Week 3: Your career as a data professional

Earlier, you briefly considered the role of artificial intelligence in data science. You may recall that artificial intelligence (AI) refers to the development of computer systems able to perform tasks that normally require human intelligence. For example, practical applications of AI include voice assistants, autonomous vehicles, automated recommendation systems, and more.

In this reading, you will learn more about the uses of AI for data work, and how AI can help data professionals better understand their data and make more informed decisions. You’ll also learn about the limitations of AI, and the differences between AI and human data professionals. 

The uses of AI for data work

Data professionals can use AI to improve their data analysis, perform essential tasks, and streamline their workflow. For example, data professionals can use AI to:

• Create predictive models to help accurately forecast future events or outcomes. 
• Automate time-consuming tasks such as data cleaning, coding, and report writing. 
• Analyze extremely large datasets. 
• Improve the quality of data by identifying and correcting errors. 
• Generate insights from data that would not be obvious to humans. 
• Provide guidance on tasks such as choosing the right algorithms and interpreting results. 
• Facilitate collaboration among team members. 

Data professionals can leverage AI to enhance the quality and efficiency of their data projects, generate valuable insights, and help stakeholders make better business decisions. 

Conversational AI tools: Bard and ChatGPT

Many data professionals now use conversational AI tools to help them analyze their data and boost their productivity. Two of the most frequently used tools are Bard and ChatGPT.  Bard was created by Google AI. ChatGPT, also known as Chat Generative Pre-trained Transformer, was developed by OpenAI. 

Bard and ChatGPT are both large language models (LLMs) that are trained on massive datasets of text and code. An LLM is a type of AI algorithm that uses deep learning techniques to identify patterns in text and map how different words and phrases relate to each other. This allows LLMs to predict what word should come next. LLMs can generate human-like text in response to a wide range of prompts and questions. 

Note: This is a general introduction to LLMs. A detailed discussion of the development and computational logic of LLMs is beyond the scope of this course. 

Tools like Bard and ChatGPT can help data professionals in a variety of ways. A data professional might ask Bard or ChatGPT to: 

• Clean a dataset by removing missing values, outliers, and duplicate data.
• Create interactive data visualizations such as dashboards and heatmaps. 
• Recommend a specific algorithm for a particular task based on the data professional’s input.
• Create a shared document to facilitate a brainstorming session among a team of data professionals. 

Note: This is a brief list of possible prompts. In another activity, you’ll get a chance to further explore Bard and discover its capabilities. 

Use cases for AI

Data professionals across industries use AI to help analyze data and generate insights for stakeholders. Here are some examples of how data professionals use AI in specific sectors:

Finance

• Analyze financial transactions to help prevent fraud and protect customers’ money.
• Analyze large datasets of financial data to help identify potential risks and make more informed decisions about investments.
• Analyze historical market data and current market conditions to help generate sound investment recommendations. 

Retail

• Recommend products to customers based on their past purchase history and browsing behavior. 
• Track customers’ interactions with the retail website to help personalize the shopping experience. 
• Analyze sales data and forecast future demand to help optimize the amount of product inventory and reduce costs. 

Manufacturing  

• Automate tasks such as welding, painting, and assembly to help improve efficiency.
• Analyze data from sensors and cameras to help identify defects in products before they are shipped to customers. 
• Analyze data from production lines to help identify ways to produce more products at a lower cost.

AI and human data professionals 

Data professionals use AI as a tool to help them understand data, make better decisions, and improve efficiency. Like all tools, AI has limitations. Human data professionals possess skills, abilities, and qualities that AI currently lacks. For example: 

• Intuition. AI models are trained on data, and they can only make decisions based on the patterns they observe in the data. Humans can use their intuition and personal experience to make decisions that are not explicitly programmed into the AI model. For this reason, it’s important to always verify a model’s output before relying on it.
• Deal with ambiguity. AI models are good at solving problems that are well-defined and have clear parameters. However, humans can identify and understand complex problems that are not well-defined and have ambiguous parameters by considering key details offered in the context of the project.
• Interpersonal communication. AI models can generate reports and presentations, but they cannot communicate with stakeholders in the nuanced way that humans can. Humans can explain the results of their analysis to fit the needs of specific stakeholders, and use their emotional intelligence to address concerns. 
• Creativity. AI models are good at following instructions, but they are not imaginative like humans. Humans can be creative in their approach to data analysis, and imagine new and innovative solutions to complex problems.
• Critical thinking. Humans can think critically about their data and identify potential biases and ethical issues. AI models are usually trained on real-world data that contains biases and are therefore likely to reflect those biases in model outputs. 
• Leadership. Humans can be leaders, and they can motivate and inspire others. AI may have difficulty understanding the nuances of human emotion, motivation, and communication. This limits AI’s ability to effectively run an organization.
• Factuality. Generative AI models are trained to output text based on patterns in language. Sometimes the model output may be very well-composed and as a result, seem reliable, but may not be factual. As noted above, it’s important to always verify model output.

In the future, product and research teams may develop updates for AI that enlarge its current capabilities. However, human data professionals will continue to play an important role in data science by using their intuition, imagination, and unique experience to solve complex problems. 

Key takeaways

Data professionals can use AI to help automate tasks, make predictions, generate insights, and communicate findings. They can leverage AI to be more productive in their work and more impactful in their organizations. Overall, AI is a powerful tool for data professionals but it is not without limitations. For this reason, human oversight and intervention is critical when working with AI and related tools. 

Additional Resources

For more information, check out Google’s approach towards building responsible AI

Activity Overview

You have learned that many data professionals now use conversational AI tools like Bard and ChatGPT to help them analyze their data and boost their productivity. Bard and ChatGPT are both large language models (LLMs) that are trained on massive datasets of text and code. LLMs can generate human-like text in response to a wide range of prompts and questions. In this activity, you’ll discover the capabilities of conversational AI by writing your own prompts for Bard. 

To review the role of AI in data work, refer to the reading about how data professionals use AI.

Be sure to complete this activity before moving on. 

LLM prompts and best practices

Data professionals can use LLMs to improve their data analysis, perform essential tasks, and collaborate with teammates. Here are some useful prompts for data science workflows:

• Data cleaning. LLMs can automate tasks such as data cleaning and coding. For example, you can ask an LLM to clean a dataset by removing missing values, outliers, and duplicate data.
• Exploratory data analysis (EDA). LLMs can perform exploratory data analysis (EDA) on datasets. For example, you can ask an LLM to create data visualizations, identify patterns and trends, and calculate summary statistics.
• Modeling. LLMs can build and evaluate models. For example, you can ask an LLM to build a machine learning model to predict an outcome, and evaluate the performance of the model.
• Interpreting results. LLMs can interpret the results of models. For example, you can ask an LLM to explain the features that are most important for a model, or generate insights from the results of a model.
• Collaboration. LLMs can help you collaborate with teammates. For example, you can ask an LLM to create a shared document for a brainstorming session with a team of data professionals.

Pro tip: Be sure to structure your prompts in a way that makes it easier for the LLM to fulfill your requests and answer your questions. 

The following suggestions are best practices for writing prompts for LLMs:

• Be clear and concise in your instructions. It is important to be clear and concise in your instructions so the LLM can understand how to help you. Details are great – just make sure they’re useful and relevant. Avoid giving the LLM unnecessary information.  
• Be precise. When posing a question to an LLM, be precise about the input (if any) and the desired output.
• Include a description of LLM’s role. This reinforces the purpose of your prompt. For example, you can tell the LLM to assume the role of a data scientist by writing “Act as a data scientist” or “You are a data scientist.”
• Provide context. Providing context allows the LLM to understand the nuances of the relevant issue and generate more informed responses.
• Try multiple prompts. Trying different prompts can provide different perspectives on a problem and enable the LLM to generate a variety of useful responses.

To help get you started, consider the following specific examples of prompts that data professionals can give an LLM:

• “Act as a data scientist and write a detailed plan for a credit card fraud detection project.”
• “I have a dataset of customer purchases at an online retail store. Act as a data scientist and write Python code for data visualization and exploration.”
• “I have a dataset of customer characteristics and churn for an online video streaming service. Act as a data scientist and create a shared document for a team meeting.” 
• “Act as a data generator and use Python code to generate a CSV file that contains mock employee data for a restaurant chain named Fast. The dataset has 100 rows and 5 columns. The columns are name, address, employee_id, department_id, email.”
• “Act as a communications expert and share best practices for explaining a data science report to a business executive with no technical background.”

Note: LLMs are powerful, but they are still under development – including Bard, which is still experimental research. As a data professional, it’s important to use your own judgment when interpreting the results. LLMs can generate insights that you may not have thought of on your own; however, it’s ultimately your responsibility to verify the results and make sure they make sense.

Step-By-Step Instructions

Follow the instructions to complete each step of the activity. Then, answer the questions at the end of the activity before going to the next course item. Please Note: This activity may be completed on any LLM of your choosing; it is not exclusive to Bard.

Step 1: Access Bard

Note: To use Bard, you’ll need to sign in with a Google Account. 

To sign in to Bard:

1. Go to bard.google.com.
2. At the top right, select Sign in.
3. Sign in to your personal Google Account.

If you don’t have a Google Account, click on the following link to learn how to create an account: Create a Google Account

Step 2: Give prompts to Bard based on a workplace scenario

Review the following fictional workplace scenario. Then follow the instructions for giving prompts to Bard at different stages of the data project. 

Imagine you are a new data professional, recently hired by a healthcare company. The company sells sustainable medical devices to hospitals and clinics in urban communities. Leadership has asked the data team to develop a machine learning model to accurately predict future sales. A powerful model will help company leaders make informed decisions about inventory management, resource allocation, overall sales strategy, and more. As the newest member of the data team, you’re excited to start your first project. 

1. Project proposal. To get started, your manager asks you to organize a kickoff meeting with the team to outline the project workflow and timeline. You want to send the invite as soon as possible, and could use some help creating a document for the meeting. Prompt: Ask Bard to create a shared document to facilitate a brainstorming session among a team of data professionals.
2. Data cleaning. Following the team meeting, you help draft a project proposal to outline key deliverables and milestones for the project. Then, the team collects the relevant data. The next step is to clean the dataset. You volunteer to perform this task for the team. The team is using the Python programming language for this project, and you’d like some coding suggestions for data cleaning. Prompt: Ask Bard to write Python code to clean data by removing missing values, outliers, and duplicate data. 
3. Data visualization. Now that the team has a clean dataset to work with, the next step is to explore and visualize the data. Your manager asks if you can help create some data visualizations to better understand the relationships between key variables. To get started, you brainstorm with Bard. Prompt: Ask Bard to suggest useful data visualizations for sales data.
4. Build and test machine learning models. As a new data professional, you are not directly involved in writing code to build and test different machine learning models. However, you want to learn more about the uses of machine learning for data work as this will be an important part of your future career, and will help you better understand the current project. Prompt: Ask Bard about the main uses and benefits of machine learning for data work. 
5. Executive summary. The data team successfully builds a model that accurately predicts future sales. Now, the team is ready to share their results and insights with project stakeholders. Your manager asks you to help draft an executive summary for a meeting with company leadership. Before you begin, you want to review best practices so you can create a polished deliverable. Prompt: Ask Bard about best practices for creating an executive summary for business executives without a technical background.

Note: Overall, Bard is a powerful tool for data professionals. However, it’s important to remember that Bard is not perfect. Be aware of Bard’s (and other LLM’s) limitations. These limitations include the following: 

• Bard is not infallible. Bard can sometimes make mistakes, such as providing inaccurate information or generating incorrect code.
• Bard is not an expert in any particular field. Bard can learn about new topics, but it does not have the same level of understanding as an experienced human data professional.
• Bard cannot explain its reasoning. Bard can generate useful output, but it cannot explain why it’s doing what it’s doing. This can make it difficult to understand how Bard works and to trust its results.
• Bard can be biased. As an LLM, Bard is trained on a massive dataset of text and code, and Bard is likely to reflect the biases that are present in that dataset. 

As a result of these limitations, Bard’s responses may be inaccurate, biased, or insufficient for your purposes. As a data professional, it’s your responsibility to verify the accuracy of Bard’s output. It’s also your job to modify or supplement Bard’s output to fit the needs of the specific project you’re working on.

Step 3: Experiment with Bard on your own

Explore Bard and discover its capabilities. Feel free to experiment with different types of prompts, use your imagination, and have fun!

Data cleaning

LLMs can help data professionals perform a number of essential tasks, including data cleaning, exploratory data analysis (EDA), and building and evaluating models.

Building and evaluating models 

LLMs can help data professionals perform a number of essential tasks, including data cleaning, exploratory data analysis (EDA), and building and evaluating models.

Exploratory data analysis (EDA)

LLMs can help data professionals perform a number of essential tasks, including data cleaning, exploratory data analysis (EDA), and building and evaluating models.

• Provide context
• Be clear and concise in your instructions
• Include a description of Bard’s role

Best practices for writing prompts for LLMs include being clear and concise in your instructions, providing context, and including a description of the LLM’s role.

computer systems

Artificial intelligence is the development of computer systems able to perform tasks that normally require human intelligence.

• It incorporates a wide range of perspectives.
• It yields more accurate project results.
• It promotes wider representation.

To counter human bias, organizations build diverse teams. Incorporating a wide range of perspectives and worldviews promotes wider representation and yields more accurate data project results.

• Create predictive models to forecast future events.
• Analyze large datasets.
• Automate time-consuming tasks such as data cleaning.

Data professionals can use AI to help them create predictive models to forecast future events, automate time-consuming tasks such as data cleaning, and analyze large datasets.

Large language model (LLM)

A large language model (LLM) can generate human-like text in response to a wide range of prompts and questions from data professionals. An LLM is a type of AI algorithm that uses deep learning techniques to identify patterns in text and understand how different words and phrases relate to each other.

As you have been learning, professional relationships can help you find job opportunities. Exploring job boards and online resources is only one part of your job search process; it is just as important to connect with other professionals in your field, build your network, and interact with the data science community. A great way to achieve these goals is by building a relationship with a mentor. In this reading, you will learn more about mentors, the benefits of mentorship, and how to connect with potential mentors. 

Considering mentorship

Mentors are professionals who share knowledge, skills, and experiences to help you grow and develop. They can offer guidance at different points in your career. Mentors can be advisors, sounding boards, honest critics, resources, and more. You can even have multiple mentors to gain more diverse perspectives! 

There are a few things to consider along the way:

• Decide what you are searching for in a mentor. Think about your strengths and weaknesses, what challenges you have encountered, and how you would like to grow as a data professional. Share these ideas with potential mentors who might have had similar experiences and have guidance to share.
• Consider common interests. Often you can find great mentorships with people who share interests and backgrounds with you. This could include someone who had a similar career path or even someone from your hometown. 
• Respect their time. Often, mentors are busy! Make sure the person you are asking to mentor you has time to support your growth. It’s also important for you to put in the effort necessary to maintain the relationship and stay connected with them.

Note that mentors don’t have to be directly related to data science. Mentors can be friends of friends, more experienced coworkers, former colleagues, or even teammates. For example, if you find a family friend who has a lot of experience in their own non-data field, but shares a similar background as you and understands what you’re trying to achieve, that person may become an invaluable mentor to you. Or, you might happen to meet someone at a casual work outing or a professional conference or meetup with whom you develop an instant connection over shared interests or hobbies. 

No one mentor may be able (or willing) to advise in all areas, so think about the skills, insights, or values you appreciate in that individual. Then, build a network of individuals that you may approach with different questions about different topics (job searches, public speaking, technical topics, different industries, etc.).

Build the relationship

Once you have considered what you’re looking for in a mentor and found someone with time and experience to share, you’ll need to build that relationship. Sometimes, the connection happens naturally, but usually, you need to formally ask them to mentor you. 

One great way to reach out is with a friendly email or a message on a professional networking website. Describe your career goals, explain how you think those goals align with their own experiences, and talk about something you admire about them professionally. Then you can suggest a coffee chat, virtual meetup, or email exchange as a first step.

Be sure to check in with yourself. It’s important that you feel like it is a natural fit and that you’re getting the mentorship you need. Mentor-mentee relationships are equal partnerships, so the more honest you are with them, the more they can help you. Most importantly, remember to thank them for their time and effort!

As you get in touch with potential mentors, you might feel nervous about being a bother or taking up too much of their time. But, mentorship is meaningful for mentors too. They often genuinely want to help you succeed and are invested in your growth. Your success brings them joy! Many mentors enjoy recounting their experiences and sharing their successes with you as well. Mentors often learn a lot from their mentees. Both sides of the mentoring relationship are meaningful!

Resources

There are a lot of great resources you can use to help you connect with potential mentors. Here are just a few:

• Meetups are meetings that are usually local to your geography. Enter a search for “data science meetups near me” to check out what results you get. There is usually a posted schedule for upcoming meetings. Find out more information about meetups happening around the world. 
• Platforms including LinkedIn® and Twitter are a great way to reach out to other professionals. Use a search on either platform to find data science or data analysis hashtags to follow. Post your own questions or articles to generate responses and build connections that way. 
• Webinars may showcase a panel of speakers and are usually recorded for convenient access and playback. You can see who is on a webinar panel and follow them too. Plus, a lot of webinars are free. One interesting pick is the Tableau on Tableau webinar series. Find out how Tableau has used Tableau in its internal departments. There are also a number of other data science related webinars available at Brighttalk.com
• Conferences present innovative ideas and topics. The cost varies, and some are expensive. But, many offer discounts to students, and some conferences like Women in Analytics aim to increase the number of under-represented groups in the field. 
• Associations or societies gather members to promote a field such as data analytics. Many memberships are free. The Association of Data Scientists is just one example. The Cape Fear Community College Library also has a list of professional associations for analytics, business intelligence, and business analysis.
• User communities and summits offer events for users of professional tools; this is a chance to learn from the best. Have you explored the Tableau or Python communities?
• Non-profit organizations that promote the ethical use of data science often offer events for the professional advancement of their members. The Data Science Association is one example. 

Key takeaways

Finding and connecting with a mentor is a great way to build your network, access career opportunities, and learn from someone who has already experienced some of the challenges you’re facing in your career. Whether your mentor is a senior coworker, someone you connect with on LinkedIn®, or someone from home on a similar career path, mentorship can bring you great benefits as a data analytics professional.

As you’ve been learning, there are many steps that go into preparing for your job search in the data career space. Before you even start applying for jobs, it’s important that you take two key steps. You need to build a professional online presence and develop your portfolio to showcase your skills to potential employers. This is also a great time to connect with mentors, who can provide professional insight and help you prepare for the application process. The interview will often be the final step in this process; in addition to all of the other job prep you’ll do, preparing for the interview will help you approach this last step confidently. To help get you ready for the job market, this reading will explore four different interview types and offer interview questions that this certificate program will help you answer. 

Establishing your professional narrative

Establishing a professional narrative, or brand, can help you build connections between your daily work and the positive influence it has on something bigger. This something bigger could be an exciting project that you helped finish or a positive change in your organization or community! This applies to the work you have done previously and the work you hope to do in the roles you apply for. Framing your experience and goals around your professional narrative allows you to develop a stronger sense of the value you bring to an organization and your own career goals. Keeping your professional narrative top of mind is important as you prepare to enter or navigate the data professional career space. Your personal brand is the value you offer potential employers. It can often provide answers to questions posed during the interview process. 

Interview question types 

There are many different types of interview questions and each organization has its own priorities for what they want to know about each applicant for each role. Interview questions typically belong to one of four categories:  

• Behavioral questions: These questions ask you to describe how you have handled specific situations in the past, and your personal characteristics. They are designed to assess your skills, experience, and problem-solving abilities, as well as your fit for the company culture.
• Technical questions: These questions ask you to demonstrate the knowledge and skills presented in your resume or portfolio. In job interviews for technical positions, an employer may ask for a demonstration of specific tasks, prior projects, or even a take-home assessment. Often these technical demonstrations are presented as a separate assignment that you will complete outside of the interview itself. These questions are designed to assess your technical skills and expertise.
• Situational questions: These questions ask you how you would handle hypothetical situations. Similar to behavioral questions, employers typically use situational questions to develop a preliminary understanding of how your skills fit the role. Situational questions are designed to assess your judgment, critical thinking skills, and ability to apply knowledge to new situations.
• Subject questions: These questions ask you about your knowledge of a specific subject or area, usually pertaining to the field or industry that you’re applying for. These questions are designed to determine how well you understand the relationship between the role you’re applying for and the broader context of the company. Employers may also use these questions to assess your understanding of how the company works in contrast to direct competitors in the marketplace. 

These are just a few examples of the types of questions you might be asked in interviews. These categories aren’t universal, and different organizations have different interview styles– they may even ask questions that combine categories. For example, an interviewer might give you a hypothetical situation and ask you for an example of a previous situation you’ve encountered that relates. This is a combination of a behavioral and situational question. Generally, the goal of interview questions is to assess your skills, experience, and general fit for the role; so keep that in mind as you prepare.

Applying course skills 

As you progress through this certificate program, you will learn industry skills that interviewers will be interested in asking about. Throughout your learning journey, it will be useful to identify and keep in mind key skills you will need to be able to discuss. The following is a list of questions that you might be asked in an interview for data professional positions. You will find questions in this list that are representative of the four interview question type categories explained in the previous section. Finishing your certificate will mean you’re prepared to answer all of these questions!

Course 1 

• As a new member of a data analytics team, what steps could you take to be fully informed about a current project? Who would you like to meet with?
• How would you plan an analytics project?
• What steps would you take to translate a business question to an analytical solution?
• Why is actively managing data an important part of a data analytics team’s responsibilities? 
• What are some considerations you might need to be mindful of when reporting results?

Course 2

• Describe the steps you would take to clean and transform an unstructured data set. 
• What specific things might you review for as part of your cleaning process?
• What are some of the outliers, anomalies, or unusual things you might consider in the data cleaning process that might impact analyses or the ability to create insights?

Course 3 

• How would you explain the difference between qualitative and quantitative data sources?
• Describe the difference between structured and unstructured data. 
• Why is it important to do exploratory data analysis (EDA)?
• How would you perform EDA on a given dataset?
• How do you create or alter a visualization based on different audiences? 
• How do you avoid bias and ensure accessibility in a data visualization?
• How does data visualization inform your EDA?

Course 4 

• How would you explain an A/B test to stakeholders who may not be familiar with analytics?
• If you had access to company performance data, what statistical tests might be useful to help understand performance?
• What considerations would you think about when presenting results to make sure they have an impact or have achieved the desired results?
• What are some effective ways to communicate statistical concepts/methods to a non-technical audience?
• In your own words, explain the factors that go into an experimental design for designs such as A/B tests.

Course 5

• Describe the steps you would take to run a regression-based analysis.
• List and describe the critical assumptions of linear regression.
• What is the primary difference between R2 and adjusted R2?
• How do you interpret a Q-Q plot in a linear regression model?
• What is the bias-variance tradeoff? How does it relate to building a multiple linear regression model? Consider variable selection and adjusted R2.

Course 6 

• What kinds of business problems would be best addressed by supervised learning models?
• What requirements are needed to create effective supervised learning models?
• What does machine learning mean to you?
• How would you explain what machine learning algorithms do to a teammate who is new to the concept?
• How does gradient boosting work?

Begin with the end in mind 

At this point in the certificate program, you are still early in your learning journey. Because of that, you have the opportunity to consider everything you’re going to learn in the context of your final goal: taking the next step in your data professional career. Part of taking that next step involves interviewing with potential employers. As you learn more and more skills and become familiar with new tools, keeping these interview questions in mind can help you frame how what you’re learning now applies to future job roles. These questions can also help you frame your focus in each course–by considering how you might use the new skills and knowledge you’re learning, you can better understand why the work you’re doing now is so important!

Starting now, you can keep your final goals in mind and continue to build them into your professional narrative. That way, by the end of this program, you will already have a strong framework for communicating with potential employers. 

Key Takeaways 

The interview is an opportunity to share how you can add value to an organization. Recognizing your growing skillset and how you might communicate those skills to potential employers is a great way to showcase not just your technical know-how, but your ability to communicate effectively too. This reading is a great resource to keep in mind as you build your skills and your professional narrative in preparation for your job search.

Active listening: Refers to allowing team members, leadership, and other collaborative stakeholders to share their own points of view before offering responses

Analytics Team Manager: A data professional who supervises analytical strategy for an organization, often managing multiple groups 

Business Intelligence Analyst: (Refer to Business Intelligence Engineer)

Business Intelligence Engineer: A data professional who uses their knowledge of business trends and databases to organize information and make it accessible; also referred to as a Business Intelligence Analyst

Chief Data Officer: An executive-level data professional who is responsible for the consistency, accuracy, relevancy, interpretability, and reliability of the data a team provides

Data cleaning: The process of formatting data and removing unwanted material

Data Engineer: A data professional who makes data accessible, ensures data ecosystems offer reliable results, and manages infrastructure for data across enterprises

Data Scientist: A data professional who works closely with analytics to provide meaningful insights that help improve current business operations

Interpersonal skills: Traits that focus on communicating and building relationships

Mentor: Someone who shares knowledge, skills, and experience to help another grow both professionally and personally

RACI chart: A visual that helps to define roles and responsibilities for individuals or teams to ensure work gets done efficiently; lists who is responsible, accountable, consulted, and informed for project tasks

Allow others to share their points of view before offering a response

• Eliminating structural errors and empty spaces
• Tagging and consolidating duplicates
• Removing irrelevant entries

• Make data accessible
• Ensure the data ecosystem offers reliable results
• Manage infrastructure for data across the enterprise

• Supervise an organization’s analytical strategy
• Manage multiple groups

Business intelligence engineer

• Someone who is labeled “consulted” is typically a subject matter expert who offers input on a task.
• Someone who is labeled “informed” is kept aware of progress and the concerns of people working on a project.
• The acronym RACI stands for responsible, accountable, consulted, and informed.

Adaptability

analytics

organizing

Standardization