Information is the bedrock of business success, so what happens when you don’t use it properly? Data science and data analytics help equip organizations with more accurate planning tools that improve business processes at a fraction of the cost and time it normally takes.

Data science and analytics are critical to business success

There are some decisions businesses can afford to neglect, for example, whether or not to allow employees to access their Facebook accounts at work. However, when it comes to critical operations that affect every facet of how a business is run, data science should be at the top of your agenda. In the next couple of years, a third of all new revenue in specific industries will be as a result of data science and AI, according to Gartner. Your company’s ability to understand and process data at lightning speed and also customize that information (which is where AI generally comes in) will determine where you land on the totem pole. If you want to survive, you need to incorporate data analytics, fast.

How to apply data science and analytics

The impact of data science and analytics go as far as the mind can stretch. Data is foundational to every aspect of your business, from customer service to logistics and career progression management. Once you move beyond rudimentary methodologies and begin to invest in these advanced systems, you can begin to reap immediate results.

Below are some areas where data science have been applied, with extremely positive results.

Data Science can improve sales, marketing, and product development

Data science can help you acquire more customers and increase the profit margin per customer. Data analytics techniques such as regression analysis and automatic data classification are just what you need to boost:

  • Marketing efficiency – data analysis helps marketers execute targeted strategies that impact specific groups and yield higher returns on investment. It can also help develop more accurate sales forecasts and revenue projections.
  • Customer satisfaction with demand forecasting – by incorporating AI with your data analysis, you can predict when customers would require certain things and be able to meet the need before it arises. This level of customer satisfaction improves your reputation and customer satisfaction.
  • Research and Development – no more botched orders or misguided designs. By analyzing industry-wide customer behavior, you can develop the right products and services with higher accuracy than ever before. This knowledge also helps marketers know what qualities in a product should be highlighted to generate more sales.

Improving cybersecurity measures

Beyond revenue growth, data science also helps with revenue protection. Cybercrime is a growing threat that needs advanced methods to handle, and this is how data analytics helps out:

  • Fraud detection – Anomaly detection algorithms let you know when certain customer behaviors are irregular. Sufficient consumer data sets help AI software develop a framework that can immediately identify transactions with a high risk of fraud. 
  • Cyber-attack prevention protocols – Data science provides much-needed support to cyber risk management. It is quite rare, or near impossible, to know when a cyber attack is about to happen. However, with predictive models, you stand a better chance of identifying when a cyber attack is about to occur and then prevent it. 

Improve industrial processes

Any business with repetitive processes generates tons of data that can be applied to create more flexible routines. Predictive analysis can help any business with cyclical productions or demand such as manufacturing, transport and logistics, retail and even hospitality identify growth areas and strategies that can help reduce costs and boost customer satisfaction. Here are a few ways it can work:

  • Routing Optimization. Once specific routes have been followed regularly, an algorithm can help decide the best paths to take that will lower journey time and transportation costs. Route optimization also applies to assembly lines and other routine operations.
  • Predictive maintenance. With so many machines in place, IoT networks can keep feeding a central system with data that can help operators identify problems quicker. You would be able to tell when a system is on its last leg before it shuts down and disrupts your entire workflow.

No growth without data

As much as data science has been talked about in the news, at business forums and everywhere else, too many companies are yet to accept it as an inevitability. The reason for this is usually confusion or uncertainty. If this is your present predicament, you can always contact a professional data analytics company. This way, you can get the knowledge you need and the expertise to compete against larger companies.