Why Data Analytics?
If you’re looking to improve your business, data analysis is a key step. By understanding the problem and gathering the data you need, you can start generating insights that will help you make better informed decisions. In this blog post, we’ll walk you through the steps of data analysis, from understanding the business problem to data visualization and finally deployment.
According to data analytics guru Bernard Marr, even though smaller businesses don’t generate as much data as larger corporations, analytics can still be a valuable tool for them. In fact, it might be more advantageous for them because they are more agile and can act quickly on data-driven insights.
Step 1: Understanding the business problem
The first and the most crucial step in the process is to get a better understanding of the problem. In data analytics jargon, this is often referred to as the ‘problem statement’. Here, you seek clarity on what you are looking for and gain an overall understanding of the core business issue. It is vital to have a clearly defined question at the start and also the corresponding data because it drives the rest of the analysis process.
Gaining a business understanding starts with identifying the purpose or the intent of the question. Once you have a good grasp of the problem, you can start to develop hypotheses about how to solve it. Then, you can use calculated metrics to verify or disprove your hypothesis.
A strategy that clearly defines the objectives of the business is extremely crucial. It saves time & resources, while also helping you avoid any pitfalls whatsoever with the data in the future.
Step 2: Analyze data requirements
Once we adopt the analytical approach, we now need to assess the type of data that we will need to carry out our analysis. This includes identifying the data formats, sources, and content for data collection.
If you compare it to cooking a dish, think of this step as writing the ingredients. We need all the ingredients (data) before we cook.
The data requirement analysis process usually has four major steps:
- Identifying the business context
- Conducting stakeholder interviews
- Synthesizing the expectations and requirements
- Developing source-to-target mapping
Step 3: Data understanding and collection
Once we know the various data requirements in our process, we can gather the data. While this may seem simple, you’ve got to be careful during this stage, as the absence of certain required data sets can cause problems later. Therefore, it is advisable to have a good understanding of the data set will familiarize you with the data set and put you in control.
This step also prepares your data for deployment by polishing it further.
Step 4: Data Preparation
The next step is to transform the collected data into the format needed for in-depth data analysis. Data preparation is the transformation of data into a state that is easy to work with.
Data is usually inclusive of missing values, inaccuracies, and other errors. Hence error correction, verifying the data quality, and joining the data sets together are a big part of the data preparation process. We see this as the longest step of this method.
The additional two steps of data preparation are:
- Converting the collected data to a structured format with all required elements
- Cleaning it to remove unwanted substances
We now have a fully polished data set to use. However, before we start our analysis, we have to visualize our data so that its analysis becomes a cakewalk.
Step 5: Data visualization
Data visualization is the step where we represent our dataset visually for analysis. You must choose a way to represent your data to reveal structures and patterns that provide you with information in your area of interest. A robust BI tool like INSIA offers over 40 stunning data visualization options to choose from!
There are different data visualization techniques, such as charts, tables, geo maps, etc. that ease analysis. It leads us to meaningful insights and in-depth knowledge.
Step 6: Data analysis
At this step, we analyze our data set to mine the required information. You must watch all the patterns and trends to deduce the answer to your initial question. Make sure there are no longer any underlying data patterns or insights missing. Using INSIA, you only need to search for the questions you want answered (just like your favorite search engine) and the platform will provide you with answers in natural language.
You can also create multiple different dashboards to bring data together and analyze regularly.
Step 7: Deployment
The last step is to create and deploy the entire pipeline for fresh incoming data. The purpose of data analytics is to help us be future-ready. It is paramount that we put the above process into action for every new dataset that is relevant for business analysis.
How INSIA can help you reach your business goals:
A robust business intelligence tool like INSIA can help you with your data analytics challenges and help your business grow to its full potential. Simply connect your data sources in a few clicks and get insights from your data instantly. The best part is that you don’t need to have any technical knowledge to operate the platform. It has a Google-like search bar with automated drop-down recommendations that provide guided insights across billions of rows of data. In addition, the Push AI feature lets you receive notifications and alerts on social engagement and conversion performance deviations.
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