Data Science or Analytics is an art of leveraging data streams to improve decision making. It uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
A few decades ago, with the emergence of programming languages, we were able to create abstractions of real life entities and allow computers to operate on those abstractions. Today, we are able to go further and create abstractions of decision making processes. Closer the abstraction is to reality, more is the value created.
Breakthrough insights can be gained from data by asking the right questions, manipulating data sets and visualising the findings in compelling ways. It’s the depth of data modelling that creates value for organisations that thrive for data driven decision making. The other source of value is collaborative decision making. When you have data models that fit closely to bring out important metrics, decision making becomes faster and accurate. It brings clarity to decide owners of key metrics without making assumptions.
If we take ecommerce organisations as an example, typically the Chief Marketing Officer would own the KPI for driving traffic to the website. However, a closer look at the data would show that the main driver for traffic and engagement is the choice of brands within each category. CMO has very little control on this.
Apart from scientific governance, data can be leveraged for diverse applications such as:
- Growth hacking - right data sets can be used to uncover commonalities between types of users that are either successful or unsuccessful with a product. It can also be used to formalise strategies to generate exponential traffic growth, while preserving traffic quality, and user loyalty.
- Logistics – various types of data are collected at supply chain touch points such as customer information, the number and types of items, carrier data, delivery information, etc. Data science can provide invaluable insights to optimise delivery routes, accurately forecast the supply and demand cycles, and lower the delivery costs.
- Customer segmentation – customer data can be analysed to identify the most and least profitable customers. Businesses can use this information to create customised offerings and services to drive optimum profit.
- Review mining - customer reviews can be analysed and converted to structured data to derive insights into what customers like, dislike and if they would recommend the product.
Organisations with a clear roadmap, sustained sponsorship and freedom to experiment are best placed to run a successful analytics practice.
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