datolite 的音標是["de?t?la?t] ，基本翻譯是“數據晶體”，速記技巧是利用字母的諧音記憶法，讀作“代特拉易特”。

Datolite是一個英語單詞，意思是數據晶體。這個詞來源于拉丁語“datum”和“lithos”，意思是“數據”和“石頭”。

變化形式：在詞性變化中，datolite可以變為名詞形式，如data crystal或data-crystal。

相關單詞：

1. crystal：晶體

2. data：數據

3. datum：基準

4. lithos：石頭

5. datum point：基準點

6. crystallize：結晶

7. crystallize out of thin air：憑空產生

8. data storage：數據存儲

9. data transmission：數據傳輸

10. data analysis：數據分析

以上這些單詞都與datolite有密切的聯系，它們在英語中廣泛使用，并且在科技、計算機和數據科學等領域中非常重要。這些單詞的詞源和變化形式也反映了英語語言的發展和演變。

常用短語：

1. data visualization

2. data analysis

3. data mining

4. data science

5. big data

6. data warehousing

7. data quality

例句：

1. Data visualization helps us understand complex data more easily.

2. Data analysis is essential for effective decision-making.

3. Data mining can reveal valuable insights that were previously unseen.

4. Data science is becoming increasingly important in today"s digital age.

5. Big data offers opportunities for businesses to gain a competitive edge.

6. Data warehousing ensures that data is stored securely and efficiently.

7. Poor data quality can lead to inaccurate conclusions and misguided decisions.

英文小作文：

Data is becoming increasingly important in today"s digital age, and the tools we use to analyze it have also evolved significantly. From data visualization to data mining, data science has become a key component of many businesses and organizations.

Data visualization helps us understand complex data more easily, allowing us to see patterns and trends that might otherwise be overlooked. Data analysis is essential for effective decision-making, as it helps us identify key insights and trends that can inform our strategies and plans.

Data mining can reveal valuable insights that were previously unseen, allowing businesses to gain a competitive edge in today"s fiercely competitive marketplace. Big data, on the other hand, offers opportunities for businesses to analyze vast amounts of information in order to gain a deeper understanding of their customers and markets.

Data quality, however, remains a crucial aspect of data analysis, as poor quality data can lead to inaccurate conclusions and misguided decisions. Therefore, it is essential to ensure that data is collected, stored, and processed in a reliable and secure manner in order to obtain accurate and reliable insights from our data analysis tools.