Data science is a popular trend in tech right now. The number of businesses that consider hiring data scientists is high and growing. The potential of these advanced technologies is tremendous – data science helps improve customer service, the communication within the team and the quality of daily scrums, influence the decision-making approach, and so on.
Still, the fact is, most companies are oblivious to all of its benefits.
If you hesitate whether or not you should implement DS in your own projects, the benefits and applications described in the post will help you make your mind up. To find out how to build a sustainable team for data science implementation, check over here.
What is Data Science For Businesses?
Data Scientists use Machine Learning and other technologies to collect and process business-related data. There are two broader objectives of the field – improving the product and enhancing the understanding of the company’s audience.
Data Science is crucial for companies that explore new local or product mar kets. It helps ensure that a new product or a service will be profitable within the modern economic landscape. Data Scientists often join forces with the marketing team. Gathering and processing insights are key to building an effective promotion strategy.
Implementing data science into SME projects is a multi-layered process. These are the stages of a sample data science workflow:
- Data gathering;
- Storing data;
- Cleaning and sorting through datasets;
- Data analysis;
- Responsive data visualization;
- Making data-driven decisions.
All of these stages require automation, are expensive in terms of data scientists costs and challenging to maintain. Why are businesses betting on data science despite all the challenges that come along with it? Let’s examine the benefits of DS for small-scale and large-scale projects closer.
Data Science Benefits For Business
1. Proactive decision-making
The modern business environment is highly saturated. That’s why company owners have to expand their objectives beyond customer acquisition. To be precise, now it’s more important than ever to be able to anticipate the needs of your clients, address them, and deliver optimized experience.
Data science is the best modern tool that allows businesses to achieve these objectives. Using DS tools, company managers can define and observe patterns in user experience, find out what kind of content, product lineup, or experience resonates with the audience to the fullest extent.
While traditional companies implement changes only when the old workflow is absolutely not working.
Pros of hiring data scientists are in making businesses more proactive, capable of predicting fluctuations in customer preferences and establishing the reputation of trendsetters in the market.
2. Improving the existing system
Examining the company’s current processes and finding ways to rebuild or enhance them is one of the main responsibilities of a data scientist. A business manager will be able to track the efficiency across all departments and improve it.
Here are some of the tasks data science helps achieve within the structure of the company:
- Track the relevance of a department’s activity and its objectives;
- Monitor employees’ performance and offer improvement opportunities based on emerging patterns;
- Compare the quality of the company’s performance on a regular basis and point out possible issues;
- Provide project managers with transparent reports on the state of their projects.
Businesses need data scientists to increase the value provided by each asset of the company. It helps company managers ensure they are moving along the right development vector.
3. Fraud mitigation
Companies use data science and analytics to increase security and evaluate management risks efficiently. The advantage of hiring data scientists lies in anticipating fraudulent activity and detecting its early signs.
Businesses across various industries heavily rely on data science for security monitoring. For instance, financial and banking service providers use big data to spot credit card misuses. Insurance firms use machine learning and data science applications to sort through applications and determine fraudulent ones.
Here are the main objectives data science helps managers achieve in terms of fraud mitigation:
- Check the authorship of any published content;
- Perform automated vetting and background checks;
- Determine patterns that are common for fraudulent activities;
- Detect money laundering schemes.
Using data science for fraud management is legal – to run their algorithms, data scientists rely on legally defensible information. You can use all collected information as evidence in court.
4. Risk management
Data science provides a new outlook on risk management as it allows company managers to notice a pattern in a seemingly unconnected chain of events. Instead of using intuitive risk evaluation methods, you’ll be able to rely on an extensive set of data points. By using linear regression, business managers can use historical data to predict future events.
Data monitoring across all facets of the enterprise gives companies a possibility to anticipate future changes in their operating mode and identify potential vulnerabilities.
Knowing how to hire data scientists, SMEs can proactively react to changes in the business environment.
5. Improving customer experience
Data science provides and unifies customer-based insights creating a wholesome system. As a client interacts with different points-of-sale, browses through the product lineup online, or checks the company’s corporate social media page, all that data will be consolidated. This way, companies can get a complete view of their customers rather than a data patchwork.
What are the practical applications of data science that bring forth customer service improvement? Here are a few common examples:
- Generating a personalized product lineup;
- Curating content that appeals to the interests and needs of a particular customer;
- Creating a consistent experience for every single user across all channels.
Data science increases the level of personalization in the brand-customer communication model. The marketing team will be able to create content and build a promotion strategy that’ll resonate with a potential lead on a deeper level.
6. Dynamic pricing
The dynamic practice is a common application of data science. It comes in handy for business owners that explore markets in emerging economies. Before upscaling, you have to keep in mind that a customer’s average salary in the chosen market might not cover the cost of a product/salary.
The contrary is possible as well – businesses from third-world countries often miss the opportunity to increase the cost of their services when expanding to a market in a different country.
Thanks to data science, the pricing policy will be determined based on the customer’s level of interest, his engagement with the marketing campaign, and the demand for a product or a service.
The model of dynamic pricing has been successfully implemented by airline companies.
7. Understanding images as a source of information
Before the sprouting growth of artificial intelligence and machine learning, automating any activity that involved analyzing graphic content was a challenge. With the introduction of data science, there’s a wide range of opportunities for healthcare, media, retail, and other industries.
Here are a couple of potential data science applications for image scanning:
- Sorting and labeling photos;
- Modeling 3D plans using 2D schemes as a reference point;
- Image recognition and tagging;
- Diagnosing patients.
Paired with deep learning methods, data science can provide more insights about the image than a human eye could notice. Analysis parameters include the point of view, the illumination level, the clutter volume, and so on.
How In-Demand Are Data Scientists Today?
In-house and remote data scientists became popular in the tech market around a decade ago. It has quickly become apparent there’s an enormous field of potential applications and responsibilities such a specialist can take on.
Nevertheless, it did come across as a surprise when Harvard Business Review proclaimed data scientists ‘the sexiest job of the 21st century’. Later statistics prove the claim true. According to the data collected by the Indeed Hiring Lab, the number of data science job openings has grown with a 256% growth rate in the period between 2013 and 2018.
With a staggering demand for data scientists, finding a skilled professional is challenging. Instead, there are dozens of DS wannabes eager to benefit from the emerging demand. What should you watch out for when hiring a specialist for your own companies? To start with, when looking for a bg data scientist, ensure a candidate possesses the following skills:
- Programming – both a database and a statistical language (usually, R or Python);
- Statistics – a solid understanding of distributions, likelihood estimators, statistical tests, and other common concepts;
- Machine learning concepts – random forests, ensemble methods, and so on;
- Advanced command of linear algebra and calculus;
- Data visualization tools management;
- Data munging – transferring data to various formats;
- Data intuition – being capable of prioritizing information and sorting it by relevance.
It’s not easy to hire data scientists – top companies and ambitious startups from all over the world are on the lookout for such specialists. The good news is, you can consider reaching out to third-party contractors that offer data-science-as-a-service. Such an approach saves business owners a considerable amount of time and money.
All companies that want to be remembered as technology trailblazers, have to consider implementing data science into their processes. This way, a business can analyze the economical potential of its new products and services, reduce the number of risks and their impact, track the team’s efficiency, and so on.
Building a sustainable framework is impossible without commitment and initial investment in data scientists hiring. There are more than a handful of challenges a company will face implementing DS algorithms. At the end of the day, these investments will pay off in a stronger bond with the audience, streamlined business processes, and increased revenue.