You're struggling to make sense of your business data. What's the best way to get started with BI?

Initiating a BI journey necessitates defining clear goals. Identify specific questions to answer with data, key metrics to track and enhance, and desired outcomes. For instance, in sales analysis, goal setting could involve identifying factors influencing revenue growth. Clear and realistic goals enable focused efforts on relevant data sources and methods. Additionally, progress measurement against expectations ensures alignment with objectives, facilitating informed decision-making and driving business success.

In many organisations Data Democratisation is a key objective to make sense of the exponentially growing data and in this journey, getting started with BI plays a vital role in achieving Data Democratisation, at LatentView, we believe BI is not just about building dashboards but understanding the larger goals first. Defining Goals is a significant milestone in making sense of the data and we believe Discovery and Vision exercise can help in defining the Goals better., some times, Organisations get carried away in the quest to address the burning needs alone rather than looking into the end state, the future might open up new possibilities and planning for the future is a key aspect in getting started with BI…

??El primer paso para implementar con éxito BI es tener una comprensión clara de lo que deseas lograr. ?Buscas mejorar la eficiencia operativa, aumentar las ventas o mejorar la satisfacción del cliente? Definir tus objetivos te ayudará a centrarte en los datos relevantes y a determinar qué significa el éxito para tu empresa. Esta claridad es esencial para guiar todo el proceso de BI.

It's important to pick the right problem to solve using BI. Is it more retail customers for banks or better asset quality for corporate loans or lower defect rates for your manufactured products? Once the area of impact is identified, the rest of the process gets structured automatically. Then you can start analyzing the current process, start collecting the right data and building relevant analytical models.

- Setting goals in Business Intelligence is like marking a destination on a map before a journey. - As a BI Developer and Data Storyteller, I emphasize the significance of starting with the end goal. - Identifying specific questions and relevant metrics is crucial for tailoring a BI strategy. - This focused approach streamlines data analysis and ensures actionable insights. - Data stories derived from this method resonate with stakeholders and foster meaningful business growth.

Choosing the data sources is one of the most important step in getting started with BI. Business Intelligence is all about getting actionable insights from the data to drive decision making..and insights will be as good as the data source we choose. Organisations are bombarded with so much of data today from various sources, it is the responsibility of the department analytical heads to decide on the right set of data sources, in line to the defined goals of their respective departments. Sometimes it is very important to pick the right combination of data sources, internal sales data and external social media data can be a good combo to understand the impact of outside vs inside innovation. Data quality check and processing follows this.,

??Una vez definidos tus objetivos, el siguiente paso es identificar y seleccionar las fuentes de datos que utilizarás. Las empresas pueden extraer datos de una variedad de fuentes, incluidas bases de datos internas, plataformas de redes sociales, sistemas CRM, y más. Asegúrate de que tus fuentes de datos sean confiables y relevantes para los objetivos que has establecido.

- Choosing the right data sources is foundational in Business Intelligence (BI). - Data quality and integration are paramount for BI success. - Relevant and reliable sources must be selected. - Data synchronization and cleansing are crucial for effective analysis. - This process facilitates insightful data storytelling and strategic business development. - Data-driven decisions are enabled, leading to growth and efficiency in organizations.

Data is the foundation of analytics and BI and we should be flexible in sourcing a large variety of data sources in the BI business. If we think about project management as an example, we need to manage, time, cost and quality to achieve an outcome to meet the scope of work. Time can be managed by schedules. We use Primavera, MS Project or other apps to build the schedules and they use databases like SQL to collect this data. To manage cost, we use applications like SAP, Oracle cost management cloud, etc and they similarly collect the data in SQL or NoSQL databases. Quality can be the same but what if we also need to consider Human Sentiment as a KPI to meet the Quality KPI, then we have a new data source in social media.

Selecting the right data sources is crucial for making sense of business data. Identify sources that directly relate to your goals, such as customer transactions, website analytics, or sales records. Quality and relevance matter, so choose data that provides meaningful insights aligned with your business objectives. When I say, choosing the right data sources are crucial, it goes without saying that a biased or meaningless datasource can lead to an incorrect analysis outcome.

Just like picking your battles, it is imperative that we choose our BI tools to make an impactful BI work., Once the Goals are defined and data sources are hand picked, the next thing is to ensure we choose the right set of BI tools. Beyond Excel / PPT, bringing in the latest advanced in BI tools can help us reach the actionable insights faster., Power BI & Fabric is already disrupting the way the BI is done.Organisations can choose with their BI tool of interest but it is important that the tools are ready not just to answer today’s problems but tomorrow’s needs too. BI tools are evolving and they don’t just build dashboards anymore.. the whole Data ingestion and Data Science work can now be done from the Frontend of the BI tools.

???El mercado actual ofrece una amplia gama de herramientas de BI que pueden ayudarte a analizar y gestionar tus datos. Al seleccionar tus herramientas, considera factores como la facilidad de uso, la capacidad de integración con tus sistemas existentes y el costo. Herramientas populares incluyen Tableau, Microsoft Power BI y Google Analytics, entre otras. La herramienta adecuada dependerá de tus necesidades específicas y del tama?o de tu empresa.

In selecting data analysis tools, align choices with needs, budget, and proficiency. Consider factors like data volume, analysis depth, and presentation style. For instance, for large-scale data processing, tools like Apache Spark may be suitable. Evaluate ease of use and compatibility with existing systems. For example, opting for Tableau for its user-friendly interface and seamless integration with various data sources. Balancing features, cost, and security ensures optimal tool selection, facilitating effective data analysis and decision-making.

- Business Intelligence (BI) tools are like lenses for viewing the data universe effectively. - Selecting appropriate tools is vital, aligning them with analysis and storytelling goals. - Tools should handle data volume and complexity while enabling insightful narratives. - As a BI Developer, I've observed tools transforming raw data into compelling business stories. - The sophistication of BI tools should match the complexity of data analysis questions.

Tools are fun and you can go as far as you can imagine but just if you have the budget to spend and skills to utilise. I believe we need to learn as many tools as we can as a BI professional to be able to adapt to the business requirements. Meanwhile, budget is playing as a main constraint to consider when we are working on a project. MS Power Platform, Tableau, Gephi are very strong tools but each one of them comes with its own cost and complexity. I have found Gephi as a very strong cost-effective solution for network analysis in social media but at the same time very complex software in comparison with Power BI that can be operated by many users as a preferred application in many companies.

Analysing data can be done in multiple ways but it is essential that we align our analysis to the overall goals..the aim is to bring stories relevant stories from the data., at the end of the day, getting actionable insights is the key.. Descriptive Analysis is the kick starter and we should ensure we don’t stop with unidimensional alone but bring in multidimensional perspectives. Diagnostic analysis should be baked in to the dashboard in such a way, these are self supporting and business users should be able to do driver analysis and ad hoc work on-the-go Predictive and Prescriptive analytics comes next and it is ideal that we always tie these to the goals and don’t do siloed analysis which may not add value to the goals

??Con tus objetivos definidos, fuentes de datos seleccionadas y herramientas en mano, es hora de comenzar el análisis de datos. Este proceso implica examinar, limpiar y transformar los datos para descubrir patrones y tendencias significativas. La capacidad para realizar un análisis profundo te permitirá comprender mejor tu negocio y el entorno en el que opera, identificando oportunidades y desafíos.

Visualization is as important as serving the food in a feast., after all the hard work in the kitchen, if serving is done properly, then the hard work may not pay off.. similarly Visualization is very important in conveying the story that was mined after all the hard work at Data processing and analysis layer… It is all about making the right charts and aligning it in a structured manner that talks with each other.. sometimes people try to use different complex charts to add flavours to the visualization but those may not be needed always.. many a times, a simple bar chart and a line chart can covey the story with actionable insights at ease… Understanding the persona is also important in Visualisation..

?? La visualización de datos es un componente esencial de BI, ya que convierte los análisis complejos en información comprensible y accesible para la toma de decisiones. Utiliza gráficos, tablas y dashboards para presentar tus hallazgos de manera que sean fáciles de entender para todos los interesados. La visualización efectiva de datos facilita la comunicación de insights y ayuda a impulsar la acción basada en datos.

In my experience I have found that the following aspects are key to visualisation: - Make visualisations easy to understand. Graphics are useless if only you understand them. - Don't duplicate information if it is not necessary. PowerBI and the other systems allow you to segment or use buttons to alter what you see on the screen. - Adapt to your client. What a middle manager wants to see is not the same as a boss who only wants to see a figure or a piece of data.

Visualization presents complex information in a visual format, making patterns, trends, and relationships more apparent. Since humans are inherently visual creatures, data visualization makes information more digestible and easier to comprehend. Instead of parsing through raw numbers, charts, graphs, and maps provide a clear and concise summary of the data, allowing for quicker comprehension and decision-making.

Visualization is often the end product in a BI project as it is the business facing part and most high level users rely on reports to evaluate the entire BI process. The human brain is wired in such a way, so as to process images, shapes and colors better than text. So it is crucial to have a crisp and sharp visualization that embodies the data and tells the users a story. This could be key performance indicators, Trend charts, pie charts or even word cloud.