As we know that data has been playing a significant role in decision-making, and there are various types of analysis that help to do leveraging data. Analytics and AI plays a pivotal role for organization in times of crisis and assists in proactive decision-making.
When we say analytics, data comes first in our mind.
Data
From the last several decades, we have tons and tons of data. In the upcoming future, we will have an enormous amount of data. That enormous amount of data has been recorded and stored, which will be of no use if we do not utilize it for analytics and AI.
If we torture the data long enough, it will confess.
By Ronald Coase.
In other words, if we explore, analyze or dig down into data, it will give us some hidden information, patterns and relationships.
Conventionally, we are using those mainly for two purposes.
1. Records to know what happened – A kind of report where we can view data of any time or duration
2. Identify the root cause why it happened – A bit of exploration or finding the root causes.
However, Artificial Intelligence and Machine Learning can effectively utilize those data for predictive analysis and give life to your organization. Consequently, it will help you to get ready for the upcoming crisis and know the hidden patterns.
Analytics and AI play a pivotal role for organizations in times of crisis.
Data Analysis
A simple definition is “Detailed Examination”. Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. (Wikipedia)
Let’s explore a bit more on various types of Analytics.
Types of Data Analysis
Descriptive Analysis
Descriptive Analysis is a baseline for organizations where the fundamental question is what happened. Alternatively, we want to find the information of any time or certain time period. We have our data stored in some data sources, and we can run reports/scripts to find what happened at any point of time. This analysis comes under hindsight. This analysis is quite common for most of the organizations.
Diagnostics Analysis
The next is Diagnostics Analysis, and in this type, we examine, discover, and drill down the data to answer why it happened. In this analysis, we are stepping from hindsight to insights, where we will have some graphs and diagrams to find general patterns.
Predictive Analysis
Predictive analysis is the next advanced level of analytics, which obviously carries more value and let us know what is likely to happen. This is where we use artificial intelligence and machine learning to forecast or predict based on the data and environment setup we have.
This is a big step for an organization to achieve this advanced level of analytics that requires implementation of AI and machine learning algorithms.
Prescriptive Analysis
Another optimized analysis, where we talk about what should be done, is Prescriptive analysis. This is the final and most advanced level of analysis. This analysis helps an organization to make decisions based on statistics and accuracy of previous analysis and likely to guarantee the desired result, such as revenue.
Cognitive Analysis
Additionally, we have Cognitive Analysis that aims to draw interference from available data and relationships and make decisions based on those prevailing knowledge bases. Furthermore, this analysis uses the findings again into the knowledge base for coming interferences. This analysis is sustainable, self-learning, and adjusts itself based on circumstances.
Where do you find yourself on this curve?
Descriptive and Diagnostic Analysis help an organization to find what’s going on with their business, with basic trends based on current statistics. However, predictive analysis enables us to forecast based on the current situations and environment setup, like predicting revenue and gross profit by end of quarter or financial year. Furthermore, we can achieve optimized analysis through prescriptive methods where we can make it happen, for instance, we can make changes to reach the target.
Visual Analytics are important
A well-designed illustration can not only represent information but also amplify the impact of that information with a strong demonstration, attracting interest, and holding attention as no table or spreadsheet can.
A good data visualization is crucial for analyzing data and making decisions quickly. It allows people to grab the core, identify relationships and catch a glimpse of emerging trends that might go unobserved with just a table or spreadsheet of raw numbers quickly and easily.
To have superior visualization, we need to have a tool like Power BI.
Power BI
Power BI is a Microsoft trusted platform for data visualization and enterprise business intelligence that provide deeper data insights and bridge the gap between data and decision-making. It is one of the best data analytics tools to provide end-to-end solutions. It provides interactive visualizations and BI capabilities that are highly scalable, secured and can connect to any data source. The platform has numerous exclusive features. Please check this link. https://powerbi.microsoft.com/en-us/
How to get started
Many organizations are struggling to get proper data analytics and interactive visualization for their businesses. This failure is mostly because of incorrect choice of BI tool and lack of proper consultant.
First, we should learn about Power BI capabilities and its licensing model. We can find it on the Microsoft site (https://powerbi.microsoft.com/en-us/pricing/) or we can reach Microsoft Trusted partners or consultants. Furthermore, we need to have skilled resources to build and manage the dashboards and reports. The skilled resource will analyze to design an architecture and roadmap for the organization that leads to the desired result. To reach advance and optimize level analytics, you can take advantage of AI and ML with Power BI.
To know about Power BI, please this link