Descriptive analytics is a type of business intelligence that examines data to help organizations gain insight into their business performance operations.
By analyzing current and past data, it can reveal trends and insights that inform decision-making.
It is the first step of a broader analytical process in data science, which can include predictive and prescriptive analytics for more detailed analysis and insights.
Video: Descriptive Analytics Definition
- Video: Descriptive Analytics Definition
- What is Descriptive Analytics?
- Why is Descriptive Analytics Important?
- How Does Descriptive Analytics Work?
- Examples Of Descriptive Analytics/Where is descriptive analytics used?
- Benefits And Drawbacks Of Descriptive Analytics
- Descriptive Analytics Tools
- Best Tips For Researchers On Descriptive Analysis
What is Descriptive Analytics?
Descriptive analytics is a type of data analysis that uses information collected from a range of raw data sources, past and present, to describe what has happened in the past.
It involves data mining and summarizing large amounts of current and historical data and presenting them in understandable data visualizations, such as graphs and charts.
This is done to gain an understanding of patterns in the data and identify any areas where the performance needs improvement.
Photo by monsitj
Why is Descriptive Analytics Important?
Descriptive analytics offers many benefits to organizations. It helps executives make more informed decisions regarding operational processes, marketing practices, financial investments, staff allocation, and more.
This type of business intelligence can also provide valuable insight into customer behavior so you can better understand their needs and preferences.
Furthermore, it provides timely feedback on the effectiveness of different strategies so your company can make changes quickly if needed.
How Does Descriptive Analytics Work?
The goal of descriptive analytics is to provide clear visualizations for stakeholders so they can draw meaningful conclusions about their operations or customers’ behavior based on pattern recognition.
Gathering data from various sources provides consultants with comprehensive datasets for analysis via various statistical techniques or machine learning algorithms such as linear regression or k-means clustering.
The insights generated from descriptive analytics then serve as the basis for further predictive modeling or prescriptive recommendations on what actions could be taken going forward in order to optimize results while minimizing risk factors.
Examples Of Descriptive Analytics/Where is descriptive analytics used?
Descriptive analytics is used by a wide range of organizations across industries and sectors. Retail, manufacturing, financial services, and healthcare are just some examples of areas where descriptive analytics provide insights that can inform decision-making.
In retail settings, descriptive analytics often focuses on Forecasting Demand – predicting sales figures based on historical trends – as well as analyzing product performance by looking at customer reviews over time or identifying which promotions are most successful at driving purchases.
Similarly, manufacturers often use descriptive analytics to monitor production efficiency over time via key indicators such as labor costs per unit produced or material waste percentages over a given period of time.
In healthcare settings, descriptive analytics looks at clinical outcomes analysis by comparing average recovery times between treatments as well as assessing patient satisfaction surveys related to appointment availability, etc.
Finally, financial institutions rely heavily on descriptive analytics when it comes to analyzing credit ratings in order to determine prospective borrower profiles most likely mortgage refinancing terms, etc.
Benefits And Drawbacks Of Descriptive Analytics
The main benefit of using descriptive analytics is its ability to quickly identify patterns within data and provide meaningful visualizations for executives to easily interpret information which enables them to make better decisions faster than ever before.
In addition, you don’t need to have a data science background to understand descriptive analytics.
However, one potential drawback relates less to the method itself but rather to how practitioners are interpreting the data, bias or lack thereof can lead decision-makers astray if interpreted incorrectly.
Additionally, since this type of analysis only looks at historical records; future events may still be unpredictable despite having reliable metrics from past experiences. Only the surface is scratched.
Descriptive Analytics Tools
There are many data analytics tools available today that offer advanced capabilities when it comes to descriptive analytics:
- Microsoft Power BI
- Oracle Business Intelligence Cloud Service ( BICS )
- IBM Watson Studio
- Qlik Sense
are just some examples.
Additionally, there are other tools specifically tailored toward financial institutions such as Riskview’s Portfolio Optimization Suite ( POS ).
Companies should take into account their own individual needs when selecting an appropriate toolset for their environment.
Photo by Pinkypills
Best Tips For Researchers On Descriptive Analysis
Here are some tips researchers should keep in mind when embarking upon projects related to descriptive data analysis:
1) Focus on quality: Ensure that all data gathered is relevant, accurate, and up-to-date; any outdated information will only lead researchers down the wrong path.
2) Ask questions: Understanding why certain patterns exist within your data can help you determine whether they’re meaningful relationships or just coincidences; drawing out the right evidence requires careful questioning skills.
3) Leverage data visualization tools: Make sure you’re making full use of software like Tableau which allows you to generate clear visuals including graphs, dashboards etc.
4) Set realistic goals: Keep expectations reasonable; know what you want out of this project beforehand; don’t jump ahead with unwarranted assumptions.
5) Never stop testing: Be prepared to invest additional resources/time continually improving upon methods used if need be; try out alternate approaches if unsatisfied with initial findings.
6) Share results: Communicate results responsibly; transparency ensures team members stay engaged with progress being made.
7) Learn from mistakes: Use failures as opportunities to build up a knowledge base and grow stronger as a data analyst next time around.
By keeping these tips in mind when performing descriptive data analysis, researchers can feel confident that the results achieved will be accurate and meaningful.
With the right tools, data analysis techniques, and a collaborative mindset, this type of analytics can provide invaluable insights into how organizations operate.
Organizations have a lot of leverage when using descriptive analytics properly due to its wide array of applications. With the right tools and knowledge, descriptive analytics can produce meaningful insights that drive better business decisions across industries.
Although it’s not without its drawbacks, understanding how to best use this type of analytics should be a top priority among decision-makers.
At the end of the day, proper utilization of descriptive analytics is essential – with it comes the power to make informed decisions that are based on data rather than just intuition.
The possibilities are endless when leveraging this data analysis process to spot trends and better understand customer needs.
By following the tips outlined in this article, researchers should be well on their way to gaining valuable insights from descriptive analytics which could potentially lead to enhanced organizational performance.
Dani Lehmer is the Founder and Head Honcho of Dani Digs In.
She is a Quality Assurance Analyst and blogger whose natural curiosity allows her
to dig in (pun intended) to help people build their businesses and satiate curiosity
in regard to data science, analysis, and crypto.
Connect with Dani on LinkedIn