Category Archives: Data analytics

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‚Äôs 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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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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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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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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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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