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Data has become the most priced and perhaps the only true inimitable asset for any business. In an interview with McKinsey, Sprint's Chief Digital Officer, Rob Roy, states that “making operations data-driven is the best course of action for any organization, but then the challenge lies in implementing the capabilities and processes in place to use the data.”
In the following section, we’ll highlight the benefits and challenges of data science for business (DSB) growth.
An M.I.T study showed that businesses using data science technologies witnessed a 5-6% higher productivity. In another study, researchers found that organizations that reveal their intentions to implement DSB tend to receive more favorable reactions from investors than those that don’t. Data science and machine learning can uncover challenging business issues and develop more effective solutions.
Fast food companies have been using data science to monitor their processes and serve customers cheap food consistently and quickly. A good example of such a company is Chick-fil-A. This American fast-food restaurant chain estimated that 30% of customers drove away due to long queues at drive-through windows. Chick-fil-A’s data science teams used product-based data to identify the bottlenecks and improve the quality of each customer’s experience.
Instead of combing through mountains of resumes, HR departments are using DSB to make speedy and accurate candidate selections. Big data is used to find the right fit for organizations by sorting through vast data points of multivariate data in corporate databases, social media, and job search websites.
Big data analysis can also uncover hidden patterns in candidate behavior, enabling businesses to accurately predict when candidates will be open to new job opportunities. This capability enables HR to maximize their outreach programs and increase their chances of encountering high-quality candidates.
Understanding your customer base by doing a targeted market analysis helps businesses know which advertising media to use, what services or products to offer, and how to use the right messaging and visuals to convert leads into purchasing customers. A 2022 Zendesk Customer Experience Trends report showed that 68% of customers expect their experiences to be personalized.
Businesses use mobile apps and software like CRMs and ERPs to store and organize sales, customer, or employee data. However, combining different or unstructured data from all these sources can be difficult, resulting in inconsistent formats as each tool collects information differently.
Walmart has overcome the data science problem of huge data volumes by relying heavily on its data science team, Walmart Labs, for R&D. The company's private cloud can process 2.5 petabytes of data per hour, making it the largest in the world. It enables Walmart to integrate data by prioritizing and combining datasets in a single repository. This integration has enabled the giant retailer to personalize customer’s shopping experience, on-time and same-day delivery, and packing optimization.
We have seen how DSB helps to improve business performance and make informed decision-making. However, the use of data science poses the risk of ransomware, theft, and attacks on data systems. Informational theft is the biggest security challenge facing businesses that hold critical data, such as customers’ personal credentials or financial information.
LinkedIn was the target of a data breach that resulted in 500 million user profiles getting exposed by hackers and attempted to be sold for a 4-digit sum in crypto. Investigations into the incident pointed to weak passwords and a lack of encryption. LinkedIn has since improved its security measures by encrypting data in transit.
Data science is a powerful tool in business for decision-making, hiring the best talent, and targeting the right audience. Businesses can use DSB to develop the right strategies through insights they gain from their customers. However, when pursuing their data analytics objectives, businesses can be confronted by different DS challenges that can hinder their progress. If the businesses follow well-formulated plans, these challenges can be efficiently addressed.
Data has become the most priced and perhaps the only true inimitable asset for any business. In an interview with McKinsey, Sprint's Chief Digital Officer, Rob Roy, states that “making operations data-driven is the best course of action for any organization, but then the challenge lies in implementing the capabilities and processes in place to use the data.”
In the following section, we’ll highlight the benefits and challenges of data science for business (DSB) growth.
An M.I.T study showed that businesses using data science technologies witnessed a 5-6% higher productivity. In another study, researchers found that organizations that reveal their intentions to implement DSB tend to receive more favorable reactions from investors than those that don’t. Data science and machine learning can uncover challenging business issues and develop more effective solutions.
Fast food companies have been using data science to monitor their processes and serve customers cheap food consistently and quickly. A good example of such a company is Chick-fil-A. This American fast-food restaurant chain estimated that 30% of customers drove away due to long queues at drive-through windows. Chick-fil-A’s data science teams used product-based data to identify the bottlenecks and improve the quality of each customer’s experience.
Instead of combing through mountains of resumes, HR departments are using DSB to make speedy and accurate candidate selections. Big data is used to find the right fit for organizations by sorting through vast data points of multivariate data in corporate databases, social media, and job search websites.
Big data analysis can also uncover hidden patterns in candidate behavior, enabling businesses to accurately predict when candidates will be open to new job opportunities. This capability enables HR to maximize their outreach programs and increase their chances of encountering high-quality candidates.
Understanding your customer base by doing a targeted market analysis helps businesses know which advertising media to use, what services or products to offer, and how to use the right messaging and visuals to convert leads into purchasing customers. A 2022 Zendesk Customer Experience Trends report showed that 68% of customers expect their experiences to be personalized.
Businesses use mobile apps and software like CRMs and ERPs to store and organize sales, customer, or employee data. However, combining different or unstructured data from all these sources can be difficult, resulting in inconsistent formats as each tool collects information differently.
Walmart has overcome the data science problem of huge data volumes by relying heavily on its data science team, Walmart Labs, for R&D. The company's private cloud can process 2.5 petabytes of data per hour, making it the largest in the world. It enables Walmart to integrate data by prioritizing and combining datasets in a single repository. This integration has enabled the giant retailer to personalize customer’s shopping experience, on-time and same-day delivery, and packing optimization.
We have seen how DSB helps to improve business performance and make informed decision-making. However, the use of data science poses the risk of ransomware, theft, and attacks on data systems. Informational theft is the biggest security challenge facing businesses that hold critical data, such as customers’ personal credentials or financial information.
LinkedIn was the target of a data breach that resulted in 500 million user profiles getting exposed by hackers and attempted to be sold for a 4-digit sum in crypto. Investigations into the incident pointed to weak passwords and a lack of encryption. LinkedIn has since improved its security measures by encrypting data in transit.
Data science is a powerful tool in business for decision-making, hiring the best talent, and targeting the right audience. Businesses can use DSB to develop the right strategies through insights they gain from their customers. However, when pursuing their data analytics objectives, businesses can be confronted by different DS challenges that can hinder their progress. If the businesses follow well-formulated plans, these challenges can be efficiently addressed.
