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Developing a successful data strategy - one that yields information required for generating higher ROI - demands a thoughtful, deliberate approach. Data analytics dramatically increase both the efficacy and volume of an organization's analytic output.
Every organization indulges in expanding channels, multi-product relationships, and changing economics. To do so without any hurdles, it is important that the team is equipped with the required knowledge and skills to derive data-driven, timely insights to earn drive growth and optimize a bigger return on investment.
With big data training, the team can draft a better strategy to gain more reliable and detailed insights more quickly. This further helps them to dive deeper into business and customer data. To achieve significant, tangible, and measurable outcomes, it is important to improve big data training and implement the right strategies to increase data analytics ROI.
Successful Business Models and Data Science
Investing in big data analytics can be one of the most lucrative and important decisions you can make for your business. According to a study, an organization that implements big data analytics into their operations results in at least 6% higher profitability and productivity rates as compared to the competitors.
As far as retailing business models are concerned, the boost in their operating margins can easily cross 60% with big data analytics whereas the reduction in cost for the healthcare sector could be around 8% - thanks to the quality improvement and better efficiency.
Organizations that are responsible for analyzing data for profitability will utilize data analytics to identify worthy marketing strategies and business opportunities, develop a targeted supply of products and services, and will also keep up with their customers' demand by delivering them on a timely manner.
Big Data Analytics and Increased ROI
Now if you are wondering why your organization is missing out on such amazing benefits, there might be a need to implement big data analytics right away. Here are the top ways you can do it:
Data science can help analyze market rates and consumer demand to identify rates that can yield maximum business in real time. The analysis will also help determine the right marketing and promotional strategies to activate the customers.
Studies show how an increase of 1% in the price can translate into an 8% increase in operating profits, considering there's no loss in the volume. Setting competitive rates create a revenue opportunity, which can lead to a higher ROI if utilized.
Current Data Streams
Only timely acquired data can be actionable. It must not only include current details but also information from the past to highlight trends and patterns. Visualization and big data analytics provide real-time data information that offers optimal options.
Therefore, it is crucial to keep your streams of data to keep up with the trend. The overall idea is to improve decision making based on important data.
Integrate Data Science in Larger Processes
A study carried out in 2016 showed how only 22% of organizations gained a bigger ROI and revenue growth after investing in data science. This is mainly because the larger processes within the organization were still missing data science integration.
These results concluded how by 2019, the majority of the large companies will have an in-house Chief Data Officer - a role that was quite nonexistent a few years ago. Larger processes require a holistic strategy for identifying opportunities and managing data, something that organizations kept struggling with for a while. Moreover, integrating big data into larger organizations processes also help with building moral and structure, platforms, architecture, and data governance.
Businesses like health care centers have tremendously increased their ROI through big data analytics. The program enables them to manage patient transfers, staffing, as well as room assignments. In short, it has made crucial and lengthy procedures simpler and short.
Better allocation of resources and efficient procedures help such business models to make better and accelerated decisions. The availability of each patient's details and history of laboratory reports, medical tests, and prescribed medications are instantly available on board.
As far as intensive care units (ICUs) are concerned, the analytics can help evaluate different data streams to find out more about the patient and the type of treatment he or she requires. All in all, the idea is to provide excellent customer service for utmost satisfaction. This can only be achieved through effective service/product delivery.
The same applies to any other business model that wants to implement data science to improve customer satisfaction and reduce costs.
Employee Cost Reduction
Another way to increase data analytics ROI is by reducing employee cost. Salaries make the biggest employee costs that an organization has to bear. With data science integration, the company has the potential to minimize man-hours required for manually entering, reviewing and editing data.
The system enables efficient use of staff resources, which means extra manpower currently used can be cut off completely to save more cost. Additionally, an organization must compare the cost of an employee with their contribution to learning the real benefit it is gaining from them.
Detect Risk and Fraud
Data analytics further equip the information security team to efficiently knock-off any fraudulent activities or risk of theft within an organization. Analytics help figures out such loopholes more efficiently. However, there's also a constant need to provide data science training to the staff to deal with such situations.
Reprioritize and Track Progress
This one's not really a step but a strong commitment to reprioritize and revaluate. To ensure you have gained the desired increase in the ROI, it is essential to track progress against priorities, confirm the alignment with available resources, and reassess prioritization to have a clearer idea about improvements.
At this point, the objective is not to compare the analytics results but to discuss if the organization has achieved the desired output in an appropriate time frame by implementing big data training. Comparing results, however, will help with supporting analyses and identifying process gaps for future improvements.