Big data analytics can drive down costs by becoming a company’s barometer for making profitable decisions. However, data analytics can be a time- and cost-intensive process and factors like cost concerns and access to high-quality and trustworthy data are still some major barriers.
We believe big data analytics shouldn’t be a luxury only for large enterprises and every business, big or small, can harness its power. But realizing the true value of data analytics requires cost optimization and ease of access to a certain extent. Let’s explore how companies can achieve these goals:
The Role of Big Data Analytics in Business
Cloud computing has transformed the way companies crunch data. Cloud-based big data analytics has a scalable computing model and offers wide-ranging features and functionalities to unlock hidden insights from diverse datasets across an organization.
Here are some crucial ways cloud-based data analytics can help a organizations seeking to achieve value through data analytics:
Cost Flexibility: Cloud-based data analytics solutions offer a much lower upfront investment compared to on-premises data centers due to the tenancy-based computing model. In addition, cloud-based solutions often come with a pay-as-you-go model which means you pay only for the utilized resources.
Transparent Pricing Models: The cloud offers different pricing models to cater specific needs of businesses. The pricing criterion is based on the Service Level Agreement (SLA) agreed between the client and the vendor and everything is transparent from the onset.
Customized Solutions: With cloud-based big data analytics, companies can get deeper value from their data analysis process. A cloud vendor can provide you with customized solutions that deliver the desired results and contribute to the organization’s growth and revenue scope.
Big Data Analytics’ Real-World Example in Business World
UPS delivers 16.9 packages and documents daily and ships over 4 billion products annually using approximately 100,000 vans. UPS employs big data for fleet optimization because of its volume. Advanced algorithms and on-truck telematics help with routes, engine idle time and predictive maintenance. Over 39 million gallons of fuel and 364 million miles have been saved since the initiative began.
How Does Data Analytics Help Business?
Improved Decision-Making: Traditional analysis methods fail to deliver deeper insights into business functions. On the other hand, big data analytics provide accurate information to perform actionable insights. With modern big data solutions, businesses can analyze patterns, trends and links to drive meaningful results that can help them build more targeted marketing campaigns and develop competitive pricing strategies. This consequently leads to a more precise product development and resource allocation, resulting in improved efficiency and increased profitability.
Personalized Customer Experiences: Customer-specific data, such as demographics, product preferences and buying behaviors, helps align business strategy to fit into the current market demands and customer’s specific needs. This data enables enterprises to optimize their offerings, enabling customer satisfaction and loyalty and increasing revenue growth.
Operational Efficiency: Predictive analytics is a subset of big data analytics that allows companies to predict impending system failures and perform timely maintenance jobs. Also, businesses can maximize productivity and minimize downtime by proactively optimizing performance and operations.
Fraud Detection and Risk Management: Data analytics helps organizations identify patterns and anomalies to detect fraudulent activity. This allows companies to take timely preventive measures.
This process enables businesses to avoid costly damages as a result of data breaches. Similarly, it helps streamline risk assessment and analysis procedures to help organizations foresee potential risks and make correct decisions to counter any projected impact on their businesses.
New Product & Market Opportunities: Big data analytics makes it possible for enterprises to develop better products and expand their outreach into new markets. Organizations are constantly fed with insights to perform gap analysis and address untapped markets. This can be done by analyzing existing market trends, looking deeper into customer feedback and performing competitor analysis.
Key Points to Consider…
Achieving positive ROI requires a well-defined strategy, alignment between data analysis and business goals and the ability to convert complex data into actionable insights. For a business to enable data-driven decision-making, it is first important to define key metrics to incrementally measure performance and growth. These metrics could be:
- Customer Acquisition Cost (CAC): Total cost incurred on acquiring a new customer.
- Customer Lifetime Value (CLTV): Projected revenue a customer can create in his/her lifetime span.
- Conversion Rate: The value in the percentage of visitors who perform the desired action.
- Click-Through Rate (CTR): Represents the total number of clicks on a link.
- Bounce Rate: The value in percentage of total visitors who leave after visiting a web page.
- Sales Revenue: Total revenue created from sales.
- Average Order Value: The average amount spent on individual orders.
- Sales Growth Rate: Total increase in sales in a specific period.
Take the Next Step!
At 4SCloud, we offer topnotch big data analytics and cloud migration solutions to businesses of all sizes. We can help enterprises unlock the true potential of their data and take advantage of the transformative power of data-driven decision-making and increase sales volume through big data analytics. Contact Us Now!