Xplore Analytics

The latest dashboard I created in MS Excel. The logic is constructed to show the current and last months data comparisons with a slicer to break down the data by products. The first pic is the dashboard, the second is the backend calculations and the third is the data model.

A bank costumer analysis we did.

Scenario:

Your data was hacked and there is a possibility that salaries may have been altered and some data may have been deleted, luckily you have a backup. Here is how I used Power Query to investigate. Open a blank Excel Worksheet and using the Data tab establish a connection to both employee files then merge the two files, I used a Full Outer join kind. After the data is merged and expanded, I used a Conditional Column to look for the differences in the two data sheets. After the Conditional Column was created and the Unaltered data was filtered out, 6 entries were found to be altered.
The data was further transformed to remove unnecessary columns, reordered the columns and a Custom Column was added to calculate the salary difference between the altered and backup data. The following is a screenshots of some of the transformation process and the result.

Data analysis is a big subject and can include some of these steps:

--Defining Objectives: Start by outlining some clearly defined objectives. To get the best results out of the data, the objectives should be crystal clear.
--Posing Questions: Figure out the questions you would like answered by the data. For example, do red sports cars get into accidents more often than others? Figure out which data analysis tools will get the best result for your question.
--Data Collection: Collect data that is useful to answer the questions. In this example, data might be collected from a variety of sources like DMV or police accident reports, insurance claims and hospitalization details.
--Data Scrubbing: Raw data may be collected in several different formats, with lots of junk values and clutter. The data is cleaned and converted so that data analysis tools can import it. It's not a glamorous step but it's very important.
--Data Analysis: Import this new clean data into the data analysis tools. These tools allow you to explore the data, find patterns, and answer what-if questions. This is the payoff, this is where you find results!
--Drawing Conclusions and Making Predictions: Draw conclusions from your data. These conclusions may be summarized in a report, visual, or both to get the right results.

What Is Data Analysis and Why Is It Important? What is data analysis? We explain data mining, analytics, and data visualization in simple to understand terms.

“The companies that are going to win are the ones who are using data, not guessing.”
–Neil Hoyne

Marketing the Future: How Data Analytics Is Changing - Knowledge@Wharton Data analytics is at an inflection point as growing concerns about security, privacy, bias and regulation bump up against its benefits.

Visit my website portfolio to see some of my recent project visualizations.

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