Similarly to human resources and physical assets, the use of big data is an integral part of running a business. With unique customer data, companies can adjust their business strategy and gain a competitive advantage.
In today’s article, we’ll explain why businesses should see data as a competitive advantage and go through different ways you can utilize it to stand out from the crowd.
In most industries, using big data is seen as the best way to outperform the competition – there are many reasons for it. First off, big data allows businesses to build data-driven strategies and make informed decisions rather than basing them on gut feeling. Secondly, with large data sets, companies can stay on top of industry trends.
Lastly, big data allows companies to introduce real-time product adjustments or put any theories they had in mind into practice.
Companies that store large volumes of data typically split it into four or five categories based on the data sensitivity. Let’s take a look at an explanation for each category:
Public data is the type of information that’s freely accessible for anyone to read, store, and share.
Private data is the type of data that’s usually protected with a password or a fingerprint.
Proprietary data usually belongs to an organization (or a company) and can only be accessed by the members of that organization. Common types of internal proprietary data are business plans, internal emails, budget spreadsheets, and others.
Confidential data is the information that only specific people at the company can access; it’s usually protected with a specialized password. Confidential data includes social security numbers, health records, financial data, credit card numbers, etc.
Restricted data is the most sensitive type of information that typically can only be accessed by a handful of people. To prevent malicious actors from breaching it, this data is encrypted and can be accessed with multi-factor authentication. Federal tax information and protected health records are a couple of data types that fall under this category.
Now, let’s dive deeper into different ways companies can use big data for gaining a competitive edge.
First off, instead of taking guesses and keeping their fingers crossed, companies can use large-scale data to make evidence-driven decisions. Take the healthcare industry as an example – it’s one of the sectors that started using high-volume customer data way back. Early on, data specialists analyzed the health effects widely-prescribed pharmaceuticals had on people. With data-enabled learning practices, pharmaceutical companies could uncover benefits and risks that weren’t apparent during clinical tests.
Another data-enabled learning example is companies that sell products with embedded sensors – i.e., kids’ toys, says the source. Having data that reveals how their product is used in real life helps companies to make informed decisions, such as adjusting the product design.
Often, data is accessible to IT departments or analytics of the company. However, to gain a competitive edge, all departments must be able to access, explore, and analyze the data. This way, decision-making processes will be improved all across the company, and various risks will be eliminated.
One of the best-known companies to use data-enabled learning across all departments is Walmart. The company has created a Data Café, which is a hub that processes and stores large amounts of internal and external data.
Walmart’s Data Café quickly analyzes the data and provides valuable insights into various issues the business is having. One time, the team couldn’t figure out why sales in a particular category suddenly dropped. The team solved it by using Data Café – quickly, they discovered that the products were listed at a higher price than the actual one.
At the same time, big data allows companies to identify market trends and unlock new growth opportunities. By analyzing large quantities of data – their own and their competitors’ – companies can see what their competitors lack and jump at the opportunity.
Also, big data opens up space for new business categories, especially for B2B companies that offer data analysis services. These companies have access to large volumes of information that reveal details about products, services, and customer behavior.
High-frequency data also allows companies to take quick action when necessary. Back in the day, companies could estimate certain metrics like consumer confidence with hindsight. Today, by analyzing large volumes of unique customer data, companies can act on the spot. Furthermore, companies can test out any theories they have and see real-time results.
By diving deep into customer data, businesses can find out what customers want before they realize that themselves. This way, they can customize campaigns based on the target audience and give personalized recommendations that speak directly to their customers’ needs.
One of the companies that follows this principle is McDonald’s – in some of their restaurants, the company uses devices that track customer interactions, collect data on ordering tendencies, and more. They then analyze this data to see the effect of menu changes, restaurant design, and employee training programs.
As you already know, gaining competitive advantage requires large volumes of company data. However, maintaining a scalable approach towards public data acquisition isn’t easy. Let’s take a look at the most common challenges associated with scraping:
Maintaining data quality. It may be difficult to ensure data quality when scraping different data types or large volumes of data in general. Since you’re scraping data from multiple sources, keeping the high quality is even harder – after all, different sources can present different results for the same data. Hence, your tool must be able to deliver accurate, real-time search results.
Layout changes. Different websites have different layouts, often causing scraping tools to break or not detect the right information. That said, your scraper must be able to adapt to different layouts.
IP blocks and CAPTCHA. To prevent suspicious activity, websites employ various anti-bot measures, such as IP bans or CAPTCHAs. For instance, if a website detects a large number of requests coming from the same IP address, they may ban it. Hence, it’s crucial that your scraping tool rotates between multiple IPs.
Another common issue associated with scraping is CAPTCHA tests. While scraping, you may be prompted to complete a CAPTCHA test (i.e., select all the images with boats) to ensure you’re an actual human, not a bot. One of the best methods to prevent receiving CAPTCHAs is to try replicating human, non-robotic behavior when scraping.
Unreliable tools and lack of knowledge. To maintain a scalable approach towards data collection, businesses need resources and expertise. If a company decides to work on their first web scraping project, they’ll need someone with professional scraping skills on their team.
Also, to ensure the web scraping process is smooth and the delivered data is high quality, it’s essential to choose a reliable solution. For example, Oxylabs’ Web Scraper API returns real-time public data from the majority of websites on a large scale. The tool handles CAPTCHA and IP blocks, allowing companies to acquire data without hassle. See the simplicity of our API in action by taking a look at this guide to web scraping Walmart.
With certain markets being saturated, it’s essential for businesses to find new ways to outperform their competitors. With the use of big data, companies now have a chance to act quickly while making informed, evidence-driven decisions and, essentially, stand out from the competition.
If you’d like to learn more about how data analysis can help with optimizing your business operations, reducing costs, and improving relationships with your customers, we invite you to check out our article on data mining and automating competitors' analysis with Python.
About the author
Senior Content Manager
Roberta Aukstikalnyte is a Senior Content Manager at Oxylabs. Having worked various jobs in the tech industry, she especially enjoys finding ways to express complex ideas in simple ways through content. In her free time, Roberta unwinds by reading Ottessa Moshfegh's novels, going to boxing classes, and playing around with makeup.
All information on Oxylabs Blog is provided on an "as is" basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website's terms of service or receive a scraping license.
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