Just because you’re a business professional does not mean you can’t or you shouldn’t pursue furthering yourself in analytics. Businesses view strategic decision making as a competitive advantage. You should too! Learning the basics behind data science not only adds value to your organization, it increases your value and thus your demand too.

About the image: Sifting through data can feel like a scene out of “The Matrix” with 1’s and 0’s staring back at you as you try to gain insight out of information. The reality is adding analytics tools to your skillset can help significantly improve your ability to gain insight and better your decision making. This is the heart of business intelligence.

My background is primarily in manufacturing, which is where I will have seen the issues that I discuss below. However, I believe these issues transcend organizations in all sectors and disciplines including banking, healthcare, manufacturing, technology, and more.

The Problem:


Companies have massive datasets of sales information stored in ERP and other relational databases. ERP systems (e.g. Oracle, SAP) are great at keeping operational data tracked and managed, but fail at presenting the information in useful ways. This is where the market for CRM developed, which has become a $24B market as of 2014 according to crmsearch.com. Further, the market for CRM continues to surge at a 15% annual growth rate. This all adds up to one thing: a massive need to analyze data.

CRM tools exist to help analyze these datasets. Customer trends can be subset, filtered, aggregated, and more as fast as you can click your mouse. However, tools like Salesforce while amazingly powerful have limitations. These tools are primarily useful for dashboards. While great for visualizations to see what is happening historically and to summarize mass information, dashboards are typically not useful for true, in-depth analysis. Organizations need to think beyond CRM.

This is where the data analyst comes in.

The Solution:


True, in-depth analysis requires data analytics by someone that can perform statistical calculations. They don’t have to be masters of the black arts. They just need to be competent in software (e.g. R, Python, and SQL) and have a general knowledge of the statistical tools available to them.

MBA professionals can and should learn analytics because the tools get results and analytics skills increase the value MBAs bring to their organization. Being able to process information for decision making is a competitive advantage. Here are a few key metrics to consider if you are an MBA considering adding data science to your skillset:

The demand for analysts is growing:

Companies want individuals with analytical skills. According to indeed.com, on a relative basis the job postings for Data Scientists have grown over 1700% from 2012 to 2016.

Data Scientist Job Trends

Source: indeed.com

While this constitutes all “Data Scientists” including those involved in machine learning, big data, and other computational disciplines, it shows the increasing trends for businesses to invest in individuals with backgrounds in analytics.

But, why?

Businesses are gaining competitive advantages through analytics

Businesses are yielding results through analytics. Here’s a few areas that businesses have identified as needs courtesy of Boston University:

  • Data Management: Data growth is exponential, and analytics projects are becoming the norm.

  • Better Decisions, Faster: Dealing with slow information impedes management’s ability to make decisions versus competition.

  • Predictive Analytics: Foresight is the ultimate competitive advantage. Being able to spot trends before competition is a huge advantage.

  • Optimizing Revenue & Cost: What company wouldn’t want to increase revenue while reducing cost? Analytics enables organizations to have this win-win scenario become reality.

Why should you care?

Data-Driven MBAs are Perfect for the Job

How do you get your boss to make a strategic decision? Data! Of course I’m oversimplifying. It’s not only sufficient to have data, but you need to communicate it in a down-to-earth, business-savvy way. This is where an MBA can leverage communication skills along with an understanding of analytics.

A huge challenge is speaking stats to non-quantitative business professionals (NQBP). NQBPs know competitive dynamics, live for SWOT analyses, and love to present on [insert business matrix of the day]. While these are very important skills to have, the current business environment requires businesses to not only view strategy from traditional competitive frameworks (e.g. Porter’s Five Forces), but also to leverage the massive datasets these companies have built over years of tracking sales, operations, and customers.

MBAs are great at analyzing and communicating. The life of a business professional revolves around meetings, presentations, and reports. By adding data science to your toolbox, you can bring an extra ability to communicate complex subjects in understandable ways to others in the organization that may not be as quantitative. Also, you can help team up with the quants to lend on each other’s natural abilities.

Ok, so what can you do with analytics?

Application Examples:


Alex Jones in his post entitled, “Data Science: Bridging the Business & IT Gap” developed a basic framework for business problems:

Business Problem Framework

Source: Data Science: Bridging the Business & IT Gap

Using the business problem framework, we can focus on a few key areas in the Market section that can have an immediate impact on sales. This is by no means an exhaustive list, but rather a few tangible examples to get you thinking.

Customers:

The lifeblood of a business is sales, which are derived from… Customers! Analyzing customer trends is a perfect application for data science. Most organizations have customer specific sales data that tracks purchase orders. This is typically in an ERP database. The data can be parsed by features and analyzed via regression or clustering to identify deep trends.

Some organizations even have databases tracking requests versus purchases, which can further help in understanding. This is typically available in a CRM database. The data can be analyzed to determine what factors correlate to a purchase. The information can be used understanding customer triggers, prioritizing which requests to work on, etc.

Products (and Services):

Products and services are essential to the business. Without the exchange or goods and services, cash does not flow into the organization. What products do your customers care about? Can you recommend another product based on the information you have available about the customer? The short answer may be yes with the help of recommender systems.

Promotion:

What promotional channels have the biggest impact? Are there interaction effects between channels (meaning does spending on multiple channels have a greater/lesser effect than the sum of its parts)? The answers to these questions can be answered by Marketing Mix Modeling, a technique that uses analytics to forecast the impact of marketing tactics.

Additional Resources:


I hope you now understand the implication of analytics in a business and how the MBA can use this to add value by leveling up in data science. To keep the knowledge transfer going, here are a few resources: