How to Get Started With Data Science: The Key to Growing Your Startup

What is data science and its various disciplines? 

The first thing to understand is that the world creates an enormous amount of data every single day, currently sitting at around 2.5 quintillion bytes. That number will continue to grow as the IoT (internet of things) expands, so knowing what data is meaningful is crucial to using it efficiently. 

This is where data science comes in.

Data science comprises a number of disciplines that involve the analysis and interpretation of data. There are five main disciplines and each can be used alone or you can use more than one and have them interconnected to give you a broader view and different perspectives. 

To decide which to use, look at how they relate to your existing or planned startup. So, what are the five? 

1. Data mining

As mentioned, a huge amount of data is created every day so you need a way of sorting that data and identifying any patterns and trends. 

Data mining uses complex algorithms to sort through data sets and highlight any patterns you can then use to inform business strategies. 

2. Statistics 

Also called predictive analytics, statistics can take data and identify patterns to predict future patterns. For example, it could help a business identify historical data and seasonal patterns and predict future patterns that should mirror or even outperform the historical trends. 

3. Machine learning (ML)

You will often have heard about ML in relation to it being part of artificial intelligence (AI) systems, particularly in contact centers. So, where does it fit into data science? Put simply, ML can be used in conjunction with algorithms to create models based on data sets. 

Different from predictive analytics, ML can learn and adapt as it progresses to give greater accuracy.

4. Analytics

Analytics could be seen as the Holy Grail of data science. It is essential to any accurate interpretation of data and is also an integral part of the first three disciplines.

Analytics helps convert data to more understandable information that can help you make important decisions in different areas of your startup. 

5. Programming

While this is an area of data science that may not always have a place within your startup, it is an essential discipline. It’s the programmers who develop and code the various models and programs that you will use to harvest and analyze any data and form predictive models. 

How can a startup use data science? 

If you have launched a startup, you will know that in most cases, budgets can be very tight, and you may need to invest in tools that manage the flow of communications such as business phone line apps – as a result, efficient use of your resources is crucial. That said, using data science can make a huge difference depending on the pace of your growth.

You also need to think about where the data comes from. If you are at the beginning of your startup journey, then data will be limited in relation to your own business and you will have to obtain it elsewhere. 

1. Data extraction

You may have limited data as you start, but your data sets will grow - and improve - with time. Being able to extract data efficiently is the foundation of all the data science you will apply. You may base initial strategies on analysis of your closest competitors but as you progress you want to use good tools that extract relevant data and analyze it so that you can tweak any strategies you have put in place.

2. Predictive modeling

Having accurate predictive models is essential for almost any business, and can be particularly useful for a startup. This is particularly true when you may have limited resources, as you want an accurate picture of what your customers are going to want in the future as well as what trends and patterns may emerge in the market as a whole.

Using a good predictive modeling tool can be an important aspect of any data science strategy. 

3. Product development and improvement

No business wants to sit on its laurels and risk stagnating. So, you want to be able to foster new ideas to improve your current products and how to develop new ones.

By utilizing data science, you can see if there are potential improvements to be made as well as analyzing reactions to new product suggestions or identifying gaps in the market or consumer needs.

4. Automated testing

Manual testing can be a slow performance, especially when you want to be able to quickly improve performance in areas such as product performance, product intelligence, or actual production. 

Using data science for automated testing reduces that time significantly, and can both validate current procedures as well as identify areas for improvement.

Why is data science so important to startups?

Organizations of every size know the value of data science to their business. Identifying seasonal patterns in product sales can not only help dictate how many units a retail or ecommerce business may buy (and the costs such as warehousing that comes with that decision) but it also has a knock-on effect on how many units a manufacturer may produce. 

The use of data science allows businesses to make more informed decisions when it comes to everything from staffing to sales strategies to the best marketing tactics for startups. For those reasons, it can be particularly important to startups on limited budgets and who are still finding their feet in their chosen marketplace. It can mean significant cost savings and making good decisions. 

By understanding the current (and future) market, and the platforms and channels their target demographics prefer, a startup can allocate resources to the areas where they will get the best results. Similarly, by understanding what their customers need and want, they can also ensure that the strategies they put in place meet those needs and wants as much as possible. 

Good use of data science can highlight previously-unseen opportunities as well as identify market trends and consumer behavior patterns that let them adjust tactics when needed. Of course, any startup involves risk-taking, but following the data can help mitigate any risks and reduce the chance of failure. 

Whether you are a sole entrepreneur or you have financial backing, all involved stakeholders want to see a positive return on investment. While they may not expect that in the short term, they will want to see the business growing and the data reflecting some degree of success. 

So, data science can both help inform your strategies and also measure how successful those strategies are.

The takeaway

Depending on your business type and model, there are many demands made on what may be a limited budget, including software that helps customers contact your small businesses easier. You need to prioritize needs and see what you can afford at different stages of your development and growth. Data science should be one of your primary priorities every time. 

There is a wide range of tools and programs available when it comes to data that include tools such as the Elasticsearch database. Choosing the tools and programs you use or invest in can be an important decision. However, there are lots of resources available online that can help you make those decisions, such as a video conferencing guide for startups. 

Startups can find plenty of ways to cut corners without sacrificing quality. For example, using templates such as a proposal for professional services template can be a great way of saving money over time. But keep in mind just how important data, and data science, will be to the growth and development of your startup.