IMP for courses answers: Why Data Analysis is so important?

IMP for courses answers: Why Data Analysis is so important?

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August 6, 2020

For most businesses and government agencies, lack of data isn’t a problem. In fact, it’s the opposite: there’s often too much information available to make a clear decision.

Let’s start our journey…

What is Data Analysis? (Data Analysis meaning, and definition)

Business data analysis is the process of collecting, transforming, cleaning, and modeling data using analytical and logical reasoning to examine each component of the provided data.

Data from various sources is gathered, reviewed, and then analyzed to form some sort of finding or conclusion, and the results so obtained are communicated, suggesting conclusions, and supporting decision-making.

Data Analysis Process consists of the following phases that are naturally iterative:  

  • Data Requirements Specification
  • Data Collection
  • Data Processing
  • Data Cleaning
  • Data Analysis
  • Communication

How to start your data analysis best practice?

With so much data to sort through, you need something more from your data:

  • You need to know it is the right data type to answer your question.
  • You need to draw accurate conclusions from the extracted data.
  • You need data that outlines your decision-making process.

In short, you need better data analysis. With the right data analysis process and tools, what was once an overwhelming volume of disparate information becomes a simple, clear decision point.

Best steps for a better decision-making process using your data analysis:

To improve your data analysis skills and simplify your decisions, follow these five steps in your data analysis process and subscribe for your data analysis live course

Step 1: Define Your Questions

You must begin your business data analysis, with the right question and design it to either qualify or disqualify potential solutions to your specific problem or opportunity, which again like mentioned before should be measurable, clear and concise

For example, start with a clearly defined problem: If you are experiencing rising costs and is no longer able to submit competitive contract proposals. One of the many questions to solve this business problem might include: Can the company reduce its staff without compromising quality?

Step 2: Set Clear Measurement Priorities

This analytical step breaks down into two sub-steps:

  1. Decide what to measure.
  2. Decide how to measure it.

A) Decide What to Measure

Using the previous example, consider what kind of data you’d need to answer your question. In this case, you’d need to know the number and cost of current staff and the percentage of time they spend on the necessary functions.

In answering this question, you likely need to answer many sub-questions (e.g., Are staff currently under-utilized?), If so, (what process improvements would help?).
Finally, in your decision on what to measure, be sure to include any reasonable objections (e.g., If staff is reduced, how would the company respond to surges in demand?).

B) Decide How to Measure It

Thinking about how you measure your data is just as important, especially before the data collection phase… Why?! Because your measuring process will either backs up or discredits your data analysis.

Key questions to ask for this step include:

  • What is your time frame? (e.g., annual vs quarter costs)
  • What is your unit of measure? (e.g., USD vs Euro)
  • What factors should be included? (e.g., annual salary vs annual salary)

Want to know more, and execute the most accurate conclusions from your data?

Join the Best online live data analysis training program and contact us to discover how the right data analysis drives success for your organization.