Companies are expected to become more transparent in the next few years, shedding some of their mysterious natures. These new developments stem from the widespread and ever-increasing prevalence of data analytics and advanced analytics. However, not all companies are eager to become data-driven companies. Like the two previous graph trends (increasingly-cheap data and data-enabled organizations), this trend comes about as the result of both the availability of data and its relevance in the business world.
Data Science & AI
Data Science Artificial Intelligence can process and make sense of increasingly large volumes of data that can only be processed and transformed by machines. As the data collected continues to grow in volume and complexity, this is where we are headed, given the potential improvements in data mining and the associated business benefits. A recent report from Gartner indicates that data is required by 75 percent of companies to identify data trends, improve business processes, increase productivity, and boost revenues.
Machine learning technology is creating new approaches to data processing. Machine learning technology may reduce or eliminate manual processes in existing business processes. This is being done in fields like financial services and healthcare. More recently, machine learning technology has become applied to many other fields, including supply chain, mining, manufacturing, transportation, education, and telecom.
However, business analysts and data scientists have warned that some of these machines and systems are not efficient. They are not flexible enough to deal with the peculiarities of real-world business processes. Many business analysts say that data scientists often build solutions that are not flexible enough to meet the needs of real businesses.
Surprisingly, these warnings are often coming from the data-enabled companies themselves. They say that machine learning may not be the best approach for processing data. Although some of these companies are implementing machine learning to process large amounts of data, in many ways they are also changing how they organize and sort the data they collect. They may also be taking some of their data from traditional systems (e.g., from enterprise data warehouses) and placing it in machine learning models.
The takeaway is that machine learning may not always improve the efficiency and effectiveness of business processes in the long run. It may be more accurate to say that businesses are putting machine learning and other data analytics to work for them. They are trying to improve the efficiency of their own business processes, not just put machine learning into them. However, if we consider the value of data analytics, machine learning may have a more dramatic effect on the business world in the next few years.
Data-Enabled Organizations and Companies Increasingly Transparent
Finally, companies are becoming more transparent in the presence of data analytics. This trend stems from the demand for transparency and the prevalence of data analytics. We are now building data-enabled organizations, where we are no longer surprised by the information that is revealed to us. Our experiences in our personal lives often carry forward into our professional life, where we are also frequently surprised by the information that we receive.