5 Steps to True Data Science

I would try to debunk complex defintion of DATA SCIENCE and give a very simple and easy to understand view of data science process.

I have attempted to create a 5 step framework to reflect a true data science process which covers all aspect of data science and bring a end to end broader perspective on Data - Information - Actionable Insight & recommendation leading to broader business impact.

Below are the 5 steps to true data science

1. Questioning : Its all about business driven curiosity. Irrespective of Position in the organization, anyone with analytical and problem solving mindset can ask a very relevant business question and to support their thought process there needs to be a curiosity framework to identify, align, prioritise and define problem statement into some shape. Process of aligning problem statement with business objective and outcomes is one of the most critical step in Data science process. Curiosity Framework allows business teams to pick problems which has maximum impact to the organization and form business relevant hypethosis. Enable them with Identification of right data sources which helps Data science teams with Data preparation process in data mart or data warehouse.
2. Engineering : As it sounds, its a true Data Engineering process involving Data Platforms & infromation Architecture for end to end Information management lifecycle. Weather you use traditional warehouse tools or next gen big data lakes it all intend to do plumbing (Designing, collecting & combining) of data from various data sources. This is often referred as plumbing process which mainly deals with data processing, quering,integrating, centralizing and maintaining all the data. Based on the defined use case a logical design is crafted which get translated into one or more physical Databases. This process also looks at how the Data will flow through the successive stages involved. This may include things like designing relational or non relational databases, developing strategies for data acquisitions, archive recovery, and implementation of a database, cleaning and maintaining the database.
3. Mining : Data Mining is not a new method and have been in existing from centuries. Data mining is the computational process of discovering hiddern patterns in large data sets leveraging various pattern recognizition methods such as regression analysis.This is the basic premise of any data driven decision making process where historical data sets are used to identify certain patterns. Over the period as data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms, decision trees and decision rules and most recent support vector machines. Data Mining is often found to be at the intersection of artificial intelligence, machine learning, statistics, and database systems. Anomaly detection (Outlier/change/deviation detection) is one of the common task performed to identify of unusual data records, that might be interesting or data errors that require further investigation.
4. Learning : Data Learning is an extension of Data Mining and often combined with Data Mining process. It is often called Machine Learning, Deep learning, artifical intelligence and so on. Data learning explores the study and construction of algorithms that can learn from experiences/patterns and make contextual predictions on available datasets. It is an advanced version of Data mining process where advanced data mining techniques like Association rule learning (Dependency modelling), Clustering, Classification or Regression are applied to predict a desired business outcome. For simplicity of this article i will not get into details of some of the technique.
5. Story Telling : Story telling is the last Mile of Data science process, which focuses on truely converting insight into recommendation and enable action. Just don't show the data but tell a story, map it with business problem, business context, make information represtation intutive powered by advanced visualisation, inforgraphics, dashboards, Power point presentation.Your data holds tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes. Any insight worth sharing is probably best shared as a data story. Below is extract from Forbes article which explains storytelling in a very nice manner. When narrative is coupled with data, it helps to explain to your audience what’s happening in the data and why a particular insight is important. Ample context and commentary is often needed to fully appreciate an insight. When visuals are applied to data, they can enlighten the audience to insights that they wouldn’t see without charts or graphs. Many interesting patterns and outliers in the data would remain hidden in the rows and columns of data tables without the help of data visualizations. Finally, when narrative and visuals are merged together, they can engage or even entertain an audience.

When you combine the right visuals and narrative with the right data, you have a data story that can influence and drive change.

credits /reference: Forbes, Wikipedia