“It’s too futuristic and not applicable to my business’s needs” – is a statement commonly heard when discussing data science, automation, AI and/or machine learning.
But is that true? Are the terms themselves too vague and undefined, thereby limiting the potential impact and application for any company?
Before your organization can leverage the terms above and implement its first party data in a meaningful way, your organization should first understand the terms, so it isn’t intimidated by them.
Today, we’re going to help clear potential confusion, by bringing our Director of Data Science in to provide clarity. Continue reading to learn:
Data science is the process of applying scientific processes to extract insights and knowledge out of data. Due to overuse and improper categorization, data science has become synonymous with analytics, which isn’t true.
Check out the clip below where Dawson Weber, Director of Data Science at Linkmedia 360, provides additional context:
In the case of data science, the journey is of equal importance (if not more important) than the destination itself. Meaning, if a team is merely providing results without the methodology or process used to determine results, it’s difficult to determine the efficacy of the results.
Data science requires a properly built framework that can be communicated.
For an organization to properly commit to data science, it must be willing to let the framework itself remove potential bias. Data science requires an objective and neutral mindset. That means, a desired result or outcome of the analysis is not the endgame in sound data science.
Rather, a data scientist can and should collaborate with clients no matter the result to make whatever the outcome is, as beneficial as possible.
Every organization has challenges that need to be overcome. Data science can certainly be leveraged to provide insights and solutions to complex challenges. Contact our team and we’ll provide case studies detailing our expertise in data science. We’ll also be happy to answer any questions your team has as honestly as possible.