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Simple definitions to get smarter: From 'big data' to 'AI'

Author: Ramkumar Dargha, AVP and Senior Principal Technology Architect, Enterprise Architecture

Today, business parlance is peppered with words such as big data, data analytics, data science, machine learning, artificial intelligence, and automation. We have all heard these terms being used; some even interchangeably. For those of you wondering what these terms actually mean, fear not. In this blog, I will attempt to demystify these new trends, highlight their significance and, most importantly, explain how they play a vital role when it comes to automation and artificial intelligence. I want to point out that none of these explanations come from industry-standard definitions or existing literature. They are drawn from my experience and shared with you in the hope of making these complex terms more comprehensible.

Business jargon 101

Let me begin with an illustration. The following diagram depicts how each trend works individually and within an ecosystem. While this may seem confusing now, I recommend you refer to it once each term is better understood.

View image

Data science - Firstly, the word 'data' refers to all types (like unstructured data) and all sources of data (like traditional data warehouses). So, data science is a field that encompasses the entire journey or lifecycle of data. It includes steps such as ingesting data, processing data, applying algorithms, generating insights, and visualizing actionable insights

Big data - This refers to data characterized by the four Vs, namely, high volume, high speed (velocity), high diversity (variety), and high veracity (abnormality or ambiguity). On second thought, we may even say five Vs, since big data adds significant value to enterprise operations! Much like data science, big data also represents the entire data lifecycle, which may cause some confusion - but let us bear with this. This brings me to the next important term.

Machine learning - Machine learning is the ability of machines to learn on their own through data - just as humans do through their environment. In machine learning, machines understand and learn from data, apply the learning and, based on the results, revise previous learning from new data. All this is done iteratively. Here, learning refers to the process by which machines convert the data to insights and apply those insights to take action. As you may have observed, data is key, particularly big data. However, ML can also use traditional data for algorithms like classification, linear regression, clustering, etc.

Data analytics - But, how do machines learn from data?  This is where data analytics comes in. Data analytics uses machine learning algorithms like those mentioned above to uncover patterns hidden in input data. These patterns are applied to new (but similar) datasets to create inferences based on past data. These inferences then become insights for future business actions. To know more about how to get data analytics right, check out my blog on "Data Analytics: Doing it Right!".

AI - In 5 steps

In my opinion, artificial intelligence (AI) has five main steps, which are described below: 

  1. Curate/acquire knowledge using approaches such as natural language processing (NLP), optical character recognition (OCR), etc
  2. Generate business rules using knowledge gained through the knowledge curation process or from insights/intelligence acquired through various machine learning techniques (as mentioned above) 
  3.  Leverage an automation engine that stores the collected knowledge and insights as code
  4. Take business actions either automatically through the automation engine or manually where human intervention is required
  5.  Use the feedback loop for continuous improvement by learning new patterns and un-learning old ones (when needed) in an iterative manner, just as humans learn, unlearn and re-learn on a continuous basis     
O   One clarification to be made here is: Some literature considers the generation of insights through machine learning and data analytics (Step 2) as part of knowledge curation (Step 1). I have intentionally separated these two here. According to me, knowledge curation is about acquiring knowledge from an information source such as literature and existing documents through NLP, search, OCR, etc. Alternatively, gaining insights through machine learning is done by applying ML algorithms on existing machine data. In my opinion, these two are distinct processes of acquiring knowledge. There are also traditional sources of knowledge such as human research, discovery, etc., that can be used to create business rules. This is represented as 'other knowledge source' in diagram and it does not necessarily come under the scope of AI.

I hope this piece has helped you better understand these complex concepts. Any thoughts or suggestions on how to improve these definitions? Please feel free to leave your comments and suggestions below.

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