September 26, 2019

WHO SHOULD BE ON YOUR ANALYTICS TEAM?

Data Analytics Team

This article is part of a four-blog series that takes an in-depth look at data visualization. Follow the links below to see the other stories:

THE SECRET TO DATA VISUALIZATION SUCCESS IS IN THE USE CASE

SIX PROBING QUESTIONS TO IMPROVE ANY DATA VISUALIZATION SYSTEM

TIPS FOR MANAGING YOUR ANALYTICS DEPARTMENT

Successful data visualizations don’t just happen. You need to have the right data, tools and workflows in place. Most of all, you need the right people. The talent you should hire depends on the size of your enterprise and the maturity of your analytics organization.

Here is a handy guide we’ve developed at WebbMason Analytics to help you choose the right team members for each stage of your analytics evolution.

Data experts for small departments and companies getting started with advanced analytics

Business and/or Data Analyst/Consultant
All data projects start on the desk of a business or data analyst. If you are working with a company like WebbMason Analytics, these analysts usually hold the job title of consultant. This person assesses the business need, captures requirements and develops process and decision flow diagrams. He has business and domain knowledge and needs to be able to cultivate relationships with stakeholders around the organization.

In small organizations, the business and data analysts may be rolled into a single job description. Where the roles are split, the business analyst focuses on stakeholder adoption and creates a framework to help the data analyst locate the information he needs. Then the data analyst identifies data sources and maps specific data assets to business requirements. He works closely with data engineers to translate raw data into business value.

Data Engineer
A data engineer is responsible for developing and managing end-to-end data pipelines. In many organizations, especially small ones, the data engineer also manages many of the components of the analytic platform, including infrastructure. Given the dependency of data pipelines on the underlying infrastructure as well as the ease of use of the cloud, data engineering and platform optimization and maintenance are tightly connected. As a result, the data engineer is often directly responsible for the running, maintenance and improvement of the analytics platform servers, storage devises, visualization platforms and other infrastructure.

To do this, she needs to have a reasonable understanding of a wide range of skills including those of data analysts, scientists, architects and others. That’s what makes her such a valuable hire for enterprises with nascent analytics departments.

Specialized talent for enterprises with an analytics center of excellence and a medium-size department

Analytics Manager
The analytics manager is a critical part of any analytics department. He implements the organization’s analytics goals, drives daily operations, assigns technical resources and prioritizes the projects and the team’s workload.

In medium-sized departments, the analytics manager is the head of analytics and may report directly to the CEO. In a larger organization, he reports to a director or vice president.

Project and Program Managers
A project or program manager guides the business analysts. They bring rigorous requirements gathering and project management methodology to the department. Program managers usually drive the strategic level, while project managers are responsible for status and tactical progress.

With additional training and experience, a business analyst may progress into the role of a project manager and then into program management.

Data Scientist
Data scientists have advanced statistical skills and are primarily responsible for creating artificial intelligence and machine learning models. Such individuals evaluate, clean and prepare data for the predictive models.

The data scientist is proficient in statistical and general-purpose programming languages such as R, SQL, Spark, SAS, Hive, Hadoop and Python.

Data analysts work with the existing numbers where as a data scientist has additional statistical training so he can predict what the numbers will look like in the future.

With additional statistical training, a data analyst may progress into a data scientist’s role.

Solutions Architect
The solutions architect’s job is to deliver scalable, resilient and secure architecture for data pipelines that produce analytics-ready datasets. He works with the client and the analytics team to define requirements as well as design and implement solutions that mine, analyze and integrate data from different sources.

The person in this role needs to have his finger on the pulse of big data analytics technologies. He needs to have a comprehensive understanding of cloud architecture, security, data storage, data ingest, data processing, data science and data visualization.

Management structure for mature analytics organizations and large departments

Analytics Director and VP
Mature departments require strategic leadership in the form of directors and vice presidents. The larger the organization, the more likely it is to have one or more directors and vice presidents.

At these more senior levels, the focus is on identifying business problems that can be resolved by an advanced analytics solution. These roles are also responsible for obtaining funding and finding projects. There is a heavy emphasis on tracking ROI and reporting value gained to enterprise leadership.

Chief Data Officer
The chief data officer is a leadership role responsible for creating both a data and analytics vision and roadmap for the enterprise. She understands the enterprise’s issues and challenges and develops strategic solutions that will be designed and implemented by the analytics center of excellence. At a high-level, she tracks the value provided to the business, milestones, funding and ROI.

SPEAK WITH A TALENT PLANNING EXPERT