Data Analyst – Data Science & Analytics

Occupational Standard

(for use in the development of Business Technology Management related job

descriptions, performance evaluations, career development plans, educational learning outcomes etc.)

Description of Position Analysis of data from a variety of sources has long been a key activity within many organizations across a variety of

industries. Despite this, today, the massive amount of data that may be available for analysis and the development of techniques permitting the successful analysis of such date have given a particular importance to this role and have led to new, emergent aspects. Data within an organization may come from many sources, is often incomplete, and may be structured and unstructured. Thus, the data analyst is responsible for importing, transforming, validating or

modeling data with the purpose of understanding or drawing conclusions from the data in order to drive operational decision-making within the organization

Position Development Advancement to manager level positions is possible through progressively responsible leadership positions and

management experience. The career path will be determined by the size, type, geographic scope, culture, and

organizational structure of the firm offering employment.

Required Qualifications

Education Post-secondary education is preferred, usually a Bachelor’s degree in a business, computing or engineering field. Follow up technical educational may also be required depending on the technologies in use at the various organizations.

Training Data Analysts require on-the-job training; however, typically organizations require that the individual will already have the required skills, knowledge, work-related experience, and/or industry courses and programs. Some organizations will send individuals to specific enterprise solutions training courses and programs to learn additional tools and

techniques.

Related Work

Experience Individuals may have experience in any of the methodologies and techniques used as a Data Analysts or junior statisticians.

Often this experience may be augmented by specific industry experience using industry or use case specific tools (e.g. R, SAS, python, etc.).

Tasks  Establish metadata management, data catalos, data standards

 Monitor the best practices followed for Master Data

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 Develop standards and guidelines for master data issues such as data convergence, data integration, data synchronization, data definitions, etc.

 Define data strategy, policies, controls and programs to ensure that enterprise data is accurate, secure and reliable

 Select analysis approaches and methods that can be used to analyze data sets in order to answer critical business problems

 Determine the structure that data must be in so that critical business and organizational questions can be answered

 Use a variety of tools to analyze data and report findings from the data analysis itself with particular attention paid to activation

 Engage with relevant internal parties and external vendors in best practice sharing and effective Data Management solution delivery

 Ensuring compliance with data architecture and data engineering principles and standards

 Selecting preferred data management technologies, analysis technologies, and visualization technologies Tools and Technology  Statistical analysis software

 Data analytics or intelligence programs

 Office productivity tools

 Software development tools and dev ops tools including language specific IDE’s, GIT, etc.

Required Competencies Knowledge

Data Analysts should have knowledge of:

 Large complex data analytics or intelligence programs

 Data, statistics, and big data concepts that relate to data analysis

 Current and emerging data analysis & statistics technologies

 Various architectures including distributed architectures

 Software development methodologies relating to analysis

 Architectural understanding of the data and big data ecosystem

 Best practices in data delivery and measurement for the individual organizations that they work for or with

 Policies and principles for the management of information

 Relevant information standards and their appropriate use

 Basic technologies and workflow for the purposes of analysis, design, development and implementation of information systems and applications.

 Organizational or industry specific terminology and commonly used abbreviations and acronyms

 Commonly used formats, structures and methods for recording and communicating data, as well as

knowledge for how this data is incorporated for system and application use.

 Architectural relationships between key information technology components and best practices in

enterprise architecture frameworks/perspectives.

 Appropriate informatics standards and enterprise models to enable system interoperability (e.g., terminology, data structure, system to system communication, privacy, security, safety).

 Key information technology concepts and components (e.g., networks, storage devices, operating systems, information retrieval, data warehousing, applications, firewalls, etc.).

 The ability to identify relevant sources of data needed to assess the quality of information & draw

appropriate conclusions

 Statistical & analytical tools, techniques and concepts

 The ability to present data and information in a way that is effective for users and consumers of the data

 Knowledge of the indicators and metrics important for the specific business that they are measuring

Skills Data Analysts should have skills in the following categories:

Technical

 Demonstrable knowledge and experience of large, complex data analytics or intelligence programs

 Statistical, pattern recognition skills

 Understanding of data concepts

111 and technologies

 Architectural understanding of the data and big data ecosystems

Contextual

 Full understanding of the organization and of its requirements and opportunities in data/big data analytics

 Experience in targeting tradecraft as well as experience in cargo screening, person screening, operational targeting

 Experience managing a team and working with senior level Government clients on consulting projects

 Strategic thinking

Personal Attributes A Data Analyst should have the following personal attributes:

 Communication skills

 Presentation and public speaking skills

 Rapport building and networking

 Innovation and creativity

 Leadership skills including ability to influence others, to lead business and 
technology programs, projects, workshops and initiatives, to inspire confidence 
and garner respect from business and technology

stakeholders

 Planning, supervision, coaching and delegation skills

 Decision making skills

 Negotiating skills

 Research skills

Abilities A Data Analyst should have the following abilities:

 Ability to explain complex concepts to lay person

 Ability to collaborate with multiple skills and cross-functional expertise.

 Ability to communicate the benefits of analytical approaches simply and clearly

 Ability to communicate with top executives, business management, IT management, solution architects, technical architects, subject matter experts, partners and 
customers.

 Ability to adapt vocabulary and style for each situation


 Ability to present appropriately to a variety of audiences, including large audiences, top executives, business and technical leaders

 Ability to present complex ideas with simple visuals.

 Ability to seek and to find solutions to a wide range of business and technology problems

 Ability to seek standardized solutions for problems where available

 Ability to find solutions across a wide range of technologies and business domains. Often solutions have budget, time or operational constraints

Work Values Individuals who are effective as Data Analysts are:

 Able to communicate at all levels of organization

 Able to present complex ideas with simple visuals

 Able to find solutions across a wide range of technologies and business domains

 Able to facilitate collaboration

 Enjoy problem-solving

 Highly analytical

 Able to work independently

Work Styles Data analysts would have the following work styles:

 Collaborative

 Cooperative

 Stress tolerant

 Initiative

 Independent

 Integrity

Essential Skills Profile A data analyst would have the following essential skills profile:

 Reading text

 Document use

 Writing skills

 Numeracy

 Oral Communication

 Thinking Skills

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 Significant Use of Memory

 Finding Information

 Working with Others

 Continuous Learning Additional Information

Physical Aspects Data Analysts work extensively in an office environment (sitting for long periods, repetitive computer and telephone use). However, Data Analysts may also be required to travel to satisfy the position function. Typically there is no heavy lifting, bending, or stooping required; however, this is determined by the needs of the organization.

Attitudes Data Analysts should have very advanced interpersonal skills – be persuasive, empathetic, able to handle pressure,

creative, have a sense of urgency, and attention to detail.

Enterprise Data Architects must exhibit leadership, people management skills, advanced negotiation skills, advanced conflict resolution skills, and organizational and planning abilities. Adaptability and flexibility are important, as Data Analysts work with diverse multicultural workforces.

Future Trends Affecting

Essential Skills The ability to speak more than one language, and an

awareness of and sensitivity to the diversity of international cultures is considered a growing need in the face of

increasing globalization. Furthermore, familiarity with opportunities and benefits associated with “green IT” (e.g.

server energy efficiency, reducing overall power consumption from IT related activities, etc.) will be of increasing importance as facilities begin to manage their overall environmental footprint while seeking short and long term cost saving opportunities. A strong understanding of cloud computing will also serve all individuals in this position very well.