How does data science help start-up post-2020



 Technology has grown by miles in recent years, which has prompted businesses to utilize it to the fullest for their growth. However, with technology, a significant amount of data is getting generated. Getting valuable and actionable insights from the data has thus become difficult. This is where data science comes into the picture for businesses across verticals. Even when we talk of startups, data scientists need to create architectures from scratch.

Data scientists have to perform multiple tasks in a startup like identifying key business metrics, understanding customer behaviour, developing data products, and testing them for their efficiency. Let us understand in detail how data science can help startups in 2020.

Why is data science important even for non-tech startups?

With the increasing competition in the business space, it has become more important than ever for organizations to master the art of personalization. Though serving the customers at the start of the journey remains a challenge, scaling up the business eventually is more challenging. Data science can help entrepreneurs ramp up their personalization efforts. Retail buyers’ persona can be identified and created based on their shopping history. This serves businesses to showcase to new customers what other people have seen, based on similar preferences. It helps in making informed recommendations.

How can startups implement data science in their business?

Rather than having data science integrated at the organizational level, it must be integrated at the team level. This includes different teams like sales, marketing, product, etc. Businesses must look forward to giving the data to the data scientists in an appropriate format so they can work efficiently.

It will be an ineffective process if companies just dump the data in the systems of data scientists. With data in a single format, businesses can give a data lake. It will bring in efficiency and make data scientists more productive. Let us have a look at the various stages of data science in startups.

  • Data Extraction and Tracking

As the first step, it is important to collect data that needs analysis at a later stage. Before proceeding ahead, you must identify the customer persona and user base. If you run an ed-tech startup and want to develop an app, you must identify how likely would its usage be. You must identify the number of users who will install the app, what would be the number of active sessions, and how likely will they spend.

This makes it important to collect the data on these parameters to identify the user base of your app. It is also essential to add specific attributes regarding product usage. Doing so will also help you identify the users who are most likely to opt-out of services and ways to prevent that.

  • Creating Data Pipelines

Being the second step in the process, you must analyze and process the data to gain relevant insights. It is the most important step that needs careful analysis. This helps data scientists to analyze the data from the data pipeline. It usually remains connected to a database, either an SQL or Hadoop platform.

  • Product Health Analysis

Data scientists need to analyze the metrics, which indicates the product’s health. Using raw data to transform it into usable data that shows the health of the products is a vital function of data scientists. Businesses can identify the product’s performance through these metrics. Several tools help data scientists perform this process. ETLs (extract, transform and load), R, and KPIs are some key metrics to analyze the product performance.

  • Exploratory Data Analysis

Exploring the data and gaining crucial insights is the next step after establishing a data pipeline. Data scientists can understand the data’s shape, the relationship between different features, and also gain insights about the data.

  • Creating predictive models

With the help of machine learning, data scientists can make predictions and classify the data. One of the best tools to forecast the behaviour of a user is predictive modelling. It helps businesses identify how well the users will respond to their product. Startups offering recommendation systems can create a predictive model to recommend products and services based on a user’s browsing history.

  • Building Products

Building products centred on data can help startups improve their offerings. This can be done when data scientists move from training to deployment. This is possible by using different tools to create new data products. Identifying the operational issues might not be possible every time. This can be overcome by manifesting data specifications on an actual product. It can handle data-related issues and prove beneficial for the startup.

  • Product Experimentation

Before introducing changes in a product, startups must conduct an analysis to identify their benefits. They need to identify if customers will accept and embrace the change. A/B testing is one of the common experimentation tools. You can draw a statistical conclusion when conducting hypothesis testing to compare the different variable versions. One of the limiting factors of A/B testing is the inability to control the users of different groups.

Data science and automation

Strenuous and repetitive tasks can easily get replaced with the help of data science. The cost reduction here can prove beneficial for utilization in other areas. This will help startups utilize their resources productively. So it will not reduce jobs but will give startups the chance to get better ROI from their employees. With increased productivity, startups can scale their operations faster. The available capital can complement the growth and increase their operational capacities.

Final Thoughts

Data science can help startups improve their product offerings and scale operations. The importance of data science is huge, as data is the lifeline of startups. As the demand for skilled data scientists is on the rise, Great Learning can help you learn data science. If you opt for a data science program, it will help you understand big data analytics, predictive analytics, neural network, and much more.

One of the excellent learning sources, these data science courses can give you a comprehensive learning experience. You can opt for a Python data science course, data scientist course, or even data science online training. Get in touch with us today to know more details about the courses and admission process.

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