How to Improve Your Business Efficiency with Data Science and Analytics

Eugene Berko

6 years ago

Article

How to Improve Your Business Efficiency with Data Science and Analytics

Business and enterprise have always crunched numbers in order to plan, grow and increase profits. Yet a new wave of data science is fundamentally changing the way companies operate. In this article, we highlight how advanced data science and analytics can transform everyday commercial operations.

Advanced data science and analytics is now widespread

Data science methods including AI algorithms and powerful data analytics are now applied broadly, from sales analytics through to industrial processes and risk management. For example, Gartner estimates that by 2021, 30% of (net) new revenue for solutions made for specific industries will now involve AI tech.

Few sectors and applications are untouched by today’s wave of data science and analytics applications. From science and industry through to financial service, e-commerce, education, healthcare, fashion, retail, logistics, warehousing, agriculture and more, the power of data is becoming readily apparent.

Applying data science and analytics

In moving beyond run-of-the-mill number crunching using simple maths, companies are discovering powerful insights in their existing data sets. These insights depend on cutting edge data techniques that are incredibly flexible, leaving almost no area of business untouched. These are just some of the areas in which data science and analytics can render large benefits:

Business growth and profitability

Data methods can help businesses acquire new customers while making existing customer relationships more profitable. Data analytics techniques including regression analysis and automatic data classification highlights insights that can assist with:

Sales and marketing. With advanced data analysis marketers can target customer far more finely, delivering a tuned message that generates real impact. Analytics can suggest how likely a sale is to go ahead, serving as a prompt for sales execs and helping businesses predict revenue.
Customer demand and service requirements. Ensuring customer requirements are met when and where needed is key to maximising profits. Data science can predict how customers will behave by analysing past behaviour, in turn ensuring inventory and staff are optimally available.
Product and service development. With deep insights, companies can do a better job of designing products and services that truly match the needs of their customers. For example, understanding which product attributes make a product more likely to be purchased helps determine in which direction to take future product development while highlighting the more marketable properties of a product.

Managing risk and security

In an age where cyber crimes and fraud are increasingly prevalent, business and enterprise must use every tool available to mitigate the risk of losses. This is not always simple to do, and data science can lend a helping hand:

Detecting fraudulent transactions. Anomaly detection algorithms have the ability to highlight transactions which are out of the ordinary. When large transaction history datasets are fed through machine learning algorithms a framework emerges which can be used to flag transactions which pose a risk of fraud.
Preventing cyber-attacks. Again, data science provides a platform for securing digital assets. Predictive models can enable the so-called adaptive security architecture, allowing to proactively prevent a cyber attack that is underway. Though cyber risk management is an established field, data science enables more intelligent management of cyber risks.

Enterprise processes and industrial operations

Repetitive processes, whether in an industrial or a service setting, generate reams of data. Applying data science and predictive analytics help car rental businesses, as well as enterprises in logistics and transportation, warehousing, retail and beyond, improve their processes, reducing costs and increasing customer satisfaction:

Routing optimisation. When aggregated, trip data can provide a basis on which an algorithm can generate optimal routes for delivery. This optimisation can even be applied in the factory setting where algorithms can determine the best methods to assemble a complex product.
Predictive maintenance. Production operations utilising IoT networks can continuously report live metrics to a central software system. In doing so software algorithms can predict which physical devices require maintenance before these devices break down disruptively.

Data is a competitive advantage

The data science revolution is underway but not yet complete. Organisations stand the chance to obtain a competitive advantage by engaging with advanced data analytics before their peers. Unsure of where to start with data?

Contact the data-savvy experts at ELEKS to get insight into the areas in which data science and analytics can improve the performance of your organisation.