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Small & Medium Sized Businesses Need Data Science

And How Students of Data Science Can Help Get Them Started

Photo by Tim Mossholder on Unsplash

Just as we are in life, in business we must also be flexible. This flexibility is especially true for small, medium, and startup businesses. But it is hard to be flexible when technology feels like it is ready to disrupt our every business step. When customers raise their expectations and competition threatens to meet them where they are.

As a data scientist, it has been my experience that such threats are best dealt with using the tools of data and analytics.

But how can we help small and medium sized businesses (SMBs) to enable greater flexibility with data and analytics? How can we ensure they set themselves up for the future and ready their businesses to evolve into data science savvy businesses?

It all starts with data at the foundation. Just as every good data science project requires the capture and engineering of data so too does a business require a data strategy.

But data strategies don’t need to start with complex storage or expensive data warehouses. Quite the contrary actually. The most important aspect of a good data strategy is simply consistency. Finding a way to consistently store information that is important to your business.

And consistency is better supported when data can be collected and stored automatically but even manual processes, with a bit of discipline, can also be great sources of data collection strategies. So long as they are consistent.

To recap, here is a list of a few ideas for setting up the foundation of a data strategy that even small businesses can take advantage of:

1. Data Sources

a. Email: Email is a great way to capture and store information for later use. I use Google Alerts to deliver insight to my inbox but email can also be used to capture customer experiences, documents, or other relevant business information.

b. Internet: The open internet has a multitude of sites that contain valuable information for your business. The trick is effectively scraping the information needed but first you need to identify which sites are relevant. Collecting a list of relevant sites can begin this process.

c. File systems: All businesses deal with the storage of information in file systems. Identify the types of data that you save a lot of in those file systems, develop some consistent way of organizing them (even if it means copying and pasting them in more than one place), and be patient as they grow in number to become a potential source to leverage with future engineering, analytics, and data science.

2. Engineering

a. Once data are stored in enough volume they need to be engineered so as to begin to derive structure that is meaningful for your business.

b. This is where data collected begin to become potentially powerful sources for future analytics and those analytics needn’t be complex at first. For example, merely pulling out say the names and gender of clients who purchase different products can begin to inform you whether or not males or females tend to prefer one item over another. Such insight can lead to more nuanced marketing strategy going forward.

c. This is also where data science tools can begin to have an impact. Data science provides an opportunity to engineer even more insight from unstructured sources that can have significant downstream value for a business.

As you consider your own role as a student of data science, begin to understand how a consistent data strategy can lay the foundation for greater business flexibility in the future.

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Brandon Cosley

Brandon Cosley

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Data Science Transformation Specialist | Start with newsletter and get my end-to-end approach to data science here www.fastdatascience.ai