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Data Science Careers

Citizen Data Scientist vs. Data Scientist: What’s the Difference?

Citizen Data Scientist Vs. Data Scientist What’s The Difference

Over the past decade, businesses and organizations have come to rely on the competitive edge afforded by predictive analytics, business modeling, and behavioral marketing. And these days, enlisting both data scientists and citizen data scientists to optimize information systems is an effective way to save money and squeeze the most from data sets.

What is a Citizen Data Scientist?

Citizen data scientist is a relatively new job description. Also known as CDSs, they are low- to mid-level “software power users” with the skills to handle rote analysis tasks. Typically, citizen data scientists use WYSIWYG interfaces, drag-and-drop tools, in addition to pre-built models and data pipelines.

Most citizen data scientists aren’t advanced programmers. However, augmented analytics and artificial intelligence innovations have simplified routine data prep procedures, making it possible for people who don’t have quantitative science backgrounds to perform a scope of tasks.

Except in the rarest of circumstances, citizen data scientists don’t deal with statistics or high-level analytics.

At present, most companies underutilize CDSs. Instead, they still hire experts, who command large salaries or consulting fees, to perform redundant tasks that have been made easier by machine learning.

What is a Data Scientist?

Data scientists — also known as expert data scientists — are highly educated engineers. Nearly all are proficient in statistical programming languages, like Python and R. The overwhelming majority earned either master’s degrees or PhDs in math, computer science, engineering, or other quantitative fields.

In today’s market — where data reigns supreme — computational scientists are invaluable. They’re the brains behind complex algorithms that power behavioral analytics and are often enlisted to solve multidimensional business challenges using advanced data modeling. Expert data scientists work with structured and unstructured objects, they also often devise automated protocols to collect and clean raw data.

Why Should Companies Use Both Expert and Citizen Data Scientists?

Since CDSs cost significantly less than qualified scientists, having both citizen and expert data engineers in the mix saves money while allowing your business to maintain a valuable data pipeline. Plus, data engineers are in short supply, so augmenting their support staff with competent CDSs is often a great solution.

Some companies outsource all their data analytics needs to a dedicated third party. Others recruit citizen data scientists from within their ranks or hire new employees to fill CDS positions.

How to Best Leverage Citizen Data Scientists and Expert Data Scientists

Ensuring your data team hums along like a finely tuned motor requires implementing the five pillars of productive data work.

  1. Document an Ecosystem for CDSs: Documenting systems and protocols makes life much easier for citizen data scientists. In addition to outlining personnel hierarchies, authorized tools, and step-by-step data process rundowns, the document should also provide a breakdown of the company’s goals and how CDS work fits into the puzzle.
  2. Augment Tools: Instead of reinventing the wheel, provide extensions to existing programs commonly used by citizen data scientists. The best augmentations complement CDS work and support data storytelling, preparation, and querying.
  3. Delegate: Pipelines that use both expert and citizen data scientists work best when job responsibilities are clearly delineated. Tasks that require repetitive decision-making are great for CDSs, and the experts should be saved for complex tasks.
  4. Communication: Communication is key. Things run smoother when all levels share results and make everyone feel part of the team.
  5. Trash the Busy Work: People perform better when they feel useful. Saddling citizen data scientists with a bunch of busy work that never gets used is a one-way road to burnout — and thus a high turnover rate. Try to utilize every citizen data scientist to their highest ability.

Implementing a Comprehensive Data Team

Advancements in machine learning have democratized the information industry, allowing small businesses to harness the power of big data.

But if you’re not a large corporation or enterprise — or even if you are — hiring a full complement of expert and citizen data scientists may not be a budgetary possibility.

That’s where data analysis software and tools — like Inzata Analytics — step in and save the day. Our end-to-end platform can handle all your modeling, analytics, and transformation needs for a fraction of the cost of adding headcount to your in-house crew or extensive tech stacks. Let’s talk about your data needs. Get in touch today to kick off the conversation. If you want your business to profit as much as possible, then leveraging data intelligence systems is the place to start.

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Big Data Business Intelligence Data Science Careers

How to Become the BI Rookie of the Year

With the new year upon us and new opportunities at hand, it’s time to get your head in the game. There’s a business intelligence all-star in us all. Whether you’re new to the business intelligence league, have recently been transferred to a new team, or are just looking to up your data game, here are some strategies that can help.

Practice Like You Play

While practice might not always make perfect, it helps you get a little closer in your continuous pursuit. Natural talent can only take you so far, making practice essential to developing skills and gaining experience. Try taking on an additional practice project dedicated to advancing your technical skills. This will ensure your improvement over time and allow you to experiment outside of the common workplace parameters of traditional methods and time constraints. 

You should begin by picking out your “play data.” However, it’s important to note that this shouldn’t be just any old data. Find data related to something that interests you, take inspiration from any personal hobbies you may have. This could range from anything such as your music listening activity, your local weather data, or even your fantasy football league. 

Practicing your skills with data you have a personal invested interest in will give you the opportunity to play and experiment with new techniques. This will also increase your chances of continuing these development efforts. These projects are also a great way to demonstrate your curiosity when it comes to data, which can be key when looking to get drafted by another organization.  

If you’re stuck on sparking an initial idea, try researching open-source data and see if anything catches your eye. Resources like the Registry of Open Data on AWS or the U.S. government’s open data library are great places to start.

You can also see Inzata’s guide on Where to Get Free Public Datasets for Data Analytics Experimentation.

Learn From the Pros

Regardless of how much time you’ve spent in the league so far, there will always be more to learn from those around you. Taking the student approach or possessing the rookie mindset is vital to continuous learning. 

Start by looking to those who have demonstrated success in their field. This can be anyone from the higher-ups within your organization to an industry influencer. You can develop these connections by asking an executive to lunch or joining various discussion forums and networking groups.

Additionally, try attending as many training’s, webinars, and conferences as you can. These virtual events are more accessible than ever due to the recent widespread transition to remote work, giving you access to thought leaders across the globe.

There is also an abundance of available content online such as books and online courses. These resources will give you instant access to decades of industry experience and valuable lessons learned through hands-on accounts.

What’s Your Next Home Run?

Your batting average when it comes to tasks and projects is crucial to long term success. You might get lucky every once in a while if you’re aimlessly swinging for the fences. It’s important, though, to make sure you are establishing attainable goals for yourself. You can think of these goals as the next home run you’re looking to hit. Having a clear vision of professional milestones will help guide you in the right direction and ultimately increase your chances of achievement. 

Studies show that you’re 42% more likely to achieve your goals if you simply write them down.

One strategy that will improve your batting average is to determine SMART goals for yourself in terms of your role and what you’d like to achieve. Now, these goals aren’t just smart in the traditional sense of the word. 

SMART is an acronym for:

  • Specific – Make your goals clear and concise. Don’t leave any room for ambiguity or confusion, these goals should be as specific as possible.
  • Measurable – Evaluation is essential. You need to be able to measure your advancement towards your goal through metrics or other methods. How will you measure your progress? What metrics or evidence will you track in order to assess your efforts? 
  • Achievable – Make sure you aren’t setting goals outside of your reach. While these goals should be challenging, it’s important to remain within the realm of attainability.
  • Relevant – Your goals should be tied to the broader goals of your organization and what your department is trying to achieve. 
  • Time-Based – Setting a timeline will help you manage your time and implement a sense of urgency into your efforts.

This strategy is effective in that it sets up clear and measurable targets, increasing your chances of knocking a project or metric out of the park. 

Your goals should be challenging enough so that you won’t be able to hit one every game and it’s by no means comparable to winning your organization’s world series, but it’s a small win helping to mark your development as a player. 

Around the Bases

Overall, this post demonstrates the many tactics and resources available at anyone’s disposal to immediately up your data game. Finding the BI all-star in you ultimately comes down to how you’re investing in yourself. Improvement is a slow and steady process, make the most of the knowledge around you and experiment with what interests you. Implement these tips and strategies to start your journey to the hall of fame!

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Data Science Careers

5 In-Demand Traits of Highly Effective Data Scientists

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Demand has consistently been on the rise for data science roles and analytics skills across all industries. In 2021, it’s predicted that there will be an additional 3,037,809 new job openings in data science worldwide as companies move to become data-driven.

Whether you’re an aspiring data scientist yourself or just looking to acquire the mindset of one, knowing the essential qualities it takes to succeed in the role can help highlight what to focus on in your development. This leads us to the question: What does it take to be a successful data scientist? Here are the traits that set effective data scientists apart from the rest.

Business Vision

The most successful data scientists commonly have the ability to understand the company’s situation from a business standpoint and always keep the organization’s overarching goals in mind. This is important to understand the why behind the data. Business acumen is the key to determining what critical business questions the data is looking to answer or what questions need to be asked.

Being a data scientist isn’t solely about writing code and developing data models. General knowledge of organizational goals and challenges can help you to start asking the right questions and develop useful queries. 

Analytical Reasoning

Due to the technical nature of the position, possessing analytical and critical thinking skills is necessary for success. Working with data is all about identifying patterns and thinking quantitatively. Data scientists need to be able to look at any particular problem objectively in order to come to logical conclusions.

Scientists should also be considering different angles and perspectives when looking at their data. Constantly asking questions and deriving insights from various points of view are crucial to driving effective and objective analysis.

Curiosity

A data scientist’s abilities should extend far beyond their technical expertise. Soft skills such as curiosity and creativity are what distinguish good scientists from great ones. The job title contains the word ‘scientist’ for a reason, not all of the answers are known! You will need to theorize, develop hypotheses, experiment, and ultimately draw conclusions on a day to day basis. 

Curiosity is needed when it comes to handling these tasks and diving deeper into the complex problems at hand. Great scientists should take an iterative approach to understand their data and be open to questioning their initial assumptions. Highly effective data scientists are always looking for the “why” and “how” behind the data in order to probe for additional information. Exploration and experimentation are vital to producing conclusive insights. 

Communication

Furthermore, another essential skill for data scientists is communication. One of the primary responsibilities of a data scientist is to communicate their methods and findings to other business units. With the abundance of data today, it can be messy and difficult to understand, especially if you are entirely unfamiliar with the data. 

It’s important to communicate insights in a way that’s easy for others to understand and drive decisions from. After all, what good is the data if no one can understand it? Any skills surrounding storytelling abilities or communication will help to properly inform key stakeholders on their queries. 

Collaboration

Whether you are working within your department or with others to collect and communicate data, collaboration is critical. Much like every job, teamwork plays a vital role in maximizing productivity. This working relationship, though, is especially important for data scientists as there is often a disconnect between business units and data science teams. Data scientists help to bridge the gap between the two business functions and allow for greater effectiveness all around.

Conclusion

Overall, many traits can contribute to the success of a data scientist. But it’s important to note that none of these traits are necessarily required or set in stone when it comes to the makings of an effective one. The role contains a number of unique responsibilities but there remains an opportunity to make it entirely your own. Consider these traits as a common data scientist’s keys or guide to data mastery when developing in their career. 

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Data Analytics Data Science Careers

Data Scientist vs. Data Analyst: What’s the Difference?

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In today’s business climate, data is the one thing everyone is looking to as a means to compete and drive better decision making across business units. But who in your organization is actually working with data and putting it to work? You’ve likely seen an abundance of job listings for data analysts and data scientists alike or may even currently be in one of these roles yourself. These positions are becoming increasingly essential across industries, the Harvard Business Review even deemed data scientist as the “sexiest job” of the 21st century.

However, the lines can often be blurred between the roles of data scientists and data analysts. So now that we’ve established the rising demand and importance of these common positions, let’s take a closer look at understanding each. Let’s explore what it means to be a data scientist or an analyst as well as some key distinctions to keep in mind. 

What do Data Analysts do?

Data analysts are versatile in their role and are predominantly focused on driving business decisions. It’s common for data analysts to start with specific business questions that need to be answered. 

Some common job functions for data analysts include:

  • Identify trends and analyze data to develop insights
  • Design business intelligence dashboards
  • Create reports based on their findings
  • Communicate insights to business stakeholders

What do Data Scientists do?

While a data scientist also works with data thoroughly to develop insights and communicate them to stakeholders, they commonly apply more technical aspects to their work. This includes things such as coding, building algorithms, and developing experiments. Data scientists must know how to collect, clean, and handle data throughout the pipeline. 

Data scientists typically require more experience and advanced qualifications, specifically when it comes to their knowledge of statistical computer languages such as Python and SQL. However, there is far more to a data scientist’s role than merely their technical expertise. They have to be able to ask the right questions and streamline all aspects of the data pipeline.

Some common tasks and responsibilities for data scientists include:

  • Build and manipulate data models
  • Optimize data collection from disparate data sources
  • Clean data and create processes to maintain data quality
  • Develop algorithms and predictive models

What’s the Difference?

While both roles have data in common, the primary difference between the two is how they work with data. Data scientists focus on the entire data lifecycle, from collection and cleaning to final insights and interpretation. A data analysts’ role is weighted at the end of the pipeline, this being the interpretation of data and communicating findings to business units.

It’s not uncommon for data analysts to transition into the role of data scientist later on in their careers. Many view data analysts as a stepping stone, where analysts are able to practice the foundational tools of working with data.

How Much do Data Scientists and Data Analysts Make?

Your second thought after “show me the data” is probably “show me the money!” Now that we’ve reviewed the similarities and differences in each role’s responsibilities, let’s get down to the numbers. According to Glassdoor, you can expect to earn an average base pay of around $113,309 as a Data Scientist. This is nearly double the average base pay for a Data Analyst which comes in at around $62,453 per year. The seemingly drastic pay difference primarily reflects the variation in technical expertise and years of experience needed.

Conclusion

Overall, there is no predetermined definition of what it means to be a data analyst or a data scientist. Each role is unique and will vary depending on the industry, there are also a number of other factors specific to each organization. Though, it’s important to remember that there is room to make the position your own and define either job title for yourself. 

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Data Analytics Data Science Careers

The Secrets to Building Highly Effective Data Science Teams

The use of data science has become increasingly essential to success in today’s business climate. As more teams and positions are being created in the field of analytics, leaders have found establishing successful data science teams to be a difficult task to manage. Let’s explore the key elements to building a highly effective data science team for your next project as well as how to maximize your current talent.

Promoting Curiosity is Key

Curiosity is at the root of any successful team or project. Data can help organizations uncover and understand almost every aspect of their business, from key marketing drivers to even forecasting seasonal changes in sales. But first, your analytics team has to be willing to continually ask the data questions and perform deep dives into the information. While data visualizations provide comprehensive value when it comes to your data, not every issue or insight can be seen from the surface. 

Additionally, curiosity and exploration are important in fostering engagement amongst analysts. Increased engagement can lead to greater involvement in projects and higher quality insights that may not have been discovered otherwise. Implementing a weekly brainstorming discussion or providing access to educational courses can assist in stimulating curiosity when it comes to how your team approaches company data.

Make Experimentation and Research a Priority

Time management is another fundamental element to team effectiveness, what you spend your time on is vital to efficiency. It’s important to assess where time could be better spent and identify any possible inhibitors or distractions.

To evaluate how your team’s time is being allocated, start by asking questions such as: 

  • What type of projects do you typically spend the majority of your time on?
  • How often do you spend time on research or long-term projects?
  • Do non-technical users in your organization have easy access to data or do they require assistance from a data scientist?

The answers will illuminate where your team’s focus lies in the day-to-day. Your team shouldn’t be constantly tasked with requests for administering access to data or answering basic ad-hoc questions. These types of tasks can easily be supported by improved infrastructure or self-service analytics tools. While your data science team might be highly proficient in these assignments, an abundance of these requests will ultimately distract your team from their data science research and higher-level work. You should make a point to limit these requests in order to emphasize research and experimentation. 

High-Level Goals are Known and Understood

The team needs to understand the end game of any project and how it fits into the organization’s overall goals. It’s not uncommon for technical teams to experience a disconnect from business teams and their objectives. Making sure these business targets are known and understood will allow your team to better communicate their findings in relation to company goals. 

Furthermore, you should clearly define how your data science team should be interacting with other areas of the organization. Team members should regularly be involved in discussions surrounding strategies to stay up to date on the key drivers behind projects across departments. This will ensure effective collaboration between your data science team and any business stakeholders. 

Feedback and Continuous Improvement

There is always room for improvement, continuous learning is fundamental to the long-term success of any team. Be sure to carve out time at the end of a project to review performance. You should not only highlight positives and team contributions but also evaluate processes or methods that could be improved. Routine feedback will assure the success of future projects and give those involved an opportunity to progress as a group. 

Overall, there are ultimately many factors beyond this list that contribute to building a highly effective data science team. While every team is unique, providing a foundation for alignment with the business side of the organization, good communication, and exploratory research is key to success. 

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Big Data Data Science Careers

8 Tips & Tricks for Data Scientists

Whether you already work in the data science field or wish to get into it, there’s a lot of benefit in always expanding your bag of tricks. The field is grounded in statistics, and there’s also a rapidly growing trend toward automation. Being tech- and math-savvy is absolutely critical. Let’s take a look at 8 tips and tricks you’ll want to know as a data scientist.

#1: Learn to Program

With data science already heavily dependent on computing resources and machine learning quickly become the top way to derive insights, coding skills have never been more important. Fortunately, you don’t have to be a full-fledged application developer. Several programming languages are being increasingly tailored to serve those who need to build their own data analysis tools. Two of the biggest languages worth keeping up with are:

  • Python
  • R

If you’re looking to perform work using modern machine learning systems like TensorFlow, you’ll likely want to steer toward Python, as it has the largest set of supported libraries for ML. R, however, is very handy for quickly mocking up models and processing data. It’s also prudent to pick up some understanding of database queries.

#2: Develop a Rigid Workflow for Each Project

One of the biggest challenges in the world of data analytics is keeping your data as clean as possible. The best way to meet this challenge head on is to have a rigid workflow in place. Most folks in the field have set down these steps to follow:

  1. Gather and store data
  2. Verify integrity
  3. Clean the data and format it for processing
  4. Explore it briefly to get a sense of the dataset’s apparent strengths and weaknesses
  5. Run analysis
  6. Verify integrity again
  7. Confirm statistical relevance
  8. Build end products, such as visualizations and reports

#3: Find a Focus

The expanding nature of the data analytics world makes trying to know and explore it all as impossible as getting to the edge of the universe. It might be fun to explore machine vision to identify human faces, for example, but that skill likely isn’t going translate well if your life’s work is doing plagiarism detection.

In order to find a focus, you need to look at the real-world problems that interest you. This will then allow you to check out the data analysis tools that are commonly used to solve those problems.

#4: Always Think About Design

How you choose to analyze data will have a lot of bearing on how a project turns out. From a design standpoint, this means confronting questions like:

  • What metrics will be used?
  • Is this model appropriate for this job?
  • Can the compute time be optimized more?
  • Are the right formats being used for input and output?

#5: Make Data Scientist Friends with Github

Github is a wonderful source of code, and it can help you avoid needlessly reinventing the wheel. Register an account, and then learn the culture of Github and source code sharing. That means making a point of providing attribution in your work. Likewise, try to contribute to the community rather than just taking from it.

#6: Curate Data Well

One of the absolute keys to getting the most mileage out of data is to curate it competently. This means maintaining copies of original sources in order to allow others to track down issues later. You also need to provide and preserve unique identifiers for all your entries to permit tracking of data across database tables. This will ensure that you can distinguish duplicates from mere doppelgängers. When someone asks you to answer questions about oddities in the data or insights, you’ll be glad you left yourself a trail of breadcrumbs to follow.

#7: Know When to Cut Losses

Digging into a project can be fun, and there’s a lot to be said for grit and work ethic when confronting a problem. Spending forever fine-tuning a model that isn’t working, though, carries the risk of wasting a significant portion of the time you have available. Sometimes, the most you can learn from a particular approach is that it doesn’t work.

#8: Learn How to Delegate

Most great discoveries and innovations in the modern world are the final work products of teams. For example, STEM-related Nobel Prize are pretty much never awarded to individual winners anymore. While the media may enjoy telling the stories of single founders of companies, the reality is that all the successful startups of the internet age were team projects.

If you don’t have a team, find one. Recruit them in-house or go on the web and find people of similar interests. Don’t be afraid to use novel methods to find team members, too, such as holding contests or putting puzzles on websites.

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Data Analytics Data Quality Data Science Careers

I’m Outta Here: The Top Frustrations of a BI Engineer

The statements below first appeared in the r/BusinessIntelligence subreddit.

I have been working as a BI Developer/Consultant for the past 5 years after graduating from University. Many people are thinking about a career in this field. I thought I would offer my perspective of the problems I have faced and what led to my decision to move away from BI. Would love to hear any opinions/advice from others.

The first point I want to raise is that things have changed A LOT in BI/Data jobs over the past 5 years and not for the better. The job does not carry the same level of respect or ‘perceived’ value in an organization. Before you all murder me, let me explain. Data has more value than ever, I agree. However, the people who extract, clean, combine and deliver this data have much lower value. I am not sure why this has developed.


Advantages of BI/Data Careers

Job title of BI sounds fancy to most people. Salary ramp-up to mid level ($80k) on par or better than other IT/Business fields. (BI does cap out much earlier than other fields).

Easy to get into a low workload job as a Excel/PowerBI/Tableau data cruncher with a mid-level salary. Progress after that is very hard unless you make shifts to other areas.

Disadvantages of BI/Data Careers

Work that nobody wants to do gets dumped into the BI department. Its role is less well defined and it’s easy to sneak the mistakes of others into “the data department.” There’s no systematic way of managing the quality of what arrives in. Once we’ve taken custody of it and a few days have passed, it’s our problem. As if somehow 7,000 emails got turned into NULL in the 2 days since you sent me your file.

I once worked with a client that ran a yearly survey to gather data. They produced a report of top 100 companies and industry trends. Nobody in the client’s company wanted to sift through over 10,000 survey responses. Nobody wanted to clean data, extract insights from survey responses. So they just sent it.

This entire workload fell to us. the external consulting company, even with our $150-per-hour bill rate. It took us weeks of work and the company paid out quite a bit. Of course, remember I did not see $150-per-hour for this work, I just received my salary, which was in the $60k range. So who benefited and who overpaid?

Another example, this time from a large enterprise. Daily data loads extract data from [HR, finance, payroll, etc.] systems. New employees are sometimes set up with different/wrong values in different systems. This causes major issues in reporting/BI tools. Senior Management was quick to blame BI. They didn’t consider the inefficient processes, or mistakes at the operational level that led to this. The HR/Finance analysts don’t care about these issues. It got so bad, eventually setting up new employees in the HR system fell to BI analysts. They main reason was that they cared the most about the data.

The end users look at the data once a month if at all. The weekly emailed static reports often go unread. Instead the end users revert back to the prior solution where data is sourced by BI analysts manually. Guess what the reason was? End users find it boring to have to use cubes to browse data or PowerBI/Tableau to manipulate data. They prefer to file a request with the BI team and let them do that work, or have analysts send them a weekly email. Or simply sit in a meeting where someone else tells them what’s going on.

Salary cap to what BI developers can earn. I find that as a BI developer, my salary peaks at around 80% of what other types of developers earn at upper levels. Market rate for me is 90-100K (USD) in house and 100-120K (USD) consulting.

This is made worse by the number of senior SQL server/DBA/BI consultants (+20 experience) in the market. You don’t need more than 3/4 years experience with a BI toolset to get the job done properly. Yet I have been on many projects where clients have asked for someone with 12+ years experience. They’re later surprised when they learn someone with 4 years experience did the projects.

Job tied to a tool/industry. I was never sure why this matters so much. The ability to learn a new tool to get the job done is under-appreciated. I have worked in finance/retail/media and government BI. But I have been told I am not skilled enough to work in x industry or with y tool that varies slightly. Add to this jobs where I see people with masters or PhD level education doing BI Analyst work. People are on-average under-utilized, in my opinion.

BI testing. The most boring, manual, but most necessary part of any BI project.

Testing SQL business logic is painful because of the lack of automated testing solutions used across companies .

Testing with popular tools (PowerBI, Tableau) is nearly always manual . (Good luck testing complex finance dashboards with complex DAX business logic.)

Source system testing is non-existent. (What happens if you change the time zone in a source finance application. Does all the data for the user we extract change at a DB level as well?)

ETL testing (good luck testing 100+ SSIS packages).

Data Warehouse testing: all too often, complex business logic is piled on top of existing logic due to source system upgrades. cube/dashboard testing. No automated solutions exist. Mainly manual.

It’s rare to find business users who will agree to do testing properly. I have seen business users resign from jobs rather than sit and test large amounts of data manually.

While a career in BI is still very attractive to knowledge workers, I wanted to share the pitfalls. I hope my experience helps others. The space still has some maturing to do. If you get with the right organization, it can still be a great career. If they let you use the right data analysis tools, it can still be a win. The key is being able to quickly understand the environment and make quick decisions.

As an employee, you should be watchful for this, but you do have some choices . As a consultant – as I was/am – you’ll often get dragged into some of the worst environments to help fix things.

Expect that.

One can easily find themselves stuck cleaning data in Google Sheets for most of each day. It’s important to recognize the signs and signals of a good BI vs. a bad BI environment. My advice: look for places where business users are actively involved in BI projects. Companies that invest in their data, and in advanced AI tools. Places where they actually care about the outcome and respect the work you do. Because it’s important. You’re important.

Good luck out there.

The statements above first appeared in the r/BusinessIntelligence subreddit.

 

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