Let’s imagine a scenario: a team of data scientists has just finished a project where they’ve created a system that can automatically identify talent in sports. The system takes raw data from multiple sources, such as player stats and social media profiles, and creates models for predicting the chances of success for any person in question. Salesforce data backup and recovery can help you in many ways.
It works with all kinds of people – young athletes who are not even professionals yet, established players on national teams, or retired legends who haven’t played for years. This is a unique new tool that can help coaches find new stars or change the entire strategy of their team; scouts and agents can also use it to get ahead of the competition and sign promising players while they’re still unknown.
The question now is: what next?
This data has already been used to create a model; however, models can be created and refined multiple times. Which option will best leverage the system’s value: continue applying machine learning to existing data to improve accuracy or find new applications for this data that could drive engagement and revenue?
To answer this question, we need to take a step back and look at all available leveraging our data. The main categories are: creating APIs, building better predictive models, or generating more business opportunities. Once we’ve decided on one area, there are various possible strategies to increase customer loyalty, increase sales volume, cross-selling products & services, etc.
Although each option is achievable for the most part, it is vital to understand which one will best leverage the system’s potential and maximize its ROI. However, some factors can influence this decision, such as project constraints or company strategy.
What are the best ways to leverage your data?
- APIs
- Models
- Business opportunities
- Increase customer loyalty
- Increase sales volume
- Project constraints
- Company strategy
Length of time machine learning has been applied to data provided by the source(s) used in model creation. [This may be out of scope depending on what you’re talking about] Once you’ve decided on one area, there are various possible strategies that you could apply.
Time length of machine learning being applied to data from source(s) used in model creation. Once you have decided upon one approach, there are several possible strategies to employ. Factors that can influence decisions: Project constraints Company strategy Time length of machine learning being applied to data from source(s) used in model creation.
It recommends three options to learn more about leveraging your data: creating APIs, building better predictive models, or using the data to generate more business opportunities. Then some factors can influence this decision, such as project constraints or company strategy. In the end, you get specific strategies for each factor. However, the tone is pretty straightforward and informative throughout, making sense since you’re giving information on leveraging your data (rather than telling a story). The only time you might want to consider changing the tone is if you were
