In todays digitized environment, the importance of data analytics as a tool for informed decision-making and strategic planning is paramount. From large corporations to parts of the healthcare industry, the ability to parse large quantities of data has become the key need for competitiveness and growth. Hence, right from machine learning algorithms to sophisticated visualization tools, the field of data analytics is evolving and new trends and innovations are being developed at a very high frequency.

Therefore, in this article, we will highlight the latest updates and trends that occur periodically to alter the field of big data analytics technology gently and explain why such changes are important in todays world.

Knowing that organizations highly demand quick ways to access meaningful insights from vast datasets, automate processes, and enhance decision-making capabilities. Therefore, AI algorithms enable advanced pattern recognition and predictive analytics, allowing businesses to forecast trends, customer behavior, and market shifts with unprecedented accuracy.

According to Gartner, by 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5x increase in streaming data and analytics infrastructures. Machine learning models automate complex data analysis tasks, speeding up decision-making processes and enhancing operational efficiency.

This synergy is not only improving traditional analytics but also paving the way for personalized recommendations in e-commerce where AI for personal finance assists consumers in managing their budgets, optimizing saving money, and making more informed financial decisions.

Real-time data analytics has become essential in todays fast-paced business environment where timely decisions can make or break opportunities. By processing data as it is generated, organizations can swiftly respond to customer needs, monitor campaign effectiveness instantly, and detect anomalies in operational processes in real-time. For instance, according to a study by IDC, organizations that use real-time data can achieve a 26% increase in revenue.

This capability is particularly valuable in sectors like online retail, where understanding customer behavior at the moment can drive personalized marketing strategies and optimize inventory management. Real-time analytics also supports dynamic pricing strategies and enhances overall customer experience through immediate feedback mechanisms.

Cloud-driven data analytics has democratized access to powerful analytical tools and computing resources, previously only available to large enterprises. Cloud platforms for data analytics technology offer scalability, flexibility, and cost-effectiveness, allowing businesses to scale their analytics infrastructure according to fluctuating demands. With features such as data integration across disparate sources and robust security protocols, cloud-based solutions enable seamless collaboration and data sharing across teams and geographies.

This shift has accelerated the adoption of analytics-driven decision-making across industries, from healthcare to financial services, to innovate faster and stay competitive in rapidly evolving markets. However, Services from ThingsFromMars.com exemplify how platforms process analytics and cater scalable solutions to grow swiftly.

Given the fact that the amount of data is increasing and the processes becoming more and more complex, Data Governance and especially data privacy, have become critical and strategic priorities of a large company. Therefore, to free the company from this risk and bolster customer confidence, data governance policies, and privacy controls have been put in place and occupy this position.

Modern NLP has unlocked new possibilities for converting vast amounts of unstructured data into meaningful insights. Organizations can now analyze social media posts, customer emails, and product reviews to understand sentiment, identify key entities, and discover emerging topics.

NLP-powered tools can automate the extraction of actionable information from text, enabling businesses to enhance customer engagement, improve service quality, and tailor marketing strategies based on real-time feedback. These advancements also facilitate more accurate trend analysis and competitive benchmarking to provide a deeper understanding of market dynamics and consumer preferences.

Edge computing decentralizes the analytics capability to where real-time insight is important and bandwidth is constrained. Faster processing, minimum time taken to transfer data, and enhanced protection of the processed data. Consequently, to analyze data for businesses, they can easily optimize data transfer costs, enhance data security, and explore other use cases of IoT solutions. Gartner predicts that by 2025, 75% of enterprise-generated data will be created and processed outside a traditional centralized data center or cloud.

However, using a Windows or Linux-based server can also be quite beneficial in the sense of Analytics since it is possible to manipulate data right on a server. Even more, you do not need to restrict the choice of automation software because you can install and utilize any utility.

Augmented analytics is democratizing data science by making advanced analytical tools accessible to non-technical users rather than only to seasoned IT professionals and data scientists. Through features like natural language processing, automated model generation, and intuitive data visualization, augmented analytics enables business users to perform complex data analyses without needing deep technical expertise. This self-service approach accelerates decision-making processes and fosters a data-driven culture across organizations. By empowering a broader range of employees to engage with data, companies can uncover new insights, drive innovation, and respond more swiftly to market changes.

Quantum computing holds the promise of transforming data analytics by tackling problems that are currently beyond the reach of classical computers. With its ability to process complex calculations at unprecedented speeds, quantum computing can revolutionize fields such as optimization, data clustering, and molecular modeling. This technology can enhance the precision of simulations, improve financial modeling, and provide breakthroughs in artificial intelligence research. As quantum computing continues to evolve, it is expected to open new frontiers in data analytics, enabling businesses to solve previously intractable problems and uncover deeper insights from their data. Moreover, according to a report by P&S Intelligence, the quantum computing market is expected to reach $64.98 billion by 2030.

As the various domains related to data analytics technology advance alongside evolving consumer trends, businesses are witnessing a transformative era in data power. From groundbreaking technologies in big data analytics such as AI and real-time analytics to edge computing and augmented analytics, the landscape of data analytics is continuously evolving. These advancements enable businesses to derive actionable insights, optimize operations, and drive innovation more effectively. Emerging forms are revolutionizing traditional approaches, allowing for more precise and dynamic decision-making processes.

Consequently, with the ever-increasing availability of data, organizations must actively contribute to new advancements to embrace cutting-edge technologies. This proactive approach will ensure they remain competitive in a fast-growing, data-driven world, and achieve a significant competitive advantage in the market.

Read more here:

Uncovering the Trends in Data Analytics & Technology - Data Science Central

Opinion And what you might actually want instead

People anchor their eyes on the next shiny thing. FOMO is part of human nature. And that applies to businesses, too. Like how Data Science became a craze for every businesss analytics function, Agentic Architecture is the red hot target on most AI radars.

Have you ever considered if you actually need it though?

Hi, I am Louis Chan, Tech Lead in KPMGs Global Lighthouse. It has been over a year since I co-founded KPMGs Enterprise GenAI-as-a-Service Platform KPMG AVA servicing over 18,000 users globally on all their GenAI needs. It is a fascinating time seeing how businesses and clients catch up to the latest catchphrases from AI, GenAI, Chat, RAG, Agent, to now Agentic Architecture.

This is an emerging field, and most of us (including me) do not know half of what we talk about. The general optimism that LLM can miraculously increase business efficiency and the fear of the impact and liability of cutting-edge technology drive the desire to gun for the next as-told, better, and more complex system.

The reality is that you dont need an Agentic Architecture, and you will be better off not using one in your business.

Excerpt from:

You Dont Need an LLM Agent. The Why & The Alternative | by Louis Chan | Jul, 2024 - Towards Data Science

Visualising AI project launched by Google DeepMind. From Unsplash image.

Gemini Pro can handle an astonishing 2M token context compared to the paltry 15k we were amazed by when GPT-3.5 landed. Does that mean we no longer care about retrieval or RAG systems? Based on Needle-in-a-Haystack benchmarks, the answer is that while the need is diminishing, especially for Gemini models, advanced retrieval techniques still significantly improve performance for most LLMs. Benchmarking results show that long context models perform well at surfacing specific insights. However, they struggle when a citation is required. That makes retrieval techniques especially important for use cases where citation quality is important (think law, journalism, and medical applications among others). These tend to be higher-value applications where lacking a citation makes the initial insight much less useful. Additionally, while the cost of long context models will likely decrease, augmenting shorter content window models with retrievers can be a cost-effective and lower latency path to serve the same use cases. Its safe to say that RAG and retrieval will stick around a while longer but maybe you wont get much bang for your buck implementing a naive RAG system.

Advanced RAG covers a range of techniques but broadly they fall under the umbrella of pre-retrieval query rewriting and post-retrieval re-ranking. Lets dive in and learn something about each of them.

Q: What is the meaning of life?

A: 42

Question and answer asymmetry is a huge issue in RAG systems. A typical approach to simpler RAG systems is to compare the cosine similarity of the query and document embedding. This works when the question is nearly restated in the answer, Whats Meghans favorite animal?, Meghans favorite animal is the giraffe., but we are rarely that lucky.

Here are a few techniques that can overcome this:

The nomenclature Rewrite-Retrieve-Read originated from a paper from the Microsoft Azure team in 2023 (although given how intuitive the technique is it had been used for a while). In this study, an LLM would rewrite a user query into a search engine-optimized query before fetching relevant context to answer the question.

The key example was how this query, What profession do Nicholas Ray and Elia Kazan have in common? should be broken down into two queries, Nicholas Ray profession and Elia Kazan profession. This allows for better results because its unlikely that a single document would contain the answer to both questions. By splitting the query into two the retriever can more effectively retrieve relevant documents.

Rewriting can also help overcome issues that arise from distracted prompting. Or instances where the user query has mixed concepts in their prompt and taking an embedding directly would result in nonsense. For example, Great, thanks for telling me who the Prime Minister of the UK is. Now tell me who the President of France is would be rewritten like current French president. This can help make your application more robust to a wider range of users as some will think a lot about how to optimally word their prompts, while others might have different norms.

In query expansion with LLMs, the initial query can be rewritten into multiple reworded questions or decomposed into subquestions. Ideally, by expanding the query into several options, the chances of lexical overlap increase between the initial query and the correct document in your storage component.

Query expansion is a concept that predates the widespread usage of LLMs. Pseudo Relevance Feedback (PRF) is a technique that inspired some LLM researchers. In PRF, the top-ranked documents from an initial search to identify and weight new query terms. With LLMs, we rely on the creative and generative capabilities of the model to find new query terms. This is beneficial because LLMs are not restricted to the initial set of documents and can generate expansion terms not covered by traditional methods.

Corpus-Steered Query Expansion (CSQE) is a method that marries the traditional PRF approach with the LLMs generative capabilities. The initially retrieved documents are fed back to the LLM to generate new query terms for the search. This technique can be especially performant for queries for which LLMs lacks subject knowledge.

There are limitations to both LLM-based query expansion and its predecessors like PRF. The most glaring of which is the assumption that the LLM generated terms are relevant or that the top ranked results are relevant. God forbid I am trying to find information about the Australian journalist Harry Potter instead of the famous boy wizard. Both techniques would further pull my query away from the less popular query subject to the more popular one making edge case queries less effective.

Another way to reduce the asymmetry between questions and documents is to index documents with a set of LLM-generated hypothetical questions. For a given document, the LLM can generate questions that could be answered by the document. Then during the retrieval step, the users query embedding is compared to the hypothetical question embeddings versus the document embeddings.

This means that we dont need to embed the original document chunk, instead, we can assign the chunk a document ID and store that as metadata on the hypothetical question document. Generating a document ID means there is much less overhead when mapping many questions to one document.

The clear downside to this approach is your system will be limited by the creativity and volume of questions you store.

HyDE is the opposite of Hypothetical Query Indexes. Instead of generating hypothetical questions, the LLM is asked to generate a hypothetical document that could answer the question, and the embedding of that generated document is used to search against the real documents. The real document is then used to generate the response. This method showed strong improvements over other contemporary retriever methods when it was first introduced in 2022.

We use this concept at Dune for our natural language to SQL product. By rewriting user prompts as a possible caption or title for a chart that would answer the question, we are better able to retrieve SQL queries that can serve as context for the LLM to write a new query.

See the original post here:

Advanced Retrieval Techniques in a World of 2M Token Context Windows, Part 1 | by Meghan Heintz | Jul, 2024 - Towards Data Science

PYTHON PROGRAMMING Learn when its worth chaining Pandas operations in pipes. 17 min read

The title of this article stresses the strengths and limitations of chaining Pandas operations but to be honest, I will write about fun.

Why fun? Is it at all important when we have data to analyze?

I dont know what works for you, but for me fun in work is important. During my 20+ years of experience in data science, Ive found that the more enjoyment I derive from coding, the more satisfied I am from completing the task. And I do mean the process of pursuing the task, not only just completing it. Of course, achieving results matters, probably the most. But trust me, if you dislike the tools youre using, all youll want is to finish the job as quickly as possible. This can lead to mistakes, as you might work hastily and overlook important details in the data. And thats something you want to avoid.

I transitioned to Python from R, and analyzing data with R is a lot of fun thanks to the dplyr syntax. Ive always enjoyed it, and I still do. However, when I switched to Python, I found myself preferring it over R. Ive never really enjoyed programming in R (note the distinction between analyzing data and programming), while

See original here:

Chaining Pandas Operations: Strengths and Limitations | by Marcin Kozak | Jul, 2024 - Towards Data Science

Image generated by AI by Author

ChatGPT is great, no doubt about that, but it comes with a significant drawback: everything you write or upload is stored on OpenAIs servers. Although this may be fine in many cases, when dealing with sensitive data this might become a problem.

For this reason, I started exploring open-source LLMs which can be run locally on personal computers. As it turns out, there are actually many more reasons why they are great.

1. Data Privacy: your information stays on your machine.

2. Cost-Effective: no subscription fees or API costs, they are free to use.

3. Customization: models can be fine-tuned with your specific system prompts or datasets.

4. Offline Functionality: no internet connection is required.

5. Unrestricted Use: free from limitations imposed by external APIs.

Now, setting up a local LLM is surprisingly straightforward. This article provides a step-by-step guide to help you install and run an open-source model on your

See the article here:

Running Local LLMs is More Useful and Easier Than You Think - Towards Data Science