With so much data in the world, and with data as such a constant in all of our lives, there is a need for a powerful combination: advanced AI and skilled Data Analysts. While AI excels at crunching massive amounts of data, Data Analysts bring their human understanding to the table. They work alongside AI to uncover the human element within the data, determining what it tells us about the people behind the numbers.
Summary
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What is data analysis?
Data analysis is the process of cleansing, transforming and modeling data to translate it into useful information for business decision-making.
In the modern world, organizations receive an immense amount of data. And more often than not it comes in the form of raw material that needs to be shaped and analyzed. This is where the data analysts come in.
Their main mission is to process the data available in information systems to find indications that can be used by decision-makers, to help them manage their business.
Analysts work with data that may involve different facets of the business such as, customers, products, or internal processes. For example, the insights provided by the data analyst can be used to improve the user experience of a website, create offers that perfectly meet customers’ marketing needs, or to optimize production processes.
Missions of a data analyst
Data Analysts have 3 main missions:
Collect data
Translate and interpret data
Reporting results in a readable way
Gathering data
Data analyst’s objective is to make the most of the mass of data collected by the company.
First of all, when faced with a given problem, he or she needs to understand wha to look for and then what type of analysis and definition to apply in order to draw useful conclusions.
If the company does not have data engineers onboard, data analysts may also be tasked with creating and modeling the company databases, and ensuring that they function properly. In this case, they will have to use data extraction methods within a digital framework.
Before embarking on the actual analysis, they must carefully select the data sources to be exploited. However, the raw data collected isn’t always usable for analysis. The first task is to clean the data, process it, and then organize it using data segmentation criteria for optimal results.
Analysis and interpretation
Once data has been collected, cleaned, and processed the analysis and definition work can begin.
At this stage, the mission is to set up analysis systems to generate relevant information based on the data collected.The data analyst needs to be able to make the link between IT data and business challenges and in order to do that it is crucial to have knowledge in business subjects like marketing, management, admin, etc.
Using analytical tools to understand and interpret data, analysts then produce reports to communicate their findings to relevant stakeholders.
Answering questions in order to make more informed decisions is at the heart of data analysis. Generally these questions correspond to four main categories of analysis:
Descriptive analysis – “what happened?”
Diagnostic analysis – “why did this event take place?”
Predictive analysis – “what can potentially happen next?”
Prescriptive analysis – “what action should be taken?”
To produce a useful report, the analyst must detect recurring patterns in the data. This data will then enable them to identify trends and insights that are valuable to the company.
Report production and distribution
The final mission is to communicate the results to the right people. To do this, the data analyst uses data visualization tools to provide reports that are clear and easily understood by everyone.
For optimal pattern detection analysts produce this type of reports on a regular basis.
Here’s an overview of data-related missions, and what the data analyst is responsible for
Data analysis before and after AI
tasks
Before AI
with AI
Data collection and cleansing
Manual, tedious and error-prone
Automated, efficient and accurate
Data mining and analysis
Time-consuming and limited by human capabilities
Fast, thorough and able to detect complex patterns
Data visualization
Manual creation of graphs and tables
Automatic generation of interactive and insightful visualizations
Predictive modeling
Development of models from scratch
Use of pre-built, optimized machine learning algorithms
Communication of results
Creation of static reports
Sharing of interactive dashboards and data histories
AI has transformed the the job to be more:
Efficient: Automating manual tasks frees up analysts’ time to focus on more complex, value-added analyses.
Accurate: AI algorithms can identify patterns and trends that humans might miss.
Data-driven: AI enables analysts to explore larger datasets and generate more comprehensive insights.
Don’t worry! AI does not replace data analysts.
Instead, it provides them with new tools and capabilities to enhance their skills and impact. Today’s successful data analysts are those who are able to leverage AI while developing their critical thinking, communication and problem-solving skills.
Some concrete examples of how AI is used by data analysts:
Fraud detection: AI can be used to analyze financial transactions and identify patterns likely to indicate fraud.
Product recommendation: AI can be used to analyze customers’ purchase history and recommend products that may be of interest.
Predictive maintenance: AI can be used to analyze sensor data from industrial equipment and predict potential breakdowns.
Enhancing sports performance: more and more athletes and teams are using the skills of data analysts to improve their performance. Data analysts can provide data based on the latest competitions, and define areas for improvement.
Essential skills in data analytics
The data analysis landscape is changing rapidly, and tomorrow’s analysts will need a new arsenal of skills to stay competitive. While artificial intelligence (AI) and machine learning (ML) are transforming the industry, these tools aren’t replacing humans; they’re complementing them. Let’s explore the essential skills for data analysts who will shape the future.
Key technical skills
To succeed in their missions, data analysts should develop a wide range of skills.
First and foremost, he or she must master data cleansing and preparation. As we mention earlier, data extraction and preparation is a big portion of the role.. Meaning that there are situations where analysts have to deal with missing or inconsistent data that could skew his analysis.
Strong analytical skills are crucial for this job. The analyst needs to identify the relevant data to answer the business question. They then transform and analyze this data to derive accurate insights. Additionally, the analyst should explore the data for trends or relationships that could add value to the company.
A strong understanding of probability and statistics is essential as it guides the analyst in data analysis and exploration. And of course, equally important is data visualization, for effective communication of their findings to an audience that may not be familiar with data concepts.
Mastering AI and Machine Learning tools:
To fully utilize the potential of AI and ML, data analysts will need to become skilled conductors. This requires mastery of programming languages such as Python and R, machine learning libraries such as TensorFlow and PyTorch, and the AI cloud platforms offered by Google Cloud Platform and Amazon Web Services.
Important soft skills
Data analysts must be able to communicate their insights** clearly to all company stakeholders.
This is all the more the case as they will be dealing with different audiences depending on the issues they will be called upon to resolve (marketers, sales people, digital managers, general management,etc.). To achieve this, they must have good oral and written communication skills.
The challenges of data analysis vary greatly from one field to another. In all cases, a good knowledge of the company’s sector of activity is an undeniable plus.
Finally, data analysts must have an appetite for problem-solving, as this is the heart of the role. On a day-to-day basis, bugs and deadlocks occur. For example, data may be incomplete, or the deadline for producing an analysis may be approaching. To overcome these difficulties, the analyst must be able to find solutions and think “outside the box”.
Communication and storytelling: AI does not replace the human expertise of data analysts. On the contrary, it complements it. Analysts need to develop strong communication and data storytelling skills to clearly explain the results of their analyses to stakeholders. It’s not enough to generate airtight technical reports; analyses must guide strategic decision-making. AI can certainly help with report formatting and the integration of human feedback, but clear, concise communication of results remains an irreplaceable human skill.
Critical thinking and strategic vision: ask the right questions and interpret the results
AI only provides tools. It’s up to data analysts to use them wisely and interpret them in the context of the business. Sharp critical thinking and strategic vision are essential to asking the right questions, choosing the right tools and analyzing the results in an objective and relevant way.
Data Analyst tools
To gather data and make sense of it, Data Analysts rely on various tools.
First and foremost, they need to master SQL to communicate with databases. Computer programming languages such as R and Python are particularly effective for statistical or predictive analysis.
The second category of tools to be mastered in the Data Analyst profession are the data analytics tools themselves. Of course, Excel can be used for this purpose, but analysts are more likely to turn to professional solutions such as Hadoop.
Finally, they also use reporting and data visualization tools such as Tableau or Looker studio.
The evolution of data analyst tools since the arrival of AI
Since the advent of AI, traditional data analyst tools have been evolving, and new ones are appearing. Whether it’s data analysis platforms, data preparation and cleansing tools, data visualization tools, cloud platforms… The greatest evolution has been around the creation of AI and Machine Learning libraries, which are designed for data analysts wishing to explore more advanced analyses and develop predictive models. Examples include TensorFlow, PyTorch and Scikit-learn.
Career in data overview
The ideal data analyst profile
Data analysts generally hold a Master’s degree in statistical sciences and studies (statistics/econometrics or a Master’s specialized in Big Data, for example).
Ideally, they have a good knowledge of the business, enabling them to respond to specific sectoral issues from a data perspective.
Over the course of their career, analysts can progress to become data scientists. In fact, data science is a logical progression. It’s still about analyzing data, but in a more advanced way, with new processes such as machine learning. This evolution also calls for a sharper understanding of business issues.
Salary
According to Data Recrutement’s 2020 salary survey, a junior data analyst in the Paris region earns an average of 30-40k a year. This compares with 42-86k per annum for a senior analyst. Of course, these salaries may vary if you work in another region of France or abroad.
How to become a data analyst in 2024?
The advent of artificial intelligence (AI) is shaking up the professional landscape of the data analyst, moving him or her towards a more strategic and collaborative role. Far from replacing human expertise, AI is becoming a powerful ally, enabling analysts to automate time-consuming tasks, analyze massive volumes of data and develop predictive models of unparalleled accuracy.
To thrive in this rapidly changing environment, data analysts need to adapt their skills and develop new abilities. Mastery of AI and Machine Learning tools, coupled with solid communication and storytelling skills, is becoming indispensable. Critical thinking, strategic vision and technological intelligence are also major assets for navigating this complex world of data.
Le Wagon’s Data Analyst training covers the whole range of data analyst skills and essential tools, from SQL to Python programming, statistics and probability. And now also include the essentials of AI.
With its project-oriented approach, our data science bootcamp is perfectly suited to your needs if you want to start your career as a data analyst, or if you want to move up the career ladder.
AI simplifies collaboration between data analysts, data scientists and data engineers.
AI fosters collaboration between data professionals to create innovative, high value-added analytical solutions. Data analysts play a crucial role as the bridge between data, tools and end-users, ensuring that analyses meet real business needs.
This collaboration helps to overcome traditional silos and create innovative, high value-added analytical solutions. Indeed, the combination of their respective expertise enables, among other things:
Rapidly exploit large volumes of data, by leveraging the power of AI algorithms to process and analyze massive data.
Develop more sophisticated and accurate analytical models, capturing the complexity of data and revealing new insights.
Design tailor-made analytical solutions, perfectly suited to the company’s specific needs and seamlessly integrated into its business processes.
Accelerate the decision-making process, by providing executives with actionable information in real time.
Ready to take your first step into data analysis and AI? Here are some useful resources:
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