How to Use Data Science to Get Ahead in Business

What is data science in business?

Data science in business helps resolve business challenges by scrutinizing data based on numbers, statistics and facts. It uses scientific methods, algorithms, processes and systems to extract knowledge from data that companies can then use to make strategic decisions. “I think that data science is making decisions based on actual numbers rather than intuition, and I think that business requires both,” said Amanda Reiman, chief knowledge officer at New Frontier Data. 

Although intuition is helpful, it’s important not to lean too heavily on a personal bias. “Every industry is evolving — consumers evolve, technology evolves, use patterns evolve,” Reiman said. “So it’s one thing to have instinct, but you also have to gut-check yourself and understand what the actual numbers say, because numbers tell a story, but they don’t have an opinion.” 

You can use analytic tools to create predictive models that simulate a variety of possible outcomes for different situations. For example, if you identify five potential ways you might want to grow your business, data science can predict the method that is most likely to work and presents the lowest risk. 

A key part of taking advantage of data science is tracking key performance indicators (KPIs) and other metrics. The collected data allows businesses to make decisions based on recurring trends.

How do you use data science in business?

Data science can have a significant impact on business areas such as market research and process automation. Check out some of the valuable uses below.

Business planning

Your strategic business moves should be backed by proper reasoning and motivation. But if you want to seize opportunities, you can’t afford to wait months for regular business evaluations. Data science gives business owners a way to make decisions much faster while also mitigating risk. 

Even if your company is too small to create a relevant data set for a particular situation, you can use freely available databases, including those from government institutions and industry associations, to collect applicable data. By gathering and analyzing relevant internal and external information, you can make informed business plans.

Performance tracking

Most data tools allow you to create custom reports based on your objective. For example, you can collect data on employee productivity and generate reports on efficiency. You can then use the insights from those reports to set employee performance goals. Some tools to measure employee performance even have built-in data analytics features.

Process automation

Most companies regularly waste employees’ time on repetitive tasks and can benefit from simplification through automation. Project managers and data scientists, working in partnership with team members, should identify workflow automation opportunities — tasks that could be performed satisfactorily by machines instead of by humans.

Data-driven automation has multiple uses, ranging from document tracking to decision-making. For example, artificial intelligence (AI)-powered digital assistants can summarize, sort, classify, and retrieve documents and conversations to save staffers time. Other deep-learning algorithms can be trained to match the skills of human workers. For instance, as you can read in our review of Salesforce Service Cloud, call center platforms can now scan phone, email and social media conversations to handle initial inquiries from customers without human intervention. 

“AI is a double-edged sword; on one hand, it definitely automates and processes things that might have taken us longer by hand, but we also shouldn’t be blindly accepting whatever AI tells us as truth,” Reiman said. “It still requires us as human beings to go over it, make sure it makes sense and make sure we investigate the things it’s claiming.”

Market research

Big data and market research are a match made in statistical heaven. By using data science to analyze your market sector, you can uncover patterns among clients, identify consumer preferences, determine the best advertising methods and even project your return on investment (ROI) for each marketing channel you use.

Client segmentation

Mass-market products are becoming less viable as consumers demand greater product customization and personalization. The only reasonable way to know what people want is by analyzing relevant data. Free tools such as Google Analytics offer in-depth insight into your customers and allow you to create customer profiles. By analyzing the data, you can identify new niches to target. [Check out our small business guide to using Google Analytics.]

Advertising campaigns

Before big data, businesses relied on focus groups and surveys to guide advertising campaigns. Unfortunately, these initiatives often generated biased insights because they included only a few participants. Now, with data science and analytics tools, every commercial, online ad or social media post can be tested for relevance with thousands of users. For instance, data gleaned from A/B testing can give businesses hard proof of the advertising campaigns that are likely to generate the most engagement.

Talent scouting

The same logic that goes into finding your ideal client applies to hiring excellent staff. Just as you need to understand your target customers’ motivations, you must understand what motivates and engages your ideal workers. Then, you can train machine-learning algorithms to identify what makes people successful in similar positions. Once you have a clearer idea of what you’re looking for and why, the real recruitment work begins — but that’s not where the use of data science ends.

For example, you can build automation tools based on the information you collected about your ideal employee and desired traits. Then, you can use those tools to sort through resumes and automatically filter out applicants who don’t match what you’re looking for. Once you’ve identified your top candidates, modern data-collection tools can pull information from the applicants’ public profiles so you can evaluate factors beyond what they’ve submitted in their applications.

What are the benefits of big data for small businesses?

Data analysis can drive business growth and revenue in several ways. Here are the main benefits.

It empowers employees.

Big data analysis shouldn’t be reserved for the C suite or data analysts. Data science helps all employees, regardless of their level in the organization, to improve their analytical abilities and make better decisions. When there is hard data to rely on, employees will feel more confident making decisions relevant to their work, without wasting time taking every issue up the chain of command. 

“No matter how strong your gut is, there’s always that feeling of, ‘What if I’m wrong?’” Reiman said. “That confidence that the decisions you’re making are cornered in something real and anchored by information — I think that’s what people really start to feel when they understand how they can use data science.”

It fuels a competitive edge.

Data science lets companies uncover emerging industry trends, sometimes before competitors see them. These unique insights allow you to establish and develop an advantage in the marketplace. For example, if you can use your data to identify which products consumers want and which products aren’t selling, you can invest more in the items that are likely to sell faster and in greater numbers.

It helps you recruit skilled talent.

HR teams can use data science to improve their chances of selecting the right candidates for available jobs. This means less time and fewer resources are wasted in finding the best people for open roles. Data science tools can provide comprehensive candidate profiles that make it easier to identify the right person for the job.

It validates critical decisions.

Data science allows you to model the effects of different decisions before you carry them out. It’s a way to experiment and conduct trial-and-error tests with minimal risk. Based on the predicted outcomes, leaders can rest assured that the path they’ve chosen is the best one for the business.

Incorporating data science into your business

Data has become a currency, a way of doing business, and the foundation for sound decision-making. Yet data by itself is just a set of numbers. To make data useful, you need to apply it. To embrace big data and take advantage of data science, you must not only collect information but also analyze and use it to optimize your business.

Jeff Hale contributed to this article.