Think Strategically: Five Steps to Superior Data Analysis
This is one piece of a three-part series that looks at the various methods, techniques, and essential steps to ensure superior data analysis.
According to Wikipedia, data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
Every day new data finds its way to the web on the scale of zettabytes. Most of it ends up in repositories where it gathers the metaphorical dust, never seeing the light of day.
Data is as good as the insights derived from it.
Once you make up your mind and decide to use data to bolster your decision-making process, you might be tempted to believe that half the battle is won. Far from it.
Since there is no universal way to perform data analysis owing to the ambiguity associated with the sourcing, analysis, and interpretation of data, businesses must look at this process strategically, in detail.
Five steps to superior data analysis
The data analysis procedure starts with a problem statement and ends with actionable insights. Each step is as valuable as the next. Failure to internalize one step may lead to the deterioration of the entire process. So, we recommend that you pay heed to every step with equal esteem.
Let’s go over the steps one at a time.
1. Define the objective
Before delving into data analysis, formulate key questions that you are seeking the answers to.
- Why has productivity taken a nosedive in my organization?
- Is there a correlation between sales and brand reputation? If so, to what extent?
- Are the customers looking for a particular kind of product?
Once you have adequately defined the problem statement, you can then begin to draft a working hypothesis that you can test along the way. Defining the objective will help you determine the sources you can extract data from, metrics you can measure, and the techniques you can use to run your analysis.
2. Procure the data
Once the pressing question is defined, you can then move on to procuring the data. Considering the nature of the data sources will ultimately define the validity of your analysis, it is advisable to spend a good amount of time finding reliable sources.
Generally, data collection begins with internal sources and moves on to external sources. Data collected from internal sources is gathered from within the company whereas data collected from your clients and competitors (or any other source outside your organization) falls under external sources.
Internal sources:
- Customer Relationship Management software
- Internal database
- Sales Analysis report
- Enterprise Resource Planning software
- And many more…
External sources:
- Google public data
- Social media data
- Government websites
- Industry websites
- Review websites
- And many more…
In the age of Big Data, it is becoming increasingly common to extract data from secondary sources to reinforce the data analysis process. While the decision to go beyond primary sources rests solely on your shoulders, doing so can take your analysis to a whole another level.
If you need to collect vast volumes of data from diverse sources on the internet, you can always avail our Concierge services, which will help you get all the data you need in a hassle-free manner.
3. Make sure that data is clean
If your data is contaminated, you might as well stop the process right here, because everything you do hitherto will be nothing more than a zero-sum game, or worse!
We’ve repeated time and again the negative consequences bad data can have on your business. For, as is accepted worldwide, bad data is no better than no data.
It is not uncommon to find missing fields in a dataset. Not to mention faulty entries and outdated data. At Grepsr, we use best industry practices like data normalization to ensure the integrity of your data. Moreover, our QA team with its proven track record implements strict guidelines to get rid of all the bad apples.
4. Start analyzing the data
Once you’ve made sure that your data is clean, you can start analyzing it! If you are working with a relatively small number of quantitative data then can use common tools like Microsoft Excel, Tableau, and Google Data Studio to analyze your data.
But for more complex applications you can use the following techniques:
- Regression analysis
- Monte Carlo simulation
- Predictive analysis
- Prescriptive analysis
- Fuzzy logic
- Factor analysis
- Sentiment Analysis
- Cohort Analysis
We’ve written at length about the various ways you can use to run analysis on your data here.
5. Share the insights
The data has been collected, cleaned, and analyzed. Now it’s time to share it with the rest of the team. This is when all the hard work you put into your data analysis finally begins to bear fruit.
You can use data visualization tools to make it visually appealing and easy to deduce. Or in other words, by using the power of data storytelling you can rally the entire team to achieve a particular goal with the insights gained from this exercise.
Keep in mind that you should not look to confirm your hypothesis but accept whatever the result dictates.
Ask yourself the following questions:
- Does the data answer the question you asked in the beginning? How?
- Does the data enable you to make safe decisions? How?
- Are there more perspectives yet to be considered?
If your findings stand strong against all these questions, you are most likely headed in the right direction.
To conclude
If there’s one thing you take away from this article it’d be to make sure that the data you collect is of the highest order. Then as per your need, you can choose the best data analysis process to glean actionable insights.
Data extraction, data analysis, or data visualization. The one thing common in all these terms is data, and you must work with the highest quality, from the get-go. For that, you can always count on Grepsr!
