You have been learning about data professionals and how valuable their work is to the companies that employ them. You also learned about technical and strategic roles for data analytics professionals. Data analysts in technical roles transform raw data into something useful for decision-making. Strategic data analytics professionals focus on maximizing information to guide the businesses they are working for. In this reading, you will learn more about the data professional career space, and see examples of some general categories of data professional roles. 

The classifications of data professional roles presented here are a very general introduction to the careers available within the data career space. When you begin to look for a job, you may encounter entry-level, mid and senior positions within each of these general categories. 

Data scientist and data analyst

Data scientist and data analyst are roles that work directly with data. These professionals gather, clean, analyze, and share insights from data with stakeholders. An increasing number of industries turn to data analysis to create insights that inform various tasks like guide decision-making, identify user preferences, or determine how to use resources more effectively. With more industries looking for data professionals, there is a great chance that you will find one that matches your interests.

Key attributes

• What they do: Uncover trends, patterns, and insights from data
• How they do it: Employ advanced modeling and statistical analytics techniques
• Entry-level data scientist or analyst positions may not require considerable data analysis experience and be less restrictive about requiring those hired to hold a traditional degree.

Sample job titles

• Data scientist
• Marketing analyst
• Data analyst
• AI analyst
• Business analyst

Data management and infrastructure 

Data professionals that work in data management and infrastructural roles are primarily responsible for the systems that distribute data and maintain its integrity. They work alongside data analytics professionals and help support their work. Their main responsibility is to ensure the functionality of data systems and the compliance with local, state, and federal regulations involving data security and ethics. 

Key attributes

• What they do: Manage data sources and the overall data infrastructure
• How they do it: Work with the tools and databases used to manage data within a business

Sample job titles

• Data engineer
• Technology engineer
• Data manager
• Data steward
• IT architect 

Business intelligence

Data analytics and business intelligence share a lot of commonalities. Both fields have professionals that use data to create insights that inform decision-making. A major difference is that business intelligence is more focused on creating processes and information channels that transform relevant data. Business intelligence professionals create tables, reports, and dashboards that empower stakeholders, giving them access to the data they need to inform the entire decision-making process on a continual basis. These roles often serve as a complement to core data analytics/data science professionals.

Key attributes

• What they do: Perform predictive analysis that enables organizations to determine likely future trends
• How they do it: Create tables, reports and dashboards that empower their organization

Sample job titles

• BI architect
• BI analyst
• BI solution developer
• BI software engineer
• Data viz & BI analyst

Additional roles for data professionals

Product development teams

The professionals in these roles manage a portfolio of customer and stakeholder analytic projects and initiatives. They often manage the analytical strategy for the organization. In these roles, experience is most likely required, and responsibilities are larger and more global.

Key attributes

• What they do: Manage analytical strategy within a project team 
• How they do it: They are less hands-on with data analysis, serving as the person a data scientist or analysts would report to

Sample job titles

• Product manager 
• Product developer
• Product lead
• Digital product manager
• Customer product manager

C-suite

This classification of roles covers high-ranking executives within an organization. The ‘C’ in c-suite stands for chief. In general, there’s a trend for the c-suite to build data-driven decision making into their processes. Individuals filling these roles within organizational leadership teams are expected to be familiar with data and analytics.

Key attributes

• What they do: Responsible for data and data professionals across an entire organization
• How they do it: They are decision makers found at the top end of a company’s hierarchy

Examples of job titles 

• Chief marketing officer
• Chief data officer
• Chief analytics officer
• Chief information officer
• Chief data scientist

Key takeaways

There are a wide variety of roles and responsibilities in the data professional career space. Later, you will learn how these roles work together and collaborate with groups of professionals with diverse expertise on data analysis projects. Having a general understanding of the roles and responsibilities of data professionals can help inform your job search. This information can serve as a reference that can help you understand the needs and expectations of companies as you navigate through job postings. Take inventory of any transferable skills you may already possess from educational or professional experience.

As you have been learning, the data analytics field is dynamic, spanning a variety of industries. For you and other data professionals searching for new job prospects, there are great opportunities across a spectrum of career fields. As you start to think about your own future role in data analytics, it can help to investigate how data is being used within different industries. In this reading, you’ll consider some examples of industries and how data analytics helps guide them. You’ll also consider the future of data analytics and how the field is still evolving! 

There are so many different industries taking advantage of data analytics in so many different ways. Here is just a sample of some of these industries and how they use data:

Data trends for the future

As you can already tell, data analytics is an emerging field with a wide range of exciting opportunities. And, even more exciting is the fact that big data is getting bigger. The need for people to understand, prioritize, manage, and analyze that information is not slowing down in any industry. Businesses will continue to rely on data-driven decision-making, fueled by both simple trend analyses and more complex techniques like predictive modeling and forecasting. 

Additionally, more companies are storing all of their raw data within large repositories accessible across the organization. As companies become more reliant on insights generated by this data, there will be many opportunities for data analytics professionals to use their skills and knowledge to organize that information and make it useful.

Innovative technologies

Innovations in accessing this data are leading to new approaches in making data interconnected—meaning that there are still new and evolving ways businesses in different industries are going to use data in the future. Technology is also ever-changing and adapting to these new needs. Because of this, there will always be exciting new tools and data solutions to explore.

Artificial intelligence will continue to have a large impact on business, helping to streamline many areas. For example, an increase in sales is understood to be a direct result of forecasting product demand. Artificial intelligence helps companies ensure warehouse supply, keep items in stock, reduce delivery time, and boost operational efficiency through automating processes.

Additionally, artificial intelligence will combine with machine learning, business intelligence, and automation to deliver more personalized services to customers.

Offering additional services will push forward innovation, bringing computer applications and the sources of stored data closer together physically. This concept is referred to as edge computing. By closing the gap between data and computation, speed improves. This results in greater support of real-time analytics and the automation necessary to support the increasing number of devices that are becoming linked through the internet of things. 

An increasing number of data analytics tasks will be automated by creating, managing, and analyzing data in edge environments. Artificial intelligence and machine learning systems are only as equitable and inclusive as the people who create and train these systems. You will learn more about how you, as a data analytics professional, will need to take steps to ensure equity in the future. 

Key takeaways

As you progress through your career as a data analytics professional, you will need to stay up-to-date with the latest trends and technologies used across different industries. The one certainty about the future of data analytics is that an increasing amount of data will continue to be generated and that new systems and innovations will continue to be developed, allowing data professionals an opportunity to learn, grow, and develop new skills.

Strategic

Business intelligence professionals and technical project managers are strategic data professionals. In their roles, strategic data professionals maximize information to guide how a business works.

data analysts

Expert data analysts explore vast and complex datasets in order to identify worthwhile business initiatives. In this highly technical role, data analysts help ensure that an organization’s data science efforts are directed as efficiently as possible.

You have been learning about skills that can help you enter and excel in the data career space. In this program, you will be building technical abilities, which are necessary before pursuing opportunities in data analytics. Job postings will include a list of the required technical skills prominently displayed. Quite often, organizations will also make note of additional skills and traits that go beyond working with data on a computer. In this reading, you will explore examples of additional skills and traits that employers are seeking when searching for data analytics professionals.

As you begin to search for job opportunities, many employers seek additional skills that are not exclusive to digital fields. In the sections below, you will learn more about these traits through excerpts found within data analytics job postings.

Being coachable

Coachable individuals are capable of receiving feedback and using that information to make improvements. At the center of being coachable is a positive attitude, and the ability to self-reflect and take steps to grow. People who are coachable usually have a growth mindset, which is a belief that hard work and determination can make them better. As a result, they view feedback from colleagues and supervisors as an opportunity to improve their skill set. 

A passion for data analysis

Employers often seek candidates whose commitment to data analysis extends beyond their professional duties. Volunteering your data skills to help a nonprofit organization is just one example, but it’s not the only way to show your commitment to data analytics. Data analysis is applicable outside of the workplace, but is often not obvious. Community projects, helping a local school organize data, and developing your own side project are a few examples of how you might demonstrate your passion for data analysis outside of the workplace. 

Another way to explore your passion for data analysis is to connect with other passionate data analysts and take on data challenges. A great website for exploring data analytics is Kaggle.com, which hosts an active online community for data scientists and machine learning enthusiasts. Users can collaborate with other users, publish datasets, use GPU-integrated notebooks, and compete with other data scientists to solve data science challenges. Participating in activities hosted by an online data science community like Kaggle can add fuel to your passion for data analysis and provide artifacts for your portfolio.

Employers are seeking passionate candidates. Job postings that identify candidates that have a passion for data analysis often include language like: 

• Seeking a passionate data scientist.
• We seek a candidate with deep curiosity directed toward diverse research interests.
• Driven. The prospect of focusing on corporate environmental impact as a data domain excites you, and you are personally motivated.

Pro tip: Include in your portfolio your passion for data analysis. Provide examples of passion projects, volunteer work, or analysis outside of employment to relay your commitment to data analysis. If you are conducting data analysis in your free time, it says a lot about your passion.

Lifelong learning

Earlier, you read about some generational shifts in data workspaces. You may recall that these shifts have included technological advances in computer interfaces, data storage, and the role of data analysis in organizations.  In fact, the one consistent element through all of these eras was change. As you progress in your career, you will continue to learn new techniques, tools, and ways of interacting with other professionals. As you have learned, staying up to date with the latest technologies and techniques is essential for data professionals. Here are samples from actual job posting:

• Candidates must balance their hands-on work with a desire to keep up with trends.
• Seeking candidates with the aptitude and enthusiasm to develop new skills and areas of expertise.
• Wanting those with a drive to learn and master new technologies and techniques.

Pro tip: Stay current by reading data-related blogs and attending workshops. Visit business networking websites to connect and learn from subject matter experts. 

Strong interpersonal skills

You may recall that data professionals and business intelligence professionals interact closely; together, they influence an organization’s decision-making. Throughout a project, a data analyst will interact with many stakeholders, from different areas of an organization. Often, these interactions will extend to include clients, users, or representatives from other companies. Additionally, data professionals will work closely with a variety of other professionals as a member of a cross-functional team. Examples from job postings include interpersonal skill requirements for candidates and will include these types of statements:

• We seek employees that can build relationships internally to transfer knowledge, consult with fellow data scientists and analysts to guide analysis, and deliver larger projects.
• Wanting to hire those with the ability to negotiate complex and/or sensitive issues; and maintain good working relations.
• Our data professionals value building strong relationships with colleagues and partners.

Pro tip: Include interpersonal skills on your list of qualifications on your resume. Revisit the material within this program (including the material on communication you will be introduced to later in this course). Find opportunities to add examples of teamwork, empathy, leadership, mediating, and active listening.

Communication

Professionals working in data, business, and technology are expected to be fluent communicators. The effectiveness of your work will weigh heavily on your ability to inspire people through the results of your analysis. Job postings can contain requirements like the following:

• Candidates must have excellent communication skills and a friendly, approachable personality.
• You’re a compelling storyteller who can communicate in succinct and inspiring ways to audiences with varied data science experience to influence real world product or feature decisions.
• A storyteller. You know that no data speaks for itself and take pride in the visualizations and narrative that you construct to communicate your careful analysis.

Pro tip: Be sure that all correspondences you exchange are professional and free of grammatical and spelling errors. Include any examples of written communication in your portfolio–these may include but are not limited to reflection pieces, executive summaries, or  project proposals. Don’t forget to include writing examples or online blog entries that describe how you have communicated in past situations.

Problem solver

The ability to resolve problems is an important part of being a data analytics professional, whether it’s dealing with incomplete data, resolving issues within an analysis, or finding the best way to communicate your results to your audience. It is important that you also quantify the results of problems that you have solved. Organizations who seek data professionals have included problem-solving in their job descriptions, such as:

• Seeking data professionals who have the capability and the strong desire to solve problems from concept development, customer engagement, and technology transition.
• Candidates must be persistent and have excellent analytical and problem-solving skills.
• A big thinker. You start with “why” when approaching a new problem, and are always wanting to generalize, synthesize, and summarize.

Pro tip: Adjust your past working responsibilities into tasks that you were able to achieve. This can be accomplished by stressing the end results of your actions. Detail how your action or task had a direct impact on the organization. If there is a measured or empirical amount of change associated with your actions, be sure to include that. Example: Analyzed data from over 3,000 users to optimize systems, which led to a 32% increase in customer satisfaction.

Key takeaways

Employers in a variety of industries are eager to hire data professionals who possess more than just technical expertise. You can highlight your non-technical abilities for potential employers in several ways. Discover ways to successfully and effectively communicate your passions. By demonstrating a commitment to professional growth and sharing examples of your ability to solve problems, you can set yourself apart from other candidates.

So far, you have been learning how data analytics professionals are making an impact in a variety of industries. You’ve also learned about nonprofit organizations, and how their efforts are making a difference for so many in need. By volunteering their expertise and time, data analytics professionals contribute to many projects that help nonprofits benefit communities all around the world. In this reading, you will learn more about how you can contribute to global efforts, including  your local community as a data professional.

Nonprofits

People hoping to further a social cause, or provide a benefit to the public, often create nonprofit groups. The main mission of a nonprofit group is to fill a need or gap that the private sector is not addressing adequately. There are rewarding and inspiring opportunities for data professionals in the nonprofit sector. At the global level, groups like Children International, Doctors Without Borders, and Care International lead humanitarian efforts across the globe. In addition to these large-scale efforts, there are many nonprofit organizations whose efforts are regionally specific, like Christopher’s Promise based in the Columbus, Ohio area.

You can apply your data skills to help nonprofit groups become more effective, allowing them to anticipate and respond to their greatest areas of need. 

You may already be aware of nonprofits that fit within your areas of interest or operate in your community. One way that these organizations compensate for their lack of resources is by finding volunteers. Often, regional or local organizations lack the resources to offer full time employment for marketing professionals or data analysts. As a data analytics professional there are many ways that you can make a valuable contribution to nonprofit organizations. In the event that you do not have a specific nonprofit organization in mind, you can research through Charity Navigator, the largest and most trusted online nonprofit evaluation and rating tool in the world. Here you will find ratings on nearly 200,000 nonprofits.

An example of a community-based nonprofit organization

Christopher’s Promise, a grass-roots nonprofit based in Columbus, Ohio, is a representative example of the types of community-based nonprofits that are making an impact all over the world. This organization was inspired by the efforts to create a bicycle for a fifteen-year old who faced the challenges of Neurofibromatosis, Cerebral Palsy, and Optic Glioma. Since 2011, this nonprofit has facilitated the design and construction of adaptive bicycles for hundreds of children, thanks to outstanding support from the local communities in the central Ohio area.

Christopher’s Promise (2022) mission statement is  “To allow all kids, despite physical limitations, the ability to experience the same hallmark childhood memories as their peers. Helping kids, be kids.” 

Volunteering data skills to nonprofits

As a data analytics professional, your skills can make a great impact on charity and nonprofit organizations within your own community or for a community whose needs resonate with you. Many nonprofits are highly dependent on volunteers for their activities due to limited resources. This situation is especially challenging for community-based initiatives that often lack the funding to hire data analytics professionals. Therefore, offering to volunteer with a local nonprofit is an excellent way to gain experience and support a cause. 

How can data analytics professionals help nonprofits?

Data analytics professionals can help nonprofit organizations in a variety of ways. Below are a few examples.

Fundraising

Using data analytics, you can predict with high accuracy which prospects are most likely to donate. In order to maximize return on marketing and outreach expenses, nonprofit organizations can use data analysis to prioritize outreach and reduce overall marketing expenditures.

Marketing

There is valuable information in a nonprofit’s data that can benefit marketing efforts. Data analysis can help determine a nonprofit’s target audience and gather insights such as donation methods, contribution history, and other demographics. As a result, the nonprofit can help guide communication with potential donors and determine the effectiveness of campaigns.

Monitoring activities

Data analysis can help nonprofits monitor resources, expenses, and daily operational needs. A nonprofit will often need assistance in keeping track of donated materials. For example, a database of donations to a food bank could help the organization determine specific food items being collected and inventory the expiration dates of food items for shelving, cycling, and distribution. Using a system of real-time inventory in conjunction with predictive data analytics could anticipate shortages and identify those donors who have helped provide these items in the past.

Data analytics has a lot to offer a nonprofit organization. Gaining deeper insight into the organization’s efforts can help them to operate in a more effective and efficient way. Analyzing data can improve various nonprofit functions, such as allocation of resources, recruiting and retaining supporters, fundraising, and conducting research. Data analysis also can help reveal patterns and power dynamics that can be used to inform decision-making. In addition to identifying individuals and communities in need of assistance, predictive modeling can also be used to identify potential recipients of a nonprofit’s services.

More opportunities to volunteer

There are organizations who are helping to pair data professionals with projects that are making a difference all around the world. 

DataKind is a global nonprofit that harnesses the power of data science and artificial intelligence (“AI”) in the service of humanity. Using data analysis, this organization guides restorative efforts in underserved communities. DataKind brings together volunteer experts from academia and industry to design innovative solutions to tough social challenges. They help social organizations apply their data to predictive analytics, machine learning algorithms, and AI in a way that both increases impact and is sensitive to ethical considerations. Visit the DataKind website to find out more about their mission and how to become a volunteer.

Another option for putting your data skills to good use are hackathons. A hackathon is an event where programmers and other data professionals come together and collaborate on a particular project. The goal is to create a solution to an existing problem. 

There are many hackathon events scheduled across the world each year. These events can take place at a specific location or remotely across the globe. Hackathon events are commonly built around a central theme or question, such as tracking industrial emissions, promoting healthy food options, or how we can improve mental health for cancer patients. If you are interested in finding listings of current hackathons around the world, visit Devpost.com.

Key takeaways

Donating your time to a nonprofit is a great way to gain experience, and it can be mutually beneficial for you and the nonprofit. The nonprofit benefits from your data analysis expertise, and you get valuable experience working with data that can be added to your professional portfolio.

Working with a nonprofit organization or participating in a hackathon event introduces you to different types of projects, enhances interpersonal and communications skills, and adds to your professional list of contacts. No matter what your level of data analytics experience, working with nonprofits and hackathons can be a rewarding side project, or even a philanthropic career option.

Resources for more information

• Charity Navigator is the world’s largest and most trusted nonprofit evaluator.
• DataKind helps social organizations identify their data and artificial intelligence opportunities, recruits and manages volunteers, and then sees the solutions are used.
• Devpost is a place to build products, practice skills, learn technologies, sign up for competitions, and grow your network.
• IRS.gov tax exempt organizations listing is a website to find charitable organizations throughout the United States.
• Statistics without borders contributes to the common good by providing free statistical, data science, and analytical services.

You have learned how data analytics can be used for good causes, like assisting nonprofit organizations. Also, you learned that data professionals need to protect privacy within data and remain aware of other considerations, like data bias and making assumptions about data. 

As a data analytics professional, you have a responsibility to handle data ethically. Data ethics refers to well-founded standards of right and wrong that dictate how data is collected, shared, and used. Throughout your career you will work with a lot of data. This sometimes includes PII, or personally identifiable information, which can be used by itself or with other data to track down a person’s identity. One element of treating data ethically is ensuring that the privacy and security of that data is maintained throughout its lifetime. In this reading, you will learn more about the importance of data privacy and some strategies for protecting the privacy of data subjects. 

Privacy matters

Data privacy means preserving a data subject’s information and activity any time a data transaction occurs. This is also called information privacy or data protection. Data privacy is concerned with the access, use, and collection of personal data. For the people whose data is being collected, this means they have the right to:

• Protection from unauthorized access to their private data
• Freedom from inappropriate use of their data
• The right to inspect, update, or correct their data
• Ability to give consent to data collection
• Legal right to access the data

In order to maintain these rights, businesses and organizations have to put privacy measures in place to protect individuals’ data. This is also a matter of trust. The public’s ability to trust companies with personal data is important. It’s what makes people want to use a company’s product, share their information, and more. 

Protecting privacy with data anonymization 

Organizations use a lot of different measures to protect the privacy of their data subjects, like incorporating access permissions to ensure that only the people who are supposed to access that information can do so. Another key strategy to maintaining privacy is data anonymization. 

Data anonymization is the process of protecting people’s private or sensitive data by eliminating PII. Typically, data anonymization involves blanking, hashing, or masking personal information, often by using fixed-length codes to represent data columns, or hiding data with altered values.

Data professionals can take additional measures to protect users and their data. Data aggregation, for example, is the process of collecting and combining details from a significant number of users in terms of totals or summary. Aggregating data ensures that information contained within datasets is shown in groups; when coupled with other anonymization techniques, data professionals can ensure compliance with data privacy and anonymization standards.

Data anonymization is used in just about every industry. As a data analytics professional, you probably won’t personally be performing anonymization, but it’s useful to understand what kinds of data are often anonymized before you start working with it. This data might include:

• Telephone numbers
• Names
• License plates and license numbers
• Social security numbers
• IP addresses
• Medical records
• Email addresses
• Photographs
• Account numbers

Imagine a world where we all had access to each other’s addresses, account numbers, and other identifiable information. That would invade a lot of people’s privacy and make the world less safe. Data anonymization is one of the ways we can help keep data private and secure!

Key takeaways

For any professional working with data about actual people, it’s important to consider the safety and privacy of those individuals. That’s why understanding the importance of data privacy and how data that contains PII can be made secure for analysis is so important. We have a responsibility to protect people’s data and the personal information that data might contain. 

If you’re interested in learning more about data privacy and ethics, you can check out the Google Data Analytics Certificate program’s section on bias, credibility, privacy, ethics, and access

As you have been learning, all data professionals are responsible for ensuring the quality, integrity, accessibility, and security of data. Data stewardship is the practice of ensuring that data is accessible, usable, and safe. Making data stewardship a normal part of your work habits will benefit everyone who relies on your analysis, both inside and outside of your organization. In this reading, you will learn more about data stewardship and receive some best practices that can assist in guiding your career in data analytics.

Respect privacy

Earlier in this course, you learned about Information that permits the identity of an individual to be inferred by either direct or indirect means. This kind of information is commonly referred to as personally identifiable information or PII. When users share personal information, they are putting a high level of trust into an organization. It is the responsibility of all who have access within the organization to help protect the privacy of their users. As a data analytics professional, it is important to be thoughtful about any personal data and exhibit great care to protect it. In different parts of the world, laws are in place to guide best practices for data privacy. Laws provide a foundation for best practices as you grow in knowledge and experience on how to support and sustain privacy. One of your responsibilities as a data professional will be to stay up to date with any change in data laws and regulations that govern data. Depending on your organization’s location or industry considerations, there may be additional regulations and policies in place. Here are a couple of regional examples:

• General Data Protection Regulation or GDPR (European Union law):
  • The GDPR is described on their website as the toughest privacy and security law in the world. It imposes obligations onto organizations anywhere, so long as they target or collect data related to people in the European Union.
• Lei Geral de Proteção de Dados Pessoais or LGPD (Brazil’s general law for the protection of personal data):
  • The LGPD is a data protection law that governs how companies collect, use, disclose, and process personal data belonging to people in Brazil. LGPD applies to companies that process data about individuals in Brazil.
• The California Consumers Privacy Act or CCPA (Privacy rights for California consumers):
  • The CCPA gives consumers more control over the personal information that businesses collect about them. These regulations provide guidance on how to implement the law.
  • Additionally, states like Virginia, Colorado, New York, Utah, and Connecticut have enacted similar legislation to protect consumer privacy in their states.

Be cautious of unintentional harm

Data analytics is expanding its influence across an increasing range of industries. Companies are using the results of data analysis to make informed decisions. Many of these decisions have the potential to impact people across a broad range of social and economic factors. It is good practice to continually strive to produce information that is accurate, while respecting cultural and social norms. 

Due to the global marketplace, decisions play out differently in different cultures. Taking these issues and considerations into account is very important for the executive team of an organization. Also, companies are known to take a position on particular politicized social and cultural issues, and these can be reflected in their policies. As a data analytics professional, you must be cognizant of your company’s policies. When presented with challenges, it is best to seek guidance from leadership within your organization on how to navigate.

Avoid creating or reinforcing bias

You have learned about bias within data and how it can have an impact on your analysis. Identifying bias is not always simple. A good practice when working with data is to keep in mind that data gathering is a task managed by humans–and that process is informed by people from different backgrounds, experiences, beliefs, and worldviews. These and other types of biases can affect the data and the results, which in turn can have an impact on business decisions. You will learn more about bias within data as you progress through the program. 

Consider inclusivity

Often In your role as a data analytics professional, you will have access to data collected in a variety of ways. You will need to consider whether the methods of data collection have excluded information from particular populations. Inclusionary approaches can expand how any organization collects and analyzes data. Building diverse research teams, communicating clearly with user communities, and engaging in careful and critical analysis that considers equity and inclusion benefits all stakeholders.

Uphold high standards of scientific excellence

The processes and technology that you will interact with as a data analytics professional are deeply rooted in the scientific method. As you continue in your data professional journey, embrace inquiry, intellectual discussion, and collaboration. Invite feedback and assess feedback. Remember, artificial intelligence still depends heavily on the instructions provided by data professionals. The more time and consideration that goes into the process of data analytics, the better the results.

Different industries have different standards. In your role as a data analytical professional, you will need to be aware of the standards for the industries you are working in. Each industry will have its own standards based on industry conventions.

Conventions that work well in the transportation industry may not necessarily be as high of a priority for the healthcare industry. For example, in transportation, data is collected to create predictive analytics models to analyze the best route based on traffic patterns. In the healthcare industry, data is analyzed in medical imaging, predicting genetic factors, and speeding up the development of treatments.

Data stewardship and ethics conversations

As you progress through your career as a data analytics professional, you will need to consider issues of ethical concern. For example, you may encounter situations where you address questions of bias or need to protect user data and personally identifiable information (PII). When these types of questions arise, many seek guidance and support from online communities of data professionals who have dealt with similar issues. The following graphics present scenarios involving these kinds of issues. You can also find text alternative versions of these conversations in the Data stewardship and ethics conversations transcript.

Key takeaways

Data stewardship is the responsibility of every data professional. This responsibility goes beyond interactions with the data. By conducting your work in ways that are socially beneficial and inclusive, you will increase your ability to identify human bias. Guide your efforts through scientific and ethical principles and stay aware of possible bias throughout the data analysis process.

Interpersonal skills

Interpersonal skills are traits related to communication and relationship-building.

active listening

A data professional practices active listening when they allow others to share their points of view before offering a response. Active listening helps to improve mutual understanding.

Data cleaning

Data cleaning is the process of formatting data and removing unwanted material.

You have been learning about the role of data analytics professionals within organizations and the work they perform. You’ve also examined some general classifications for data professional roles.  Additionally, you explored what a typical data analytics profession might experience during the initial days in a new position. Large organizations often have more data needs, requiring the creation of teams to take on projects. In this reading, you will learn more about how organizations build data analytics teams and look at some best practices when constructing them.

Designing data analytics teams

The idea of companies creating teams of data professionals developed over time and largely out of necessity. Originally, the responsibilities of data collection, management, and analysis fell onto the CIO (chief information officer) of the company. CIOs in the past would typically hand the responsibilities over to the IT (Informational technology) team. 

Gradually, organizations began separating data functions as the wealth of information stored grew. Additionally, new technologies emerged and data-related tasks became more specialized.

Data professional profiles within large organizations

Earlier in this course, you were introduced to some data professional profiles.

• Data Scientist
• Data Analyst
• Data Engineer
• Analytic Team Manager
• Business Intelligence Engineer

These profiles are very generalized categories that can help you focus your eventual job search. In reality, many employers are looking for more than data professionals. They are looking for project stakeholders, dependable team members, and great co-workers.

Building the team

The data needs of larger companies requires the creation of a team of skilled professionals. The members of these teams each have a specialized area of expertise. Some will come from business backgrounds, some with project or staff management experience, while others will have more technical skills. While there is no checklist available for companies to follow when putting together their team of data professionals, the needs of their organization can help guide them.

Creating a team of data analytics professionals is very similar to how sports franchises put together championship-level rosters. Coaches and general managers are always looking to upgrade and enhance the capabilities of their teams. While having a superstar athlete can help deliver a consistent performance at a certain position, their abrupt departure can compromise the team’s overall performance. A better approach is to develop a well-balanced and collaborative team. 

Employers want to hire someone who has more than data analytic skills. They are selecting the best fit for their organization. Candidates who bring additional experience and skills beyond data analytics are most often seen as most favorable. Strong interpersonal and communication skills, experience working in business, or within a team dynamic can enhance data analytics. There is a very good chance that you already possess some of these skills.

Five principles for data team building

After you join a data team, there are still challenges to building a data-driven organization. Below you will find five principles that can guide any organization to becoming more effective when facing the challenges of data analysis, regardless of the structure of your data teams within the company. 

1. Adaptability

Data platforms, networks and storage options need to allow flexibility. Each data professional will have their preferences as to the tools and their approaches to analysis. Remote and on-site employees need to have access and the ability to work with all data and use the tools of communication they feel are the most productive.

Organizations need to facilitate a data platform that allows open access to resources for all users. Instead of specifying specific software solutions, organizations can expand their pool of candidates by allowing individuals to integrate the tools and applications they are most comfortable and experienced with. Flexibility invites a wide range of experience and enhances data teams, adding experience and additional perspectives.

Just as organizations need to be adaptable, those seeking opportunities in data fields should be committed to learning new skills and technologies. Data workplaces are undergoing constant change, as you learned earlier. As a data analytics professional, you will continue to learn and grow as newer technologies or regulations emerge.  Accept the challenge presented by new circumstances and let yourself feel energized when presented with opportunities for professional growth.

Pro tip: Keep your desire for learning. Expand your knowledge through online data science communities and educational opportunities.

2. Activation

Access to data analysis results require someone with the background and experience of a data analyst. Even small-scale data operations require skills that go beyond the scope of other professionals within an organization.

To become more data literate, organizations should cultivate new habits and integrate them into their daily work routines. Dashboards and other accessible interfaces can help promote the use of data analysis, enabling wider usage of data analysis, promoting communication and fostering cross-departmental collaboration.

Vital to the success of an organization’s efforts to transform its internal habits and culture are the interpersonal skills of its employees. When a company is seeking to expand data literacy, its data professionals play an important role. There will be opportunities to help promote understanding among peers and colleagues. The time spent answering questions and promoting understanding will help others appreciate you and your contributions to the organization.

Pro tip: Maintain positive professional relationships through effective communication. Your ability to share insights is just as important to an organization as your analyzing skills.

3. Standardization

An organization needs to set criteria for the standardization of data practices and procedures. Standardization helps to promote best practices, and communication and transferability of information between teams. When users can share optimized code and other assets it saves development time and streamlines projects. An organization that builds a culture of collaboration embeds best practices into work behaviors.

Identifying candidates with capabilities beyond data analysis is essential to developing and standardizing best practices. To develop best practices, organizations need employees that contribute innovative solutions to problems. 

Pro tip: Become a problem solver. Obstacles within a project can turn into opportunities for innovation, which can transform an organization.

4. Accountability

Data analysis is a complex and dynamic process that requires a high level of accountability. To promote responsibility, organizations need a ‘paper trail’ that allows examination of their entire process.

Accountability adds transparency, explainability, and security to data teams and projects. It also helps to eliminate layers within an organization, while aligning business goals and customer values. Transparency in workflow allows organizations to answer specific questions about the data analysis process. Data analytics professionals can use project metadata to examine and communicate specific elements within their prediction models, giving data teams the collaborative ability to make adjustments with more precision.

One way to promote accountability is to extend your ability to communicate throughout your workflow. Within data analytical projects, the transfer of ideas extends beyond individual correspondence. It also includes information in project notes, records kept within projects, and proper tagging within metadata. Individual processes and ideas are the incubators of standards of practice and communicating these ideas effectively can improve an organization’s accountability.

Pro tip: Data analytics professionals are more than repositories of information. It benefits everyone involved in a project if you are able to communicate your knowledge and observations. Identify what would be most valuable to others and provide the information.

5. Business impact

Often, the inability to estimate the impact on the business can block data analysis projects. Organizations are not considering all available data analytical solutions during the planning stage.

Organizations should look at approaching data projects with the widest field of view in the planning stage. This requires a thoughtful approach including considerations beyond data analysis.  For example, the difficulty of integration, commitment of resources, and changes to the project timeline. Businesses need to consider more data solutions options and identify which benefit the project the most.

Broadening the scope of an organization takes time and consistency of results. To achieve this, a data team must become a trusted resource for insight and a positive influence on an organization’s decision-making process. An effective communication strategy, strong interpersonal skills, and a track record of problem-solving will earn the trust of the organization. 

Pro tip: Focus on communicating clearly with stakeholders. Maintain a commitment to consistency between what you have promised and what you will deliver. 

Key takeaways

Organizations carefully consider the individuals they bring into their organization and seek candidates that embody qualities that go beyond data skills. Additionally, companies can influence their adaptability by hiring data professionals that embody the spirit of lifelong learning, effective communication, interpersonal skills, and the ability to solve problems.

Assessment of Exemplar

Compare the exemplar to your completed activity. Review your work using each of the criteria in the exemplar. What did you do well? Where can you improve? Use your answers to these questions to guide you as you continue to progress through the course. 

RACI matrix #1:

• Access to data: Based on their general roles within an organization, the business intelligence engineer, analytics team manager, and data engineer have all been identified as responsible for providing access to the data. The data scientist, who is more responsible for working with the analytics team to draw insights from the data, is just consulted. 
• Create models to analyze data: The data scientist, who is primarily concerned with drawing insights from the data, is responsible for this task. The business intelligence engineer and analytics team manager are consulted. The data engineer, who is concerned with infrastructure, is kept informed. 
• Drive insights and recommendations based on data: Similarly to the previous task, the data scientist is the primary team member responsible for this task. 

RACI matrix #2: 

• Ensure data compliance: Data compliance is part of developing and managing databases, which is the responsibility of the data engineer. Other team members are consulted or informed, but the data engineer is tasked with the actual responsibility of ensuring compliance.
• Chief data officer: Although all of the tasks in this project are the direct responsibility of other team members, the company’s chief data officer is accountable for them—meaning this role has the ultimate responsibility for ensuring this task is completed. 

Key Takeaways

In your work as a data professional, you might encounter the RACI matrix. It is a tool that many organizations use to structure their projects. It also helps to outline, communicate, and understand the responsibilities of data analytics professionals and other cross-functional team members. As a data professional, RACI can also help you consider how to structure your communication with other team members about a project. Understanding how data teams are organized will set you up for better collaboration with your future colleagues!

Data engineer

A data engineer is responsible for ensuring an organization’s data ecosystem offers reliable results. They also manage infrastructure for data across the enterprise and ensure that the data is accessible.

insights

Analytics or insights team managers supervise the analytical strategy of an organization. They are often responsible for managing customers and stakeholders and serve a hybrid role, operating as both data scientists and decision-makers.

Organize information and make it accessible

Business intelligence professionals organize information and make it accessible. They do this by synthesizing data, building dashboards, and preparing reports.

Glossary terms from Course 1, Module 2

Aggregate information: Data from a significant number of users that has eliminated personal information

Artificial intelligence (AI): Refers to computer systems able to perform tasks that normally require human intelligence

Data anonymization: The process of protecting people’s private or sensitive data by eliminating PII

Data stewardship: The practices of an organization that ensure that data is accessible, usable, and safe

Edge computing: A way of distributing computational tasks over a bunch of nearby processors (i.e., computers) that is good for speed and resiliency and does not depend on a single source of computational power

Hackathon: An event where programmers and data professionals come together and work on a project

Nonprofit: A group organized for purposes other than generating profit; often aims to further a social cause or provide a benefit to the public

Open data: Data that is available to the public and free to use, with guidance on how to navigate the datasets and acknowledge the source

Personally identifiable information (PII): Information that permits the identity of an individual to be inferred by either direct or indirect means

Sample: A segment of a population, often used to infer parameters of the whole population

Technical project managers, Business intelligence professionals

Hackathon

personally identifiable information

• Increase the likelihood that the data represents the population as a whole
• Protect individuals

The entire population

Artificial intelligence

all data professionals

Avoiding subtle biases in data work