Business use cases harness data science to help business users to make sense of how Big Data methods can be put to use in their organization.
Businesses have utilized and integrated data science within their operations, however, these can further be improved to tailor-fit the needs of companies. You may have heard the term ‘data science use case’, if not, then you might have an idea of how it’s used on platforms such as Facebook, Twitter, and Spotify.
Before talking further about how to identify what business use case is fit for your organization, you must first take into consideration certain aspects prior to diving into the creation process.
For starters, let’s define what a ‘data science use case’ and ‘business use case’ is. Data science use case, which is broader than the latter, provides organizations evidence-based studies and application of decision-making in action with the use of data science. On the other hand, a business use case, which can or cannot be a data science use case, is a sequence of business actions looking to solve a specific business problem with predetermined results.
Both use cases harness data science to help enterprise or business users to make sense of how Big Data methods can be put to use in their organization.
The considerations business owners have to take note of for identifying business use case are the objectives, types of data, problems, and planned timeline.
- Objectives or the goals set by the organization give an idea of what type of business use case should be applied in your company's Big Data – what improvements you might want to fix or streamline within your operations or how you would make use of data science or machine learning.
- Data sets that are too large and complex to handle need to be addressed especially when considering what business use case you'll be choosing. It's important to ensure that the data sets you're targeting to address will match with the business use case and bring about a positive outcome that is easy to use.
- Similar to objectives, you have to know the problems in your company's operations and what you would want to resolve. Likewise, the risks that you will have to consider are important in order to avoid any future repercussions or failures in the long-term use of data science.
- Just like with every type of project or study, you will have to set a timeline to know when it will be tested, used, and launched for your business to use.
Once you're done evaluating the foregoing considerations within your organization, you can begin with the process of identifying the business use case.
- When picking a business use case, you have to take into account what data would be required and clearly define what is the expected outcome for the business use case.
- After capturing the data, clean and transform it, and then apply an algorithm.
- At this stage, you can test the hypothesis to see if what the organization wants to achieve can be obtained using the captured data and applied algorithm.
- In case it is not achieved you can go back and either tweak the data or the algorithm.
- Once you're done tweaking, you can rerun it a few more times to ensure that the business use case can be accomplished smoothly.
It's important to note, however, that if it still fails or what you want is not yet achieved then the business use case might not be the right fit. In this case, you might have to search or create a different business use case.
Use cases of data science, if it matches the requirement and expected outcomes of businesses, can bring in several benefits for an organization not just with decision-making but also externally with how it would positively affect consumers driving an increase in value and revenue. To know more about what use case will fit with your business, feel free to reach out to One Connect Solutions.