Big Data & Analytics

The scope of the business analysis is all-encompassing as it works across the broad aim of business improvement with technology or without it. The spectrum of activities that fall under this field is a bit of management, finance, strategy making, dealing with both external and internal customers, ensuring regulatory and compliance issues are dealt with, strategising for better efficiency, cost analysis and everything that falls in between. Uninteresting example is the development of the iPod. It was known that Hitachi had developed a mini-storage of 1 GB data capacity and were unable to take it to the market. At the same time, Apple had developed a music app that could not be used independently of storage capacity. Steve Jobs put these two together to produce the hugely popular invention he called the iPod. He was a successful entrepreneur. The above success story can not only be inspirational, but it can also clearly demonstrate how a smart business analyst can use ML, AI, data analytics and predictive technology to create innovative products and steer the enterprise to success and huge profits. Across the board the last decade has seen technological advances and replacement of repetitive jobs by AI. The need for business analysis and profits for the enterprise will never end. Technological advancements will also continue happening. However, most importantly the need for smart analysts with domain-expertise has far exceeded the supply. With technology implementation, most roles across various departments are collaborative and involve other teams and specialists across the organisation. Scope for experienced and tech-savvy business analysts role is growing exponentially. The job role of the Business Analyst is lucrative and offers great payouts. Business analyst role is one of the front line management roles responsible for a variety of management and strategy processes. The need of the hour is to have expertise in technology related to functioning. This would obviously mean acquiring a new skill set if you are employed or re-training those employees vital to the organisation. Retrain and re-skill at Imarticus Learning. They offer an IIBA-endorsed and recognised Business Analysis Certification programme. The topics covered here help you gain the much-required expertise and immersive exposure to Business Analysis, and the technology powering techniques and frameworks on BABOK 3.0 which has recently been prescribed as the norm.

If you're ever planning to make a career in either of these fields, its important to learn the difference between the two. Business Analysis vs Data Science Difference #1: Definition
Put simply, the overarching goal of a business analyst is to help grow a business in a given market under certain conditions. They have a direct influence on critical financial decisions made in the company. They use data to create and change policies, improve productivity and stabilise systems. A data scientist, on the other hand, is responsible for collecting, processing and reporting findings from a massive data dump. Data scientists convert raw data into structured, meaningful silos which are then used to generate reports on trends or make forecasts. Business Analysis vs Data Science Difference #2: Analysis
A business analyst perceives data, insights and requirements from the perspective of the business and its overall operational systems. A data scientist observes and visualises the relationship between data in a database. While data analysts derive insights from data dumps, business analysts take what is needed to make the business function better and meet certain milestones. Both problem-solving roles are highly data-focused; the difference is that they enter at different stages of the operation and manipulate data to different goals. Business Analysis vs Data Science #3: Skills
While the skills required for both roles might overlap by virtue of dealing with data, in the end, there are some differences. The following skills apply for data scientists:Programming: Where premade software might not be flexible enough, data scientists must be equipped to make personalised changesSoftware: Data scientists must be well-versed in a plethora of tools serving different purposes, from statistics to visualisationData management: From collecting to segregating and organising massive data dumps, data scientists are expected to be adept at managing and manipulating data
When it comes to business analysts, the following apply:Analytical skills: By virtue of their job role, business analysts need to be excellent at analysing data and immediately spotting significant information and leveraging itTechnical understanding: though not as much as data scientists, business analysts are expected to understand databases, basic software and visualisation systems to interpret dataSoft skills: Business analysts need to network and brainstorm with multiple key players, so t.