The right approach in data analytics: Solve business problems like a pro!

If you are reading this article, you might already know that data science helps solve business problems by combining data, statistical concepts and domain expertise. And as domain experts, data analysts work with all of this data to provide meaningful business insights. In case you are one, how do you comprehend problems and make smarter choices on the job as a data analyst?

I am sure that you apply?tools, write codes and look for new technologies to make your life easier. Thanks to the evolution and open-source nature of data manipulation and coding languages, data science is no more as complicated as rocket science! Even then, most aspiring data analysts revere complex algorithms, tools and processes. Some of them get overwhelmed by such technical jargon and desert the field mid-way!

Powerful, complex algorithms don’t always guarantee business problem resolution. So how can you add value to your business and improve existing processes in the long run? I have listed some important points below that can help you unearth maximum business benefits through data analysis.

1.??Know the purpose of data analysis in your business

As a data analyst, you need to record, analyze, and dissect data and offer decision-making insights to the Business. Basis this data, they can predict customer trends, behaviors, interpret and deliver services in meaningful ways and boost the top-line and bottom-line.

2.??Adapt a mindful approach to data analysis

You might agree when I say that data science is an ocean. And as a novice or professional data analyst, some important factors you may consider are:

1. Understanding the business objective
2. Exploring the deeper meaning behind data
3. Analyzing the events based on your data findings
4. Presenting these insights in easy English to the leadership/Business

This way, you can contribute to high-level decision-making with ease.

3.??Focus on problem-solving rather than writing complex algorithms?

You have to understand depth vs. breadth and drive it with the power of your logical solutions. Initially, you may or may not always convert the logical solution to clean code in the language of your choice. It’s only at a later stage that you have to apply your deep problem-solving knowledge. The code you write or the algorithm you devise can be as basic as possible, but it will be effective only when it solves the business problem. So give precedence to the problem statement and business context over the technical coolness of the solution. ?

That reminds me that Lilian Pierson, an acclaimed data science consultant and author of many data science books once said, “Most business managers and organizational leaders couldn’t care less about coding and complex statistical algorithms. On the other hand, they are extremely interested in finding ways to improve their operational efficiency.” ?

4.??Chuck fancy new algorithms and technologies

As they say, simple is better than complex, and complex is better than complicated. Algorithms are data structure searching techniques, with which you should be acquainted. However, applying complex algorithms is not always required. In fact, it is possible to resolve most business problems in machine learning, using a handful of basic algorithms. For example, you can rely on Gradient Boosting, Decision Trees and Recurrent Neural Networks. The No Free Lunch (NFL or NFLT) theorem states that any two optimization algorithms are equivalent when their performance is averaged across all possible problems. It also implies that there is no single best machine learning algorithm for predictive modeling problems, such as classification and regression. So, go deep into a select few algorithms rather than trying to learn every new algorithm that gets published in scientific white papers.

5.??Get a strong hold on the basics

As said already, the importance of basics in problem-solving approaches cannot be undermined in data analysis. I remember how an acquaintance could not clear an interview for a Data Science position at a leading tech giant because he was unable to answer some fundamental business and excel-related questions on data manipulation, even though he was thorough in Machine Learning Algorithms, Topic Modelling, Classification and other complex concepts. Strange but true!

6.??Do not stress about tools!

Remember that no algorithm is of use if it does not help the end-user to solve the problem or add value. Also, it is pointless to worry about random debates such as Tableau vs PowerBI or Python Vs R coding language. These tools will help you solve a problem but are not solutions themselves. All in all, Machine Learning is just a part of the solution and not the overall solution. And new complex technology is not only demanding and expensive but also difficult to explain to the business leader who may not be familiar with complex tools!

To sum up

As a new or professional data analyst, never ignore the fact that every business wants to improve processes and unravel profitable outcomes. So, focus on problem-solving vis-a-vis industry challenges and stop stressing about complex codes and tools. Rather, go back to critical thinking and begin with a simple algorithm design. If you are just beginning your data science voyage, just keep it simple and I wish you good luck. If you are a professional or an expert in the field, would you agree to the points I’ve cited above? Let me know your thoughts in the comments below!