What Exactly is the Analysis Process: Steps and Methods to Make Decisions
If we talk about today's business trends, almost every business is collecting data of their customers. And there is no shame in it, to collect data for better convenience. The information they collect from you may include any android application usage, social media interactions, and many more ways. As we discussed above, it's important for almost every business better to understand their customer's needs through the analysis process.
But what exactly is the data analysis process, and which steps or methods are used by businesses to make a better understanding with their customers. Well, this article will highlight the significant steps and procedures for better decision making.
- Part 1:What is Analysis Process?
- Part 2:Steps for Analysis Process
- Part 3:Methods of Analysis Process
- Part 4:Software for UX Designer
What is Analysis Process?
So, what exactly is the analysis process? In simple words, the process of examining, cleaning, transforming, modeling data with the discovered valuable information, notifying outcomes, and assisting decision-making. The whole process is known as the analysis process.
In simple words, the process of data analysis or analysis process is to generate some useful information from different sources and make some decisions on that Analysis. Well, these are the exact definitions of the analysis process; what if we consider it in our daily lives?
In our daily life of data analysis, we make decisions of our everyday life by thinking about the past and making particular decisions by considering the history. We analyze our past or future happening and make decisions on it. So, everything we go through to make decisions is nothing but an analysis process. So that's data analysis for business purposes.
What does your business mean to you? Of course, you want a successful business in all respect. For that needs to grow faster and faster to meet the today's competition. That's where your business needs an analysis to grow in your life.
What if your business was growing at a reasonable speed, but suddenly it stops growing? So what's needs to be done to get your business back on track? Here's the simple thing, all you have to do is just take time and look around where you have done something wrong. Acknowledge your mistakes make plans to avoid those mistakes in the future.
Steps for Analysis Process
Without the proper execution of the data analysis process, it's impossible to make any decisions for your business. To improve your skills and make better exceptional decisions for your business, you must follow the following five data analysis process steps.
1. Why You Need Data Analysis
Every analysis process starts by defining the right questions. It's essential, to begin with, clear and straightforward questions. Avoid lengthy questions that don't qualify your specific problem. The questions must be specific to your particular situation.
Some typical questions for business problems are:
- How to minimize production cost without affecting the product quality?
- How to increase sales opportunities on current resources?
- Do our customers consider our brand in the right way?
2. Collection of Data
You've created a roadmap and built a team for the data analysis process; now, the next step is how and where you should start collecting the data.
In the data analysis process, you must start collecting data from internal sources with the help of CRM software, ERP systems, and other marketing tools. Then go for external sources like social media, reviews from different sites, and many other places.
3. Data Cleansing
Data cleansing is the most crucial step in the data analysis process. It's important because not every piece of data is suitable for your analysis process. To generate perfect results, you remove duplication, data inconsistency, and abnormal data from your Analysis. This stage requires 60 percent of the time throughout the whole Analysis—more time-consuming than any other step.
4. Analyze Data
This step considers as the last step before interpretation of the data analysis process. As the outline of this suggests, this step requires data manipulation and analyzing the data through data mining techniques.
5. Interpreting Results
Finally, time to interpret the results from the data analysis. This step is as crucial as analyzing the data because it shows how the business will gain from the previous actions.
So, these were the five important steps for the data analysis process for a business to make better decisions for the future.
Hold on a sec; the above steps are not enough to complete the data analysis process. There are some techniques and methods that need to follow for the execution of data analysis.
Methods of Analysis Process
Many techniques or methods exist on business perspectives. Some of them are as follows:
- Text Analysis
- Statistical Analysis
- Diagnostic Analysis
- Predictive Analysis
- Prescriptive Analysis
1. Text Analysis
Begin with text analysis; it is also referred to as Data Mining. This data analysis method recognizes the large number of data set using tools or databases. It takes raw data as a piece of business information.
2. Statistical Analysis
It's nothing more than a collection of statistical Analysis, interpretation, presentation, and modeling of existing and useful data. Statistical Analysis followed by the following categories:
- Descriptive Analysis: Summarize the data in a meaningful way that pattern might emerge from the data.
- Inferential Analysis: In Inferential Analysis, you can find different conclusions of the same data by different groups.
3. Diagnostic Analysis
Diagnostic Analysis is instrumental in identifying the behavior or patterns. This technique is used in the data analysis process when a new problem burst into the business process.
4. Predictive Analysis
It's ultimately the opposite of the Diagnostic Analysis. As the heading suggests, we'll predict the future happening in this data analysis process. It shows "what will happen" by using the previous data. As we mentioned before, this data analysis process predicts future outcomes based on past and current data.
5. Prescriptive Analysis
Prescriptive Analysis gathers the perception from previous data analysis to decide which actions need to take to a particular problem. Most companies rely on Prescriptive Analysis because they think predictive or descriptive analysis doest show promising data performances.
Software for UX Designer
We've thoroughly gone through every step of the data analysis process. Now, there's a tool for UX designers to increase their experience level of user experience designing. Wondershare Mockitt is highly-recommended by many experienced and professional graphic designers. Most of them are still loving its unique features.
Wondershare Mockitt
Like Adobe XD, Invision, Figma, and other graphic designing tools, Wondershare Mockitt is an online prototyping tool that can enhance your creativity to the next level. The unique features of Mockitt make it different from other graphic designing tools. Most of them are just like downloadable applications, which support only a limited number of operating systems, but not with the Mockitt. It's an online software that is capable of running almost on every operating system presents today.
Features of Mockitt
Mockitt supports lots of unique and amazing features. But here we'll discuss those form which UX designers can take benefits from:
1. User-Friendly Environment
Mockitt has the best and easiest user interface for designers. No one would say that we could not create designs on it.
2. No Coding Required
One of the advantages of using Mockitt is, you don't need to remember any code for your design.
3. Sketch Plugin
You can import mockups directly from Sketch within few clicks.
4. Animation
Mockitt also supports animated prototyping. And we think this is the best feature for the UX designers to use animation in android/iOS designs.
