The Intersection of Data and Entrepreneurship

As you know, data and entrepreneurship are deep interests of mine, and recently I’ve been looking into the intersection of these two fields. As someone who is passionate about both fields, I’ve been exploring how data can benefit startups and small businesses. In this post, I want to share some key takeaways from my research on how data science can add value to any company, and how entrepreneurs can use it to make better decisions, identify opportunities, and refine their target audience.

View at Medium.com

https://medium.com/geekculture/machine-learning-data-science-and-artificial-intelligence-a45a2ffe9639

he articles being referenced below can be found at…

Reading these articles has taught me a lot and I have written about some of the key takeaways I found from these articles.

Knowledge is Power… Knowing something or even having educated predictions influence your choices. “But if you knew everything you know now, you most certainly would have made different choices [5 years ago] and would have been better off.”
This is proactive action!
Proactive action pleases potential and existing customers
A company utilizing data science should collect both quantitative and qualitative data.
Your data doesn’t have to be complicated… Collect simple, already able-to-be-tracked things. AKA your sales, your sale prices, number of customers. Just start collecting
Data science adds value to any company by…
- Allowing management to make better decisions
- Taking action based on trends
- Identifying opportunities
- Identification and refining of target audiences
- Recruiting the right talent for the business

The more you know, the better choices you can make. And this is where data science comes in. Collecting both quantitative and qualitative data can help companies gain valuable insights into their customers, products, and market trends. It doesn’t have to be complicated – start by tracking simple metrics like sales, sale prices, and number of customers. By taking proactive action based on data, companies can please potential and existing customers, identify new opportunities, and recruit the right talent for the business.

So how exactly can data science add value to a company? Firstly, it allows management to make better decisions. By analyzing data trends, companies can identify patterns and make informed decisions that align with their goals and values. Secondly, data science can help companies take action based on trends. This means being able to predict what customers might purchase, make targeted ads, and tailor recommendations. Companies like Amazon, Stitch Fix, and Netflix have successfully implemented this type of cognitive insight to improve their customer experience. Thirdly, data science can help identify and refine target audiences. By understanding your customer’s needs and preferences, you can create better products and services that meet their expectations.

Entrepreneurs at the core want to build successful businesses. To achieve this, they need to make the right decisions, analyze market trends, and leap at opportunities while knowing exactly who they cater to. They need to take risks, but they need to be highly calculated risks. This is where data science can provide valuable insights. By analyzing customer data, entrepreneurs can identify new opportunities and create products or services that meet their target audience’s needs. Moreover, data science can help entrepreneurs refine their target audience and better understand their customers’ needs, leading to more successful marketing campaigns and sales.

But it’s not just about how data science can benefit entrepreneurs. More and more data scientists are moving away from working for big, corporate companies and are forming data startups. These startups offer data scientists more versatility, creativity, and freedom, allowing them to innovate and make a difference in the industry.

Three Big Ways

There are three big (different) ways that data can be used in a business.

Process Automation	Cognitive Insight	Cognitive Engagement
This is the simplest and cheapest way a business can utilize Artificial Intelligence in a business. They can use existing data to determine which processes they can automate and allow the business to grow without spending more money. This is relatively easy compared to the other ways data can be used because the “Intelligence” component is pretty limited. The technology is typically limited to only automating that one process without “learning” and self-optimizing. This was the most common type and was utilized in administrative and financial capabilities	This is the second most common utilization of data in businesses. (The first is process automation). This is using algorithms to detect patterns and interpret the meaning. Think about this like analytics but cranked up to level 1000. This is used to predict what a customer might purchase, to make targeted ads, and to make tailored recommendations. You can see this applied in companies like Amazon, Stitch-Fix, and Netflix. These models (due to the machine learning behind them) tend to be much more detailed and intelligent. This means that it can continue learning and stay up-to-date.	This is a relatively complex way to utilize data in a business as language is a complex topic. As the name suggests, cognitive engagement is the use of data to engage with customers. This can be through chat boxes or virtual assistance. This category includes 24/7 virtual customer service, sites to answer questions, recommendation systems with rich language and imaging, or health treatment recommendation systems. The goal is not to reduce headcount but to handle growing numbers of employee and customer interactions without adding extra staff, therefore not eliminating customer service jobs.

SANJITA SRINATH