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With modern solutions and the evolution of technology come new challenges and unintended pitfalls. This concept rings true with regard to how data is handled across industries on a daily basis. When we look at the healthcare, finance, retail, transportation, and energy sectors, the information being leveraged is either misused, not understood or not absorbed properly by established systems, and often not being kept as secure as it should be. Adding to this is the aging infrastructure still present in certain sectors, making it understandable why datafication is necessary.
"Datafication" is the process of taking different aspects of our world and transforming them into digital information that we can use. Analog sources of information, such as physical events, natural phenomena, and human behaviors can be processed, analyzed, and stored through the use of computers and digital tools. These sources, alongside other fountains of information like various transactions, social media posts, and machine data, make up "big data." The sheer amount, consistency, and complexity of data have made it a crucial pain point today. However, through the use of open-source frameworks for distributed data processing, visualization tools, cloud computing, Machine Learning (ML), Artificial Intelligence (AI), and other advanced technologies, talented software developers can aid various sectors in understanding and utilizing this data to achieve their forecasted revenue growth, provide significantly improved services, and get ahead of the curve.
One interesting thing to note is the seemingly complex process of metamorphosing analog information into digital, readable code involves equally complex technologies. Internet of Things (IoT) devices are devices fit with sensors that collect data about the physical world. The information gathered, such as humidity, temperature, motion, and light, is then processed by big data technologies, like open-source frameworks such as Apache Spark. Once the data has been processed, developers use Machine Learning to analyze any potential patterns within the clean data, deliver insights, and make predictions. Additionally, by employing Deep Learning, another subset of the grander Artificial Intelligence, different sectors can be enabled with advancements like Natural Language Processing (NLP) and image recognition. With these capabilities, industries can also utilize cloud computing to achieve the flexibility and scalability that is necessary for efficient data processing and storage, effectively allowing organizations to harness the power of digital transformation.
Through datafication, industries across the spectrum of modernity can reap the rewards of valuable insights and improved efficiency, and even drive innovation within their processes and operations. In healthcare, industry professionals face significant challenges managing the copious amount of medical data generated daily. This, mixed with concerns of potential data leaks, has led Grand View Research to forecast a CAGR (Compound Annual Growth Rate) of 21.4 percent from now until 2030 in the healthcare industry in order to meet these challenges and provide improved treatment, patient engagement, and overall operational efficiency. Luckily, the technologies at our disposal today, such as Artificial Intelligence, will make good use of that investment.
This trend continues beyond healthcare. The transportation and logistics sectors can use datafication to reduce fuel consumption, enhance fleet management, and optimize route planning. Plus, professionals within this industry can benefit from real-time tracking of vehicle and cargo conditions.
Within the finance sphere, The Business Research Company (TBRC) has released a report that forecasts Artificial Intelligence utilization within banking will reach $27.76 billion in 2027. This impressive draw from just a single technology implementation speaks volumes about how the process of datafication can be of huge benefit. Managing delicate pieces of financial data securely, ensuring compliance and standards, and addressing potential bias within established systems can be quite simple with the proper understanding of the information at play. Through datafication, AI tools can improve the customer experience, easily detect fraud, and improve investment decision-making while keeping private information secure, as privacy plays a pivotal role in challenges addressed across all of these fields.
Datafication has changed from a possibility to a certainty. The informational needs and regulatory demands that coincide with turning analog to digital can be staggering. However, practically every sector can see the benefits of safe and clean information through the use of fundamental and impressive tools such as Artificial Intelligence, cloud computing, Machine Learning, and other big data technologies. E-commerce businesses can develop targeted ad campaigns; hospitals can provide more personalized care while improving cybersecurity; and banks can secure their customers' information and prevent fraud.
The adoption of AI has seen significant increases across multiple industries. Overall, the revenue forecast for AI development and implementation is expected to reach $1.345 trillion by 2030, according to a report from MarketsandMarkets. This projection is not only astounding but also totally conceivable. Today, our modern processes are being hindered by limited data utilization. Luckily, a team of well-versed software developers can bring organizations that much closer to operational efficiency, increased revenue, and improved customer satisfaction.
Chetu, Inc. does not affect the opinion of this article. Any mention of specific names for software, companies or individuals does not constitute an endorsement from either party unless otherwise specified. All case studies and blogs are written with the full cooperation, knowledge and participation of the individuals mentioned. This blog should not be construed as legal advice.
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