Research is an essential process necessary to stay on top of trends and gain a competitive advantage in both business and social ecosystems. Not only can high volumes of data be captured today but with the power of machine learning can be organized, sorted, and analyzed. Actionable insights can then be determined and workshopped by expert researchers and business professionals.
Machine learning, artificial intelligence, and Big Data all play key parts in developing more advanced, nuanced research projects that have the potential to drive businesses forward into a new age. With the data available working passively in response to changes is no longer the only approach. Predictive technologies can better help business owners and executives understand market trajectories and plot a path that puts their company at the forefront of their field.
At the heart of all research is the researcher
Though machine learning, AI, and of course Big Data all play crucial roles in enabling advanced research and analysis, they are ineffective without the critical thinking of a researcher. In order to properly leverage these technologies and transform analysis into action more executives need to consider advancing their skillset into business research. An online DBA can help executives and business owners alike build the necessary skills that will allow them to design and deliver research findings that make an impact both in their company and in their field. The goal of online doctorate programs like these is to give executives the ability to identify and solve complex business problems. Not only do doctorate programs help executives develop the essential research skills, but they guide professionals through their first comprehensive research project.
The role of machine learning and AI in research
Machine learning and artificial intelligence go hand in hand. To date there is no true AI. Instead, artificial intelligence systems as we know them today are machine learning programs that work to adapt based on changing data models.
Machine learning and AI are critical tools used to extract meaning from data. This allows businesses the ability to easily adapt to changing market conditions, improve operations, and develop a fine-tuned understanding of their business needs and what their consumers want.
Extracting data and meaning is only the first step, which is why professionals need critical research and analysis skills to plot and design the research framework and to then make try to form conclusions from the results.
The role of Big Data in research
Big Data refers to the full scope of data collected by every device and the Internet of Things (IoT). On its own Big Data is an unusable set of data that cannot be comprehended. It takes the work of a machine learning AI program to sort, extract, and analyze the data at hand.
Possible applications of machine learning, AI, and big data in business operations
Machine learning (ML) is consistently used in business analytics and has become the go-to method of increasing the scalability and effectiveness of operations. The popularity of AI as a method is growing exponentially and is set to continue to grow in the future. Current estimates by the Accenture Institute for High Performance assume that AI will help double economic growth rates by 2035.
Broad AI deployment across all sectors is estimated to increase growth from 2.6% to 4.6% in the United States of American and increase UK markets from 2.5% to 3.9%. In numbers it is estimated that AI use and development may contribute $9.3 trillion USD in the US market and $814 billion in the UK market.
Machine learning artificial intelligence is becoming increasingly enterprise ready, meaning that it is becoming more widespread in larger settings and, more importantly, is used more ubiquitously throughout departments to increase operations efficiency.
Challenges in machine learning analysis
There are several challenge that researchers must work around in order to successfully adopt machine learning in their own research and business operations. Machine learning is still in its relative infancy, and the very process behind machine learning itself is the premise that it grows as it learns. The more machine learning programs are used the more useful the results.
On top of having a lack of trained professionals that understand how to create, manage, and execute commands within machine learning programs there are also a few significant challenges that businesses will need to overcome in order to properly improve their operations.
-
Poor quality data
Poor quality data is a rampant problem within machine learning. Data may be heavily biased, inaccurate, or data sets may simply be incomplete. While there is little that you can do when you are inputting data from outside sources, working to ensure that you collect the relevant data and that it is correctly catalogued and stored is the number one step to improve the quality of data, and therefore the analysis that can be extracted from said data.
-
Lack of available data
Privacy concerns help protect customers and businesses but when it comes to extracting meaning from data it will complicate data collection. While newer technologies like blockchain may be a solution to anonymize data so that you can work from large demographic data sets it is not widespread enough to be implemented everywhere.
-
Vague goals
Having all the data you need at your disposal is useless without clear parameter’s. Programs need clear and succinct orders in order to extract meaning that is valuable for your business and operations. Setting clear and quantifiable parameters and goals will improve output. Improving your own ability to research and use the program in question and training all who work under you or in the same role is essential to gaining the most benefits from machine learning, artificial intelligence and Big Data.
-
Coding errors
Machine learning is an evolving process. At any point in that process an error can occur that can immediately affect the program or results. Having techs and experts on hand that know how to properly design and manage such a system will help improve data capture and analysis. The goal of AI machine learning is that a lot of the work is automated, but even in autonomous systems engineers and maintenance are essential.
