Building an Analytics Team From the Ground Up

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Collection, analysis and consumption of relevant market data are key to business success. That is why entrepreneurs, investors and business managers need to invest in an analytics team to help them analyze and leverage data. A great analytics team can give a business an edge over the competition. As the data industry continues to expand and top talent is in demand, it is important for leaders to assemble and retain a talented team of analysts. Learn more about the current data analysis industry, roles and necessary skills, and how to attract and retain the top talent in the data analysis industry. Created by Villanova’s Online Master of Science in Analytics degree program.

Current State of the Data Analysis Industry

Scientists have projected that the digital universe will grow to 45 trillion gigabits by the end of the decade. This is a 50-fold growth. There are currently 20 universities offering data science programs. Out of these, only 6 of them offer undergraduate programs while the rest are graduate schools. This shows that data analysis is a highly specialized field of study.

By 2018 the demand for data analysts will rise by at least 50% throughout the world. This is one of the reasons why in 2012, the Harvard Business Review called data scientist as the sexiest job in the 21st century. The national average salary of a computer scientist is $60,476.

What is a Data Analyst?

Before looking at the skills required by a data analyst, it may be prudent to first define what a data analyst does. The main responsibility of a data analyst is to take business data, such as sales figures, logistics and market figures), analyze it and use the information to help managers make better business decisions. While managers usually make decisions based on what they think is the right decision for the business, they usually need to back their decisions with facts, and that is where data analysts come in. Usually, data analysts are required to present all the data they have collected and analyzed. The presentation must be in a simple format, which the average person can understand. After presentation of the information, the analyst will recommend a number of options for the managers to consider.

Skills Needed by a Data Analyst

As you can see from the above definition of a data analyst, these professionals need a myriad of skills to do their job. Some of the most important skills include; business analysis, data analysis, data architecture, social/behavioral analysis, mathematics and statistics, and data interpretation and visualization skills. To succeed in this industry, data scientists need five core competencies; Programming, Statistics, Machine Learning, Data Visualization and Data Munging. From these core competencies of a data scientist, you can see that there are two main backgrounds where data scientists can come from. These are; computer science/programming and mathematics/statistics. Anyone who has an undergraduate degree in these fields can join a graduate school to study and become a data analyst. Other fields, such as actuarial science, are also related to this field.

Building a Data Analysis Team

Building a successful data analysis team in an organization is not easy. This is because data analysts require a conducive environment in which to work. Business owners need to keep this in mind when assembling a data analysis team. In that regard, the following are the five foundational elements of a successful data program:

Culture: This is the ability to make data driven decisions and implement process and behavioral changes across the organizations.

Skills and Tools: The organization must have a technology infrastructure and an employee base with adequate skills to effectively use these tools and interpret results.

Data: In order to utilize data for analytics, the organization must have an underlying infrastructure to support it. For instance, there must be a system for collecting, storage and archiving data in the right format to make retrieval and analysis easier.

Process: The organization must have processes for analysis and reporting.

Expectations: The organization must uphold consistent expectations for its use of analytics.

If an organization has these five elements, they can easily assemble a successful team of data analysts and run a successful data analysis program that will benefit the entire organization.

Key Roles on a Successful Data Strategy Team

Data Evangelist: The data evangelist must be an expert in a specific area of business. This can be marketing, finance, human resources, sales and so on.

Contextual Analyst: This is a person who is able to read, understand and interpret data. In other words, the contextual analyst must understand data.

Data Custodian: Data can be manipulated, corrupted, stolen, deleted or simply lost, so there must be someone who is tasked with safeguarding the data. This is usually the data custodian. A successful data strategy team must have a caretaker of the organization’s data.

Data Visualizer: The average person may not understand data in its raw form. Most people often require graphics to fully understand the data. For this reason, the data strategy team in an organization must have a data visualizer with design skills and an ability to turn innovative data into compelling graphics.

Neuro Analyst: This is an important member of the team as they are able to develop strategies to present data in ways that people can easily understand.

How to Deal with the Current Shortage of Data Analysts

The best strategy for bridging the gap between demand and supply of data analysts is to train more of them. In that regard, college recruiting is a great start. Organizations should develop internship programs to attract the brightest students and make the transition into the business world easier for them. An internal data analytics education program is also a great idea as it can help convert existing employees into data analysts. Lastly, data analysts must have split roles. There should be different data scientists responsible for analyzing data and managing data as this will improve efficiency of operations.

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