4 Types of Data Analytics that support Artificial Intelligence
Data analytics is the process of extracting, transforming, loading, modeling, and drawing conclusions from data to make decisions. It’s the “drawing conclusions” bit that BI tools are most concerned with, as the extracting, transforming, and loading steps generally happen at the database level.
The best type of data analytics for a company to rely on depends on their particular stage of development. Data analytics involves a four-stage process that builds on itself and uses machine learning (ML) and AI in the final phases to predict outcomes and suggest options to respond to those predictions.
In this article, we shall focus on the 4 types of data analytics we encounter in data science:
This is the most common of all forms. It provides the analyst with a view of key metrics and measures within the business. This is the backbone of reporting. There seems to be no possibility to have BI tools and dashboards without it. Ad hoc reporting and canned reports are two categories of it. A canned report is one that has been designed previously and contains information around a given subject. Ad hoc reports, on the other hand, are designed by you and usually aren’t scheduled but are more in-the-moment. They’re useful for obtaining more in-depth information about a specific query. Descriptive analytics is a preliminary stage of data processing that describes or summarizes raw data to yield useful, interpretable information for further analysis. In describing the past, descriptive analytics lays the groundwork for how these events may affect future outcomes. Data aggregation and data mining organize data in order to identify patterns and relationships that may not otherwise
be apparent. Then queries, reports, and data visualizations can further yield deeper insight.
Predictive analytics may be the most commonly used category of data analytics as it is used to identify trends, correlations, and causation. The category can be further broken down into predictive modeling and statistical modeling. But, it’s important to know that these two really go hand in hand. Predictive analytics is all about forecasting. Whether it’s the likelihood of an event happening in the future, forecasting a quantifiable amount or estimating a point in time at which something might happen — these are all done through predictive models. Industries that most commonly use predictive analytics are marketing, financial services, and insurance
companies, as well as search engine and online services providers. In order to predict the likelihood a particular event will happen it applies statistical analysis,queries and ML algorithms, while using measurable variables, predictive analytics apps work to anticipate likely behavior by individuals, machinery or other entities.
The diagnostic analytics phase focuses on why something happened, characterized by techniques such as drill-down, data discovery, data mining and correlations. Even more importantly, diagnostic analytics lets you understand data faster. Diagnostic data analytics help answer why something occurred. Like the other categories, it too is broken down into two even more specific categories: discover and alerts and query and drill-downs. Query and drill-downs are what you’ll use to get more detail from a report. Well-designed business information (BI) dashboards incorporating reading of time-series data (i.e. data over multiple successive points in time) and featuring filters and drill down capability allow for such analysis.
The final phase, prescriptive analytics, attempts to find the best course of action, solution or outcome among various choices, building off descriptive analytics’ insight into what happened and predictive analytics’ forecast of what might happen. Prescriptive analytics is where AI and big data meet to help predict outcomes and what actions to take. This category of analytics can be further broken down into optimization and random testing. Using advancements in machine learning, prescriptive analytics can help answer questions like “What if we try this?” and “What is the best action” without spending the time actually trying out each variable. Basically, it can help you test the right variables and even suggest new variables with a higher chance of generating a positive outcome.
Now that you’ve got a good idea of the four different types of data analytics, consider using their more descriptive category names within conversation and writing. Doing so can help to reduce the number of possible misunderstandings and help to deepen the knowledge of those around you of the different types of data analytics.
As per the IDC research report, up to 90% of large companies will generate some sort of revenue from DaaS (Data as a Service) in 2020.
Gartner has predicted that both edge computing and cloud computing will become complementary models to each other. This trend will eventually decrease latency and data-processing costs for organizations by 2020.
Gartner has predicted that more than 40% of all data science-related tasks will be automated by 2020. ML (Machine Learning) technology and its advancements can drive this automation.
While different forms of analytics may provide varying amounts of value to a business, they all have their place. To find out how data analytics could bring further value to your business, please drop us an mail to arrange for a chat.
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