Tag Archives: data analytics

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 , .

Business Analytics Team Structure: Roles/ Responsibilities

business analytics team structure roles and responsibilities

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 a full-time or part-time team member or both. It might seem logical to hire full-time analysts 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 needs then it may be more cost-effective to use freelancers rather …

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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. 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. 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 are defined as insights which can help in making decisions and taking action. Actionable insights can be used to …

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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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ESG Metrics & KPIs: What ESG team Needs to Know

ESG KPIs and metrics

This blog post is geared towards ESG professionals primarily associated with the procurement department in any organization. ESG initiatives are important for organizations to measure their ESG performance. It is of utmost importance to understand ESG KPIs / metrics and how to track ESG metrics. ESGs can help companies improve their operational efficiencies, environmental impact, financial position, governance, transparency, and societal contributions while managing risks. Data analytics can play key role in identifying KPIs, data needed for that KPIs and building dashboards for tracking those KPIs. What is ESG? ESG is an acronym that stands for Environment, Social, and Governance. ESGs encompass issues such as ethics, diversity, social justice, employee …

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Posted in Data analytics, Procurement. 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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Data-Driven Decision Making: What, Why & How

data driven decision making what why how

Data-driven decision-making is a data-driven approach to making 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 data that they have collected. The goal of this type of decision-maker is to make informed decisions rather than quick ones. 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! What are the different types of decisions? The following represents different types of decisions made in an organization on a day-to-day …

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Digital Transformation Strategy: What, Why & How?

digital transformation what why and how

Digital transformation is a digital strategy that aims to change the way an organization operates. It’s not just about digital marketing anymore- digital transformation includes all aspects of digital engagement from customer service, product development, and delivery, operations, etc. And it requires a holistic approach to digital transformation without any silos or strategic gaps in between departments. In this blog post, we will cover what digital transformation is and why organizations should take advantage of this strategy. We’ll also look at how digital transformation is happening in different industries. What is digital transformation? Digital transformation is a digital strategy that aims to change the way an organization operates. It helps …

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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 , .

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 , , , .

Data Analytics – Different Career Options / Opportunities

data analytics career options

Data analytics career paths span a wide range of career options, from data scientist to data engineer. Data scientists are often interested in what they can do with the data that is analyzed, while data engineers are more focused on the analysis itself. Whether you’re looking for a career as a data scientist, data analyst, ML engineer, or AI researcher, there’s something for everyone! In this blog post, we will different types of jobs and careers available to those interested in data analytics and data science. What are some of the career paths in data analytics? Here are different career paths for those interested in data analytics career: Data Scientists: …

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Posted in AI, Career Planning, Data analytics, data engineering, Data Science, Machine Learning. Tagged with , , , .

Using Theory of Change to Design Data-driven Solutions

theory of change for data-driven decision making

Have you ever wanted to design a solution for an issue but weren’t sure how to do it? One theory that can help is the theory of change. The theory of change provides a framework for designing solutions by focusing on the steps needed to achieve desired outcomes or results. It also helps identify what needs to happen in order for the solution to be implemented successfully and realizing the desired outcomes. The theory of change when combined with data-driven decision making can result in great impact. In order to design solutions that have an impact and are sustainable, it is important to understand the theory of change as well …

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Actionable Insights Examples – Turning Data into Action

data to insights to action - actionable insights examples

In this post, you will learn about how to turn data into information and then to actionable insights with the help of few examples. It will be helpful for data analysts, data scientists, and business analysts to get a good understanding of what is actionable insight? You will understand aspects related to data-driven decision making. Before getting into the details, let’s understand what is the problem at hand? The school authority is trying to assess and improve the health of students. Here is the question it is dealing with: How could we improve the overall health of the students in the school? We will look into the approach of finding the …

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

Starting on Analytics Journey – Things to Keep in Mind

Analytics Journey - Things to Keep in Mind

This post highlights some of the key points to keep in mind when you are starting on data analytics journey. You may want to check a related post to assess where does your organization stand in terms of maturity of analytics practice – Analytics maturity model for assessing analytics practice. In the post sighted above, the analytics maturity model defines three different levels of maturity which are as following: Challenged Practitioners Innovators At whichever level you are in terms of maturity of your analytics practice, it may be good idea to understand the following points to come up with data analytics projects. Believe that a lot of prior work is required …

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Data Quality Challenges for Analytics Projects

data quality challenges for analytics projects

In this post, you will learn about some of the key data quality challenges which you may need to tackle with, if you are working on data analytics projects or planning to get started on data analytics initiatives. If you represent key stakeholders in analytics team, you may find this post to be useful in understanding the data quality challenges.  Here are the key challenges in relation to data quality which when taken care would result in great outcomes from analytics projects related to descriptive, predictive and prescriptive analytics: Data accuracy / validation Data consistency Data availability Data discovery Data usability Data SLA Cos-effective data Data Accuracy One of the most important …

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Posted in Analytics, data engineering, Data Science. Tagged with , , .

Top 10 Types of Analytics Projects – Examples

Most common analytics projects

In this post, you will learn about some of the most common types of data analytics projects which can be executed by the organization to realise associated business value from analytics projects and, also, gain competitive advantage with respect to the related business functions. Note that analytics projects are different from AI / ML projects. AI / ML or predictive analytics is one part of analytics. Other types of analytics projects include those related with descriptive and prescriptive analytics. You may want to check out one of my related posts on difference between predictive and prescriptive analytics. Here are the key areas of focus for data analytics projects: Cost reduction: …

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