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Making Data Analytics Human For Decision Making

Data intelligence is an important aspect of every organization. It lays the foundation for data analytics and decision making by the company executives. However, data collection and analysis are conducted by computers that store them in languages that are not comprehendible by humans. Thus, in order to facilitate decision making, it is imperative to make data analytics available in natural human languages.

Once the metadata is annotated in human languages, it provides information about events such as when, what, where, how and why they occurred. When the information is available in tangible form, it can be used to gain situational awareness and stimulate thinking by forming patterns or relationships between data. Formatting the data by forming visuals such as tables, charts, and graphs help in understanding the patterns and interdependency of various factors. This understanding defines the course of future actions to achieve desired organizational goals.

In order to understand how to make data analytics human for decision making, let us consider the following aspects:

Type Of Data:

Traditionally, metadata management focused on technical metadata including platform, structure and physical characteristics. However, as the business organizations are now relying extensively on data analytics, equal focus is being laid on collection and correlation of business metadata (business rules, associated applications, and business capabilities) and semantic metadata (business terminology and ontology).

Finding Data Patterns:

A large amount of data is collected on a daily basis. But in order to gain meaningful results, it is required to understand the relationships in the data. An example of data mapping for understanding interrelationships between entities, their properties and relationships is ‘Knowledge Graphs’ pioneered by Google. Although such graphs provide good information, they alone cannot be used for reliable decision making. Thus, more related data has to be collected from parallel platforms and databases to create a ‘Knowledge Platform’.

As the information is classified in classes and concepts across different datasets, it makes it easier to interlink and find related information. Businesses tend to make use of query languages to search for information across the contents of enormous datasets.


After understanding the patterns of data, the next step is to form a data narrative. It includes reasoning and learning in addition to data patterns. To create a narrative, it is important to understand three things:

  • Types of questions that may be asked based on data patterns
  • Answers to these questions
  • Questions that will arise based on previous answers

Data patterns may indicate information such as the effect of a variable on business metrics. But data narrative includes answers to questions such as ‘If metric goes up with time, how will it affect the business?’, ‘Does the metrics accumulate over time or is it point-in-time?’ and ‘What does it mean for our sales?’.

Decision Making:

The data narrative forms the basis of decision making. The decision makers of an organization analyze the narrative, visualize the supporting data, and test the hypothesis to identify gaps. The final decision conveys the required actions for achieving business innovation and goals.

For more information on making data analytics human for decision making, call Centex Technologies at (972) 375 - 9654.