Data Analyst 101 - Blog , Intermediate

Apple Watch enabled monitoring the data throughout the day and data is captured automatically instead of manual data entry. It was possible to monitor performance on a daily/weekly basis and stay motivated or take corrective actions to get back on track. It took 6 months of data before indicators are shown.

Applying this example as a framework to a business (department or overall) scenario:

Identify what data can be measured automatically on daily or weekly basis in an automated way based on how you operate(like website visits)
Set a realistic goal (based on your business type, identify what is a realistic goal)
Take actions to achieve goal on regular basis (run campaigns to attract visitors to the site)
Monitor streak of achieving goals (how frequently are you meeting goals)

To achieve this in a strategic mode, identify one area at a time.

Digital transformation has made it easy for any organization to measure their activity and convert that data into useful metrics. Just like fitness data is critical to your personal wellbeing, operational data of business is also important for business wellbeing. Recording data as business is happening will improve data quality and reduce errors when data is manually recorded after a delay. Automate this as much as possible to reduce gaming the data.

Podcast: CDO Battlescars

Sat, 31 Oct 2020 12:41:15 -0700

If you are looking for some inspiration please follow and listen to relevant podcast episodes here.

This is hosted by Sandeep Uttamchandani. "The motivation of this podcast series is to give back to the data community the hard-learned lessons that I and my peer data leaders have gathered over the years!"

Links

P odcast

Data for Humanity initiat ive (providing data insights for non-profit organizations)

Datasets for use - MySQL

Sun, 25 Oct 2020 17:18:09 -0700

This post will include list of mySQL databases that you can restore for practicing your data analysis skills.

Please click link to download dataset: Books Data

Please check this article for some suggestions on how to use this dataset

Please check the following article for instructions on how to restore a dataset from backup

Restoring mySQL backup

Data Quality - Testing Tools

Sat, 17 Oct 2020 09:57:45 -0700

Data Quality is a challenge that needs to be owned by Data Professionals. Given the volume of growing data we deal with and complexity of data transformations we implement in our jobs, we always need options to reduce tedious effort in testing and ensuring data quality.

Data Validation needs to be thorough (100%) as spot checks are not sufficient.
Data Quality is more important for business critical functions
Data Quality monitoring in production requires need to be automated as manual checks are not scalable.

We recommend checking out following tools. Use the trial version to implement your scenarios before you recommend adoption for your organization

QuerySurge

Datagaps

If you want to list any other tool or options you have used on this page, please let us know

Integrating Python with MySQL

Thu, 15 Oct 2020 23:13:39 -0700

Recommended Article for data analysts learning Python and SQL

Full article written by Sanjana is posted here

A data analyst or data scientist get lots of opportunity to work on Python and SQL. Python with its enormous library support provides data analyst with a magic wand to manipulate and visualize data. Likewise SQL helps us store millions of records and establish relationships with other tables for optimal data management. There are scenarios that require a Python program to directly load data into a back-end database. For example, if a data frame is dynamically modified periodically or at random instances, uploading the csv file for each data update into the database is not feasible. In this post, we will go through how to integrate a Python program with MySQL database.

Enabling Data Driven Culture

Sat, 03 Oct 2020 09:41:51 -0700

Converting an organization that is not used to having a ‘data driven’ culture to become one takes considerable effort. Following are steps we recommend

Analyze data that is already captured: Start with what you have before starting new data initiatives. Use the data to tell a story of what is happening

How are we performing? – What is total of ?
Are we improving? – What trends are we seeing? Increase, decrease or no change?
How can we look at the data in different ways?

2. Build ‘Data Democracy’: Make data accessible to everyone. Everyone who is impacted by that data should be able to view the data. This establishes trust and transparency.

What data is available?
How is it collected?
Can everyone come to same conclusion looking at the data?

3. Ask for data: Encourage team members to ask for data. Data should be asked by every team member as evidence whenever there is a shift in direction.

What data is causing us to make this change?
Are we sure? do we need to analyze the data further?

Are you doing any of the above? Is this proving effective? Share your thoughts.

In future articles, we will cover what steps can be taken to take this culture to next level.

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Predicting Customer Churn

Sat, 03 Oct 2020 09:33:22 -0700

As a growing SaaS company Acme was focused a lot in growing customer base. Sales and Marketing took lot of time and assuming existing customers are happy with product was a reasonable thing to do.

However, Acme had usage analytics in place to monitor login activity every week. During one of these reviews the team noticed gradual drop in usage by the largest customer. Operations team reached out to Client/Account team to assess the situation. They learned that client users have started using an alternate product and yearly contract will not be renewed.

Analytics helped ACME predict customer churn and be prepared for the impact much before the event happened. This also presented an opportunity to recover customer.

Action:

Analytics may not deliver insights when they are built but regular monitoring will provide early warning signs to something that can be prevented.

Note: This is real use case and name of the company changed for confidential purposes.

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Enabling Data Literacy

Mon, 14 Sep 2020 22:22:13 -0700

Article Summary: Data Literacy

Key Point in referenced Article

"To that third point, data analysts teach technical courses on writing SQL and visualizing data in Tableau. Kalb teaches one about data literacy, including pitfalls to avoid in data interpretation, and on how to be a “data translator” and "data storyteller."

Read the full article below:

Chief Data Officers Priorities for 2020

Note from us: Most of the data in companies is in databases and writing queries to extract right data is first skill and then showing the in simple visualization using Tableau is quickest way to show value. Practice your skills enough to be competent in these skills.