Category Archives: Data analytics

ESG Metrics & KPIs and ESG Reporting Concepts

ESG KPIs and metrics

This blog post is geared toward Environmental, Social & Governance (ESG) professionals looking to understand different aspects of ESG and some metrics that can be reported via ESG reports as part of their organization’s ESG reporting in relation to representing the sustainability aspect of their business. An understanding of different aspects of ESG can help you in getting started with ESG initiatives and ESG reporting. ESG initiatives can help companies improve their overall sustainability factor while creating a positive impact on environmental, social, and governance issues.  Getting started with ESG-related practices in your organization or department (such as procurement) requires a set of ESG initiatives and related performance measures including …

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Posted in Data analytics, Procurement. Tagged with , .

AI / Data Science Operating Model: Teams, Processes

data science operating model

Realizing value from AI/data science or machine learning projects requires the coordination of many different teams based on an appropriate operating model. If you want to build an effective AI/data science operation, you need to create a data science operating model that outlines the steps involved in how teams are structured, how data science projects are implemented, how the maturity of data science practice is evaluated and an overall governance model which is used to keep a track of data science initiatives. In this blog post, we will discuss the key components of a data science operating model and provide examples of how to optimize your data science process. AI …

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What is Data-Driven Decision-Making? Why & How?

data driven decision making what why how

Data-driven decision-making is a data-driven approach to making decisions including business decisions. This data can come from data analysis, data visualization, or other data resources. Data-driven decision-makers use data in their decision process and they make decisions based on the actionable insights generated from the data. The goal is to make informed decisions while ensuring transparency across the stakeholders. In this blog post, we will discuss what data-driven decision-making is, how it differs from other types of decision-making, and why you should consider going for this method in your business! Before we dive in and understand what is data-driven decision-making, lets understand what are first principles of decision-making? What are …

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What is Data Quality Management? Concepts & Examples

what is data quality and why is it important

What is data quality? This is a question that many people ask, but it is not always easy to answer. Simply put, data quality refers to the accuracy and completeness of data. If data is not accurate, it can lead to all sorts of problems for businesses. That’s why data quality is so important – it ensures that your data is reliable and can be used for decision-making purposes. Data is at the heart of any enterprise. It is essential for making sound business decisions, understanding customers, and improving operations. However, not all data is created equal. In order to make the most out of your data, you need to …

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Machine Learning with Alteryx: Examples

Alteryx machine learning use cases

Alteryx is a self-service data analytics software platform that enables users to easily prep, blend, and analyze data all in one place. It is a powerful tool that can be used in a variety of machine learning scenarios. It can be used to clean and prepare data, and develop, evaluate and deploy machine learning (ML) models. It offers a variety of features and tools that can be used to preprocess data, choose algorithms, train models, and evaluate results. In this blog post, we will discuss some of the ways that Alteryx can be used in machine learning. We will also provide examples of how to use Alteryx in machine learning scenarios. …

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Analytical thinking & Reasoning: Real-life Examples

analytical thinking 1

Analytical thinking and analytical reasoning are two concepts that are often misunderstood. Many people think that they are the same thing, but this is not the case. In fact, analytical thinking and analytical reasoning are two very different things. Analytical thinking is an important aspect of analytical skills. Most of us do not realize how to use analytical thinking and often end up solving the problem incorrectly or half-heartedly. As data analysts or data scientists, it would be of utmost importance to acquire this skill well. In this blog post, we will learn these concepts with the help of some real-life examples. What is analytical thinking? Analytical thinking is the …

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Difference between Data Science & Data Analytics

data science vs data analytics

What’s the difference between data science and data analytics? Many people use these terms interchangeably, but there is a big distinction between the two fields. Data science is more focused on understanding and deriving insights from data, while data analytics is more focused on using pre-determined algorithms to make decisions or take action. In this blog post, we’ll explore the differences between data science and data analytics in greater detail, with examples of each. The following are key topics in relation to the difference between data science and data analytics: Different forms / purposes Different techniques Different Skillsets Different tools Different forms: Data Science & Data analytics Data science is …

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Differences Between MLOps, ModelOps, AIOps, DataOps

MLOps vs ModelOps vs DataOps

In this blog post, we will talk about MLOps, AIOps, ModelOps and Dataops and difference between these terms. MLOps stands for Machine Learning Operations, AIOps stands for Artificial Intelligence-Operations (AI for IT operations), DataOps stands for Data operations and ModelOps stands for model operations. As data analytics stakeholders, it is important to understand the differences between MLOps, AIOps, Dataops, and ModelOps. For setting up AI/ML practice, it is important to plan to set up teams and practices around AIOps, MLOps/ModelOps and DataOps. What is MLOps? MLOps (or ML Operations) refers to the process of managing your ML workflows. It’s a subset of ModelOps that focuses on operationalizing ML models that …

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Posted in Data analytics, Machine Learning, MLOps. Tagged with , , , .

Business Analytics Team Structure: Roles/ Responsibilities

business analytics value chain

Business analytics is a business function that has been around for years, but it’s only recently gained traction as one of the most important business functions. Organizations are now realizing how business analytics can help them increase revenue and improve business operations. But before you bring on a business analytics team, you need to determine if your company needs full-time or part-time team members or both. It might seem logical to hire full-time staff members just because they’re in demand, but this isn’t always necessary. If your business operates without any external data sets and doesn’t have complex reporting and advanced analytics needs then it may be more cost-effective to …

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Posted in Data analytics, Data Science, Machine Learning, Product Management. Tagged with , , .

What are Actionable Insights: Examples & Concepts

actionable insights concepts examples

The idea of actionable insights is something that has gone mainstream across different departments in any and every business due to the onset of digital transformation initiatives at large. Today, actionable insights are at the heart of many successful business decisions, and are used to help your company grow further than ever before. Actionable insights are key to any data analytics initiatives which are at the heart of digital transformation. Analytics centered around actionable insights is also termed actionable analytics. In this blog post, actionable insights are explained with examples along with few actionable analytics tools which are used when dealing with actionable insights. What are actionable insights? Actionable insights …

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OKRs vs KPIs: Differences and Examples

Objectives and Key Results - OKRs

The difference between OKRs and KPIs is not always clear, but the concept is a great way to measure the progress towards achieving your business objectives. As business analysts, product managers, or project or team leaders, it is important to understand the OKRs & KPIs differences. In this blog post, we will discuss OKR vs KPI and how each can be used to measure different aspects of your team’s and organization’s performance in relation to achieving your goals. We’ll also go over real-world examples for both OKRs and KPIs so you can get a better understanding of how these metrics work in real life. What are OKRs? OKR stands for …

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Posted in Analytics, Data analytics. Tagged with , .

Data Governance Framework Template / Example

data governance framework template

Data governance is a framework that provides data management governance. It’s the process of structuring data so it can be governed, managed and used more effectively. Data governance framework forms the key aspect of data analytics strategy. This blog post will discuss key functions of a standard data governance framework and can be taken as a template or example to help you get started with setting up your data governance program. What is Data Governance Framework? The data governance framework is intended to put some structure around how data can be managed and used in an organization based on well-defined rules and processes around a variety of data related operations and decisions. Data …

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Posted in Data, Data analytics. Tagged with , .

How to Create Data-Driven Culture: Key Steps

how to create data-driven culture

In today’s competitive business environment, companies are looking for the cutting edge they can get to stay ahead. One of the ways to beat the competition is by establishing a culture of data-driven decision making. In this blog post, we will explore how to create a data-driven culture that values data analytics and provides actionable insights into what needs to be done next in order to create a future-ready digital organization. What is data-driven culture? Data-driven culture is about creating an organization that is data-driven, where everything from business processes to culture supports the need for data-based decision making. In other words, every step of a business process must be …

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Data Readiness Levels Assessment: Concepts

data readiness levels assessment

Data readiness levels (DRLs) and related assessments are an important part of data analytics. Data readiness levels is a concept where different stages represent the quality and maturity of data. Data science is becoming increasingly popular, but not all companies have the right level of data readiness for this type of work. Performing data readiness levels assessment is important because it gives an insight into the quality and quantity of your current datasets and helps determine future success of the data analytics project. This blog post will explain what data readiness levels are and why assessment tests are important in relation to them. What are data readiness levels? Data readiness …

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Posted in Data, Data analytics, Data Science. Tagged with , .

Relationship: Analytics & Data-Driven Decision Making

analytics and data-driven decision making relationship

Data analytics is a topic that many data-driven organizations are becoming increasingly interested in. Data analytics often includes the process of analyzing data to find insights that can be used to make decisions. But what does this mean? How are different types of analytics related to data-driven decision-making? This blog post will explore how an organization’s use of data can help them make better, more informed decisions. Before getting into the details, lets quickly understand how business analytics is related data analytics. There are a number of facets that business analytics and data analytics have in common. In both the cases, the common steps include dealing with gathering data from …

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Key Architectural Components of a Data Lake

data lake architectural components

Data lakes are data storage systems that allow data to be stored, managed and accessed in a way that is cost-effective and scalable. They can provide a significant competitive advantage for any organization by enabling data-driven decision-making, but they also come with challenges in architecture design. In this blog post, we will explore the different components of data lakes, including the data lake architecture. Before getting to learn about data lake architectural component, lets quickly recall what is a data lake. What is a data lake? A data lake is a data storage system that allows data to be stored, managed, and accessed in a way that is cost-effective and …

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Posted in Architecture, Data analytics, Data lake. Tagged with , .