Data Analysis and its Importance For Intelligent Data-Driven Business Decisions.
Data is being generated at a rate like never before — from medical records to financial data, sales data, business data, accounting data etcetera. However, one major problem we have is the abundance of data but little information.
For any business to turn profitable, the top management and the workforce must be highly reliant on informed decisions, and those decisions are dependent on the kind of information provided.
In past times, businesses relied on spreadsheets and other simpler forms of crunching numbers and analysing data. Nowadays, the world of analytics and data intelligence has opened doors for companies of all sizes to look at more in-depth data relating to past and present figures, trends and how they will relate to future forecasts and decision making.
What is Data Analysis?
Data analysis is the transformation, interaction and modelling of diverse forms of data in a way that is meaningful and provides insight for a company/organization’s decision-making process for future undertakings.
In shorter terms, data analysis help in Data Driven Decision Making (DDDM). Data-driven decision making involves making decisions that are backed up by facts from data rather than making decisions that are intuitive or based on observations alone.
The processes involved in data analysis include:
- Data requirements: The type of data to be collected for analysis of the specified problem
- Data collection: fetching relevant data from different sources.
- Data Processing: organizing data in a structural manner — eg. tabular format.
- Data cleaning: removing erroneous data, incomplete data, or duplicate data.
- Data Exploration: using descriptive statistics like mean, median, mode, etc to understand the data
- Modelling, algorithms and visualisation: visualising the data to see trends, current happenings in the data and building models to predict future occurrences based on the current data.
- Communication: reporting your findings to the end-user (top-level management or the business team)
The analysis of data in an organization can be grouped into the following components:
Descriptive analysis
This helps organizations understand what happened in the past, which in this context could include a minute ago to a few years back. This component exposes the relationship between products and customers with the goal of knowing what approach to take in the nearest future — basically learning from past behaviours/interactions in order to influence and make better decisions in the future which will, in turn, bring better outcomes.
Examples include company reports that simply provide a historic review of an organization’s operations, sales, financials, customers, and stakeholders.
Ad-hoc reporting, interactive data visualization using BI tools like Tableau, Microsoft Power BI, Looker, Qlik , and so on fall into this type of data analysis.
Descriptive analysis sheds a light into knowing what occurred and when, which forms the basis for diagnostic analysis in knowing why it happened.
A relatable example of using a data visualisation tool in making data-driven decisions can be seen in Coca Cola’s use of Tableau to drive business growth and profitability across the company
Predictive Analysis
It is aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. With the help of sophisticated predictive analytics tools and models,
any organization can now use past and current data to reliably forecast trends and behaviors from a minute to a few years into the future.
An example of how predictive analysis is used in organizations is in the financial services where credit risk models are developed. Also, an organization can predict whether providing additional information or incentives on their product will increase the likelihood of a completed transaction.
Time series analysis using python, R and any other advanced automated machine learning tool also fall into this type or analysis.
Prescriptive Analytics
This is where the insights gathered meets action. It forms a basis for recommendation, gives a number of possible actions to take in order to get a desirable outcome. In essence, multiple futures or outcomes are predicted and thus allow organisations to assess the best course of action to take and the result of that action, beforehand. Recommender systems and models are a good example of prescriptive analytics.
Waymo -formerly of Google- self-driving car is an advanced example of prescriptive analytics put into use. The vehicle makes inferences on every trip that help the car decide when and where to turn, whether to slow down or speed up, and when to change lanes — the same decisions a human driver makes behind the wheel.
Importance of Data Analysis For Decision Making in Organizations
The importance of data in decision making lies in consistency and continual growth. It enables organizations to create new business opportunities, generate more revenue, predict future trends, optimize current operational efforts, and produce actionable insights. That way, you stand to grow and evolve your empire over time, making your organization more adaptable as a result.
With proper data analysis, Organizations become more agile, detect new business opportunities sooner, respond to market changes more quickly, become exceptionally customer-focused and get more competitive.
Data-driven decision making leads to greater transparency and accountability, and this approach can improve teamwork and staff engagement. In organizations that prioritize data-driven decision making, goals are concrete, and results are measured. Team members often feel a greater sense of control because they can see the goalposts clearly, and interactions may become more positive because discussions are fact-based rather than driven by ego and personality.
PayPal collaborated with Rapidminer (a well known automated data analytic tool for predictive analysis) to gauge the intentions of top customers and monitor their complaints. According to a case study from Rapidminer, Han-Sheong Lai, Director of Operational Excellence and Customer Advocacy, and Jiri Medlen, Senior Text Analytics Specialist at PayPal, wanted to gain a better understanding of what drives product experience improvement. They needed to analyze customer feedback in order to do this successfully.
After 2 to 3 months working with the software, PayPal was reportedly able to classify customers as “top promoters” and “top detractors”. This enabled them to arrive at the top complaint areas (customer login issues).
Another scenario of the importance of data-driven decisions was a case of one of Seamfix products — an innovative clock-in software for contemporary businesses to track employees in any location. The app uses facial recognition and Geo-Fencing technology to confirm that the user is who and where they say they are by taking selfies during clock-in. At a point, we noticed that some organizations had issues with wrong clock-in (employee clock-ins were marked as invalid even when they clocked-in correctly). This was corroborated by complaints from organization admins.
After proper analysis, we were able to identify two causes of the issue (wrong device coordinates and wrong clock-in radius set by the organization’s admin) and recommend solutions for our clients.
Conclusion
Data-driven analysis can pay for itself through cost savings and higher revenues. Most organizations collect data, usually for record-keeping and compliance, but many don’t do anything with this information. Often, they incur storage costs for warehousing the data. So, why not analyse it and see if it tells any stories? Stories which can make the organization grow and cut costs in the long run.
Thank you for reading. I would love to know your opinion and don’t forget to clap If you enjoyed this.
References
https://en.wikipedia.org/wiki/Prescriptive_analytics
https://securitytrails.com/blog/data-intelligence
https://www.smartsheet.com/data-driven-decision-making-management
https://www.datapine.com/blog/data-driven-decision-making-in-businesses/https://yourstory.com/2013/04/business-intelligence-helping-data-driven-decision-making-2
https://emerj.com/ai-sector-overviews/predictive-analytics-5-examples-of-industry-applications/
