Artificial Intelligence and Business Analysis

The world has now become a whole with technology and is changing at an unprecedented rate. This change creates new needs. With the rapid development of technology, the way of doing business has begun to reshape in the era of digital transformation. Technologies such as artificial intelligence, cloud computing, data analytics and blockchain are among the most important technologies that create this effect.

Son on yılda Yapay Zeka, Makine Öğrenimi araştırmalarının yeniden ortaya çıkmasıyla birlikte, üretimden pazarlamaya kadar her endüstri sektöründe robotlar, dijital asistanlar, akıllı makineler, otomatik botlar ve uygulamaların kullanımında hızlı bir artışa tanık olduk.

Yapay zeka ve robotik alanındaki hızlı ilerleme birçok meslekte insan iş gücüne duyulan ihtiyacın azalacağı yönünde sinyaller veriyor. Akıllı makinelerin yapay zeka dünyasındaki iş analistlerinin yerini alıp alamayacağı konusu ise hala belirsizliğini koruyor.

Öncelikle Yapay zekanın ne olduğuna bir bakalım.

Artificial Intelligence is a broad term that describes how computers are programmed to perform certain tasks that can be performed by humans.

Artificial Intelligence relies on machine-based learning driven by data inputs to simulate human-like tasks. Rather than writing code, incremental algorithms are trained to adapt to our likes, dislikes, and behaviors (for example: Amazon product recommendations and self-driving cars). This is not intelligence in the sense that computers are smart and now understand the meaning of life; It is a phenomenon in the sense that they improve their decision-making abilities to predict actions based on large datasets.

You've probably heard many terms related to Artificial Intelligence. According to IBM, artificial intelligence developed in three main stages,

  • Basic Artificial Intelligence with clustering algorithms (first attempt at unsupervised systems learning), decision trees and rule-based systems (1950-1980)
  • Machine Learning (1980-2010) with neural networks (multi-layered algorithms integrating a feedback mechanism to learn from mistakes) and deep learning (a family of algorithms based on complex algorithms and unsupervised learning)
  • Cognitive Learning (2010-present) aims to simulate human thinking using machine learning technologies and other cognitive science disciplines.

Business Analysis, on the other hand, relies mainly on collecting and interpreting information using very specific skills and a fairly standard workflow. Business analysts are responsible for bridging the gap between IT and business by using data analytics to evaluate processes, identify requirements, and provide data-driven recommendations and reports to managers and stakeholders.

Business analysts interact with business leaders and users to understand how data-driven changes in process, products, services, software and hardware can improve efficiency and add value. They must articulate these ideas, while balancing them with what is technologically possible and financially and functionally reasonable.

Basically business analysis

  • Data collection from different sources
  • Analyzing data to generate useful insights
  • Interpreting new information
  • Suggest action plans
  • Implementing plans
  • The control of the results of the implemented plans may consist of stages.

By following these stages, we can achieve a high level of performance in our work. At the start of the job, we perform routine tasks to collect and analyze data quickly and accurately. Based on our experience, we can determine the best action plans. We then help stakeholders understand and implement these plans.

If we look at the future evolution of the Business Analyst,

In the next 10 to 20 years, with the myriad contributions of AI, we can expect to see a massive rebound in economic success, at least in the developed part of the world. If humans and machines learn to work together now, "physical-digital teamwork" could soon transform workplaces around the world. The availability of low-cost hardware and advanced software has the potential to develop a new world of business analysis, where essential routine work will be performed by machines and innovative technologies.

Looking at the current AI research and the rapid growth of automated systems in business analysis, we can predict that within the next decade the human Business Analyst will collaborate and collaborate with AI.

Intelligent systems will be able to make decisions like business analysts by working and learning with humans. AI and machine learning will shorten the chain between customer data, customer insights and decision-making processes. In this case, will artificial intelligence replace analysts? The answer is both yes and no. Although not replacing business analysts in the future, we can say that analysts will be a member of automated systems for advanced analysis. AI will easily replace low-value jobs within the business scope, so analysts will focus on business analysis to create higher value.

AI programs often require huge amounts of data to be successful. It therefore acquires its information, for example, from audio streams, video and photos in real time. He has no knowledge of how to define and communicate the requirements around him with such resources.

This is one of the important opportunities for adapting business analysis to AI projects. The next question in business analytics and AI is how we can use AI to do a better job as business analysts. The business analyst will create the necessary business processes and requirements to enable the AI ​​to learn on its own and understand the content. The business analyst can design the right data repositories and interfaces that AI will need.

Business Analysts' strengths against artificial intelligence include analytical thinking, problem solving and communication skills, behavioral competencies and business knowledge, interaction skills.


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