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Data Analytics Simplified in 5 Steps – Part 2

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In continuation to our part 1, here are the next 3 steps that you can master in Data Analytics course Malaysia.

Data Gathering and Scrubbing

For the last 15-20 years, the two steps were often discussed separately. However, in the last 12-18 months things have changed dramatically with the adoption of AI-based automation in data science. While the tools and techniques of data gathering remain same, the addition of advanced automation capabilities have equipped BI teams to harness quality data in real-time.

The current phase of data gathering involves organization of information and scrubbing it for real value through case studies, questionnaires, surveys, polls, projective trends, and physical observation. Since data gathering is a time-consuming and the costliest phase in the data analytics cycle, enterprises are leveraging tools that do the part in short, and accurate manner.

Every second, data teams get 2 GB information from various sources. With the advent of Big Data, BI teams are flooded with information that may not serve the purpose in the current scheme of analytics.

The data collected may lose its shiny side with time – a factor called ‘decay’ comes into play. On top of it, much of data gathered may not be relevant to the operations anymore.

So, the data needs to be cleansed and removed for inaccuracies, redundancy, duplication, and some time – even, malware that cybercriminals junk in. These defects can lead to further complexity in data assessment and eventually provide false results to the decision-making teams.

To avoid falsified outcomes, data analytics teams conduct simultaneous cleansing processes to get rid of ‘dirty data’.

Why quality Data Analysis is hinged on identifying dirty data?

Poor data capture and analysis can derail the entire intelligence workflow. Human errors add to the automated systems. Lack of standardized processing in the first three steps of data analytics lead to 75% of the failure incidents in any model. That’s why predictive intelligence platforms take 2-3 months to complete their workflow models.

It is essentially a key phase of data analytics—but remember —  no amount of data cleansing would help if you have gathered ‘junk data’ in the first place.

Finally, Data Analysis and Visualization

All the efforts that you have taken to gather, clean, and store data would showcase its value to the decision-making team here. There are many techniques that top Business Analysts and Data Science teams adopt to represent their information in a story mode. These could be through Data Visualization and Exploratory data analysis.

Through this final step of data analysis, teams finally test the original hypothesis and attempt to summarize the information gathered in a way that can be easily understood, calculated and applied to various related business processes.

For example, in data visualization techniques, Reporting Dashboards present data in a visual form – in the form of graphs, heat maps, charts, and tables. From the heaps of data-mountains, we can zero microscopically onto the final outcomes.

You are now ready for the next phase of Data Analytics Malaysia.