How can you interpret advanced BI analytics and metrics?

It's crucial to select the most appropriate visualization for a specific type of analysis and not repeat the same analysis with different charts, as this can confuse the audience. Tools like Power BI and Tableau offer dynamic visualization capabilities that allow us to create interactive and informative visuals. Leveraging these tools, we can present data in a more engaging and comprehensible manner, enhancing the audience's understanding and retention of key insights. The dynamic nature of these visualizations also enables users to explore the data at their own pace and uncover valuable information.

Interpreting advanced BI analytics involves key steps. Firstly, grasp the business goals. Understanding the context helps align insights with what matters most for the business. Secondly, ensure solid data modeling. Properly organizing and structuring data lays a strong foundation for meaningful analysis, enabling clearer interpretations and informed decision-making.

The key is IMPACT. The appropriate data visualization is the one that delivers a message with impact. Data visualization is a great tool that, when leveraged effectively, achieves the unachievable at convincing your audience.

Data visualization improves the way how presenter wants to show the data and also makes it better for the audience to understand and derive meaning from it. ● Selecting the visualization type is critical based on the data and values. Showing simple charts is much better for relatability based on the audience demographic. ● Post the chart selection, it is as much important to highlight either the trend, a specific month (in a bar chart) to make it a part of storytelling For creating these visualizations, there are various tools: ● Excel and Power BI are free to use and save your visualization ● Tableau is much simple and free to use but the save option is better for open data as it saves the visualization on the public cloud system

Data visualization is here to help us understand complex data, spot patterns and outliers, communicate findings, and aid decision-making. To have better results we need to be very clear with purpose or the objective. Different visualizations have different strengths, weaknesses, and purposes and one should understand what fits best for a situation. It is crucial to have reliable, valid, and up-to-date data before creating a visualization. Additionally, data visualizations should tell a story and convey a message relevant to the business problem or objective. so while one creates a visualization, it is important to consider the audience, the purpose, and how it can help them comprehend and act upon the data.

First, we should know the answer of this question: What is your Data Quality score? What is the data quality score for specific dataset? If you don't know, then you might need to invest on a Data Quality platform before jumping to advanced analytics. For non-technical data users, I would recommend KNIME :) to discovery your data, transform, apply some data models.

Prior to delving into data mining, it's imperative to underscore the significance of data quality. The age-old saying, 'garbage in, garbage out,' aptly underscores that the quality of input data is paramount, as it greatly influences the relevance and reliability of the outcomes. In the realm of data mining, both Python and R are great tools. They offer rich libraries and packages tailored for data analysis and machine learning.

From a business perspective, data mining can be considered a very important part of a STAR framework. Taking an example in an online travel agency in partnership with a low-cost carrier, agency has to transfer funds in the LCC carrier account for customers to book flights. But recently they have observed that the funds are reducing more than usual. S - Situation: Proper allocation of funds T - Task: To check the reason on why the funds are reducing drastically and based on that suggest actions which will help in better fund allocation. This step requires data mining to find the correct reason A - Action: Use demand forecast model to predict sales and transfer funds R - Result: Proper allocation of funds based on the model

I am not sure what value add interpreting data modeling would add in this context. It seems to me like the beginning of this article should discuss how having a solid data model (that you understand) is the first step along the way to interpreting advanced analytics. I would say that having a well documented and commented data model will help others connect to and use that data model, and improve maintenance - so that you can draw insights and analytics from the data.

Data modelling is a foundational step in data analytics and data visualization. The data model itself is less about interpreting data and more about correctly aligning and linking data so that multiple datasets can be analyzed, interpreted, and understood as a whole.

It's super crucial for you to decide which model you will use Star Schema or live connection. It can cost you years of effort or save thousands hours of work. TAKE THE TIME to analyze and review what your endgame is and how far your project can be extended in the future.

In Business Intelligence, Insight generation is crucial. This helps in finding a pattern, finding relationships in date and also in predictions. Based on these insights you can make informed decisions that can help your business.

Establishing a robust data architecture is paramount for cultivating an effective data model and fostering a cohesive data culture. In the realm of business intelligence, it is imperative to cater to both larger groups and individuals without compromising the integrity of data sources. Two key recommendations include implementing a centralized ETL layer to establish a singular source of truth, promoting uniformity in data interpretation across stakeholders and reducing the strain on data sources. Additionally, maintaining simplicity and consistency in visualizations through design themes and report development guidelines enhances data interpretation, fostering trust among stakeholders.

The key thing I would recommend for BI experts is to really understand the business objective. What is the business goal, how is the goal being measured? Once you understand the goal, consider what actions the users of your solution need to take if the goal is not being met. Can you surface up the specific actions they need to take in that visualization e.g. if sales are lower that projected can they click into a chart and immediately see that the biggest contributor was lack of inventory. After you design the visualizations to easily drive action ensure as other have said that the data is of high quality and that the modeling and logic you apply is accurate.

Before diving into the metrics, grasp the business context. What is the objective of the analysis? Are we looking at sales performance, customer behavior, operational efficiency, or something else? Then know what each of these metrics represents. For example, understand the difference between metrics like Customer Acquisition Cost (CAC) and Customer Lifetime Value (CLTV) if you're analysing marketing data. Examine the data over different timeframes. Are there visible trends, seasonality, or patterns that emerge? For example, a spike in sales every December might indicate holiday shopping patterns.

understanding your data is very important before you go on building dashboards for it. check how you want your data to be strcutured what are the common linking fields, what you want to get out of the data set, once these are clear even like 50% you are half way there in your viz journey. The rest is all about which chart is appropriate and how to build it

Without any added context, metrics are mere numbers. Along with supporting information like, historical trends and averages, Industry benchmarks, same metrics for similar long standing product offerings from the same company etc. , an analyst can interpret the value of current metrics and derive insights and recommendations.

Utilizing key measures instead of adding a custom field to your fact tables can save you size and speed. Remember that any column field you add to your table will be added to each row as a data point Vs key measure is just a DAX based calculation, super easy, quick and doesn't require your model to increase in size significantly.