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Data has replaced oil to become the most valuable commodity in the world. Today, data and data analysis is not just a luxury for businesses to grow, but a need to stay relevant in the competitive world of businesses.

In this blog, we will understand why data analysis is important and how you can leverage it to generate actionable insights.

According to data analytics guru Bernard Marr, while small businesses have lesser self-generated data in comparison to bigger corporations, it doesn’t mean that analytics is off-limits for them. It is rather more suitable for them because they’re more agile and can act quickly on data-driven insights.

Today, problem-solving is data-driven as we seek to leverage data for answers like never before. Many organizations still cannot do this despite the availability of abundant resources.

Unlike before, we no longer lack adequate computing power or availability of data. Yet, our ability to engage data in the decision-making process is still low. The problem is that most of us do not understand the questions to ask from the data- what we are seeking and why?

The solution lies in setting up a process to maximize data efficiency in solving the business problem at hand.

In this blog, we explore a method that helps anyone become an expert in data analysis. So, let’s start.

Step 1: Understanding the business problem

The first and the most crucial step in the process is to get a business understanding of the problem. 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 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. Then breaking down the objectives that support this purpose. After that, organizing and planning can continue.

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 know 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 the data expert’s method is the same as 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, we must take a lot of precautions, as the absence of certain required data sets can cause us problems later. Therefore, it is advisable to have a good understanding of the data set we use.

It is quite crucial since having a clear idea about the data sets will help us at later stages. It will familiarize us with the data set, put us in control, and make us comfortable in playing around.

This step also prepares our 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:

  1. Converting the collected data to a structured format with all required elements
  2. And 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 their analysis becomes a cakewalk.

Step 5: Data visualization

Data visualization is the step where we represent our dataset visually for analysis. We choose a way to represent our data to reveal structures and patterns that provide us with information in our area of interest.

We adopt different data visualization techniques, such as charts, tables and geo maps, etc to 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. We watch all the patterns and trends to deduce the answer to our initial question. We make sure there are no longer any underlying data patterns or insights missing.

Moreover, we 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 data that is relevant for the business analysis.

Once these steps are in place, it becomes extremely easy for business stakeholders to

  • Track performance deviations
  • Analyze data and
  • Execute steps that are required for the company to stay ahead of the curve

Final Thoughts

We have now found the answer to our question and are ready to make data-driven decisions. The methodology has helped us adopt a very objective approach to answer our question, to make our results very accurate.

Once completed, we are now in the position of becoming a data expert. We can use our newly gained knowledge to make decisions that help us to achieve our goals and enable our businesses to conquer the corporate world.

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