What is Descriptive Analytics?
Descriptive analytics is the analysis of past data in order to better understand company developments. The utilisation of a variety of historical data to make comparisons is referred to as descriptive analytics. Descriptive analytics is the source of the majority of widely reported financial measures.
Descriptive analytics involves “What has occurred in the corporation” and “What is going on now?” Let us consider the case of Facebook. Facebook users produce content through comments, posts and picture uploads. This information is unstructured and is produced at an extensive rate.
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Facebook stats reveal that 2.4 million posts equivalent to around 500 TB of information are produced every minute. These jaw-dropping figures have offered popularity of another term which we know as Big Data. Comprehending the information in its raw configuration is troublesome. This information must be abridged, categorised and displayed in an easy-to-understand way to let the managers to comprehend it.
Business intelligence and data mining instruments/methods have been the accepted components of doing so for bigger organisations. Practically every association does some type of outline and MIS reporting using the information base or simply spreadsheets. There are three crucial approaches to abridge and describe the raw data:
- Dashboards and MIS reporting: This technique gives condensed data giving information on “What has happened”, “What’s been going on?” and “How can it stand with the plan?”
- Impromptu detailing: This technique supplements the past strategy in helping the administration to extract the information as required.
- Drill-down reporting: This is the most complex piece of descriptive analysis and gives the capacity to delve further into any report to comprehend the information better.
Working of Descriptive Analytics
There are five steps that describe the overall working of the descriptive analytics. These steps are as follows:
Determine the business metrics and KPIs
The organisation must choose the metrics it wishes to create based on the company’s aims and purposes or the fundamental business goals of each group inside it. For example, an organisation that prioritises growth may put an emphasis on analysing quarterly profit gains.
At the same time, the accounts receivable department of the organisation could keep track of exceptional days’ sales and other indicators that demonstrate how long it takes to recover money from clients.
Obtain the required Data
You must identify the data sources for such information once you have defined the business goals and associated KPIs. Because the essential data may be distributed over several files and apps, this process may be difficult. For the information to be accurate, it must all come together.
Data extraction and preparation
Data extraction is the most time-consuming activity in the data analytics process. It includes data transformation, duplication and cleaning, among other things. This is, nevertheless, a critical step in ensuring correctness. Additionally, data cleansing may be required to eliminate inconsistencies and errors in the data.
Advanced data analysts employ data modelling, a framework embedded in information systems that aids in the preparation, arrangement and organisation of a company’s data. Data modelling is the process of defining and formatting complicated data in order to make it useable and actionable. Besides data modelling, data automation is a great tool that is used for doing this type of work for you and saving time for your employees.
After the data has been properly organised, it should be analysed. Otherwise, having the data in the first place is pointless. Data analysis may integrate numbers with business KPIs to generate insight. Organisations can use a variety of tools to do descriptive analytics, including business intelligence (BI) software and spreadsheets. For example, a sales manager in an organisation could want to keep track of average sales revenue or monthly revenue gained from existing or newly acquired clients.
Once business analysts have completed all of the essential stages, they need to deliver the data. This data must be communicated with internal and external stakeholders. The business analyst uses data visualisation and presentation tools, such as charts and graphs, to accomplish this task.
For example, the stakeholders frequently prefer reports that are visually appealing such as bar charts, pie charts, or line graphs. Finance professionals, on the other hand, may prefer data presented in figures and tables.
Advantages of Descriptive Analytics
Some advantages of descriptive analytics are as follows:
- Descriptive analytics is thought to be helpful in discovering variables and emerging ideas that may then be investigated further through experimental and inferential investigations.
- Descriptive analytics is beneficial since the margin for error is low because the trends are extracted directly from the data attributes.
- Descriptive analysis does not necessitate a high level of statistical knowledge or experience. Descriptive analytics delivers the data required to efficiently answer those queries, regardless of when or how frequently they are asked.
- Descriptive analysis is thought to be more comprehensive than most other quantitative approaches. It provides a clearer picture of an event or phenomena. To do descriptive research, it can employ single variable or multiple variables.
- Descriptive analysis is said to be a superior strategy for gathering data since it portrays relationships in a natural way and shows the world as it is. As a result, this study is very genuine and near to mankind, as all of the patterns are based on research into the data’s real-life behaviour.
Disadvantages of Descriptive Analytics
Some disadvantages of descriptive analytics are as follows:
- Descriptive analytics just recounts what has occurred without attempting to understand the causes or anticipate what will occur next.
- It is also usually confined to relatively simple studies involving two or three variables.
- The focus of descriptive analytics is solely on historical performance and it makes no attempt to explore further than the data’s surface