There are a number of buzzwords in the field of data analytics that, while significant, are ill-defined due to their complexities. For non-technical people, words like “big data,” “cloud computing,” and “data-driven” might be confusing. However, establishing a strong knowledge foundation by explicitly defining these concepts early on is essential to success in a data analysis profession.
Learning the language of data analysis can help you have a better grasp of the subject and give you the ability to put what you’ve learned to good use. Once you’ve figured out what “data-driven” is, you can start using it in your decision-making and data analyst job.
“Big data” is perhaps one of today’s most used terms. But, exactly, what is “big data”? The term is frequently used to define the size and complexity of data. If a huge amount of information has been retrieved from a little amount of content, it could be labelled “big data.”
So, what exactly does “data-driven” imply? This word refers to a decision-making process that entails gathering data, extracting patterns and facts from that data, and using those facts to draw conclusions that impact decision-making.
The practice of making organizational decisions based on actual facts rather than intuition or observation alone is known as data-driven decision making (or DDDM).
Today, every business aspires to be data-driven. “Let’s not use the facts; our intuition alone will lead to excellent conclusions,” no corporation, group, or organization claims. Most professionals recognize that in the absence of facts, biases and erroneous assumptions (among other things) can impair judgement and contribute to bad decision-making. Despite this, 58 per cent of respondents in a recent study claimed that their organizations make at least half of their routine business choices based on gut feel or intuition rather than data.
So, how can you make sure you’re making data-driven decisions that are free of prejudice and centred on clear questions that will help your company succeed?
Professionals must achieve the following in order to properly use data:
A well-rounded statistical data analyst is well-versed in the industry and has excellent organizing skills. You have to analyze the issues that exist in your industry and competitive market. Identify and comprehend them completely. This core understanding will enable you to draw better inferences with your data in the future.
Before you start collecting data, you should first figure out what business questions you want to answer in order to meet your organization’s objectives. You can streamline the data collection process and prevent wasting resources by establishing the precise questions you need to know to inform your strategy.
Assemble the data sources from which you’ll be pulling information. You could be combining data from several databases, web-based feedback forms, or even social media.
While it may appear that coordinating your multiple sources is simple, discovering common factors across each dataset can be a huge challenge. It’s tempting to focus just on the immediate aim of using the data for your present purpose, but it’s also prudent to consider whether the data could be useful for future initiatives. If that’s the case, you should work on developing a strategy for presenting the data in a way that’s usable in a variety of settings.
Surprisingly, a data analyst spends 80% of his or her time cleaning and organizing data and only 20% of his or her time actually performing analysis. This so-called “80/20 rule” emphasizes the significance of having clean, organized data before attempting to decipher what it means for your company.
The term “data cleaning” refers to the process of removing or correcting incorrect, incomplete, or irrelevant data from raw data before analyzing it. To begin, make tables to arrange and classify what you’ve discovered. Make a data dictionary, which is a table that lists all of your variables and explains what they imply in the context of this project. Data type and other processing parameters could be included in this information.
After you’ve completely cleaned the data, you may start analyzing it with statistical models. You’ll begin building models to test your data and answer the business questions you identified earlier in the process at this point. Different models, such as linear regressions, decision trees, and random forest modelling can be tested to see which strategy is appropriate for your data collection.
You’ll also need to consider how you’ll display the data in order to respond to the inquiry. You can present your findings in three distinct ways:
- Descriptive Data: Only the facts.
- Inferential Data: The facts plus an interpretation of what those data mean in the context of a certain undertaking.
- Predictive Information: An inference based on facts and recommendations for the next steps depending on your reasoning.
Clarifying how the information will be presented can assist you in remaining organized when it comes time to evaluate the data. Often times coping with all the data in a short amount of time is quite difficult. Especially when the deadline is just around the corner, then it is highly recommended to consult Statistical analysis service to help you relieve the burden.
Coming to a conclusion is the final phase in data-driven decision making. “What new knowledge did you discover through the collecting of statistics?” ask yourself. Regardless of the pressure to uncover something completely new, a good place to start is by asking yourself questions to which you already know—or believe you know—the answer.
Many businesses frequently make assumptions about their goods or markets. They may think, for example, that “a market for this product exists” or that “this is what our customers want.” But, before looking for fresh information, examine your present assumptions. Demonstrating that these assumptions are valid will provide you with a basis to work from. Alternatively, disproving these assumptions will help you to eliminate any incorrect claims that may have been negatively harming your organization unwittingly. Remember that a great data-driven choice frequently raises more questions than it answers.