What advanced BI techniques do you want to learn?

You need to add value. The most important thing to keep in mind as a data analyst is that regardless of its name, the essence of data visualization is not really about the visual representation of the data. It is about creating value by revealing relevant facts and relations otherwise concealed from plain sight, and by doing so, telling a story that can aid in the understanding of matters and generate a meaningful impact. Acknowledging that more often than not, less is more, especially for a wider audience, any data study presentation should prioritize engagement over raw data. Simplicity is key to effective communication, and an emotional connection with the audience, not a logical one, holds the key to driving change.

The main part about Data Visualization is storytelling. Anyone can do a storytelling, only some can do a effective story telling. In today's world of AI, I guess I would like to explore more capabilities of AI Insights into my personal life. If I could do my own prediction of stocks/shares, my life would be much easier.

I want to explore advanced techniques in data storytelling to effectively communicate insights through visualizations. 1. Mastering interactive and dynamic visualizations for real-time data exploration. 2. Learning advanced techniques in data storytelling to effectively communicate insights.

Data visualization with good storytelling behind the data makes the visual more engaging and interactive. Learn to integrate storytelling into data visualizations while designing your findings for stakeholders. Its helps audience to understand before you start presenting.

Absolutely agree that data visualization is crucial! However, its real power comes alive when paired with storytelling. Visualization makes data accessible, but storytelling makes it compelling and memorable. It’s the narrative that transforms raw data into actionable insights, helping stakeholders understand the 'why' behind the numbers. Effective storytelling in BI ensures that the visualized data not only informs but also drives strategic decisions.

You do not need to reinvent the wheel! Much like data visualization crafts stories with data, data mining decodes the language of information, unveiling insights hidden within the intricate layers of data complexity. While no two problems are identical, a set of proven methods becomes your best ally. Exploring literature on the subject of how others tackled and resolved similar complex challenges can enhance your approach and yield better results for the challenges you may one day encounter yourself. A great place to look for such knowledge is within the pages of scientific papers. Becoming acquainted with scientific publications and forming a habit of doing so can quickly become immensely valuable.

Data Mining requires strong base in mathematical models that are used behind the scenes in many python programs. If you can master the algorithms and are able to implement the same using code, you will go a long way.

In my opinion Data mining involves understand each business data deeply, every use case is different, When data mining is done then the reporting analytics part would be easy and efficient for results. Data mining is to understand the patterns and hierarchy of existing business data and to refine or organize it to get the data governance in place.

Se quer dominar técnicas avan?adas de BI, a minera??o de dados é fundamental. Ela revela padr?es e tendências em grandes conjuntos de dados usando estatística e IA, ajudando a identificar oportunidades e resolver problemas. Explore tópicos como clustering e regress?o, e use ferramentas como R, Python e SAS.

I believe after gathering or storing your data this is very important step to follow. data can be messy, unorganized and may not be in proper shape but you should make sure you process and refined your data before staring work on it. It may Involve finding abnormalities like Null Values, Blank records, duplicates and checking data type for fields and where ever possible normalizing the data or denormalizing data as per requirement. then we can perform few more operations to get some trend or pattern figure out from the data like merge, union, joins or inserting some condition this all can be performed while doing ETL or on SQL side or any other platform like R, Python, SAS these all platforms are very well equipped with this all functions

To trust data, we must first be able to trust processes. One of the biggest mistakes one can make when working with data is considering that each data point is a fact, because it is not. Every bit of data is just that. And the circumstances and methods by which it is produced or collected are the first steps in attributing the quality value to such a bit of data. A simple row or column shift in an Excel file, a name change in a CSV file, or perhaps a seemingly inoffensive disregard of the proper data governance policies may turn any analytic effort down the road. As a rule of thumb, the quality of the data can only be as good as the quality of the processes it goes through from its production to its consumption.

Learn how to tell stories with data to explain findings to people who may not know much about technology. Also, discover ways to find unusual things in data and use graphs to understand patterns. Make sure you understand how to explain what your AI models are doing in a clear way.

To enhance accuracy, reliability, and trustworthiness in decision-making processes.You must have set standards through which you validate the quality of data as it ensures the results which will be great for businesses to take the right decisions

When stakeholders or users first put hands on a BI tool, they will be cautious. They want to validate that in fact the system shows the same information they get from their manual reports or Excels. They are hesitant, they are not jumping in trusting what they are seeing. So imagine how it will impact if they start finding mistakes? It will set up an alarm in them, saying that this doesn’t work, that is not useful and must importantly that they can’t rely on it. It will impact adoption and make the trust gap even wider. It’s important to work in parts/subjects. Make sure the data is correct alongside the BI path before involving any user

Selon mon expérience, la qualité des données dépend beaucoup des personnes qui créent les données dans le système. La phrase est bien connue "garbage in, gargabe out." L'implication de ces personnes est primordiale dans le processus d'amélioration de la qualité de la donnée et peut sauver beaucoup de temps et d'énergie à corriger a posteriori. Le message à passer aux personnes par les managers est donc que la qualité de la donnée est le sujet de tous et pas seulement du service informatique.

For me, the data model is sometimes underestimated. A good data model not only captures the peculiarities of the business and the data that is produced but is also capable of evolving with the information needs that are required. I have seen data engineers academically defend their models and they are partly right. That is, their models may be technically correct but still not be aligned with the needs of the stakeholders.

A well-crafted data model streamlines engineering tasks and enhances their efficiency, but... There is no universally applicable model and not a single definitive perfect data model for every use case, nor should be. A comprehensive and overarching understanding of the business activities and processes is crucial in its development and maintenance. But what does it mean? It means that data models are a complex and that they often evolve over time within organizations just as organizations also evolve. Data models are not static entities; they are living structures that require adaptation to challenges such as changing business needs, technological advancements, data volume or variety, regulations, etc.

Data Modelling is a center of BI Activity or we can say all the BI work divided into 2 parts 1-Before data modeling and 2-After data modelling A well defined data modelling will not just insure smoothness of work but also help in performance issues. Basically In this step we define how all the tables will be related to each other their joining condition, creating a schema will help to avoid unnecessary burden on data. this cab be done on BI side or backend side on databases. Creating data modeling on backend side will definitely help as then BI tool wont have to process joining condition. Creating well crafted Stored Procedures or Views will help to avoid these step on BI side on you can start work with just single Table.

The data model is the heart of your BI project, it should be designed in collaboration with the business, technical(ETL Developers, BI Developers, Viz/UI Developers), and end users, the effective data model can not only help with your BI or reporting strategy, but it will also help with your whole data strategy, one of the thing that I'd like to mention is that the biggest asset companies hold today is data and if you cannot model the data to analyze that data is unused, effective data model will empower your business users and your whole company, please look into Ralph Kimball or Inmon for more details on different techniques

Irrespective of the BI tool you use having the sophisticated Data modeling is Key - It helps to have proper query performance. - It helps to maintain Data Integrity. - It helps to accurate Data analysis and Visualization - It is also scalable.

Your ETL is key in all of this. It’s how you will be making sure the information you’re importing into your BI tool is exactly what you need and of course, it’s precise. Some use the ETL to perform calculations to have the value of a KPI from here, and some do these calculations directly in the BI tool. It doesn’t matter the path you choose, bottom line is that if your ETL doesn’t work ok and accurately, you will be facing data quality issues, and errors that will take a while to find. Don’t disregard the importance of the ETL process

Datalakes are the silver bullet in the era of data. This is thanks to their inherent simplicity and flexibility. Nonetheless, each organization must vigilantly monitor its practices and protocols to forestall the transformation into a dataswamp. A dataswamp is the term used when an organization's data environment becomes inundated with massive amounts of unstructured and/or inadequately managed data. This data is frequently of subpar quality, lacking proper organization, metadata, or governance, and consequently becomes challenging to navigate, analyze, or employ effectively, unnecessarily amplifying the efforts needed to extract valuable information.

We could say that these four types of analytics represent a bidimensional progression. On one hand a temporal progression going from understanding historical data to predicting future outcomes and optimizing the results of the chosen actions. And on the other hand going from highly structured data to the realms of of unstructured complex data by the hand of ML & AI. Keep in mind that there will always be new tools and new frameworks and platforms, though the knowledge required to to tackle the understanding of the challenge is the real skill you are most likely to be able to transfer and the most valuable tool you have.

Data analytics requires quality data, tools, skills, clear goals, visualization, storage, cleaning, learning, collaboration, and ethics.Afterall you will gain insightful discoveries, informed decisions, and actionable results.

One of the biggest mistakes I see with Business Intelligence (BI) analytics, from stakeholders and analysts alike, is mistaking “business health” (KPI/metrics reporting) as “business intelligence”. To clarify, business intelligence is narrative, enlightening, and most importantly actionable. In my experience, this is what sets data analytics teams apart from real BI teams. Your analyses should be structured in a way such that you are telling a story backed by data, that reveals previously unknown information, that defines clear steps towards solutions. Don’t just present fun facts. Tell me what you know, how you know it, and what we can DO with that new knowledge.

Trabalhando como Analista de Inteligência de Negócios, priorizo estar sempre em desenvolvimento e atualizada as ferramentas disponíveis para realizar a inteligência de negócios. Atualmente, desenvolvo mais com o Power BI e portanto, estou estudando de modo aprofundado medidas DAX, modelagem de dados e transforma??o de dados para proporcionar insigths e análises mais avan?adas aos meus projetos.

Data Analytics is a vast area to be dwell and dive into, this branch concentrates on multiple streams like data cleaning, data modeling, governance, reporting, management. All these areas together would yield data analytics solutions to end users and choosing tools for a data analytics project depends on business requirements.

I think that before we even get into analyzing data, it is important to think about what data we do not have and whether the data we do have is adequate. Sometimes, the data is just not sufficient and a data gathering exercise would thus need to be started instead.

Asking the right questions is likely the most valuable skill of a data professional. While at a surface level a business process seems simple enough, the layers of semantics, end user preferences, business objectives, and technical feasibility, are all complexities to consider. Having a framework of questions included in your project management methods will help ensure you capture the information you'll need to commence a successful project. Secondly, iteration. Work with your stakeholders closely to understand their needs, and show them what you're thinking quickly and openly. Through these iterations and feedback, we can develop products that deliver value, create competitive advantage, and evolve with our use cases.

Absolutely agree with you Tim, I think asking open questions will fill the gaps between business and data analyst/insights developer. I enhance this questioning part should be a mandate step in the process. Moreover the iteration should be like a continuous interactions with business every month or maybe half yearly can bring in more effective decisions that business can make over the time with improved insights.

If you’re not leveraging Embedded BI and Ambient BI, you’re already behind! Integrating analytics directly into business applications is revolutionizing how we access and use data. It provides seamless, contextual insights right within the workflow, leading to faster and more informed decision-making. Plus, higher adoption rates mean more widespread data-driven decisions.

Natural Language Processing (NLP) is an advanced BI technique that focuses on understanding and interpreting human language. It involves the use of algorithms and AI to analyze and derive meaning from unstructured text data, including customer feedback, social media posts, and support logs. NLP techniques include text mining, sentiment analysis, topic modeling, and entity recognition. Ultimately, NLP empowers businesses to make data-driven decisions by extracting valuable insights from the vast amount of textual data available. It enhances the understanding of customer needs, helps identify market trends, improves customer service, and supports various strategic initiatives.