You must have heard people talk about how optimising data has helped improve their business. They are increasing their revenue, detecting customer churn before it happens and using forecasting tools to coordinate an efficient supply chain.  But you are here, wondering what data and data science mean and how it fits into your dream of infusing more technology in your business.

  • Businesses are increasing their revenue, detecting customer churn before it happens and using forecasting tools to coordinate an efficient supply chain.

Perhaps you’ve been in conferences and heard business owners talk about what and what not they are doing with machine learning models. If this is you, you can stop worrying. This post is what you need to gather enough knowledge about the field of data. It is what you need to move your business to the next level.

What is Data Science?
What is Data Science?

Firstly, let’s define what Data Science means. It is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. It transforms raw information into valuable insights that make decision making for businesses easier.

By applying machine learning models on big data (numbers, text, images, video, audio), Data Scientists manufacture artificial intelligence systems designed to execute tasks that otherwise would have required human efforts. In turn, these systems generate insights which business analysts and owners can interpret into tangible value. Next, we will discuss what ‘Big Data’ means.

Big Data and Data Science

Big data can be said to be defined as a large amount of data too much for the human mind to process and traditional computing systems to handle within a given timeframe. To qualify data as big, data has to meet requirements known as the 5 Vs of big data. High volume, velocity, variety, veracity and value are the attributes that define this data set.

2.5 quintillion (10¹⁸) bytes of data are generated daily. That is a lot of data generated from literally everything. Data science helps make sense out of this load of data.

What Your Business Will Benefit From Data Science.

Increased Revenue. Improved market forecasting. Faster business innovation. All of these come from better business decisions informed by data science.

Everyone wants to make better, fact-based decisions. Businesses would rather have accurate predictability over guess work, improve client satisfaction, maximise market differentiation, provide exceptional user experience and the list goes on. By applying the knowledge of data science on clean and optimised data, data scientists offer insights to the questions organisations have about making  decisions.

The Process: From Data to Value

From collecting data to receiving insights about how you can move your business forward, there are steps involved in the application of the knowledge of data science. Automated data machine tools are the best way to enjoy data analysis. They take away the stress of manually merging all your data, cleaning them and training models to be deployed.  

Automated data machine learning platforms are the best way to enjoy data analysis.

Acquire Data:

If there is no data to process, then data science can’t profit you.  But not to worry, if your business has been operating even for a month, then you’ve already generated data you can use from client information, details on past transactions, revenue generated and so on.

Clean Data.

This used to be really stressful, but automation has made a lot of things easy. Because data obtained may sometimes have repeated values, some values may be missing and have to be filled in. The quality of insight generated is largely dependent on the quality of data imputed, hence this step of cleaning data is crucial. This process is automated and has been highly simplified on the Voyance Data Pipeline platform.

The quality of insight generated is dependent on the quality of data imputed, hence this step of cleaning data is crucial.

This process is automated and highly simplified on the Voyance Data Pipeline platform.

Build, Train and Deploy Models.

After data has been collected, cleaned and loaded into the data warehouse, the next steps is to use a Machine Learning platform to build and train  models that produce predictive results.

Monitor with an ML Orchestration tool.

To deliver the best results, there is a need for monitoring, after a model has been deployed. You want to keep an eye on the accuracy of its predictions. Businesses that enjoy the maximum benefits from their deployed models are those who use Ml Orchestration tools to supervise the models they’ve sent out and keep improving it.

Data Science In Finance, Health and Transportation.

Whatever field your business is in, investing in data science will yield dividends for you. Not only will the results generated increase your return on investment significantly, but they will also help you create a better buying experience for your buyers.

Companies in Fintech prevent fraud by utilising the power of data science. They do this through applications designed to monitor transactions in real-time, proactively identifying abnormal patterns and actions depicting fraudulent activities.

Technology derived from the understanding of data science has been useful in the building of explainable and accurate healthcare predictions.  A good example is the prediction of the severity of the CoVid19 pandemic in Singapore.

By analysing historical data and observing behavioural patterns in the booking data over a short period, airlines can now predict which passengers will cancel their flights. This helps them plan better and reduce losses. This same tool is applied by other businesses where appointment cancellations are a source of recurring losses.

Data Science at Voyance

Even for organisations with near-unlimited resources, increasing data science efforts can be tasking. At Voyance, our mission is to democratise access to data science via the automated machine learning platform that enables your business analysts, data engineers and other professionals in tech to get their best job done.

Learn more about data science, download the free guide to understanding data science here.