluxicon), Celonis or the PowerBI addons for this. Also very useful for tracking the true customer journeys: what really happens beyond the nice flow chart...

Whether it's Agile, Scrum, or Waterfall, choose a project management style that suits the project’s needs and the team’s working style. Not every project will do well in every methodology.

Data catalog! So much time could be saved having a concise, consistent, and easily accessed data catalogue. Users have questions about what they see in the end tools/reports. Let's enable self-service there too. The ideal solution also will foster two communication about the context/meaning of dimensions/metrics with the history of those conversations between business and technical users being maintained for later reference for improvements and to speed onboarding. If someone leaves, their replacement can see the discussion that took place to get to the now. Faster ramp up to functional analysis!

When considering data analysis, it's important to define clear and precise questions to focus on the most important insights. This helps guide the analysis process and ensures that the results are relevant and actionable. Additionally, gaining hands-on experience in related roles like data analyst, supply chain analyst, or logistics coordinator can provide valuable skills and knowledge for becoming an inventory analyst. Updating your resume and portfolio with relevant information and examples, as well as preparing for interviews and assessments by researching the company and industry, practicing data analytics skills, and expressing enthusiasm for the position, are all important steps to increase your chances of success.

Data Analytics is inextricably linked to communication. As a data analyst, you are interpreting data points, describing patterns, and predicting trends. In order to do your job effectively you must know your audience and what their bandwidth is. One helpful piece of advice I got when I was early in my career is that every organization has its own language. This is doubly true in GovTech. In acronym-laden agencies with complex deliverables that demand attention to detail, it is the norm for components to have their own shorthand. Sometimes components within an organization will have different names for the exact same thing. Understanding how their language works, rather that knowing it inside and out, is what will help you speak to them.

Un consejo muy práctico y sumamente útil que ayuda a reducir riesgos, errores, problemáticas y a aumentar las probabilidades de éxito de nuestro proyecto es algo que pocas Organizaciones realizan, y esto es, realizar una prueba piloto, reducida, acotada y con bajo o nulo costo, en el cual se integren las distintas plataformas en las que estamos interesados para el análisis de datos en nuestra arquitectura de IT y de esta manera tener una prueba de concepto que nos garantice que la arquitectura y las herramientas funcionarán como lo esperamos. La gran mayoría de vendors de software están dispuestos a contribuir en estas pruebas piloto por lo que puedes tomar ventaja de eso para garantizar que lo que necesitas se cumplirá satisfactoriamente.