Most people begin their journey thinking that they need a statistics foundation or a statistical concept to understand data analytics. But what if I told you these are the least important components to solving real world problems?

Understanding the Real World Context

The foundations of data analysis are unique to every situation because the most important statistical techniques involve understanding the real world issues, not mathematical data sets or concepts.

Most business people don't have confidence in their skills and worry about failing, so when they turn to data analysis, they feel overwhelmed by huge numbers, statistical concepts, and paralyzed by their lack of knowledge.

In the end they don't know where to start and fail to do anything because it's too difficult for them.

What they need to do is take a step back from worrying about the data analytics format and look towards the issue that surrounds the data.

Data Analysis Starts with Questions

The first step in any data analytics project is figuring out a question or problem statement:

Why are we doing this?

What does success look like?

How will we measure success at the end of our work?

You need answers before you can move on. These questions help us build an analytical foundation from which all other decisions flow as we get into more technical aspects of building models, exploring insights, hypothesis testing and relevant big data analysis techniques.

Your introduction to the data analysis should be built around basic but key questions.

Dates, times and timestamps are an important part of your data foundation. Fundamental benchmarks related to the issue hep form that data foundation.

Benchmarks are questions such as:

What is the date?

What time did that happen?  

Was it before or after a certain event?  

Are we looking at data for one day, three days, six months or twelve years from now?

These types of questions will help you understand what type of analytics work to do on this set of data — especially if data sets vary in terms of duration.

A real world few examples:

- If I want to look at how my campaign impacts web traffic for next week, should I use last year's numbers as my base line or this month so far?

- What would be the starting point for a failing insurance company to search data using client relations and forecasting conversion rates based on email interaction.

What would be the starting point for a failing insurance company to search data using client relations and forecasting conversion rates based on email interaction. Determining what your starting point is will help you to see how far off the conversion rate actually is, and whether it's worth investing in new programs.

Don't be afraid to fail

Failing is a fundamental aspect of success. If you don't fail, you won't know how to move forward in order to learn, so you need to experiment with the data science and explore the central foundations of data so you can expand your knowledge on the vast number of support tools at your disposal.

When you analyze data, you will have many options and it can seem daunting. But don't be afraid of the complexity of the process because that's where all good things come from.

The foundation on which you build your plan for analysis is important to keeping track of what success looks like, how success will be measured at the end and why we are doing this work.

It’s natural for us as humans to want a quick fix or solution.

So sometimes it seems easier to feel like failures when we get our first introduction to terms like data science or even by the idea of doing algebra or programming; and for some of us we may choose to learn or review these concepts to complete a course or attain a certificate, but failing these courses should not set us back in our career or in being able to complete advanced and descriptive data analytics for management.

The foundation on which you build your plan for analysis is important to keeping track of what success looks like, how success will be measured at the end and why we are doing this work.

The best thing about failing is that its free. The central component to failing is that gives you an introduction to techniques which don't work for you.

An overview of key mistakes when handling data will assist with better data management.

Benefits of a Losing Approach:

- Allows for increased creativity when exploring data as there are more possibilities - Puts less pressure on the researcher because failing doesn't mean "wrong" or that they're stupid; instead, means that there was something they didn't account for which could be solved next time around with more information up front.

- When examining big data sets like financial history, failure can lead to greater understanding due to variance between different variables over large periods of time. For example, if we go back 100 years and track stock prices vs wheat prices

Data Analytics Means Listening For Feedback

The easiest way to not fail at building a foundational understanding of how data analytics works is by listening for feedback throughout each phase of your work: What questions are we asking? How are we trying to answer those questions? Where did our assumptions come from? Who helped us with this project so far? Were they helpful or unhelpful in their contributions?

This type of "fail forward" approach will lead you through any problems that arise, big and small.

Failing allows for feedback, allowing you to get an overview and seek support.

Ever since the first person stepped up and said "I have an idea," feedback has been crucial to innovation. Feedback is required for improvement, validation of ideas, and development of new ones. Yet in today's digital world where data analytics are king, we're often too busy analyzing to listen for feedback on our analysis.

Making sure you're listening for feedback from your customers or stakeholders so that they know their voices are being heard is a good way to develop innovative solutions while also strengthening relationships with those who matter most – your community and wider society. 

Data analysis should not feel like a lesson in regression

When I first started working with data, I was intimidated by the technical aspects of it. It felt like a lesson in regression that only an advanced statistics student could understand. The more I learned about data analysis and what types of questions to ask, the easier it became. 

If you are not a math nerd, data analysis may seem like an exercise in regression. But with the right tools and knowledge, it can be fun!  failing is important

Data mining can be completed without heavy programming techniques or tools. In this new age of computer forward analytic learning.

Why you don't need solid Statistics Foundations to get the work done

A major basic misconception is the idea that an extensive education and deep knowledge on statistical analysis, or a university program complete with courses on related statistical concepts are needed to get data analysis done.

This is simply not true. You can get data analysis done anytime anywhere because there are a lot of key tools that give you statistical probability.

A data analytics tool such as Tableau is an excellent tool for data analysis because it helps you to visualize your data.

Tableau

This visualization process allows you to see trends, patterns and correlations that are otherwise difficult to spot by just looking at numbers. This tool simplifies statistical data, making visualizations like linear regression seem basic and digestible.

Imagine learning how to use this tool in a self paced data analytics course, where big data is not just an online introduction to math management, but a linear look into how the foundations of data analysis can be determined by real world business issues.

So if you have a good understanding of the tableau interface and can get use with its different features then this will be enough!

Tableau interfaces:

The Tableau Interface provides an intuitive view of running queries across multiple dimensions or through time. Tableau also has tools such as filtering, sorting, marking rows in categorical views, drilling into subsets of these results using dashboard panels (this is done by clicking on tabs), generating visualizations based on summary statistics without having any source query defined beforehand

What do you need to understand?

Understand your business over trying to explore advanced statistical techniques. The foundations in data analysis are in context of the problems and not mathematical ability.

This allows to be able to use statistical techniques, without the foundational skills of any statistician.

Understand that it's about how you're using data, and what people need information for - rather than just being able to use statistical concepts. It is important to understand what happens in a data analysis process and to know when it’s necessary.

It's important not just for how you do the analysis, but also whether or not you need statistics at all.

It’s important because it teaches us more about our mistakes which help us learn! It also helps establish a sense of competency in subjects such as math or science by demonstrating an ability to play around with mathematical concepts without necessarily acquiring any mastery yet.

Why not Try Tableau for yourself?

Incus Services provides a free 14 day trial of Tableau. Don't be afraid to download give it a try.

If you want to become more data driven without having to learn how to code or program then we suggest starting with the Three Most Powerful Analytics Techniques Framework

Feel free to reach out if there's anything we can help with.