How to Build a Data Analytics Team & Start Getting Results

Did you know that Merck, the $40 billion healthcare company, used data analytics to reduce manufacturing lead time by 30% and inventory carrying costs by 50%?

Xerox used big data to reduce attrition in their call centres by 20%, which is no small feat.

And yet, data and analytics aren’t just useful for businesses and the corporate world.

Did you know that Germany’s national football team built a data analytics team that provided data-driven insights to cut down average possession times from 3.4 seconds to 1.1 seconds?

This might not sound like much, but it’s what allowed them to dominate their opponents in most of their matches. It’s also what eventually helped them to win the 2014 world cup in Brazil.

The Rise of Big Data

Big Data & Data Analytics started off as trendy buzzwords only a handful of years ago. Today, they are now front and centre in driving decisions and delivering results for businesses and organisations of all sizes.

The importance of data in business decision making continues to grow.

Now you may not have the size and scale of Merck or Xerox, however, a strong data analytics team in this digital economy is a worthwhile long term investment. Building this capability in your business might be just the thing you need to take your company to the next level, surge ahead of your competition and usher in a new phase of growth.

Big data analytics examines your data to uncover hidden insights or patterns providing businesses with the ability to identify new opportunities. This leads to smarter more agile business decisions that gives you the competitive edge in a crowded market.

Before you begin investing in data analytics, you should first ask yourself a couple of questions.

1. Is Your Organisation Ready for Data Analytics?

Most companies agree that data analytics are extremely important, but they also confess that they don’t possess the in-house capabilities needed to begin. In fact, 40% of all surveyed CIOs admit to having a skill gap in this domain within their organisations.

The talent required to build and staff a data analytics team is a very specialised sort and not readily available from within the conventional ranks. It’s hard to grow your data team internally. You may have to source these skills from the external market.

Do your leaders have the courage to accept these truths when they are revealed? Can they make changes to strategy if data dictates it?

2. How Will You Use the Data?

Data analytics done merely for the sake of it can quickly become a massive waste of time and resources.

The second question you have to answer is what problem you are trying to solve. If you want the company to take action on the insights generated from big data, they have to address a current or future business challenge.

Are you hoping to increase sales and revenue? Or cut costs?

Do you want to use data to improve your processes and overall efficiency? Or manage change?

Will your analysis be channelled to other machines and systems? Or do you intend to have your insights consumed by people within the company?

Your answers to these questions will clarify for you if you should invest in data analytics and how your big data team should be structured.

Building Your Data Analytics Team

Once you are satisfied that your organisation has both the environment and the need for a data analytics team, you can get down to the actual business of building the organisation.

The size of the team will depend on several factors, including the number of problems they need to work on, the volume of data to analyse and finally your staffing budget.

However, any analytics team typically consists of five key roles, each with a distinct set of responsibilities.

We’ve described them below using the most prevalent industry nomenclature. However, keep in mind that actual job titles do vary on occasion.

The Domain Expert

The domain expert possesses a strong understanding of the business and is especially familiar with the actual problem that your team is trying to solve.

They are the person who keeps the larger team focused on the issue at hand and can take the insights generated from the data to propose business actions.

While their expertise in data science isn’t necessarily their strongest suit, the domain expert is a vital member of any analytics team. They serve as the most direct link between the analytics team and the larger organisation.

The Data Scientist

The data scientist is the brains of the outfit. They are typically a statistician with a mastery of mathematics and quantitative techniques.

Data scientists are usually the prima donnas of the big data world.

The data scientist decides how to process the large volumes of data and which analytical tools to use. They are an expert on methods of regression, correlation and predictive analysis.

They design the analytical methods and builds the hypotheses alongside the domain expert.

The Data Analyst

The data analyst understands data systems and knows how to handle massive amounts of data. They manage the storage, retrieval and warehousing of data systems.

Once the data scientist has designed the studies and defined the hypothesis, it is the job of the data analyst to introduce the data into the actual test algorithms.

Data analysts are also often referred to as database administrators or data architects.

The Data Artist/Data Journalist

Even after the data has been processed, it takes a certain amount of skill to be able to present the data in a manner that is easily consumed.Enter, the data artist, also known as the data journalist.

A data artist understands how to take volumes of data and present it in a visual, consumable manner. They can depict flows, relationships, linkages and patterns that turn obscure data into understandable trends.

This is an essential skill set for teams that generate data for consumption by other people and not machines. Data artists are a new but increasingly important skill set that will become highly sought after with the growth of big data analytics.

The Executive Sponsor

While the executive sponsor isn’t technically on the data analytics team, their role is no less important.

Any venture into the field of data analytics requires strong executive buy-in and support.

To this end, the executive sponsor is the person who invests in the capabilities and outputs of the team. They often take the lead role in defining the team’s mission and in creating its charter.

They lends their credibility and organisational capital to the group while providing oversight, guidance and mentorship to its members.

They can mobilise resources, people and time to drive the data analytics strategy and agenda across the organisation.

Data Skills: High Demand, Low Supply

Once you’ve decided on the appropriate structure for your data analytics team, you have to ensure that you staff it with the very best talent.

The data analytics sector is a white-hot market for talent, and sourcing the best people can be a challenge. In fact, it is predicted that by 2018, the demand for data analytical skill sets will increase by 50%.

Getting Started

You can gain a huge advantage if you engage a firm that has an established practice and global reach. A professional agency can give you access to the most accomplished candidates from a wide variety of sectors within a highly targeted skillset.

The Big Data sector is a challenging field with high demand and limited supply. If you have a specific set of roles you’re looking to hire, check out our capabilities. We can help you fill requisitions for a variety of positions in this field.

We understand that each of our clients is unique and has their special requirements. And so we offer multiple service models, designed to meet your needs.

If you’d like to learn more about how we can help you build your organisation, contact us today for a no obligation consultation.

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