What advanced analytics techniques do you use in your BI projects?

Machine learning algorithms are employed to analyze data and uncover patterns, even in complex and large datasets. BI projects may use machine learning tasks such as customer segmentation, fraud detection, recommendation engines, and sentiment analysis. NLP techniques are used to extract insights from unstructured text data, such as customer reviews, social media comments, and emails. NLP can be applied in BI to perform sentiment analysis, topic modeling, and text categorization. Incorporating a chatbot into your BI environment can enhance user engagement, accessibility, and overall BI adoption. It can transform how users interact with data and provide real-time insights to support informed decision-making across your organization.

Diving into advanced analytics for Business Intelligence is like having a superpower in the data game! Data mining digs up hidden gems, predicting trends is like having a crystal ball, and text mining turns words into gold. Stats? They're not just numbers; they tell stories. Geospatial adds a cool location twist, and visualizations turn data into stories at a glance. Big data tools handle the heavy lifting, while prescriptive analytics helps us nail the perfect outcome. These tricks aren't just choices; they're the secret sauce in the data-driven world, making BI not just about numbers but about rocking innovation and smart decisions.

The choice of advanced analytics techniques in a BI project depends on the specific objectives, the nature of the data, and the requirements of the business. The techniques like Data Visualization, Prescriptive Analytics, Data Integration, Data Mining etc. can help organizations gain a competitive edge by making data-driven decisions and extracting valuable insights from their data. BI projects often leverage a variety of advanced analytics techniques to extract meaningful insights from data. Always looking for new ways to use advanced analytics to help the clients achieve their business goals.

Data mining is like a data adventure. It helps you find valuable insights in your information treasure chest using techniques like classification, clustering, and anomaly detection. With data mining, you can understand your customers better, spot your most valuable items, and even uncover any hidden issues or fraud. It's the key to making smarter decisions with your data.

No Tool or Technique can provide a relevant solution unless you are clear of the question, query or objective. Have you even seen Google giving you a prompt without you even asking it? Similarly the success of tools/ techniques depends upon the business problem to solve. Depending upon what the business needs, techniques like Visualization, AI/ML, NLP or Big Data have to be shortlisted

Machine Learning in simple world is like teaching a children to learn how to walk,talk and perform task, Continuous learning improve its efficiency and after that you have just to provide dataset based on its efficiency it enhanced your ability in every Area and sector of IT sectors surrounding us

Machine learning (aka statistic) is a powerful tool for finding correlation, among other things, in your data. Whether you’re trying to find correlation between categories or dependent and independent variables, you can use machine learning algorithms to uncover insights you never knew existed. For example, want to know what factors lead to someone signing up to a newsletter? Well with a logistic regression model you can estimate the probability of new sign ups based off how long the user spends on your homepage.

Machine learning makes life easier to identify recurring patterns in large datasets. It is a constantly evolving process which needs monitoring on a regular cadence. Different industries require different techniques depending on market volatility, product requirements and infrastructure constraints. It needs certain business acumen to decide what goes into these models to have the +ve business impact

Your explanation of machine learning is spot on. It's a transformative approach to computer programming where algorithms learn from data and improve their performance through experience. The practical applications are vast, ranging from predictive modeling and recommendation systems to process optimization and automation. Machine learning has the potential to revolutionize various industries by enabling data-driven decision-making and delivering personalized experiences to customers.

ML for data quality, mostly for record linkage! One can also use ML to detect potential DQM issues via detection of outliers, worst predictions.

I used word clouds five years ago and it was surprising how quickly you could navigate the data. NLP allows you to do this with more data and better insights with less manual intervention. Power BI added some of this a while ago allowing you to use NLP questions as a co-pilot say "why are our YoY sales down in X?" I expect this to grow into full reviews based on expanded data automatically collated identifying patterns from industry experts.

Incorporating predictive analytics approaches like machine learning algorithms, regression analysis, and time series forecasting into business intelligence initiatives has proven to be beneficial, based on my experience. With the aim of assisting in strategic decision-making, these methodologies reveal latent patterns, predict future trends, and generate data-driven forecasts. To further increase the efficacy and value of BI solutions for clients, I also use techniques like clustering and anomaly detection to find important insights inside big datasets.

You've provided an excellent overview of natural language processing (NLP). It's a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. The techniques you mentioned, such as tokenization, sentiment analysis, and topic modeling, are fundamental to NLP's ability to extract meaning and context from text and speech data. The applications of NLP are diverse, from automating text analysis tasks to developing conversational AI systems like chatbots and voice assistants. It's a pivotal technology in making sense of the vast amount of natural language data generated today.

Natural Language Processing (NLP) ist eine Technologie, die sich mit der Analyse und Erzeugung von Text und Sprache mithilfe von natürlicher Sprache besch?ftigt. Sie umfasst Methoden wie Tokenisierung, Stammformreduktion, Lemmatisierung, Parsing, Sentiment-Analyse und Themenmodellierung, um Text- und Sprachdaten in ihrem Kontext und ihrer Bedeutung zu erfassen. NLP wird eingesetzt, um aus Kundenbewertungen, Rückmeldungen oder Umfragen wichtige Erkenntnisse zu gewinnen, Zusammenfassungen zu erstellen oder Berichte und überschriften aus Daten zu generieren. Darüber hinaus erm?glicht NLP die Entwicklung von Chatbots, Sprachassistenten und Konversationsschnittstellen, die eine interaktive Kommunikation mit Kunden und Mitarbeitern f?rdern.

NLP can help a lot when there is difference in the wording but the synonyms are meaning the same, such as salary and wage. But if you complicate enough with the negations such as not as this, but not, as this - you are going to have a problem.

Data visualization is the easiest method to display the results of the analysis to the stakeholders. It empowers them to clearly understand the story happening behind the scenes using the collected data. There are ample tools available in the market for data visualization such as Tableau, Power BI, Qliksense, etc. Every tool has it's pros and cons. Just like you compare different phones before buying one, an organization needs to know which tools would best suit their scale and necessities. It's almost mandatory these days to have one of the above tools and not just rely on MS'Excel for the reporting needs. It's also opening up a lot of job opportunities for skilled people in this domain.

Data viz isn't just pretty charts – it's storytelling! ??? Recently, we revamped our client portfolio reports using Power BI. Gone are the days of mind-numbing spreadsheets! Now, risk exposure is a vibrant heatmap, and asset allocation? A sleek treemap that even my grandma could understand! Our crowning achievement? An interactive dashboard showing portfolio performance against life goals. Seeing retirement dreams as a filling progress bar? Game-changer! ?? Of course, we had our "oops" moments. Our first attempt at 3D charts made bull markets look like cliff dives. Talk about mixed signals! ?? Remember, in BFSI, good viz turns data into decisions. Keep it clear, keep it relevant, and watch those AUMs grow! #WealthTechViz

The goal of a good visualization is easy to comprehend and intuitive. All the elements within the visualization should tell a story and outcome - positive or negative.

I usually use these technique in order to tell a story based on business data. In my opinion this is the key for use data visualization.

For DataViz there is an invaluable thing - dimensionality reduction, especially if you work with high dim datasets and you want just a general overview. Another thing - ability to extrapolate simple processes... though I'm still to discover usable tools for such tasks.

More than 4 quintillion bytes of data is generated every year, More than 5 Millions emails send every second, Social Media post 9 Millions across different Platform, 800 hours of video were uploaded on YouTube per minute, these are some basic number the corporate world data and data in closed loops is beyond our imagine. So in order to analysis these kind of Data we need data lakes, layers of warehouse and data mart in order to convert this data into information we used Big Data Analytics terminology

Additionally, big data analytics enables the identification of complex patterns and hidden trends in data, providing valuable insights for strategic decision-making. By integrating data from different sources and formats, organizations can create a more comprehensive and accurate view of their business and the environment in which they operate. This not only increases operational efficiency but also drives competitiveness by allowing for real-time customization of products and services according to customer needs. Ultimately, the effective use of big data analytics can transform organizations, driving innovation, efficiency, and growth.

Uma das técnicas de análise que n?o pode mais ser ignorada é o uso de IA generativa. Hoje, é possível que uma pessoa sem conhecimento profundo em Inteligência Artificial (n?o faz ideia do que sejam modelos clássicos, Scikit-learn, PyTorch ou tensores), utilize e aproveite as vantagens do avan?o das IAs. Ferramentas como o playground da Open AI permitem que você coloque a IA generativa para executar trabalhos que seriam feitos por humanos, teste-os e ainda gere o código necessário para implementa??o, completamente pronto, na linguagem que você desejar. As empresas que se aproveitam disso no seus processos de BI já est?o ganhando vantagem competitiva no mercado.

Hey there! I wanted to share a little tool I've been using lately - Proposal Assistant. It's an AI-powered gem that helps me whip up technical writing into polished proposals, all right in Google Docs. Honestly, it's been a game-changer, saving me heaps of time while keeping quality top-notch. No big boasts here, just a handy trick I thought you might find cool too!

AI in finance? It's like giving your data a PhD! ???? First AI model we deployed felt like science fiction. Now? It's our bread and butter! ???? Pro tip: Use Power BI's AI Insights. It's like having Einstein as your data analyst! ???? Remember when our AI spotted that micro-trend in forex? Cha-ching! ???? In BFSI, AI isn't just fancy tech - it's our crystal ball and superhero rolled into one! ????♂? Don't just analyze - let AI synthesize. Every data point's a neuron in our digital brain! ???? Finance without AI? That's like a calculator without batteries. Power up! ????

All data platforms/tools (even the most mature, powerful, and innovative ones) have bugs and missing features/capabilities. However, many/most can be fixed/extended/improved with external/third-party tools. Example: Power BI is an extremely powerful, feature-rich, and innovative data modeling/visualization tool, but if you want Calculation Groups or Object-Level Security in your model, you'll need an open-source tool called Tabular Editor, which can connect to either Power BI Desktop or the Power BI web service and add those [supported but hidden] features to your Analysis Services Tabular dataset. Same goes for Tableau, Looker, Qlik, etc. Every data platform/tool has bugs and limitations. Use external/third-party tools to overcome them!

Outcome is always key - what does your stakeholder need, what is their requirement and why. Don’t try and force different tools or methods into solving a problem, understand the problem and select the right tools for the job.

It starts with stakeholders- defining who are your stakeholders, which function they belong to and what action they will take. Any visualisation will show you insight but getting ‘actionable’ insight is the key and that requires a deeper understanding of problem. Tool, tech stack all can be decided later on as well.

Tool selection is really important for all topics in the Business Intelligence space!! What software are you guys using? Any comments or observations from custom developments could be interesting to explore as well..

First of all it's important to say that BI is intrinsically linked with : - management of the universal data model and data governance catalog - data quality - reference data and master data management These may be evident, but still considered advanced techniques. Moreover, most projects don't use a unified centralised consolidated view as an intermediate step in data processing for DWH. I don't know, why. It is a very useful strategy for more or less complicated projects and it pays later in simplified data flows.