Everything you need to know about Data Analytics



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The overall social media users globally grew by 9.2% over the last ten years. This growth is outpacing the current global population growth, which stands at just over 1%. Understanding the impact of this growth on the emerging use of the internet on your business is critical.

Digital marketing has become the next frontier for business as internet use grows at a steady pace. But how can companies monitor, track, and analyze the shift in customer behaviors while pushing products and services online? Data analytics is one of the best solutions to this question.

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So, what is data analytics? This is often the first question that every business looking to push products and services online ought to ask. Whether you want to boost your business or blog, understanding what data analytics entails is your first task.

What is Data Analytics?

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Most people assume that data analytics and data analysis refer to the same thing. In reality, data analytics is a unique and different concept from data analysis. The latter is a subcomponent of data analytics.

data analysis
Business partner showing graph on tablet | Image credit: giggsy25/Freepik

Data analytics is a discipline that encompasses the complete management of data. The entire process involves the collection, organization, storage, and analysis of data to help your business or blog improve the process of delivery.

Analytics uses data and math to answer and solve critical business questions and dilemmas. If you are wondering what data analytics is, it’s essential to understand the basics. Businesses use data analytics to discover relationships, predict results, and even prepare automated decisions.

Data analytics as a discipline has come of age more so with the changing approach to technology in business. The process of data analysis allows businesses and blogs to scale up by making progressive decisions based on shifts and trends.

How does Data Analytics work?

When thinking about data analysis, it’s essential to understand how the approach works. The data analytics lifecycle is easy to understand. The cycle involves three critical stages that often determine how useful the analytics process will be to you.

1. Data Stage

This is often the first stage in the data analytics process. Data in the modern business context comes in thick and fast. Unless you have the right mechanisms to make use of such data, it might end up not helping you as much. With the current business competition, making sense of both structured and emerging data formats is a must.

One reason most people have been unable to embrace data analytics is due to the complex process involved in the data stage. The data stage involves four essential steps that determine how successful your decision-making process will be.

Data Preparation

The first step involves data preparation. During this stage, you’ll need an intelligent analytics platform to ensure proper cleaning and sorting of the data. If you can access a reliable data analytics platform, you might have an easier time preparing such data.

Data Management

The next step in the data stage is data management. You need a proper data management strategy to help you sort and store such data in a manner that can help your business. It’s not enough to prepare data if such data isn’t useful to you when it comes to utilization.

Data Governance

The third step in the data stage is the process of data governance. At this stage, a business focuses on ensuring efficient and effective use of the data, enabling the organization to make faster decisions.

Data governance also defines who can take specific actions on the prepared data. You also get to understand the particular situations that warrant the use of particular data.

Data Quality

The last step is the determination of data quality. At this stage, your business or blog is ready to use information obtained from the varying data sources. The question that stands out at this stage is the level of quality of the data.

Data quality is measurable based on its ability to fit your intended use and operations. Passing this phase involves checks and evaluations to satisfy that the data is high quality.

2. Discovery

This stage is all about exploration. You now have data in your hands but must find ways of making the data useful.

The discovery stage allows you the chance to visualize the data and make sense of results. During this stage, you also have the opportunity to build models that can help you arrive at the right algorithms.

During the discovery stage, you have the chance to assess various factors that might affect the utilization of the gathered information. Such factors include the data size, business needs, training, and data points.

When building a business model that will thrive online, this stage allows you the chance to explore and experiment. However, you also need an expert in programming to help you document and compare the varying models. If you succeed beyond the data discovery stage, you won’t have to worry about the process of decision making.

3. Deployment

Beyond the discovery stage, the data analytics curve proceeds to the deployment stage. By this point, you have gathered enough data, made the necessary adjustments and improvements to the models, and developed a seamless integration plan.

The next phase in the process should be the utilization of the data. Here, you need to narrow down the models and the results to make sense of the data. The data models can now be useful in predicting new data sets to help you make better decisions.

The phase can be as simple as generating reports for your future online marketing objectives, or complex if your goal is to create various market-related predictions.

You can now utilize Data Analytics Platforms

One of the most challenging processes when dealing with digital marketing is the gathering and application of data. Unless you understand the answer to ‘what is data analytics?’ and how to apply the various models that emanate from the data analytics process, your online marketing endeavor will be unfruitful.

Now that you have a basic grasp of what data analytics is, it will be much easier to use big data analytics to your advantage. All you need is a reliable analytics platform.

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