Report written by Marina Ghazaryan.
Data-driven decision-making is the process of making organizational and strategic decisions based on actual objective data instead of on intuition or observations. Today, every company in every industry aims to minimize the likelihood of business decisions going awry. Data is usually the answer.
Before making inferences from the available data, it is important to go through the process of identifying and amassing core information about the company’s industry, its competitive landscape, and its customers. Simply put, the more a company knows, the more accurate the decision can be.
It is also important to compile and coordinate all data sources. Data analysts typically perform their work following the ‘80/20 rule’, which means that they spend 80 percent of their time cleaning and organizing data, and the remaining 20 percent performing the actual analyses. Having clean and orderly information is essential. Generally speaking, data cleaning is the process of preparing raw data for analysis by removing data that is incorrect, incomplete, or irrelevant.
The next step after cleaning the data is analyzing the information using various statistical models. Here, the data analyst can start to build models to test the available data and attempt to find initial answers to the business questions identified earlier in the process. Testing the different models (linear regressions, decision trees, random forest modeling, and so on) can help the analyst to determine which method is best suited to the data set. It is also important to decide on the best way to present the information, ideally in an effective, and often visual, way.
Data analysts use these three common ways to present the information:
And finally, the cycle comes to an end with a conclusion stage, which is aimed at supporting the stakeholders throughout their deliberation and decision-making process. The more the findings are presented in an understandable manner the better the chances of successfully propelling the company’s strategy onward.
Dennemeyer is one of the heavyweights of the IP landscape. The company was founded almost 60 years ago in Luxembourg but has since grown to have offices in over 20 countries across six continents, offering worldwide representation to clients regardless of their language and time zone.
There are several methods that can be used for assessing an organization’s performance. These typically focus on highlighting the company’s internal resources, strengths, competitive advantages, as well as its weaknesses. Examples of internal analysis tools used for such purposes include gap analysis, strategy evaluation, SWOT analysis and the McKinsey 7S Framework. In In other words, internal analysis reveals where an organization excels, what it is good at and where it needs improvement. In any business, there may be many different scenarios that warrant conducting an internal analysis.
Impressed by the level of inadequacy shared around the world these days, we have taken it upon ourselves to make our very own collection of myths and beliefs about the translation and localization industry.
The language industry is rapidly evolving and you don’t want to miss out on the latest developments. Nimdzi has created a list of more than 60 influencers in the Localization industry based on a variety of criteria and active engagement on social media. The candidates included in the ranking are all professionals with proven experience in translation, localization, and globalization.