Why discovery analytics is more useful in analysis of businesses? – By Aditya Abeysinghe
Data Discovery
Businesses generate large amounts of data today with diverse types of systems used. These systems vary from systems where data are analyzed for direct business benefits to systems that are used for indirect benefits such as those used in ensuring customer relationships using social media, email and assistance using virtual (chatbot-based) messages. Analyzing these data from systems that have diverse functions is often costly for a business and also most data are not that useful to be analyzed. Therefore, data discovering process on which data to be used in analysis is often useful to avoid costs and gain only required knowledge for business analysis.
Analytics on data discovered
Unlike other types of intelligence used for business analysis, intelligence used in discovery of sources to be used is only used by analysts who have knowledge on which sources are useful. The types of sources and the quantity is dependent on the use case. Therefore, analysis on which sources are used, discovery analysis, depends on whether those sources will be beneficial for a business.
The approach used for data discovery includes analyzing data using a data discovery tool. A data warehouse may be used to store data but this is often not the approach for medium or large-scale businesses. For these types, a tool may be directly coupled to the system’s data storage and discovery may be performed system-wise rather than using a single data warehouse.
Comparison to other analytics
Descriptive analytics is a type of analytics where statistics is used to describe and/or summarize data. It is often used before other types of analytics for analysis when data from data discovery is available. Descriptive analytics is useful in identifying patterns in current data and then visualizing them. Using this type, past events or current events can be understood. It is used to describe what is contained in the data by analyzing data using statistical methods. Raw data is used during this type of analysis and data is cleaned before analysis. Therefore, data that is useful to be analyzed need to be selected before using this analytics method.
Predictive analytics is another type of analytics where statistics is used to predict on the data. It is often used after descriptive analytics but is always not used after other types of analytics. Predictive analytics is useful in identifying trends in data and to predict on future trends that might be useful. Using this type, businesses could focus on trends that could make profits, losses or points which could improve core activities to prevent cost or time overruns. Both data that has been used for analysis or raw data is used for predictive analytics. Data that can be used to predict is useful before using this analytics method.
Why is data discovery important?
Analytics on data requires what sources of data to be used, the quantity of data and also quality of data so that it will be useful for businesses. As seen in other types of data analytics such as descriptive and predictive analytics, data that is useful is needed to make summarizations or predictions to be useful. Decisions and insights based on data is inaccurate and not important if only data which is important is not selected. Therefore, discovery analytics is often given more value than other types in data-driven business analytics.
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