Data analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision making. Data analytics is important across many industries, as many business leaders use data to make informed decisions. A sneaker manufacturer might look at sales data to determine which designs to continue and which to retire, or a health care administrator may look at inventory data to determine the medical supplies they should order.

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services.

1. Storage and management of data
2. Data cleansing
3. Data mining)
4. Data analysis
5. Data visualisation
6. Data integration
7. Data grouping

1. Regression analysis. This method is used to estimate or “model” the relationship between a set of variables. You might use this to see if certain variables (a movie star’s number of Instagram followers and how much her last five films grossed on average) can be used to accurately predict another variable (whether or not her next film will be a big hit). Regression analysis is mainly used to make predictions. Note, however, that on their own, regressions can only be used to determine whether or not there is a relationship between a set of variables—they can’t tell you anything about cause and effect.
2. Factor analysis. Sometimes known as dimension reduction, this technique helps data analysts to uncover the underlying variables that drive people’s behavior and the choices they make. Ultimately, it condenses the data in many variables into a few “super-variables”, making the data easier to work with. For example: If you have three different variables which represent customer satisfaction, you might use factor analysis to condense these variables into just one all-encompassing customer satisfaction score.
3. Cohort analysis. A cohort is a group of users who have a certain characteristic in common within a specified time period—for example, all customers who purchased using a mobile device in March may be considered as one distinct cohort. In cohort analysis, customer data is broken up into smaller groups or cohorts; so, instead of treating all customer data the same, companies can see trends and patterns over time that relate to particular cohorts. In recognizing these patterns, companies are then able to offer a more targeted service.
4. Cluster analysis. This technique is all about identifying structures within a dataset. Cluster analysis essentially segments the data into groups that are internally homogenous and externally heterogeneous—in other words, the objects in one cluster must be more similar to each other than they are to the objects in other clusters. Cluster analysis enables you to see how data is distributed across a dataset where there are no existing predefined classes or groupings. In marketing, for example, cluster analysis may be used to identify distinct target groups within a larger customer base.

1. Informed & improved decision making. When big data joins forces with artificial intelligence, machine learning, and data mining, companies are better equipped to make accurate predictions. For example, predictive analytics can suggest what could happen in response to changes to the business, and prescriptive analytics can indicate how the company should react to these changes. Additionally, enterprises can use data analytics tools to determine the success of changes and visualize the results, so decision-makers know whether to roll the changes out across the business.
2. More effective marketing. What makes organizations stand out is the unique approach they choose to market their products. By using data analytics, companies can pinpoint precisely what customers are looking for. Data enables businesses to do in-depth analyses of client trends, which companies can then utilize to develop successful, focused, and targeted marketing.
3. Better & personalized customer experience. By sharing their data, customers expect companies to know them, form relevant interactions, and provide a seamless experience across all touchpoints. Being able to react in real-time and make the customer feel valued is only possible through advanced analytics. Data allows for interactions to be tailored to the customer’s personality, thus helping to understand their attitudes in order to deliver personalization in a multi-channel service environment.
4. Streamline operations. Data analytics may also enable enterprises to identify possible ways to optimize processes and increase revenues. It assists in identifying potential issues, thus preventing them from happening. This allows enterprises to see which activities have produced the best overall outcomes under different circumstances.
5. Mitigate fraud. Adequate data analytics capabilities will provide the highest levels of fraud protection and overall security for your firm. Using statistical, network, path, and data methodologies for predictive fraud will ensure that real-time threat detection methods, automated warnings, and mitigation drive fast reactions.