Let’s get down to it. We know data science is beneficial, and the importance of it for businesses today can fall into two categories.
The first is if you have business problems that you know can be solved by machine learning, you need someone to build specific machine learning applications to achieve your business goals. The second is that data scientists are inherently excellent problem solvers and strategists who can help discover new revenue streams and new value propositions, which is something that can be overlooked in many businesses. However, not all businesses are ready to leverage data science. 

The Role of a Data Scientist

Today, the role of a data scientist can be underutilized and misunderstood. Many people within this position spend most of their time answering tickets and following requests. Where data scientists actually thrive is being deeply embedded within an organization and working with business stakeholders to help uncover problems a business is facing and craft solutions.
Data scientists are valuable where a machine learning service or product is the core of a business, and where machine learning and data science can help enhance a business’ current services or products. For example, Google, Uber, Lyft, and Airbnb have core products that are foundationally built on machine learning and data science. With data-focused companies, data scientists can be some of the first hires that are made due to their skills being central to the business’ success.
For companies that don’t hire data scientists right away, they may find the most value in business development, data engineering, and data analytics. Data analysts and data engineers can unlock amazing insights and lay the groundwork for data scientists to have the most impact.
Once this foundation is laid, data scientists can build more advanced analytics, statistical models, and machine learning models to push the capabilities of a business even further, even if machine learning itself is not a core product of the business.
If you’re interested in more about the role of a data scientist and what it takes to get there, check out our post on transitioning from an analyst to a data scientist.

When to Implement Data Science Into Your Business

So when do you need to implement data science into your business? Typically, it’s later than you think. For data scientists to be successful, they first have to have access to reliable data. Data scientists are not magicians, and if you have bad data to begin with, you won’t get the results you need from anything they build. 
It is essential for businesses today to follow the below structure, starting from the bottom up, in order for a data science practice to be successful. 
Data Science Foundation
What’s important for businesses to understand is that once you hire a data scientist, you won’t have advanced models being generated right away. You need a fully supported system with data engineers, data analysts, and data scientists working together in an organized fashion to develop useful machine learning. Another option is you can have someone outside of your organization, like Cognetik, to help determine what your goals and objectives are.
For companies that are looking into a whole set of new tools to help them gain a competitive edge, optimize their business, and generate new revenue streams from insights off of their data, then data science may be in the near future. This is especially the case if a business needs answers to questions requiring elements of prediction or prescription. 
When a business has that desire or is at least thinking about the kinds of questions data science or machine learning practitioners can answer, it may be time to implement data science into the business. If you’re still wondering if data science is in your immediate future, check out Cognetik’s Data Science Checklist below.
When to Implement Data Science


Implementing data science into a business should be examined on a case-by-case basis. Even though hiring data scientists to help develop strategic solutions and deliver new revenue streams sounds amazing, don’t try to run before you walk!
Whether or not machine learning is one of your core products or services, it is crucial to build a strong data foundation of best practices data engineering, and insight data analysis first.
If your business has untrusted, sparse, or dirty data, it is essential that this data is wrangled and organized before bringing data scientists to your organization. Stakeholders must trust the underlying data so that the output of any data science project can be trusted.
No matter where you are in the data science journey, contact us at Cognetik. Our expertise in various services and products in the analytics industry can help guide you in the right direction.

About the author

Jefferson Duggan

Jefferson Duggan
As a principal data scientist, Jefferson uses data not just to learn, but to answer the most important question of “why?”. His background in mathematics and physics allows him to develop end-to-end predictive analytic and machine learning solutions. Jefferson is also skilled in developing internal and external data science strategy.

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