May 21, 2020

Solving Business Problems with Data Science

Data Science – This term is everywhere these days.

More and more folks are reading about on Google. The term is unknown to many, so it’s exciting to discover something that can potentially affect their lives.

Many of those folks are business owners. They’re in a hurry to learn the business benefits of data science as well as how it can give them a competitive edge.

Are you one of them?

In this article, you’ll find out how data science assists businesses in solving complex problems:

  • advance supply chain performance
  • enhance workforce engagement and job satisfaction
  • improve data security
  • develop more secure and effective business software.

Let’s now get straight to the point.

1. Improve Supply Chain Performance

If you’re someone engaged in supply chain management, you know that you need more data about its performance. Not just any data: the right data at the right time and gives accurate insights to make real-time decisions.

Data science can help you get there.

Here are the main things you’ll find interesting:

  • more accurate demand predictions. Prescriptive and predictive analytics make it possible for businesses to process more data related to their supply chains. As a result, they can build predictive algorithms that help with discovering optimal aggregation levels and production scheduling
  • more efficient customer support. Thanks to AI-enabled chatbots, businesses can already automate their customer service. The customers can get answers to common questions like expected delivery times quicker without having to wait for a human agent
  • advanced inventory management. Large companies often struggle with inventory optimization and recognition of bottlenecks. To counter these issues, Amazon has recently implemented a data analytics solution. It automatically assigns warehouses based on the location of customers and vendors to reduce distribution costs.

The power of data science in the supply chain is its ability to define important patterns and make quick decisions based on real evidence. This is something that companies desperately need to automate and make their supply chains more predictable and flexible.

2. Enhance Workforce Engagement and Job Satisfaction

Although HR doesn’t seem to be a field where data scientists would engage, the truth is pretty much different.

Companies with large workforces – online writers services, consulting platforms, etc. – are actively looking for solutions to retain their top talent and keep the workforce engaged. Many have found “people analytics” to be a great way to go.

Deloitte has recently released ConnectMe, a digital workplace dashboard for companies. It contains all HR-related information that both HR professionals and employees can access relevant content and projects.

For HR people, in particular, solutions like ConnectME can be incredibly useful to track and manage work.

The tool collects and analyzes employee-related data all the time, so the user can monitor engagement, performance, and get regular reports. That data can also assist with measuring employee development and learning outcomes.

3. Improve Data Security

Did you know that software applications, especially those working online, are your weakest security link?

Cybercriminals certainly do, so businesses need to consider cybersecurity.

To ensure the highest protection levels, companies need to develop secure software and employ the latest security solutions. Machine learning has been recently added to this mix as a tool for detecting cyber-attacks quickly.

Now, predictive analytics is the next candidate that improves the security of stored data. There are a few ways in which this data science-enabled solution does that:

  • real-time search for potential attacks. Catching a potential problem often is the best way to minimize the impact. Predictive analytics-based algorithms process data in real-time and can find unusual patterns and trends in data to indicate possible attacks
  • automation of security processes. With the algorithms doing their thing, data security specialists can work on other critical tasks requiring human intervention. Moreover, the chance of human error in processing data also decreases thanks to the ability of the algorithms to categorize information and prioritizing incidents
  • processing huge amounts of data. Dealing with enormous data volumes is a major challenge for cybersecurity specialists. Data scientists can help them to handle this task by providing the algorithms.

With cybercrime becoming more targeted and effective, employing the latest data analytics solutions may soon become a must for keeping online data safe.

4. Develop More Secure and Effective Business Software

Data science has also been making its way into the area of business software development. Automated software testing is one of the best examples where it can make a lot of difference for companies.

Recently, more and more testers have begun learning data science to be able to use algorithms to find issues. It makes perfect sense. The process of testing is about data and patterns, which is exactly what data algorithms are created for. They also ask similar questions like QA to help define how software can work better.

Here are some ways in which data science can enhance software testing for businesses:

  • reduce workload and save time. Often, testers have to process enormous volumes of data in short timeframes, which is not always possible. By using data mining techniques with super-fast computational speed, they can quickly look into logs and save lots of time
  • find patterns in unstructured data quicker. This is one of the super-powers of data analytics that testers can really take advantage of. They can apply algorithms, highlight patterns in data, and understand the underlying structure. As a result, it would be easier for them to find anomalies and other issues. These algorithms are being already applied in the financial industry to find traces of potential credit card fraud
  • prediction of future values. Testers can use data science methods to process historical data and predict future values, even in complex conditions. These results can also help with defining if a specific app is using too many resources or starting to go beyond the expected parameters.

Software testers are increasingly finding new, more efficient ways to do their job with data science methods. Clearly, they can take advantage of automation and other solutions, so we should see more data science applications in the field.

Is Data Science Important for Businesses?

Absolutely.

Can it benefit every business?

Yes, because every business in every industry uses data.

From a small software development company to a retail giant like Amazon (which, as we already know, is already using it), businesses will be investing more in learning what data algorithms can do for them.

Already, data science has a nice portfolio of business problems it can solve. Learning how to apply it to these problems can, indeed, make a lot of difference for companies.

Author’s bio. Daniela McVicker is a blogger and a freelance writer who works closely with B2B and B2C businesses providing blog writing, copywriting, and ghostwriting services. Currently, she is a contributor to Essayguard. When Daniela isn’t writing, she loves to travel, read romance and science fiction, and try new wines.


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