7 Key Members of Every Big Data Team

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Now that the age of Big Data is upon us, businesses all over the world use information collected by analyzing data to make important strategic decisions, allowing them to spot opportunities to gain an advantage over the competition they would have otherwise missed. If you want to take full advantage of what Big Data can offer your business, having a team of highly skilled professionals with diverse skills is essential.

In the last few years, highly distinct roles have emerged among those who work with Big Data. While all of those team members work towards the same objectives, the way they each do it will be different. Here is an overview of the seven main members you’ll need to put together a successful and highly efficient Big Data team.

1. Software Engineers

Software engineers play a key role in your Big Data team by creating the software that allows you to collect the actual data. They work to put together both the back and front end of systems responsible for collecting and processing data. While applications running on desktop PCs are still highly popular in some industries, most software being developed now is made to run on mobile devices or in a web browser.

Software engineers are able to use their knowledge and experience to not only create the software applications themselves, but also provide valuable advice on how your company chooses technology and how it implements these choices.

2. Statisticians

Statisticians keep your Big Data team running by using math to collect, analyze and interpret the data other team members have acquired during the course of their duty. They’re also very good at determining which method to use to collect data for a specific purpose.

Statisticians commonly use programming languages like Stata and Perl, although it’s common for them to be familiar with other ones as well. Their contributions are especially valuable if your company needs to collect data through methods like surveys, field experiments and focus groups.

3. Data Hygienists

When dealing with Big Data, it’s rare that all of the information contained in a data set is accurate, relevant and useful. For this reason, your team will need someone to “clean” the data and refine it, ensuring that it’s suitable for use during its entire lifecycle. For example, a data hygienist could look over the data set to ensure that values denoting time or currency are all logged in same manner.

They can make adjustments manually or use a variety of software tools to automate part of the process. By making sure your data is stored and presented in a uniform manner, data hygienists help prevent errors that can cause serious problems further in the data’s lifecycle.

4. Data Architects

The data you collect will often come to you in a rather unstructured manner that it difficult to do anything meaningful with. Sometimes even reading the data can be an exercise in frustration. By hiring a data architect, you’ll solve this problem, as they have the ability to take all your data and transform it into sets that can be easily worked with by your entire organization.

As data engineers, they’ll develop a wide range of methods to improve the quality and efficiency of your data. They’ll also help you implement these methods across your organization. In order to do their job effectively, these team members will need to have a solid knowledge of database technologies, including various IBM, Oracle, Hadoop and Teradata.

5. Data Scientists

Once your data has been neatly organized, data scientists come in by creating complex analytical models that take advantage of the gathered data to give your business information that is extremely valuable. For example, a data scientist is able to create a model that analyzes purchases to predict future customer behavior. This information can then be used to segment customers into different categories, optimize product pricing, and develop promotional offers to acquire new customers and keep the existing ones loyal.

While data scientists may not always be highly experienced programmers, they still have a deep knowledge of industry-leading analytics frameworks. The programming languages they’ll be the most familiar with will tend to be those specific to Big Data applications, such as SQL, R and Python.

Simply put, your data scientists will spend a lot of time looking over the data your organization has acquired to get the maximum value out of it. Once ready, they’ll report the results to all main stakeholders, such as heads of various departments within your company.

6. Visualizers

The role of visualizers is to ensure the data you have can be understood by those who need to see it, whether they’re working for your business or third parties like vendors, clients and potential business partners. A visualizer is skilled at taking raw data and changing its format into something easier to understand. This could be graphs, lists, tables, infographics, slides and even short animated videos.

Although visualizers will have the knowledge of various business tools, their specialty is using Big Data frameworks, as well as agile development. This helps them interpret the information they see correctly and present it in the right way for the right audience.

7. Business Analysts

Business analysts interact with various members of the Big Data team, as well as the main stakeholders of a company. Their role consists of ensuring that everyone on the Big Data team knows the organization’s key objectives and is actively working on achieving them.

What makes a business analyst so valuable in mainly their very deep knowledge of a company and the industry it operates in. This lets them look at data and spot insights that will help a business move in the right direction. More specifically, they oversee processes to make them as cost-effective as possible, spot industry trends that could have an impact on the organization and ensure that the end-user gets what they need.

Business analysts have excellent communication skills, allowing them to have meaningful interactions with all members of an organization’s hierarchy. Additionally, they leverage their industry knowledge to recommend ideas that can be applied right away.

When putting together a Big Data team, it’s important that you create an operational structure allowing all members to take advantage of each other’s work. Your company will also need to have the technological infrastructure needed to support its Big Data. This can be done by investing in the right technologies for your business type, size and industry.

About the Author

Josh McAllister is a freelance technology journalist with years of experience in the IT sector. He is passionate about helping small business owners understand how technology can save them time and money. Find him on Twitter @josh8mcallister



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  1. Mike Farrar says

    I would propose that you left out the role of the Interpreter, who is responsible for translating (usually poorly expressed) business goals into appropriate analytic plans, and who is also responsible for explaining technical analytic results to business users to give them the confidence to move forward with implementation.

  2. As an HPC analyst and system admin for years, I’ve sped up data science workflows 400% in some cases just showing them how to use the resources they already have properly. I’m surprised you didn’t say you need someone who actually understands computers and operating systems, because none of these seven typically do and often big data themselves into stasis.