How Do You Define Unfair Bias in AI?


Art is subjective and everyone has their own opinion about it. When I saw the expressionist painting Blue Poles , by Jackson Pollock, I was reminded of the famous quote by Rudyard Kipling, “It’s clever, but is it Art?”

IT 109

Why Data Driven Decision Making is Your Path To Business Success


We read about it everywhere. The term ‘big data’ alone has become something of a buzzword in recent times – and for good reason.

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The quest for high-quality data

O'Reilly on Data

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners.

Tukey, Design Thinking, and Better Questions

Simply Statistics

Roughly once a year, I read John Tukey’s paper “The Future of Data Analysis” , originally published in 1962 in the Annals of Mathematical Statistics. I’ve been doing this for the past 17 years, each time hoping to really understand what it was he was talking about.

How to Solve 4 Common Challenges of Legacy Information Management

Speaker: Chris McLaughlin, Chief Marketing Officer and Chief Product Officer, Nuxeo

After 20 years of Enterprise Content Management (ECM), businesses still face many of the same challenges with finding and managing information. Join Chris McLaughlin, CMO and CPO of Nuxeo, as he examines four common business challenges that these legacy ECM systems pose and how they can be addressed with a more modern approach.

How to Perfect Your Data Culture Recipe


One of the first questions new clients generally ask us is: “How do we maximize the value from our data?”. Data Strategy

Waterfall to Agile: A Necessary Mindset Shift For Business Analysts

BA Learnings

I tend to describe the agile approach as a way of working; A targeted way of working that allows us to make changes, respond to customers’ needs and manage uncertainty with minimal delays, and without needing to wade through “red tape”.

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Strategy is Unleashing the Potential of Enterprise Data


Enterprises across the globe are waking up to the fact that data is an asset that requires its own strategy. Those that treat it as such are now seeing substantial returns on their investments. Data Strategy

Analytics and Business Intelligence for a Data-Driven World

David Menninger's Analyst Perspectives

Ventana Research provides unique insight into the analytics and business intelligence (BI) industry.

Research quality data and research quality databases

Simply Statistics

When you are doing data science, you are doing research. You want to use data to answer a question, identify a new pattern, improve a current product, or come up with a new product.

Automating ethics

O'Reilly on Data

Machines will need to make ethical decisions, and we will be responsible for those decisions. We are surrounded by systems that make ethical decisions: systems approving loans, trading stocks, forwarding news articles, recommending jail sentences, and much more.

The Magic of Intent: Start Knowing The Goals of Your Users

Speaker: Terhi Hanninen, Senior Product Manager, Zalando, and Dr. Franziska Roth, Senior User Researcher, Zalando

It's important to know your users - what are their preferences, pain points, ultimate goals? With user research and usage data, you can get a great idea of how your users act. The tricky part is, very few users reliably act the same way every time they use your product. Join Terhi Hanninen, Senior Product Manager, and Dr. Franziska Roth, Senior User Researcher at Zalando, as they explain how they were able to reach a new level of user understanding - by taking their user research and segmenting their users by point-in-time intent. You'll leave with a strategy to change how your product team, and organization at large, understands your users.

5 Weird and Hilarious Uses of Data Science

Analytics Vidhya

Introduction “Ripley’s Believe or Not” features some of the weirdest and most bizarre facts from around the world. How about creating our own Ripley’s. The post 5 Weird and Hilarious Uses of Data Science appeared first on Analytics Vidhya.

Top 5 Tips For Conducting Successful BI Projects With Examples & Templates


BI projects aren’t just for the big fishes in the sea anymore; the technology has developed rapidly, the software has become more accessible while business intelligence and analytics projects implemented in various industries regularly, no matter the shape and size, small businesses or large enterprises. With the help of online data analysis tools , these kinds of projects have become easy to manage and agile in performance. But sometimes, they can also be tricky: it’s not just pushing one button and expecting your business intelligence to fly over a rainbow. To truly harness the power of a successful BI project, companies must develop a solid plan of action and in this post, we will provide the top tips for developing and executing analytics and BI projects with the help of BI tools, followed by business intelligence examples from different industries. Ultimately, this will bring a solid level of understanding and indispensable potential that business users could implement in their own working environment. From Fortune 100 companies to small business owners, BI tools and technology are becoming the standard to oversee historical, present, and future data of business operations. But what makes these projects successful and what to look out for? We will cover this question, and much more, but first, let’s start with a bit of background. Exclusive Bonus Content: Do you know what is a success in BI? Get the short and sweet version with our free executive summary! What Is A BI Project? A BI (business intelligence) project is a term used to describe the planning, assessment, development, and implementation of business intelligence in a company, mainly BI tools that will help managers to solve business problems and derive actionable insights. These projects require cooperation between various company’s processes, technology objectives, and data while contributing to set business goals, usually defined by a detailed business intelligence strategy. Top Tips To Create A Modern BI & Analytics Project. To get started in this journey, here are the top 5 tips to successfully create a BI project. 1. Create a solid BI project plan. It is of utmost importance to create a compact BI project plan that you can refer to periodically and track your progress. When conducting business intelligence projects, the more information you gather at your starting point, the better controlling you will have during the process. Involve relevant stakeholders and answer questions such as who will work with the BI? Is it intended for analysts, C-level executives or department’s managers? You can also conduct interviews and ask each relevant person directly to avoid communication issues between departments after the project and online BI tools are already implemented. This kind of structure will ensure proper foundation so later you won’t have to face pitfalls and misinterpreted information. 2. Define goals and objectives. In correlation with the planning processes, defining your endgame and setting the right KPIs will create success. While there are numerous KPI examples you can choose from, only a few of them will help you answer specific business questions. If you work in finance, financial analytics will be the backbone of your operations. On the other hand, if you’re in the HR industry, then an HR dashboard could be the best answer you’re looking for. The essential element in this step is to be able to answer in what way your company or organization makes business decisions, and how the quality of these decisions is measured. Another useful advice is to start small; you have to walk before you can run. 3. Clear the clutter and define a timeframe. After you have established your plan and defined goals, it is time to clear all the information clutter and define a timeframe. To be able to fully reap the rewards that an analytics project and BI can deliver to your organization, it is not just significant to own the KPI management process. By now, you should have already identified business questions you need to answer, and it’s time to get your hands dirty. As the old saying goes, timing is everything , so make sure you develop a schedule for implementing and approving all the relevant processes. Do you need one month or six months to finish the project and start using the BI tool? If you’re confronting setbacks, it might be useful to engage with additional business intelligence consulting to be on the safe side. 4. Concentrate on technicalities. At this stage, you have developed your plan, set the timeframe, identified and communicated with relevant stakeholders, and now it’s time to choose the right dashboard tool. Since every project initiative is different, it would be wise to establish a framework on what you need from a tool. What kind of database you’re currently working with and do you need various data connectors to unite all your flat files, databases, marketing analytics, social media, etc. Working with the right partner that can deliver all your requirements is an invaluable choice and you should be able to choose based on your budget and scope of the project. Keep in mind that BI and analytics projects are business programs in their core. You will need the help of the IT department, but the “business first” perspective will make your life easier and focus on the big picture. 5. Implement your BI solution and measure success. Our final tip to develop a proper BI and analytics project focuses on the implementation and measuring the success of your initiative. It is often hard to evaluate and quantify the level of success of utilizing a BI solution, but a simple calculator as shown below can provide you with an idea of how much you can save each year: To see the full scope of the calculation, you can visit our business reporting page. We have answered the question what is a BI project, provided a roadmap of tips you need to follow in order to successfully implement such initiatives, and now we will focus on real-life business intelligence projects examples and templates that made companies’ processes more productive, saved costs, and increased efficiency. Exclusive Bonus Content: Do you know what is a success in BI? Get the short and sweet version with our free executive summary! Real-Life BI Projects Examples And Templates. Here are shining examples of real-life business scenarios in which a BI and analytics project is used to improved efficiency, productivity, and enabled smarter decision-making processes in their operational and strategic efforts. 1. US-based financial services provider. Requirements : Real-time access to vast amounts of data. Fast implementation. Availability to all managers. Maximum security and data privacy. Reducing the reporting time. Challenges : Reducing IT involvement. Decentralizing the decision-making processes from one person to 10. Facing the challenges of poor data quality, dispersed through a number of spreadsheets and databases, this financial company was unable to track financial data in real-time and generate valuable insights needed to ensure their vendor payment, managed by the accounts payable department, is accurate and fast. They have already experienced a few business disputes and wanted to avoid such scenarios in the future. Additionally, they wanted more control over their working capital and the cash conversion cycle data in order to increase management productivity and operations. After deciding to implement a business analytics project with the help of a data dashboard , their efficiency skyrocketed. We can also see below a visual business intelligence project template which can be used in any finance department or company: **click to enlarge**. The final result was reducing the time of comprehensive financial reporting processes, automating calculations and gaining access to data in a single, central location. A testament to the supremacy of using a financial dashboard to enhance internal performance. 2. Human resource department in a corporate setting. Requirements : Improving recruitment methods. Self-service access to information. Budget-friendly. Multidimensional analysis. Automating processes. Challenges : A comprehensive view of the entire recruitment process. Performance of the team should be tracked on a weekly basis. Providing a foundation for weekly meetings. This is one of our business intelligence projects examples that expound on the HR level in a corporate setting in the US. The company struggled with their recruitment funnel and didn’t have up-to-date information on the costs, turnover rates, and top performing agents that can share their knowledge and educate the rest of the team. The final BI project template looked similar to this visual: **click to enlarge**. The manager gained a clear, birds-eye view of the department’s performance and crucial HR KPIs that provide instant insights through the employment of a powerful BI solution. Their reporting process was time-consuming and employees were facing challenges with weekly meetings when they needed to provide accurate data and deliver fast responses. By utilizing a comprehensive HR dashboard, every stakeholder had an interactive visual which they could access any time, from any device, and decrease the amount of time needed to generate HR reports. The automation of the reporting process enabled more efficient time management which employees could use to perform other relevant HR tasks. Another testament to the power of using HR analytics tools. Exclusive Bonus Content: Do you know what is a success in BI? Get the short and sweet version with our free executive summary! It’s Your Turn! To summarize, here are the top tips for creating a successful BI project: Create a solid BI project plan. Define goals and objectives. Clear the clutter and define a timeframe. Concentrate on technicalities. Implement your BI solution and measure success. Now that we have provided BI projects examples and templates that professionals and managers can use for their own purposes. Creating business analytics projects by following our tips and leveraging these examples to your advantage can create a much more stable business and better performance level. To see it in practice, you can start creating your own projects with our BI tool, for a 14-day trial , completely free! The post Top 5 Tips For Conducting Successful BI Projects With Examples & Templates appeared first on BI Blog | Data Visualization & Analytics Blog | datapine. Business Intelligence Data Analysis Reporting

KPI 283

How to overcome reporting challenges in your business


The ability to generate accurate, relevant and timely reports is critical if a company is to remain competitive in today's marketplace. However, as many executives know, traditional (static) reporting methods have a range of shortcomings.

The Top Seven Technology Trends for 2020


We have reached the end of 2019 and just like in previous years, I am looking ahead to see what organisations can expect next year.

Encouraging Innovation in an Established Product Culture

Speaker: Richard Cardran, Chief Creative Officer and VP Strategy, HIA Technologies

Innovation is both a process and an outcome. The best way to begin innovating your products is by innovating your internal process. We'll explore the challenges, solutions, and hands-on techniques for becoming a successful "agent of change" within a well-established product culture. We'll examine the importance of UX and user-centric feature analysis, the adaptation of Agile Methodologies to the creative process, as well as a way to drive successful culture change for setting expectations and winning approvals with cross-functional stakeholders. Innovation and Leadership go hand in hand. Join Richard Cardran, Chief Creative Officer and VP Strategy, HIA Technologies, as we assess some case studies to see how to lead with a clear strategy well-defined tactics, and an unbiased understanding of the fundamental question: "why are you innovating?"

Glossaries of Data Science Terminology

Rocket-Powered Data Science

Here is a compilation of glossaries of terminology used in data science, big data analytics, machine learning, AI, and related fields: Glossary of common Machine Learning, Statistics and Data Science terms. Data Science Glossary on DataScienceCentral. Data Science Glossary.

Laying the Foundations for AI Success


AI is now well into its ‘early adoption’ phase, with businesses throughout the Middle East and Africa clamoring to launch new initiatives. AI & Machine Learning

Game (Theory) for AI? An Illustrated Guide for Everyone

Analytics Vidhya

Overview What is Game Theory? And how does it apply to artificial intelligence (AI)? Game theory for AI is a fascinating concept that we. The post Game (Theory) for AI? An Illustrated Guide for Everyone appeared first on Analytics Vidhya.

Want to Build Machine Learning Pipelines? A Quick Introduction using PySpark

Analytics Vidhya

Overview Here’s a quick introduction to building machine learning pipelines using PySpark The ability to build these machine learning pipelines is a must-have skill. The post Want to Build Machine Learning Pipelines? A Quick Introduction using PySpark appeared first on Analytics Vidhya.

Embedding Operational Reports: Everything Product Managers Should Know

Speaker: Dean Yao, Sr. Director of Product Marketing, Logi Analytics

Businesses are run with analytics - but companies continue to struggle with interpreting, analyzing, and distributing data. Operational reports help get information to the people who need it most, in formats they understand, and in a timeframe that matters. Join the webinar to learn how embedding operational reports can give your users a precisely formatted, ready-to-analyze view of their operational activities. World-class software teams are embedding operational reports to empower end users with interactive data visualizations, detailed information, and highly precise formats that can be shared via email, PDF, print, or online.

Data Management on Display at Informatica World 2019

David Menninger's Analyst Perspectives

This year, I attended Informatica World 2019, Informatica's annual user conference. The main focus this year was on the cloud with a heavy does of AI.

You can replicate almost any plot with R

Simply Statistics

Although R is great for quickly turning data into plots, it is not widely used for making publication ready figures. But, with enough tinkering you can make almost any plot in R. For examples check out the flowingdata blog or the Fundamentals of Data Visualization book.

Artificial intelligence and machine learning adoption in European enterprise

O'Reilly on Data

How companies in Europe are preparing for and adopting AI and ML technologies. In a recent survey , we explored how companies were adjusting to the growing importance of machine learning and analytics, while also preparing for the explosion in the number of data sources.

5 Key Reasons Why Data Scientists Are Quitting their Jobs

Analytics Vidhya

Introduction The stock of a data scientist is at an all-time high right now. There aren’t too many professions out there that can rival. The post 5 Key Reasons Why Data Scientists Are Quitting their Jobs appeared first on Analytics Vidhya.

Your 2-Part Metrics Audit for High-Value Products

Speaker: Sam McAfee, Product Development Consultant, Startup Patterns

You know what they say: what's measured improves. As product managers we're in a golden age of being able to get all sorts of metrics and run all sorts of experiments. But what are your measurements and analytics focused on? Are they really truly objective? Do they contribute to the ultimate vision of your product? And is everybody clear on that vision? Join Sam McAfee, Product Development Consultant, as he takes you through a two-part measurement audit. First, you'll learn how to make sure your measurements actually align with your product strategy. And second, you'll learn how to evaluate your culture of using measurements, so future experiments will more consistently provide high-value results.

Top 10 IT & Technology Buzzwords You Won’t Be Able To Avoid In 2020


The modern world is changing more and more quickly with each passing year. If you don’t pay attention to new changes or keep up the pace, it’s easy to fall behind the times (and the market) while other companies beat you to the punch. The solution?

How to determine who owes you money


The ability to determine how much outstanding money is owed to your company is vital to the ongoing management and sometimes survival of a business.

5 Data Science Career Trends for 2020


Netflix reached its current level of success, as a popular streaming service and content creator, with its original show, House of Cards. The secret of the successful making of this popular TV show was the extensive data-research that went in.

Add Shine to your Data Science Resume with these 8 Ambitious Projects on GitHub

Analytics Vidhya

Overview Here are eight ambitious data science projects to add to your data science portfolio We have divided these projects into three categories – The post Add Shine to your Data Science Resume with these 8 Ambitious Projects on GitHub appeared first on Analytics Vidhya.

Assessing and Fostering a Culture of Innovation

Speaker: Magnus Penker, CEO & Founder, Innovation360 Group

Welcome to an interactive empowering session on how to sharpen your future through innovation management, which can help guide your company’s goals. During this webinar, Magnus Penker, international thought leader and author, will dive into how to assess and foster culture and capabilities for innovation.

Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics


Will you please describe your role at Fractal Analytics? I am the Chief Practice Officer for Insurance, Healthcare, and Hi-Tech verticals at Fractal.

4 Unique Methods to Optimize your Python Code for Data Science

Analytics Vidhya

Overview Writing optimized Python code is a crucial piece in your data science skillset Here are four methods to optimize your Python code (with. The post 4 Unique Methods to Optimize your Python Code for Data Science appeared first on Analytics Vidhya.

Everything you Should Know about p-value from Scratch for Data Science

Analytics Vidhya

Overview What is p-value? Where is it used in data science? And how can we calculate it? We answer all these questions and more. The post Everything you Should Know about p-value from Scratch for Data Science appeared first on Analytics Vidhya.

How Do Analytics and Business Intelligence Vendors Stack Up?

David Menninger's Analyst Perspectives

I am happy to share some insights gleaned from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements.

Measure the Immeasurable: Beyond Vanity Metrics

Speaker: Sari Harrison, Product Management Instructor, Product School

As a product manager, it's your job to realize your product’s vision by executing your chosen strategy. It’s also your job to deliver value to the business. Ultimately, these two outcomes are aligned so the temptation is to focus primarily on business metrics. Doing this can cause you to lose focus on the real value you are trying to achieve, in favor of moving the vanity metrics such as launches and time spent. Join Sari Harrison, Product Management Instructor at Product School, as she explains how to use immeasurable success criteria along with your more standard KPIs to deliver products that don't just get used a lot, but deliver real value.