In the times of globalization and multinational corporations, it seems that tourism should flourish naturally, without the need for additional changes. However, the recent pandemic crisis clearly states: there are no modern industries protected from black swan events today. That’s why companies should always look for ways to reach even higher performance or profitability levels. And what’s the best option for this task? Tech innovations, of course.

Today, we will discuss the importance of Big Data in domestic and international travel, the advantages of this technology, its limitations, and perspectives.

The Essence of Big Data

In the beginning, let’s understand the Big Data concept. According to different sources, Big Data is a sector that works with extremely large information packages. This field focuses on scales unreachable for traditional data processing. It requires special software and hardware to gather, store, share, and analyze info to get valuable insights. When processed properly, data sets can turn into industry-changing ideas.

The actual size of Big Data isn’t carved in stone but it’s about petabytes, exabytes, and even zettabytes. IDC reports that there will be 175 zettabytes or 175 trillion gigabytes of information in the world by 2025. In 2020, this parameter is projected to reach 50 zettabytes. Big Data is an indispensable part of IoT development, healthcare, media, and IT in general. It boosts various industries on the worldwide level: trade, manufacturing, shipments.

Generally, this sphere can make any market sector more efficient. One great example is the travel industry.

How Innovations Benefit Travels

Mainly, Big Data and general data science for tourism are centered around analytics. It’s useless just to capture millions of gigabytes because raw information doesn’t add value. It benefits businesses only after proper processing. Thus, Big Data in travel can help on three levels of analytics: descriptive, predictive, and prescriptive:

• Descriptive aspect relies on historic data with simple parameters of customer travels such as the most visited countries or average expenses per year. Descriptions help to segment people and understand their past behavior better.
• Predictive analytics pushes the service further. On the basis of available information, it targets individual clients to forecast their future behavior. This approach requires large data sets, cleansed and optimized. But it also delivers much more value.
• Prescriptive category includes the most innovative solutions for experienced teams. Combining top-quality data with machine learning and powerful processing, it suggests the best actions for clients or may even automate these actions.

Today, descriptions are widely used by different companies while predictions and, especially, prescriptions are not as popular. We expect more innovative services to grow thanks to better access to clear data and cheaper computing. The brightest emerging trend now is propensity modeling – super-personalized offers for each individual traveler.

Below, you can find five of the most prominent Big Data advantages for the global tourism industry.

1. Better Customer Experience

The whole travel industry is built around clients. By collecting and analyzing user data, businesses can understand the preferred services, destinations, general behavior patterns, and needs of customers. Respectively, it allows companies to target people and deliver the most personalized offers: prediction-based discounts, live support, AI assistance, etc.

2. Market Research Opportunities

Apart from user-related insights, Big Data can help to gather information about the industry in general and competitors. By analyzing reviews, forums, social media, and rival websites, travel brands can spot relevant trends, weaknesses, and strengths. Needless to say that the research is vital for companies that want to stay at the forefront of tourism.

3. Reputation Management

The mentioned reviews together with internal feedback may help to maintain a flawless reputation of your travel firm or hospitality service. Especially, it’s important for businesses with a lot of clients. Big Data tools optimize processing of feedback and show which areas require urgent improvements, according to clients.

4. Revenue Management

There’s no secret that businesses want to earn money. Among other benefits, technologies serve this goal, too. Big Data gathers internal financial info (product/service prices, demand, actual revenue) with external insights (other market rates and factors that affect them). Hence, companies can better manage their own finance to optimize revenue flow.

5. Strategic Forecasting

Finally, forecasting analytics is an extremely prospective field of Big Data. It comes together with improved customer experience as it’s the main goal of data processing. Travel firms can forecast behavior to offer new services, predict fares to adjust own offerings, and analyze market trends to present the demanded things before competitors.

Tourism-Oriented Big Data and Modern Challenges

However, it’s crucial to understand that Big Data (as any technology) isn’t a magic wand that solves all business issues at once. Instead, it can be compared to a screwdriver – a tool that you use to fix something, clearly understanding why and how you should do it.

Nowadays, Big Data in travel faces several limitations. One of the freshest problems is hyped COVID-19. The virus outbreak impacts global industries heavily but tourism suffers the most, on a par with supply chains. Countries close their borders, airlines cancel flights, attractions close, and people ask for ticket/booking refunds globally.

On the one hand, it’s an undoubtedly negative trend for data-focused processes. Various travel industry players lose revenue and have to limit tech investments. Nevertheless, Big Data helps to predict and fight the outbreak. Data science also may be useful for tourism teams that can understand the changing wishes of clients and adapt processes respectively.

As for other – more traditional – travel Big Data challenges, here are a few examples:

• Data privacy and regulatory issues. Data gathering and processing are pretty complicated because of close attention to user privacy. Regulators list strict rules designed to improve customer control over personal info so businesses should be careful.
• Demand for skills/knowledge. Big Data remains in the list of innovations so it requires significant experience and practical skills. Not all travel teams can afford in-house data experts. Simultaneously, amateurs may harm operations.
• High technology costs. Probably, this one is the most essential issue. Big Data strategies require huge investments. A lot of teams fail to get quick and sufficient ROI so it’s vital to analyze everything before implementing Big Data tools.
• Potential performance dips. As you know, the industry accumulates trillions of gigabytes of data. Computers often face difficulties with processing. And better computing functions require more investments. This fact returns us to the previous point.
• The problem of inadequate data. The mentioned zettabytes represent general data of different quality. However, to get really valuable insights, you will need accurate info. And, again, data cleansing requires more time and money expenses.

What we’re trying to show is that data processing may be highly beneficial. But people and companies that work with Big Data should do it carefully. Let’s summarize it together.

Moving Further

Data science is evolving fast nowadays. Most likely, the famous Big Data section will become a new normal pretty soon. In other words, as the information volume gradually increases, companies will try to get and analyze it more often. Eventually, all parts of the global ecosystem (including travel brands) will move away from old-fashioned Small Data.

The main question here is: who and when should adopt innovations? We think that not all of the existing businesses. If you realize that Big Data can improve your operations and bring the required ROI, build your custom solution and benefit from it! Otherwise, monitor trends and implement new tools slowly. It’s better to be the second than the last, isn’t it?