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It is always valuable if you ask your data the right and meaningful questions. Today businesses are spending a lot of their time to get the right answers from their data. The benefit of asking the right answer to your data can lead to your business success. In this article we have listed some hints that you can use when playing with your data. Hopefully, this will help you optimize data preparation for the analysis process and ensure that you have all the important steps and bases covered.
What is the importance of a good question?
In our primary education, teachers always say that “if you don’t understand the concept ask questions” but the question should be relevant to the subject. The same way in data analytics is that it is also good to ask questions, but you decide in advance what you want to ask. The fact is that you will not get a ready solution unless you ask it specific questions about data analysis.
For some reasons, if you are unaware of what questions to ask from your data, you should once again look at your business problem that you are working on. If you assume that you are currently solving and it needs any data, then you need to find out if a data-driven approach can enhance your existing solution. This can be achieved with machine learning models but they are only as valuable as what you are asking your data for.
Questions are more important than answers and for correct answer you should always ask a meaningful question for several reasons:
- Unless you understand or know the question, an answer is meaningless.
- If you think about the question then you can unlock only one answer and consequently the value of an answer is tied to the quality of your questions, among other things.
- One question will often give rise to many more than you would have ever thought you had not asked before.
Uncovering the best questions to ask for your data
When you combine Big Data and Modern Data Science, it empowers you to ask questions in a completely new way and by going to predictive analytics through descriptive analytics you can find new branches of querying data relationships and patterns you are bound to.
You may also like: Unlocking the potential of better data science workflow
Today many organizations are following a data-based approach to asking questions. Consider an example, if your job profile is a marketing manager and your data model has a variety of attributes that you want to gather in your CRM. The question arises such as “How many contacts were we in with our last campaign?” Or “Which is our most successful channel for acquisition?” These are simple questions that provide information that is helpful.
All businesses, even the most successful ones, can benefit from asking the right questions. After all, you can’t get answers without questions, and the quality of the answers you get depends on how good your questions are, and how you ask them.
Top Data Analysis questions to improve your business performance
By analyzing which industry, you are running, and which competitors your business likes to perform, the data analysis question should be clearly defined. Poor identification can result in faulty interpretation, which can lead directly to business efficiency, general results, and problems.
What problem are you trying to solve?
It is better to analyze your business first. Research which metrics and KPIs will benefit your company. Consider the future of the business and start working. Can you influence this development? Identify where changes can be made. If nothing can be changed, then there is no point in analyzing the data. The next step is to consider your data scientists. Encourage them and make them pay attention only to known issues and opportunities, as well as more tangible insights. This approach will help the team to gain confidence.
Where will your data come from?
The next step is to identify data sources. Here you need to put a lot of effort on scanning the raw data. You need to pay attention to which data to choose and which you are ignoring. But remember to save your unselected data for future analysis. Your next challenging task is to store the data in a location in a folder or database to be filtered. Be open-minded about your data sources at this stage – all departments of your company, sales, finance, IT, etc., have the ability to provide insights.
Do you need to change the data?
The question here is, do you need to change the data? The answer will be yes. There is a need to manually change or manipulate the data for effective analysis. This situation occurs when tables and datasets use different formats for the same information, or inconsistent or duplicate values appear in the dataset or when you try to group the data in different patterns.
What standard KPIs will help you?
Of course, Key Performance Indicators (KPIs) are important in business. But, when push comes to shove, KPIs are only useful when you identify the right ones for your business. And they will only deliver mission-critical data if you use KPIs and analyze what they tell you on a regular basis to inform your decision making.
Let’s see this through a straightforward example.
How can you approve data quality?
You already know that data is coming in the form of multiple sources, and they can be in clean or inaccurate format. All sources collected within a business may not have valuable information stored that benefits the business. So, to use the information and from which source it is coming from should be one of the top questions to ask about data analytics.
Data shows your business reality so don’t forget to filter your data set and take advantage of data analytics in real life.
According to Crowdflower’s survey, it was noted that most data scientists spend time on activities below:
- Around 60% time spend on organizing and cleaning up data.
- Nearly about 19% of the time given on collecting datasets.
- 9% of time utilized in mining data to draw the pattern.
- Training of datasets needs 3% of time.
- 4% time spent on refining the algorithms.
- And the rest 5% of the time are utilized for other activities.
When you are satisfied with your data and have made sure of the quality of your data, you are now ready to move on to the next step in the data analytics process.
Do you need to additional consolidate the data?
In many cases, you need to create a new table on top of your existing one. This can be done by funnel analysis, in which you want to have basic information about an ongoing, multistage process and will create different buckets in which to classify each record.
Decide the statistical analysis techniques you need to do
There are several statistical analysis techniques you can use. But here question is which techniques you need to select. Here we will introduce 3 most prominent used technique use for business analysis:
Regression Analysis: Regression analysis in statistical modeling is a set of statistical procedures for estimating the relationship between a dependent variable and one or more independent variables.
Cohort Analysis: Cohort analysis is a type of behavior analysis that breaks data into data set in related groups prior to analysis. These groups usually share common characteristics or experiences within a defined time-span.
Predictive & Prescriptive Analysis: Prediction and prescriptive analysis works on analyzing current and historical datasets to predict future prospects, including future potential and risk assessment.
What ETL processes need to be developed?
ETL stands for (Extract-Transform-Load). This is the most important step while analysing your data. The process of ETL is to read the data from the database(source), transform it into a different form, and finally load it to another database. ETL makes the work easy for data scientists because ETL tools break down the data to access and to analyse data easily. These tools provide an effective solution for IT departments. By using ETL tools data scientists do not have to manually extract information from a various source or you don’t have to need any advanced knowledge of doing this work.
What data visualization will help you?
Now you are ready with your data but your process is not complete yet. Data visualization is the most important step in data analysis. Effective presentation of your data will give positive impact to your user. You need skill to choose best charts and graphs for data visualization.
There are a number of data visualization tools available online. These tools can effectively prepare data and interpret the results.
Who is the end user of your results?
Finally, we have reached the end of the process. Here comes the questions about who is the end user of your data. Some basic questions come to mind when we are considering the end user and their expectations
- What they really need from the data
- How data benefits them
- If they have the technical skills to find information from data
- How long will it take them to analyze the data?
- Whether they are able to reach the goal for what they are analysing data?
You must know the answer to the above question. This will help you decide how detailed our data report will be and what data you should focus on.
Pay attention to your user whether they are inside the organization or customers. If your report is designed for your organization, then you are the best person to know the insights of your organization. You have to know all the details of the company and plan accordingly which report will be beneficial for your staff members. On the other hand, if the report is used by external parties or clients, then you have to focus on collaboration identification. The report should be easy to use and take action so that it is easy to understand and does not require any IT team to help.
In this article we have discussed all the process for your business success by applying the right question for data analysis. With the help of the above guidelines, you can formulate questions that can help you make the necessary business decisions. Always remember good questions will give you a good result. To start your own business analysis, EzDataMunch provides you with a lifetime free trial software. Just register on EzInsights to set up your free trial.
Abhishek is working as a Web Graphics Designer at EzDataMunch. He is involved in Maintaining and enhancing websites by adding and improving the design and interactive features, optimizing the web architectures for navigability & accessibility and ensuring the website and databases are being backed up. Also involved in marketing activities for brand promotion.