# Tag Archives: data analytics

## Actionable Insights Examples – Turning Data into Action

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 …

## Starting on 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 …

## 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 …

## Top 10 Types of Analytics Projects – Examples

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: …

## Predictive vs Prescriptive Analytics Difference

In this post, you will quickly learn about the difference between predictive analytics and prescriptive analytics. As data analytics stakeholders, one must get a good understanding of these concepts in order to decide when to apply predictive and when to make use of prescriptive analytics in analytics solutions / applications. Without further ado, let’s get straight to the diagram. In the above diagram, you could observe / learn the following: Predictive analytics: In predictive analytics, the model is trained using historical / past data based on supervised, unsupervised, reinforcement learning algorithms. Once trained, the new data / observation is input to the trained model. The output of the model is prediction in form …

## Analytics Maturity Model for Assessing Analytics Practice

In this post, you will learn about data analytics maturity model which you could use to assess where does your business / organization stand on the path of using analytics to drive business value. If you represent decision-making stakeholders group and want to assess your organization readiness / capabilities to deploy analytics in order to create business value creation, you may find this post useful enough. Here is a list of other articles I posted in the recent past in relation to strategic data analytics: Top 10 analytics strategies for great data products Top 5 data analytics methodologies Here are the three broad categories / levels of data analytics maturity model: Analytically …

## Top 5 Data Analytics Methodologies

Here is a list of top 5 data analytics methodologies which can be used to solve different business problems and in a way create business value for any organization: Optimization: Simply speaking, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values (also termed as decision variables) from within an allowed set and computing the value of the function. An optimization problem consists of three things: A. Objective function B. Decision variables C. Constraint functions (this is optional) Linear / Non-linear programming with constrained / unconstrained optimization Linear programming with constrained optimization Objective function and one or more constraint functions are linear with decision variables as continuous variables Linear programming with unconstrained optimization Objective function …

## Machine Learning Use Cases in Procurement

This post represents some of the important machine learning use cases in the procurement domain. These use cases can also be categorised as predictive analytics use cases for procurement. The list is not aimed to be exhaustive. However, some of the most important ones are listed. In case, you would like to add one or more use cases which I might have missed, pls feel free to suggest. The following are five key business function areas / department in procurement department. Demand management Category management Supplier management Sourcing management Contract management In all of the above function areas, there can be multiple use cases which can take advantage of machine …

## Data Science – Descriptive Vs Predictive Vs Prescriptive Analytics

This article represents key classification or types of analytics that business stakeholders, in this Big Data age, would want to adopt in order to take the most informed and smarter decisions for better business outcomes. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are the key categories of analytics which are described later in this article: Descriptive Analytics Predictive Analytics Prescriptive Analytics What is Descriptive Analytics? Descriptive analytics answers the question or gains insights into or summarize, “What has happened?”. This could be seen as first stage of business analytics and still accounts for the majority of …