The Correct Way to Carry Out Data Science in Business.
Using Data Can Get Messy
But when it is done well, it’s knowledge-rich. Data science done well can help the business managers make informed decisions to drive your business forward, improve your efficiency, increase your profits, and ensure you meet you achieve your organizational goals.
Data science can go wrong. When that happens, the answers you get won’t quite make sense, and you can’t explain what you learned. This often means you’ll be stuck doing further analysis, which costs both time and money.
Common Data Missteps
Fishing: this refers to analyses that are done without predefined research questions. Without knowing the questions to ask, there is a high likelihood you’ll come up with misleading findings. Without strong business hunches (or educated hypotheses) you’re not really doing science.
Plug and Play: this refers to filling in holes with whatever data is available or using easy analysis methods, instead of using the data and analysis that is most appropriate. Despite the lakes and oceans of data, to answer specific questions or address revenue growth opportunities, you may need to ensure the intelligent collection of new data.
Garbage In = Garbage Out: no matter how functional your analysis is, if the data itself is terrible (meaning error-filled or not right for the question) the results won’t have meaning.
Incorrect Analysis: not all analysis methods are right for all data. If the analysis is wrong, the answer will be too.
Good Data Science Looks Like
The Right Questions: Correctly defining what question you need to answer will move you towards growth.
The Right Methods: Identifying the data, the correct study design and the correct analysis to yield the right answer for your business is critical.
The Right Data: Statistical treatment of your data will ensure the right answer.
The Right Analysis: Correct statistical analysis and consumer science knowledge will ensure the right answer.
The Right Answer: At this stage you should be able to identify the correct answer and be able to explain how you arrived at the results. The right answer means you can take the next step: data extrapolations – implemented data algorithms, models, artificial intelligence algorithms and machine learning.
When you have the right answer, you put them to work. Good data science means you know what you need to know, and you can act on that knowledge with confidence.
How Horizon Can Help
We’ve been practicing strong data science for more than 25 years. We can help you know what data you have, what data you need, and what will give you the right answers to increase revenue, improve efficiency, and grow your business.