Data Analysis Methods 101 – Turning Raw Data into Actionable Insights

You will be surprised to know that we generate 328.77 million each day (source: explodingtopics.com). And according to one estimate, these figures will rise to 181 zettabytes by 2025 (Source: statista.com). It goes without saying that organisations globally are on the lookout for data analysts. They need skilled analysts who can analyse this colossal volume of data, uncover hidden trends, and help them to reach data-driven decisions. There is a long roster of data analysis methods and tools to complete an analysis project. Put simply, data analysts are one of the most in-demand professionals today.
The blog below delves deeper into the inner workings of the analysis process and its significance in the contemporary business world.

What is Data Analytics?

Data analytics is all about uncovering valuable insights from the massive amounts of data we generate daily. It involves methods for sorting, categorising, and analysing information to reveal hidden trends and metrics. This helps improve the overall process and efficiency of an organisation. Insights derived from analytics also reveal cost-cutting zones and leverage an economical operation.
For example, manufacturing companies use data analytics to optimise machine performance by analysing runtime and downtime data. Gaming companies employ it to enhance player engagement through reward structures. Content creators tailor content placement based on analytics insights to increase clicks and views.

Roadmap for Data Analysis Methods

Data analysis involves a series of steps to unlock insights from information. These steps include:

• Data Collection:

The entire data analysis process commences with the collection of raw data. As an analyst, you will have to gather information from various sources relevant to your analysis.


• Data Cleaning:

Once collected, you will have to purge the data of all impurities to churn out the most unadulterated version possible. Thus, the second step is all about removing errors, duplicates, and inconsistencies to ensure data accuracy.


• Data Exploration:

Analysts need to derive a comprehensive understanding of the collected and cleansed data to comprehend the inherent nature of the dataset. Thus, they have to study the patterns and characteristics of the filtered data before heading to the analysis part.


• Data Transformation:

This is one of the most crucial data analytics techniques as you proceed to the core analytics part. This is where analysts have to convert and organise the dataset to prepare the lot for analysis.


• Data Analysis:

We have reached the heart of the analysis process. This is where the analysts carry out the analysis process to uncover valuable insights from the dataset. The procedure involves extensive implementation of statistical and machine learning methods, and related tools. Leading data analytics training programs cover all major analysis tools like Power BI, Tableau, Excel, Jupyter Notebook, and more.


• Data Visualisation:

Raw findings gathered from the analysis could be too baffling to comprehend for others, say the boardroom executives. This is where you need data visualisation tools. These tools help to create visual representations with charts and graphs to bring more clarity to the findings. You can join our Power BI course to learn more about data visualisation.


• Data Interpretation:

The visualisation process will further help the analysts to draw meaningful conclusions from the analysed data.


• Data Reporting:

Next, the analyst will present the findings and recommendations clearly and concisely.


• Implementation:

The final stage is about putting the data findings into real-life action. You will help the management to implement decisions or strategies based on the insights gained from the analysis.

Why High Demand For Data Analysts?

Businesses capture statistics, quantitative data, and information from multiple external and internal channels. But this humongous dataset is largely cluttered and need meticulous sorting and organisation. Data Analysts shoulder the baton to simplify this huge raw dataset to empower informed decisions and boost efficiency. Here are some reasons that highlight the importance of data analytics:

• Informed Decision-Making:

Data analytics empowers organisations to base their decisions on concrete evidence rather than gut feelings. Informed choices lead to better outcomes.


• Real-time Insights:

With real-time data analysis, businesses can make immediate adjustments, seize opportunities, and address challenges promptly.


• Data-Backed Strategies:

This is one of the major benefits of data analytics. Crafting strategies based on data and business analytics helps businesses understand customer preferences better. It further empowers them to tailor their offerings as per the evolving shifts in the changing business landscape.


• Identifying Trends:

Analytics help to discover patterns and possible emerging trends. These data-driven predictions enable businesses to anticipate shifts in the market, consumer preferences, and industry dynamics.


• Enhancing Efficiency:

Insights derived from thorough analysis empower companies to optimise their processes, streamline operations, and reduce inefficiencies. It further leads to cost savings and improved productivity.


• Customer Understanding:

Extensive analysis of customer behaviour and preferences enables tailored marketing strategies. This helps to create personalised experiences and, eventually, stronger customer relationships.


• Competitive Edge:

Businesses that harness data analytics always boast a competitive advantage. They are capable of responding swiftly to changes, staying ahead of rivals, and adapting to evolving trends.

Conclusion

With data ruling the world, the demand for data analysts is only soaring. These professionals help organisations make evidence-based decisions, identify market trends, streamline operations, and connect with customers. With data analytics, businesses can thrive, adapt, and lead in an ever-evolving landscape, ensuring a bright and data-driven future.
DataSpace Academy’s Certification In Data Analytics is strategically designed to render a competitive edge in the job market. The course focuses on major topics like how to work with SQL databases, and Python, create data visualisations, and apply statistics and predictive analytics in a business setting.