A high level or 30,000 feet process view of drawing insights from any data set.
Data driven decision or near accurate insights driven decisions are a mix of optimised data sources, right selection of algorithms, right mix of variable & hyper parameters, factoring insights from consumer sales data, promotions and events data, qualitative and qualitative market research, competitor products and its market influence, seasonality, pandemic influence, consensus planning and perhaps few more.
The role of end to end or integrated planning & data capturing is as important for the success of accurate insight discovery so that a company business and data leaders do not miss out on any specific data or business leakages within the eco-system.
Data processing, insight discovery and using the data for the benefit of the business and its customers is compulsory to build a strategic foundation for the business. Businesses today cannot afford to ignore a single channel of acquiring customers owing to the growing competitive threats, and complete washout of a company from a region or territory due to not capturing consumer insights from social functional and emotional aspects!
Given the above context, the steps involved in the whole insight discovery process are
1. Selecting the right data sources and detecting outliers in the data set, and Machine learning/Artificial intelligence can be used to detect outliers in the given data set.
2. Selecting the right algorithm to process the data which has been simplified with the adoption of Machine Learning and Artificial Intelligence, and various Machine Learning models can be co-discovered and co-developed alongside the processing of the data. The Machine Learning Models speed-up the process of algorithm selection and give the near accurate insights with multiple repetitions as the machine process different sets.
3. The more data sets you process on a given use case for your company or for your clients , the role of data scientist to is to first analyse which algorithm is best suited for the processing of this data which is cumbersome takes in-exorbitant amount of time as the data scientist has to process 500–600 iteration per combination(primary data x number of factors affecting the data) perhaps using all the recommended algorithms to derive the closest insight, whereas machine learning models helps to discover the patterns in the data sets faster and helps in scenario planning and choosing the most appropriate algorithm and save time and computing power.
4. The representation of insights in a meaning full way is another important aspect of communicating the value to the stake holders covering the statistical forecasts in multidimensional graphical and tabular formats covering specific insights such as contributing, cannibalising and churning factors.
I hope the article is useful to freshers, aspiring data scientist or companies looking to adopt data science in their business.
I am reachable on rahul_aulak@hotmail.com for any specific insights.
