As 2023 comes to an end and 2024 is on the horizon, software engineers and beyond put in hour upon hour to make the next big thing that will revolutionize the world. Data analysis takes innovation to another level to essentially predict the future by evaluating the past and uncovering the patterns of the present. Before diving right into the deep end, let’s take a look at the fundamentals of data analysis.
What is Data Analysis?
Knowing the past makes seeing the future possible and this is how data analysis operates; data analysts collect and evaluate massive amounts of data to reveal trends, tendencies, insights, and patterns to make predictions and more informed and intelligent decisions.
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To arrive at their final insights and trends, data analysts undergo a process that includes the following steps:
Pose a question: at the onset, you need to know what you want to research; with a simple question or idea, a firm starting point. With a question or idea that you want to answer, you have more guidance into what kind of data you need to collect for the next step.
Collect data: you can get data from a great number of places; depending on the requirements you choose, it may be more personalized data in the form of surveys, interviews, or focus groups. Other forms of data include social media monitoring, transactional and online tracking, and online forms.
Clean the data: when completing forms and surveys, many clients won’t answer all the questions or they may even answer incorrectly. Because of this and other problems like duplicate records, analysts must review and comb through the data to avoid skewed results.
Analyze the data: now that the data has been thoroughly sifted through, it’s time to use data analysis tools such as Microsoft Excel, Python, Apache Spark, R, Microsoft Power BI, and many more. Analysts will choose a data analysis tool based on the data type that they're trying to evaluate.
Interpret the data: for many analysts, this is the most exciting step because they finally discover answers to the questions they posed at the beginning. What's even more interesting is when they stumble upon data that indicates trends and insights that they didn’t intend they’d discover!
Present the data: with all of this new information, analysts can present their findings to their bosses, companies, teams, or whoever is willing to hear about them. To present this data, they create presentations or infographics that show their findings in a visually pleasing way.
As soon as data analysts answer their question, they move onto the subsequent project, the next question rises to the surface, and the cycle begins once again. The potential of data analysis is unrivaled since it can provide extremely valuable insights, but it all starts with a simple question or idea.
Emerging Technologies in Data Analytics for 2024
In addition to understanding the ins and outs of the data analysis process, data analysts need to have a wide variety of skills and expertise in strategies and techniques. Wearing many hats, data analysts need to know how to:
Data cleaning
Since unreliable data skews the results and makes it unusable, data analysts have to ensure that they sift through the data both efficiently and accurately before adding it to the approved data set.
Descriptive statistics
These statistics are essential for data analysts to calculate since they provide the data with the mean, percentiles, median, standard deviation, and mode.
Exploratory data analytics
By using techniques such as visual exploration, data profiling, and summary statistics, data analysts can more thoroughly and deeply understand the data and as a result, will generate the answers to their questions. Important patterns, relationships, and insights can be uncovered in this technique.
Machine learning algorithms
The power of machine learning is unmatched in the hands of a data analyst and selecting the right algorithm results in attaining those strong predictions and classification.
Data visualization
When data analysts create data visualizations, some information can become clearer when looking at the bigger picture. Putting the data into graphs and charts makes it much easier to see beyond the numbers.
Natural language processing and text mining
Textual data calls for a different way of handling the data: natural language processing and text mining. By extracting topics, classifying text, conducting entity recognition, and analyzing sentiment, data analysts can uncover other insights.
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Inferential statistics
Hypothesis testing, regression analysis, and confidence intervals are techniques used to analyze and test relationships and evaluate the relevance of their findings.
Clustering and segmentation
With data visualization in front of them, data analysts can observe and take note of clusters and segments in the data to reveal insights and hidden trends and compare the similarities and differences amongst clustered data points.
Time series analysis
When time is involved in your data set, time series analysis techniques are a fantastic way to take a magnifying glass to the various trends and patterns that aren’t visible without the whole timeline.
These skills and more are essential for data analysis jobs throughout the tech world; however, the field continues to evolve, with data analysts who boast a wide and expanding skill set in high demand. The next year promises to be a pivotal one that provides another leap forward for data analytics; take a peek into the future and see what awaits the future of data analytics in 2024.
Artificial intelligence and machine learning powered data analysis:
Artificial Intelligence (AI) and Machine Learning (ML) are transforming data analytics by automating complex processes and uncovering patterns that were previously undetectable. For instance, Netflix uses AI and ML algorithms to analyze user data and recommend personalized content, significantly enhancing user engagement and satisfaction.
Although artificial intelligence has driven a huge amount of data analysis insights and trends for many years, the trend will continue growing in 2024 and as machine learning algorithms become more creative, sophisticated, and intelligent, they will carry an even heavier workload.
According to a report by Gartner, the adoption rate of AI and ML technologies in data analytics is projected to grow by 40% annually through 2025, highlighting their crucial role in driving business intelligence and operational efficiency .
Not only are AI and machine learning algorithms capable of analyzing data more quickly than before, but they are also dealing with more complex data from videos, images, and text. Artificial intelligence and natural language processing working in tandem are revolutionizing how text data is analyzed and interacted with for many companies.
Hybrid and multi-cloud technology
For many data analysis companies, adaptation and flexibility are key and by having access to several types of cloud services that provide business and computing solutions for a wider range of needs, companies will thrive.
In addition to broadening solution options, companies with data across several cloud services will have security superior to those with just one cloud service; in the face of a data breach, companies will have less downtime, thanks to data available on another cloud service.
Data democratization
When talking about data democratization, remember that it allows non-data literate professionals to access the information and as more and more people realize the potential and power of data analysis, companies want their full staff to learn from it.
Data may be presented to employees like software that analyzes the data without the help of a data analyst; Instagram Insights would be an apt example. The insights and informed decisions extracted from data are priceless and low-code businesses can leverage it to find further success.
Edge computing
As real-time analysis becomes more essential for not only data analysis but also Internet of Things devices, edge computing will take the lead in providing this data on the edge of the network closer to its source and as a result, can analyze the most current, up to the moment data.
For devices like autonomous vehicles and industrial automation, evaluating available information and making instantaneous decisions is a necessity.
Source: Edge Delta
Augmented reality
Since data visualization is such a powerful tool to interact with data, the use of augmented reality to visualize it allows people to immerse themselves in data in a real world context. AR will also be beneficial for data analysts who collaborate from separate locations to complete their work and other future applications for augmented reality will appear in 2024 as it is leveraged for more innovative purposes.
It’s evident that data analysis has contributed tremendously to both the tech and non-tech worlds. Data analysis has pushed the limits of creativity and innovation for cloud computing, artificial intelligence, machine learning, and many more new technologies and will provide further motivation for 2024 and beyond.
Steps to Integrate Emerging Technologies in Data Analytics
Evaluate Your Needs: Determine your specific data analysis requirements.
Choose the Right Tools: Identify suitable AI, ML, cloud computing, or blockchain tools that fit your needs.
Invest in Training: Ensure your team is trained to use these new technologies effectively.
Implement Gradually: Start with small projects to assess the technology’s impact before scaling up.
Monitor and Optimize: Continuously track performance and make necessary adjustments to optimize the implementation.
If you see data analytics as an attractive and exciting part of your future, check out our Data Analytics Bootcamp and become part of the data revolution.
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
