How Data Analytics Can Lead to Cost Optimization
Data is the oil driving digital economies. Data is everywhere, it has acquired the center stage. As humans, whatever we interact with is packed with data whether visiting a mall, shopping online, going to a doctor, playing sports or finding potential customers for our businesses. Data tells the story of our preferences.
Data-centric organizations can revolutionize conventional ways of working and create new opportunities.
But the question is, how do we put this data in to perspective and generate actionable insights that help businesses make informed decisions. This is where data analytics forms the core of data-centric organizations.
Data Analytics is the process of extracting and categorizing behavioral trends and patterns about key performance indicators and business operations using data. This in turn helps automatically trigger business actions.
Data analytics uses Business Intelligence and analytical processing methods like predictive analysis and data mining to run multiple queries that scrutinize data and filter based on the requirement.
“By the end of the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.”
Manually maintaining big data is a gigantic task. It is resource intensive and time-consuming resulting into higher costs. This brings us to another question, ‘How can you optimize the business cost?’
Cost Optimization is a business-oriented process to reduce expenses while maximizing business value. It is a process of optimally utilizing the resources without compromising security and ensuring customer satisfaction. Optimization includes acquiring actionable insights from the data collected, estimated time to evaluate the results and applying appropriate algorithms for achieving desired results.
Cost optimization is a mixture of Digital, IT and Business that helps your company to achieve business goals on time.
Here are some tried and tested methods for optimizing business cost:
- Digitize the processes, employ relevant algorithms to manage and maintain data which can add value to your business.
- Management should work with employees to identify pain points in the business processes to improve efficiency and minimize costs.
- Formulate strategy that can handle real-time customer queries and send immediate responses.
- Manage data in a way that can be used when needed.
- Decision makers should apply Business Intelligence, Machine Learning, and Artificial Intelligence for automating the processes.
Let us now understand how Data Analytics can help reducing expenses of the company.
Cost Optimization using Data Analytics
- Consistent data management can help minimize last minute complexities and the costs involved
- The generated data can be specific to internal employee, processes, operations or about customer experience, partner interactions, etc. Monitoring the trend of such data using Data Analytics will help to streamline overall process while increasing ROI and optimizing spends wherever avoidable.
- Analytics help in shortening testing cycles which can optimize time and money invested.
- Using Predictive Analytics for proper decision making, businesses can provide a customized experience to each client or solve complex problems with relative ease.
How Data Analytics is helping the following industries optimize cost
Modern businesses driven by technology generate high volumes of data that is usually difficult to manage. Therefore, enterprises need to find ways to use data in a meaningful manner to get better insights.
For instance, data collected from social media when scrutinized using data analytics technique helps us to understand customer preferences better. Listening to the voice of customer can help to accelerate the product design process, reduce cost and time-to-market.
On the other hand, in the manufacturing industry, inventory management is crucial to regulate stock and supply chain. However, huge amounts of data generated and collected from inventory management process can be used effectively for demand forecasting. Data analytics gives an opportunity to analyze the inventory stock better and reduce cost of storing significant amounts of unwanted products.
The implementation of predictive analytics can give you complex view of the manufacturing operations such as overproduction, idle time, logistics, inventory, raw material availability, etc. This helps in cost optimization and increase in company’s ROI.
Predictive Analytics is a set of statistical techniques that includes data mining, predictive modelling and machine learning to analyze current and historical data to make predictions about future or unknown events.
Recently, Cygnet Infotech provided an AI-powered Predictive Demand Planning Solution for Transport Services in the Middle East. The solution reduces the operational cost and helps identifying taxi demand by employing time-series techniques on a real-world data set.
Vishakha Bhagia serves as a content writer for Cygnet Infotech. She is an engineer who loves to lend her voice to making technology easier to understand. She uses the best of her experience in sales and engineering to make her write-ups thorough, convincing and simple. She spends her free time exploring places, learning new skills and reading fiction.View All Posts