data-team

As it’s been repeatedly said, digital business can not happen without data and analytics at its core. Technology can be a point of failure if not handled properly, but it is often not the most important roadblock to progress. In Gartner’s annual Chief Data Officer survey, the top roadblocks for success were human factors – culture, resources, data literacy and skills. A similar pattern emerges from another study, Gartner’s CEO and Senior Business Executive Survey, where “Talent Management” was listed as the “number one organizational competency to be developed or improved.”

In this article, we would like to focus on the key data and analytics roles & leaders that are essential for enterprises seeking a data-driven organization.

Support roles

Chief Data Officer

The Chief Data Officer, or CDO, is a senior executive responsible for enhancing the quality, reliability and access of data. They are also in charge of creating value from their data assets and from their data ecosystem in general. Through data exploitation and by enabling all forms of business outcomes through analytics, the CDO can produce more value with their enterprise data. There are many variations of the title such as CAO (Chief Analytics Officer), CDAO (Chief Data & Analytics Officer), CDIO (Chief Digital Information Officer), etc.

See more in our article “What is a Chief Data Officer?

 

Data & Analytics Manager

As the title implies, the Data & Analytics manager is responsible for managing the data & analytics center and is responsible for its delivery throughout the entire organization. They are a key contributor to the strategy and vision for the data & analytics department, they build the roadmap and are responsible for budget and resource planning. Besides measuring the performance of their analytics team, they are also responsible for tracking the contribution of data analytics in regards to business objectives.

 

Data Architect

The Data Architect, also referred as the Information Architect, strengthens the impact and proves recommendations on business information. They make the information available and shared across the company by presenting how information assets drive business outcomes. They “own” the data models. They understand the impact various data analytics scenarios on the overall IT architecture (such as data science or machine learning) and work closely with the business department.

Analysts

There isn’t a single type of analysts, but rather a spectrum of analysts. Their roles depend on their use cases and vary by responsibilities and skill requirements. There are data analysts who have a foundational understanding of statistical analytics. They are, or work closely with domain experts to support business areas, processes, or functions.

 

Project Manager

The project manager is responsible for the successful implementation of all projects in the enterprise portfolio. They plan, execute and deliver projects in accordance with business priorities. Throughout the project’s lifecycle, the project manager tracks their project’s status and manages their teams to limit any risks. They are the primary point of contact for data and analytics initiatives.

 

Data Roles

Data Engineer

A Data Engineer involves collaboration across business units and IT units and is the practice of making the appropriate data accessible and available to various data consumers (data scientists, data analysts, etc.). They are primarily responsible for building, managing and operationalizing data pipelines in support of data and analytics use cases. Also, they are responsible for leading tedious tasks such as curating datasets created by non-technical users (through self-service data preparation tools for example).

Without data engineers, data & analytics initiatives are more costly, take longer to deploy, and are prone to data quality and availability problems.

Data Steward

Data stewards are the first point reference for data in the enterprise and serve as the entry point to access data. They must ensure the proper documentation of data and facilitate their availability to their users, such as data scientists or project managers for example. Their communication skills enable them to identify the data managers and knowers, as well as to collect associated information in order to centralize them and perpetuate this knowledge within the enterprise. In short, data stewards provide metadata; a structured set of information describing datasets. They transform these abstract data into concrete assets for the profession.

See here for more information on Data Stewards

Analytics roles

 

Data Scientists

A data scientist is responsible for modeling business processes and discovering insights using statistical algorithms and visualization techniques. They typically have an advanced degree in computer science, statistics or other related fields. Data Scientists contribute to building and developing the enterprise’s data infrastructure and supports the organization with insights and analysis for better decision making. They predict or classify information to develop better action models.

 

Citizen Data Scientist

Contrary to data scientists, a “Citizen Data Scientist” is not a job title. They are “power business users” who can perform both simple and sophisticated analytical tasks. They can execute a variety of data science tasks, supported by augmented analytics tools for data discovery, data preparation, and model deployment. Potential citizen data scientists will vary based on their skills and interest in data science and machine learning.

See here for more information on citizen data scientists

 

AI / ML Developer

Artificial intelligence / Machine learning developers are increasingly responsible for enriching applications through the use of machine learning or other AI technologies such as natural language processing, optimization or image recognition. They embed, integrate and deploy AI models that are developed by data scientists or other AI experts either offered by service providers or developed by themselves. Other key skills include identifying and connecting potential data assets, data quality, data preparation and how these are used for model training execution.

 

Conclusion

The growing importance and strategic significance of data and analytics is creating new challenges for organizations and their data and analytics leaders. Some traditional IT roles are being disrupted by “citizen” roles performed by nontechnical business users. Other new hybrid roles are emerging that cut across functions and departments, and blend IT and business skills.

By putting together these must-have roles, your enterprise is a step closer to becoming data-driven.

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