Blog

Here, I have a mix of blog posts written by myself and from other authors in the realm of business analtics. With topics such as the role of business analytics in different industries and augmented analytics. I have included links to the original articles where applicable, which can be accessed by clicking on the image for each article.

Role of Business Analytics

11/09/2023

Business analytics can be defined as leveraging value from data. Data can detect new opportunities and niches in the marketplace, which can develop new products and services and aid the business in decision-making. (Acito et al., 2014). It involves using statistical and quantitative data and predictive and explanatory models. This data is turned into insights to aid the business in decision-making (INFORMS, 2016). There are many possibilities for the application of analytics in business, technology, and popular media. For example, It is often used to identify trends in employee turnover to allow the firm to intervene. Also to segment customers in order to effectively market towards loyal buyers.

Data Analytics in the music industry

14/10/2023

Today I decided to research into the application of data analytics in the music industry. I learned that music services such as Spotify can recommend songs based on user preferences, and Shazam can identify songs by comparing sound snippets to digital databases. (Acito, F et al., 2014). ...Firms like Indify use data analytics to identify artists with high engagement on social media. This firm predicted the rise in popularity of the singer Khalid by analyzing his cross-platform growth. This involves growth across social media sites like YouTube, Instagram and Spotify (Complex News, 2019). Data analytics are also often used by labels to decide what platform an artist’s music is most popular on.

Microsoft Office Visit

19/02/2024

This week I had the opportunity to attend a Power BI workshop in the Microsoft Office in Dublin. Our group had a class with a junior employee where she showed us how to use Power BI. ...She showed us the colours to use to make a more professional presentation, usually cool blues, and which graphs are better to show which insights. She also promoted the ‘copilot’ tool which can aid us in creating valuable insights. We then had a panel of experienced Microsoft employees to whom we could ask questions. This was an extremely valuable experience. After this, we were given a tour of the office. My favourite part was the digital waterfall in the entrance.

What is Augmented Analytics? A Beginner’s Guide

21/03/2024

I read an article from Austin Chia who explained augment analytics. Augmented analytics is the use of artificial intelligence (AI) and machine learning (ML) techniques in the process of data analysis. It goes beyond traditional business intelligence (BI) by automating data preparation, insight generation, and even decision-making. ...This means that augmented analytics tools can help users explore and analyze large amounts of data in a faster, more efficient, and accurate way. With this new technique, users can uncover hidden patterns, trends, and insights that may not be easily visible through manual analysis. Augmented analytics uses AI and ML algorithms to process and analyze large datasets. These algorithms are designed to learn from the data, identify patterns, and make predictions or recommendations. This is tools like Tableau, and Power BI.

Becoming a Sustainability Analyst: How to Do it in 2024

21/12/2023

Today I read an article by Elliot Gunn about being a sustailabiloty analyst. A sustainability analyst combines expertise in data analysis with a deep understanding of environmental and social responsibility. Sustainability analysts assess and interpret a wide range of data, everything from environmental impact studies to corporate social responsibility (CSR) initiatives....Becoming a successful sustainability analyst requires a combination of education, technical skills, and soft skills. To be successful in this field requires staying on top of these trends and learning technical best practices to produce data-driven insights for positive change. Continuous learning is especially important in rapidly evolving fields like this one.

How To Use AI To Empower Your Data Analytics Workflow

20/12/2023

Today I read an article by Natalia Tomenko about using AI. Whether it’s with augmented analytics or just through AI-boosted tools nowadays, embedding AI data into your workflow is a matter of business survival in competitive conditions. In this article, you’ll find out how data analysts can use AI to enhance their existing work routines, without any coding skills and rebuilding of systems....First, let’s review the main ways modern AI is used in data science and analytics.The first one is to use AI-powered analytics platforms. They have tons of functionality to help perform data analysis from start to finish. Those include Tableau, Microsoft Power BI, and RapidMinder for example. The second one is using AI tools meant for creating models and doing predictive analysis. This is quite an advanced topic, so if you’re interested in it it’s probably worth checking out our full guide to AI data analytics tools.

Machine Learning in Production — How to Operate a Model factory

20/11/2021

Today I read an article by Julien Kervizic about Machine Learning in Production. He wrote about Understanding the Machine Learning lifecycle. There are multiple phases within a machine learning model lifecycle, from data collection to data cleansing, experimentation, debugging, or deprecation. Building a model factory means simplifying and automating each of these steps as much as possible to gain the fastest pace of iteration....When looking at leveraging data to power an ML model factory, there are different things to consider. What initial data will be fed for the model training? How will the various labels be populated? What will be the metrics needed to monitor the model’s performance? Approaches such as DMAIC from SixSigma methodology provide a Data collection plan as their empirical approach to process improvement and problem-solving. Most Machine Learning initiatives attempt to optimize processes and (should) embody similar improvement process methodologies.

Expert Interview: Beyond the Buzzword – Understanding the Ethical Implications of AI

27/11/2023

Today I read an article by Matthew Deery about the ethical implications of using AI. This was a blog post about an interview with Willliam Tracy a North Carolina native with a masters and BS in mechanical engineering, and a BA in toy design, an expert in linkage design and optimization....When asked 'What are the ethical implications of using synthetic data in training models?' He said it’s best to always consider where the data comes from in the first place. One solution to get around using ethically-fraught real-world data is to use a set of synthetic data instead. Synthetic data is said to remove ethical issues, as the data is generated and not real. However, it’s still important to consider how the data was created and if there was inherent bias added while it was created. While running the data, it’s possible for there to be increased model error in the final product, because synthetic data doesn’t capture all real-world aspects and uses. Finally, if the synthetic data doesn’t capture real-world issues, this may allow for unforeseen ethical issues when the model is finally exposed to real data.

Data Analyst Training

No Date

Today I decided to research the data analyst profession. Here is a snippet of an article by Thinkful.Data analysts are professionals who process raw data related to products, customers, and a company’s performance. Then, they translate this data into an easy to understand format for business stakeholders. ...Once they’ve seen the data, their job is to gather insights from it. Once these insights have been made, business managers are able to make informed decisions to benefit the business.As more businesses realize the importance of using data to understand their market and customers, the demand for data analysts has grown significantly. More and more organizations are using data analytics to help their businesses grow.

Data Analyst Tools

No Date

Today I decided to research the most commonly used tools in the data analyst profession. Here is a snippet of an article by Thinkful.Once you have centralized your data, you can get down to the business of analyzing it for business insights, which you will then use to plan strategies that will help grow your business. Some of the most powerful and user friendly data analysis tools include Power BI, Sisense, and SAP BusinessObjects ...Microsoft Power BI is one of the best business insight platforms for data analysts. It can support a broad range of data sources, and allows you to easily group assorted reports and dashboards into an app for easier distribution. Sisense is a platform that aids in the processing and visualization of almost all your business data. It's quite popular among more technical developers and data analysts who are no doubt enamored by the arsenal of drag-and-drop tools and dashboards that facilitate interaction and collaboration.

Python for Business: Optimize Pre-Processing Data for Decision-Making

13/01/2022

Today I found an article by Kayla Matthews about the importance of the use of python for pre-processing in the business. Here is a snippet: Comprehensive data processing requires robust data analysis, statistics, and machine learning. As a high-level, open-source programming language, Python possesses a firm grip over these functionalities. Consequently, Python has become one of the most efficient instruments for data preprocessing. ...Python has many libraries like NumPy, suitable for machine learning, and supports high-level mathematical functions, making it great with algorithms. It’s completely free to use and distribute for both private and commercial use. Python has some built-in data analysis tools that make the job easier for you. Also, you can use ML-powered algorithms, utilize versatile data mining, and many other very efficient functionalities that make it very flexible and productive to use for Data Preprocessing.

Is Your Business Primed For Growth? How Data Can Address Internal Barriers

6/07/2018

Today I found an article by Larry Alton about How Data Can Address Internal Barriers. Here is a snippet: In the past, many companies hired consultants to gain an outsider perspective on day-to-day practices, but today you can maximize professional insights by turning to business intelligence (BI) tools.The two most important tools are 'Ramping up retention rates' and 'Assessing group dynamics'. ...Retention rates: Whenever an employee leaves your company, no matter the circumstances, they need to take an exit survey. As you bank exit interview data, you begin to get a sense of what you could have done differently to keep valuable staff on board and save your company money. Group Dynamics: Your employees are your greatest asset, and you shouldn’t let your business stagnate because you’re not fostering their talents and relationships. Instead, add some structure to the human element of your operations and take a data-driven approach to HR. It can put your business on the road to an exciting future.

Deep Learning vs. Machine Learning: What Enterprises Need to Know

No Date

I decided to research into the difference between deep learning and machine learning.The terms deep learning and machine learning are sometimes used interchangeably, but that's a mistake. They're closely connected, but not synonymous: Deep learning shares machine learning's core capability of improving its performance without human intervention, but it's much more structurally complex....Machine learning is an offshoot of artificial intelligence that uses algorithms to examine large data sets and learn from them, without any human intervention aside from the initial programming and training process. Deep learning is an advanced subset of ML. It's distinguished by its interconnected and at least triple-layered series of nodes known as an artificial neural network (ANN). The various layers in a deep learning model allow it to process massive amounts of structured, unstructured, or semi-structured data and analyze it with a degree of complexity modeled after the human brain.

Machine Learning for Fraud Detection: Fundamentals and Benefits

No Date

I found an article which outlined the benefits of machine learning for fraud detection. Here is a snippet. For the purposes of fraud detection, ML implementation begins with the training phase in which the machine learning algorithm is "taught" how to recognize the signs of a bogus transaction. ...Detecting fraud amid all of that simply isn't possible without high-level automation, due to the volume of data involved. A modern machine learning system is not only built to handle such data volumes, but also to thrive and improve its performance as it ingests and processes more information.5 key anti-fraud ML use cases: Phishing detection, Identity theft prevention, Debit and credit card fraud, Payroll fraud mitigation, and Forgery detection.

Fight financial crime with fraud analytics

No Date

This post is from the teradata website about fraud detection. Here is a snippet. In the technology-driven business world, data security and integrity have never been more important. This is particularly true in sectors commonly targeted for fraud, including banking, financial services, and healthcare. The term fraud analytics refers to all the core data analytics and data science methods, tools, and best practices that are used to stop and mitigate financial fraud. ...The most advanced analytics tools used to detect major types of fraudulent activity automate most of their processes. This is only achievable at scale through artificial intelligence (AI)—often specifically through machine learning (ML) tools. All major ML training methods can be leveraged to prepare optimal analytics solutions for fraud detection. Supervised learning, for example, is beneficial for engineering traditional banking, insurance, and healthcare fraud analytics systems, all of which rely heavily on large volumes of historical data. By contrast, newer financial methods, such as open banking, won't have amassed as much data from the past, so unsupervised learning makes more sense for fraud detection in those contexts.