Data analysis is the manipulation, crunching and juggling around with data to derive purposeful results discovering various options and possibilities. Data analysis process adopted by us involves five distinct steps as listed below:

Defining of the Question.

Set measurement priorities.

Collection of Data.

Analyse Data.

Interpret results.

Defining the Question:  (Step: 1)

To end up with the desired results we begin the process by asking concise, clear and measureable question or questions specifically designed to either qualify or disqualify potential solutions to our specific problem or opportunity. Examples of such questions may be like:

Can a company reduce its staff without compromising quality? OR Can a company increase production without taking in additional work force? Etc.

Setting of clear measurement Priorities: (Step 2)

The second step is that of setting up clear measurement priorities, which is broken down to two sub-steps: (i) What to measure and (ii) How to measure.

What to measure obviously should be complying with the question(s) decided in the step one? For the example questions set above, the appropriate measure would be to know the number and cost of current staff and the percentage of time spent by them on the production process. OR we may need to know the average annual/ monthly/ weekly/ daily/ hourly production done by current work force and within what time etc. To get the desired answers to those primary questions we may need to ask many more sub-questions.

As for How to measure, is as important as how to measure, especially prior to the phase of data collection. The questions to be asked in the sub-steps are like:

(i) What should be taken as the measuring unit?

(ii) What time frame should be fixed?  

(iii) What are the factors that should be taken into consideration etc.?

Data collection is the Step 3 of the analysing process.

Now that the questions have been defined and the measurement priorities set, it’s time to collect data keeping in mind certain important points which are very vital for the purpose of collecting appropriate data for our purpose. Points such as collecting of information from existing data, organising the storing system even before the collection begin and any other arrangements that ought to be done before, during and after the process of data collection should be taken care of.

Data Analysing (Step 4)

After the collection of data that suitably answers the question(s) in the step one, the actual manipulation of data in many possible ways which allows our data experts to calculate the mean, maximum, minimum and standard deviation of the data.

During this step it may become necessary to revise the original question, hence collect fresh data to answer the revised question or it may be that the questions remain the same but additional data need to be collected. All the above activities are performed by our experts with intense care. To carry on with these analysis, various tools and softwares are taken to use.

The ultimate step is the Result interpretation. Here attempts are made to find whether or not the results of the data analysis satisfactorily answer the original question that had been put in step one, if yes, how? Whether the result reached at is able to defend against any objections, if the answer is yes, how? And finally to consider whether there are any loop holes, whether or not the conclusion has any limitation or if anything that has been left out of consideration. If the interpretation of the data holds up under the above tests and considerations than it is likely that a productive conclusion have been reached. It is now time to decide the best course of action with the data analysed results.

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