In the data economy, data is king. Today, any business — small, medium, or large — thrives on its data assets. The recent trend of offering data-driven insights as a service to the outside world has opened up a profitable revenue channel for businesses. Cloud computing and hosted analytics have brought data-as-a-service to the desktops of ordinary business users, which was unheard of even a few years ago.
As the global business environment is fast moving toward “all things digital,” as predicted by Gartner a while back, artificial intelligence (AI), machine learning (ML), and deep learning (DL) will play as important roles as Data Science in reshaping businesses across the world.
Data Management vs. Data Strategy: A Framework for Business Success talks about building business models and revenue streams around enterprise data assets. The author also mentions that it is critical for businesses to understand the sharp difference between Data Management and Data Strategy, because the latter facilitates the former through a carefully guided framework of plans, programs, policies, and practices.
Currently, all well-developed business models include a defined Data Strategy to guide data-management activities throughout the organization. In a defined Data Strategy framework, Data Management goes far beyond basic database management tasks and creates a blueprint for data collection methodologies, Data Governance, data access and control, data privacy and security, and much more.
In such a data-centric business environment, it is only normal to expect newer and better data technologies in the global IT market, threatening to displace human data scientists and business analysts in the near future.
In the podcast Demystifying AI and Machine Learning for Executives, McKinsey’s senior partner Tamim Saleh discusses advanced data technologies, especially AI and allied sciences, explaining how they can be best applied to actual business situations. According to Saleh, the human scientists begin interacting with machines via the input data, and this process is often slow and erroneous. In the next stage, when image or voice analytics are combined with ML, the machine adopts more human qualities and starts to behave like an agent.
From Data Science to AI & ML: The Technology Transition
According to a Newgenapps blog post, the emergence of Big Data has helped Data Science projects to scale up fast. These favorite buzz words often trending on technology news sites combine principles of mathematics, statistics, computer science, data engineering, database technologies, and more. Data Science may be viewed more as the technology field of data management that uses AI and allied fields to interpret historical data, recognize patterns in current data, and make future predictions. In that sense, AI and subsets of AI like ML and DL aid data scientists to accumulate competitive intelligence in the form of insights from data stockpiles.
AI can be defined as a broad scientific field with many sub-disciplines — all collaboratively working toward automating business processes and enabling machines to behave more like humans. Fields like ML and DL, though offshoots of AI, have made intense penetrations into the territories of neural networks, thus pushing Data Science into the next level of automation where voice, image, text recognition, and virtual reality have merged to create an awesome “digitized business ecosystem.” Newer technologies related to the basic practices of Data Science and AI are still evolving every day, and now with Big Data, cloud, IoT, edge, and serverless — who knows where it all ends?
The Digital Journey that Does Not Seem to End
Data Science, which remained hidden behind on-premise data centers suddenly started gaining tremendous visibility throughout the enterprise — all due to the emergence of AI. The overnight transformation of business processes and day-to-day decision-making, further fueled by Big Data, Hadoop, and the rise of social, mobile, and IoT channels, brought data technologies to the forefront of business operations. Today, data rules in businesses, and this trend is not about to diminish in the foreseeable future.
Data Science vs. AI vs. ML vs. Deep Learning uses different types of ML algorithms to distinguish the applicability of the algorithms in real-life Data Management projects. One aspect of Data Science is the “business of analyzing data,” which relies of AI-enabled advanced tools like ML and DL algorithms or neural networks to make predictions and forecasts for competitive advantage. That means business data analysis begins only after the data is processed through these advanced tools.
“Data Science” is the more holistic term encompassing the “collection, storage, organization, preparation, and end-to-end management of data,” while the AI-enabled technologies control and facilitate data analytics in business operations. Data Science, artificial intelligence, and machine learning work in tandem to exploit data for a wide variety of business benefits.
A blog post from Data Flair comparing Data Science with AI, ML, and DL contrasts the benefits of Data Science versus those of AI, ML, and DL. The marked difference Data Science and AI-enabled data technologies is probably the learning algorithms, which train on vast amounts of data. The machine learning and deep learning algorithms train on data delivered by Data Science to become smarter and more informed in giving back business predictions. In that sense, Data Science and AI share a symbiotic relationship, a complete give-and-take in both directions.
Contrasting Features between Data Science, AI, and ML
Contrasting Features Between Data Science, AI, and ML contains a sophisticated relationship analysis on the three distinct fields of Data Management. According to this post, it is machine learning that connects AI with Data Science. ML and DL, on the other hand, have a parent-child relationship, while AI and ML may be described to share a similar parent-child relationship. Thus, artificial intelligence, machine learning, and deep learning are hierarchically placed in the data-technology ecosystem with AI at the top and DL at the bottom.
Though Data Science itself is an inter-disciplinary field, when data scientists enter the realm of data analysis, they begin at the top automation level of AI, working their way down to DL with increasingly more complex and more challenging analysis tasks. The neural networks function like the human brain, and intense analytics activities require a “brain-simulator kind of environment” to resolve highly complex business problems.
So, the broad field of AI all its sub-fields may be looked upon as the solution enabler for Data Science.
This post on Springboard blog takes a keen look at the basic difference between a data scientist and an ML expert. The post states that while the ML expert remains pre-occupied with building useful algorithms throughout the project lifecycle, the data scientist has to be a little more flexible in role-playing — switching between different data roles according to the needs of the project. Data Science and AI overlap on “data,” which provides an opportunity to collaboratively build business solutions.
An article by Variance Explained deals with this area of overlap among the three fields. It points out that while AI and ML provide answers to business problems, the data scientist finally comes to build a convincing story through visualization and reporting tools for the consumption of a broader business audience. The business audience may not understand what a random forest is, but once the data-driven story is in front of them, they immediately understand the relationships and patterns among different business components, along with their future impact on business. The data scientist is, undoubtedly, more of the domain expert than an AI or ML practitioner to be able to build the final story from data-driven insights.
The narrow differences between AI and ML are best understood through their applicable “use cases and implementations,” according to Artificial Intelligence vs Machine Learning. AI and ML work together to automate human activities like customer service (digital assistants), driving vehicles (self-driving cars), and offering personalized recommendations (recommendation engines). One benefit of using AI and ML is often understated — the benefit of making huge cost savings by eliminating human workers from these functions.
In Machine Learning vs. Deep Learning, the author points out that in ML, the training algorithms learn from a single layer, while in DL, the same training of algorithms happens in multiple layers, in what is known as “unsupervised learning.” The learning in DL closely simulates human learning conditions.
The Closing Note
The race for research and market applications in artificial intelligence, machine learning, and deep learning is on, and will continue well into the distant future. Businesses that are investing and implementing advanced data technologies most suited for their unique business environments are well positioned to remain ahead of competition through the next decade.
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