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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.

Drug Discovery Through Artificial Intelligence

Artificial intelligence has garnered immense applications in various industries including banking, manufacturing, and healthcare. A branch of healthcare that is benefitting from Artificial Intelligence is the ‘Pharmaceutical Industry’. It is met with new challenges in the face of new viruses, mutated antigens, drug-resistant strains, etc. on a daily basis. Additionally, it has become common to see the rendition of once eradicated diseases such as polio. Under these conditions, traditional R&D can be very time consuming and costly.  

Traditional drug discovery methods are objective driven and work well for targets whose structure and interactions in the cell are understood. However, most of the cellular transactions have complex pathways.

In order to overcome these challenges, Artificial Intelligence-powered drug discovery offers an effective alternative. Following are the ways in which AI transforms the drug discovery process:

  • AI-powered drug discovery follows a data-driven approach that is based on the vast patient datasets. The data is studied and categorized by complex algorithms into understandable information for facilitating drug discovery at a faster pace.
  • It applies machine learning to study new incoming data for recognizing new opportunities and information.
  • The algorithms search through vast databases of compound structures to identify a compound that can bind to the antigen protein (even if the structure of the target protein has not been yet identified). This saves a lot of time when compared to manual screening of compounds that can act as drug candidates.

How It Works?

  • The first step is to take sample from people with and without a disease. Also, samples are taken from people who are at different stages of disease progression.
  • The sample data is then extracted into genomics, proteomics, metabolomics and lipidomics for identifying the target.
  • AI and Machine Learning software study the information to identify any differences between the disease and non-disease states, proteins, and other features that may impact the disease state.
  • The identified proteins and metabolites are considered to be target candidates by the software.
  • The candidates are then queried against databases of patents, publications, chemical libraries, clinical trials, and approved drugs. This facilitates a precision-medicine approach by offering a means to triage the patients in an in-silico manner before entering a clinical trial to determine the effectiveness of a potential drug.

Benefits Of AI-Driven Drug Discovery:

  • AI does not rely on predetermined targets which rules out the chances of subjective bias.
  • AI amalgamates the latest technology in biology and computing to develop algorithms for drug discovery.
  • AI offers high predictive power to define meaningful interactions in drug screening. This reduces the chances of pursuing false potential drugs.
  • AI moves drug discovery to a virtual lab where screening results can be obtained at a faster pace and efficiency.

For more information on transforming drug discovery through AI, call Centex Technologies at (972) 375 - 9654.