The advancement of data science and advanced forms of analytics has resulted in a variety of applications that provide improved insights and business value in the enterprise. Data science practices provide organizations with the capabilities they require to extract valuable information from ever-increasing amounts of highly variable data.

According to Our World in Data, the drivers of global data growth include increased internet access, broadband access, mobile phone use, and social media use. Organizations are adopting a data-driven culture in order to avoid falling behind their competitors and to see results. According to a Forrester report, data-driven companies “grow at a rate of more than 30% per year.”

Why is Data so Valuable? 

If data does not reveal anything, it is of little to no value in and of itself. The value is not in the data itself, but in what is done with it. Data-driven businesses derive value from data analytics, which is the process of analyzing data to gain business insights. The data can then be used to add business value by solving problems or improving processes. Data has value because it allows business leaders to make informed decisions that can lead to improved business performance and stronger customer relationships. 

The examples below highlight some of the numerous advantages of data-driven decision-making for businesses.

Improve Customer Service:

Organizations can use data to determine what customers prefer.

Identify New Business Opportunities: 

Data can reveal insights that can assist businesses in generating additional revenue streams by innovating and developing products and services that meet consumer demands.

Increase Sales and Improve Processes: 

Every company wants to increase revenue. Data is critical in identifying and translating data into revenue opportunities in a competitive global marketplace.

Obtain the First-Mover Advantage: 

Data and analytics can assist organizations in responding to market changes more quickly. Businesses can gain a competitive advantage by leveraging data analytics to predict future trends, identify consumer behaviors, and detect new business opportunities more quickly.

Business Analytics for the Translation Industry

Over a four-decade period, the translation industry has experienced regular adaptation shifts caused by changes in the business and technological environment.

Over the years, translation service providers have helped to bridge diplomatic gaps between countries, fostering international relationships and market developments. Such services have been critical for businesses looking to expand into international markets and require accurate translation and localization of marketing materials. The global language services industry was worth between $47.5 billion and $48.4 billion USD in 2020, after doubling in size over a decade, compared to $49.6 billion in 2019. Moving into 2021, when the heat of the pandemic had started to subside, the overall market value rose considerably to $51.6 billion, according to Statista. 

Data is everywhere in the translation industry, from translation memories to project data, which includes not only basic project data but also data about marketing/sales, bookkeeping, and human resources. Data analysts’ deciphered data has aided language service businesses in sorting out their operations. Data has been used by translation service companies to identify areas of excellence and areas for improvement.

Starting with your human resources, language service businesses should track things like:

• The amount of time it takes a freelance translator to respond to job assignments.
• The daily/weekly/monthly volume that a translator (in-house or freelance) can translate.
• The feedback provided by project managers and/or clients.

Data analysts can conduct an analysis of your clients to identify those with the highest volumes; the number of interpretation/translation/certified translation jobs per language combination; client ratings submitted per project or per manager; the number of projects successfully delivered for each client; or the number of reworks.

When it comes to language data, it is critical to focus on the leveraging rates, or the volumes and quality of text pre-translated either with machine translation or in context with translation memories, and so on.

Conclusion

Overall, the translation industry is evolving at a fast pace. The world is rapidly adopting advanced technologies to improve its quality of life. Furthermore, translation technologies are evolving to deal with massive amounts of digital data. A data-driven mindset can help you make better decisions, set more specific goals, and grow your translation business.