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Data Analytics Processes

by Linda D. Yelverton

Data Analytics

Data Analytics Processes

As the world’s population increases, and its need for resources such as energy, food, water, and data expands, it is clear that Data Analytics will become more important. Data Analytics is the process of collecting, organizing, interpreting, using, and managing large sets of information about a company’s activities and how they affect the business. It can be used to understand the “big picture” – what a company does in relation to its customers, markets, and the environment. It can also be used to find patterns, trends, and predict future events.

The first step in a good data analytics process is to collect and analyze the available data. Organizations need to be able to store all the data. They also need to be able to access it from anywhere in the world. Organizations that are “scale-able” are required to be able to access and manage massive amounts of data. This leads to the second step of data analytics – the analysis and use of that data.

There are many different types of analytic techniques. There are some problems that organizations have difficulty with. Some of these problems are:

* Segmentation. Data segmentation is an important part of Data Analytics. Organizations must be able to segment their data so that a section of the data is used and analyzed at a time, making sure that it is still useful.

* Graphs. Graphs are a great way to present the results of a research study. They can be used to show relationships between variables and summarize the results of a research study. This makes the graphs and other visual representations easier to understand.

* Machine Learning. Machine Learning is the process of taking information from all over the place and determining the relationship between the items. Examples of this technology are image recognition, natural language processing, and mathematical algorithms. These are all methods of finding correlations in all sorts of data.

* Predictive analytics. These models are used to determine the next action to take based on past patterns of behavior. For example, a predictive analytics program could determine what action is needed to save a plant that is threatened by a disease.

* Identify problems. This is the final step in Data Analytics.

* Project analysis. This type of analysis takes place during the data gathering phase, looking at the company’s internal processes. Companies will also look at how their products or services fit into the whole company, which factors make up the whole, and how external factors are affecting the overall value of the company.

* The need for data. As the needs of the business grow, organizations need to be able to integrate all of their resources. For example, companies may be looking at the need to build a new IT system, or changing the way their data is stored.

* Steps to follow when starting a project. This is the most important steps to follow when starting a Data Analytics project. This step covers everything from finding a team to develop the software, deciding on the technology and finding the right people to implement it, to the next step – finding the right clients.

Each organization is different, and each large company has its own set of needs and challenges. There are different types of Data Analytics projects. Some require the hiring of external developers to complete the project, while others only require internal software developers to implement it.

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