Data Analytics Basics with Kristina Sorrelli @KCSorrelli #vcbuzz

Data Analytics Basics with Kristina Sorrelli @KCSorrelliNowadays marketers have more data than they can process.With multi-platform marketing channels and advanced tools we have access to, how to start using all this information for better ROI?

Let’s discuss!

***Add #VCBuzz chats to your calendar here.

***Please sign in here to follow the chat -> twchat.com/hashtag/vcbuzz

About Kristina

Kristina Sorrelli @KCSorrelli is data analyst, strategic planner, relationship builder, and cross-functional leader. Kristina is VP of Operations/Client Service at BVK

Kristina Sorrelli @KCSorrelli is an adaptable and transformational leader with an ability to work independently, embrace change, and developing opportunities that further establish organizational goals

Follow Kristina on Flipboard and Linkedin

Questions we discussed

Q1 How did you become a digital marketer? Please share your career story! Why did you decide to obtain degree in data analytics?

I began working in the advertising and marketing industry when I was 23. I was and administrative assistant working on the Shoney’s Big Boy (the Andy Griffith restaurant) account.

Now, remember, the first webpage went live in 1991 and smartphones weren’t mainstream until the early 2010s. We were still full speed with commercials, newspapers, magazines, radio, direct mail, and so on.

I went from there to an Assistant Media Buyer at another ad agency. Moving on to another agency as a Media Buyer/Account Supervisor. After that I somehow I found myself accepting a job with a CMR(Certified Marketing Representative). Better known as a National Yellow Page Placement Agency.

I worked my way from Account Manager to VP Operations/Client Service. As you know the Yellow Page industry had to reinvent itself or fall to the wayside. We began offering listing management, mobile marketing. We were a Cliff Note of a general agency.

However, we were about 8 years behind the curve and the damage was to great to overcome. After 17 years, on April 2017, we closed the doors.

I knew I didn’t want anything to do with the Yellow Page Industry and I needed to reinvent myself. I had experience with healthcare, retail corporations, large franchisee accounts, managing staff, culture structuring.

I had something to offer but I was missing one huge element when I began interviewing. A clear understanding of digital analytics. I searched and found a course that fit my schedule and covered a broad array of skills needed to analyze data.

I learned the practical and technical skills needed to analyze and solve complex data problems. This program covered a broad array of technologies like Excel, Python, JavaScript, HTML, API Interaction, CSS, SQL Databases, Tableau, R, MySQL and more. 10 hours a week in class and 20-40 hours outside class work. This was 2 years’ worth of course work condensed into 6 months. BTW: I loved every minute of class and got to know so many fabulous people that I now consider friends.

Q2 How to become better at analyzing data? Are there certain steps we need to take?

Before I started the course, I had absolutely no knowledge of what technology was used or needed to decipher data. I started the class as green as green could be.

This meant I was going to have to work harder than 99% of my classmates, who all had varying degrees of experience coding yet far more than me.

Yes, it’s hard. Yes, you will question your sanity at times. Yes, you will feel overwhelmed. Yes, there will be languages you hate, JAVASCRIPT!!, and ones you love, HTML/Python.

Yes, you will spend 49% of your time coding and the other 51% trying to find why it’s not working. For me it was usually a missing semicolon.

There were several things I had to overcome in order to really get my head around the coding process. First, there is no data analyst that knows nor can remember everything!!!!

There isn’t one way to get the answers, which means there’re are endless code variations to get the results you’re want. Next, Google is your best friend.

Last, copying code or blocks of code to use in your project is not cheating. Once I understood and embraced these points, I felt much more confident in my abilities.

There are certain steps you need to follow to insure you will be able to code in any environment. You must understand and learn the programming concepts instead of just learning the syntax.

If you understand the concept of say “if or while”, then you can take that concept and utilizing it within any language with just some altering of the syntax. You don’t want to just know how to code specific syntax.

What will happen is when it comes to implement that code won’t know when or how to use it. So, take the time to understand why you’re doing something and the results it will provide. This will make all the difference in the world.

Q3 What can marketers achieve by becoming better at data analytics? What is the implementation in today’s world?

By understanding the process and the way in which the data can be manipulated.

Marketers can produce better more specific reports that helps the client reach their target audience with more precision and allows the consumer to receive marketing more directly related to their interests and needs.

For instance, there is accompany that provides detailed reporting utilizing social listening and text analytics. Their search results are segmented by hundreds of audiences like Moms and Gen Z.

They utilize billions of voices and online conversations and turn it into valuable and insightful information for brands. Giving brands the opportunity to better understand the consumer and target the most relevant audience.

Insights delivered over 80 different industries. This is a game changer for marketers to be able to provide better ROI for the client.

Another benefit for marketers who become more familiar and better understand data analytics. Another benefit for marketers who become more familiar and better understand data analytics.

They can be a better catalyst between the client and the analytics department. This allows the marketer to ask questions that provide more technical details and insight when requesting reports from the analyst.

It depends on what you want to do within the world of analytics. I wanted to learn from start to finish how the information was transformed from raw data to specialized reports. I like knowing that I could pull data and do something with it not just read a report. knowing code also gave me better insight into what is and isn’t possible, time constraints, and what information is available for consumption. This makes me a better marketer, more empathetic/understanding of analysts, and I can then give clients realistic expectations. I hated when the sales staff would go out and sign a client only to find out they promised them things we weren’t capable of fulfilling. It’s a very bad look for the company. I don’t want to do that when I create a marketing plan since most plans include some form of analytics.

Q4 Who should we follow and what books should we read to become better at data analytics?

Who to follow:

  • CTO of Amazon
  • Jeff Hammerbacker: Jeff along with DJ Patil coined the term ‘Data Scientist’. Jeff is the Founder and Chief Scientist of Cloudera. Previously, he led the Data Team at Facebook
  • Cindi Howson: Gartner VP Research, Business intelligence, data & analytics. Product insights from hands-on testing.

Books:

  • Tableau Data Visualization Cookbook: Ashutosh Nandeshwar
  • Tableau Dashboard Cookbook: Jennifer Jane Stirrup
  • Business Intelligence and Data Mining Made Accessible, by A. Maheshwari – Best for: the newbie who has no idea what data science even means
  • Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic
  • Deep Learning with Python 1st Edition by Francois Chollet

I am really trying to get up to spead on Tableau so that’s why I stared with those book.

Websites:

  • w3schools.com one of my top go to websites when I’m searching for help. Great examples and test code that you can play around with to make sure it will performs as expected.

Q5 What are your favorite data analytics tools?

Please remember I am still a newbie with several of the languages. I found myself drawn to the ones I felt made more sense.

Github is an absolute must. GitHub is a website and cloud-based service that helps developers store and manage their code, as well as track and control changes to their code. It’s also very useful in allowing employers to see what you’ve done.

Jupyter Notebook is my personal favorite. Jupyter Notebooks are powerful, versatile, shareable and provide the ability to perform data visualization in the same environment.

Tableau is a powerful and fastest growing data visualization tool used in the Business Intelligence Industry. It helps in simplifying raw data into the very easily understandable format.

Data analysis is very fast with Tableau and the visualizations created are in the form of dashboards and worksheets.

FYI: I began my data analytics course in June of 2018 and finished in December of 2-018. If I’ve missed some key points, tools, or topics please forgive me. I’ll get there 🙂

Our previous analytics chats:

VCBee

Viral Content Bee is the free platform for social media sharing helping you get more shares for your high-quality content

More Posts - Website

Follow Me:
TwitterFacebook

Leave a Reply

Get notified!Grow your following, connect to inflencers!