Data Isn't Important, But How You Consume It Is - Deepstash
Data Isn't Important, But How You Consume It Is

Data Isn't Important, But How You Consume It Is


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Data Isn't Important, But How You Consume It Is

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There are two types of data

  1. Raw, unstructured data, consisting of numbers or lines of code.
  2. Processed data, which is raw data that has been cleaned, organized and transformed into information (i.e., knowledge).

Raw data is unusable, while processed data is key to driving business decisions.


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  • Descriptive Analytics evaluates historical data to identify patterns and trends. It is the most common approach to data consumption as it centers on summarizing data and identifying general trends.
  • Diagnostic Analytics reviews data to determine why an event occurred in the past. Through data mining and correlation, diagnostic analytics spots trends and anomalies — an operation that’s a step beyond the functions of descriptive analytics.
  • Predictive Analytics analyzes data to forecast or predict future patterns. Predictive analytics, popular among large corporations, involves consuming data in a way that proactively drives business decisions and revenue.
  • Prescriptive Analytics applies descriptive and predictive data to test multiple variables and calculates the best possible outcome. Prescriptive analytics compiles the results of all other methods to provide recommended courses of action.


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  • Third-party analytics. Numerous tech companies, such as Google and Facebook, specialize in collecting and analyzing the data of other businesses and providing elementary analytics to owners. These analytics are an excellent tool for small businesses with limited budgets or no need for deep statistics.
  • Internal data analysts. Mid- to large-size companies sometimes hire a dedicated data analyst whose sole responsibility is to oversee the processing and organization of all their data. Bringing on such a specialist provides the greatest, though pricey, flexibility. 
  • Custom-built data platforms. Organizations that process a massive amount of data can custom-build data analytics platforms from scratch. These platforms include robust dashboards that analyze millions of datasets in real-time. Their implementations have streamlined the operations of many multinational corporations. 


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Our digital economy has evolved beyond the point where data collection alone can facilitate success. 

Now, getting ahead requires collecting and evaluating data to gather crucial insights on market demand, running lean and generating consistent returns.


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