World roam around the data. While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.
The responsibility of data analysts can vary across industries and companies, but fundamentally, data analysts utilize data to draw meaningful insights and solve problems. They analyze well-defined sets of data using an arsenal of different tools to answer tangible business needs: e.g. why sales dropped in a certain quarter, why a marketing campaign fared better in certain regions, how internal attrition affects revenue, etc.
Data analysts have a range of fields and titles, including (but not limited to) database analyst, business analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst. The best data analysts have both technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients.
Data analysts can have a background in mathematics and statistics, or they can supplement a non-quantitative background by learning the tools needed to make decisions with numbers. Some data analysts choose to pursue an advanced degree, such as a master’s in analytics, in order to advance their careers.
Data scientists, on the other hand, estimate the unknown by asking questions, writing algorithms, and building statistical models. The main difference between a data analyst and a data scientist is heavy coding. Data scientists can arrange undefined sets of data using multiple tools at the same time, and build their own automation systems and frameworks.
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 visualizing the findings in compelling ways. It’s the depth of data modelling that creates value for organizations 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 organization’s 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 formalize 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 optimize delivery routes, accurately forecast the supply and demand cycles, and lower the delivery costs.
Customer segmentation – customer data can be analyzed to identify the most and least profitable customers. Businesses can use this information to create customized offerings and services to drive optimum profit.
Review mining – customer reviews can be analyzed and converted to structured data to derive insights into what customers like, dislike and if they would recommend the product.
As many companies from MNC to start-ups have made way towards success using this giant technology. We Whizian are proud to be part of it.