Data science and artificial intelligence opportunities and challenges
Data science and artificial intelligence opportunities and challenges
Artificial intelligence induces enthusiasm, but it is looked upon with a certain degree of caution as well. Machines are developing basic human capabilities, bringing productivity to work and comfort in life.
AI has now successfully transcended the sphere of innovative labs, and now is looked upon as a technology that renders tremendous possibilities for inducing a transformation. There is however a number of challenges posed by Artificial Intelligence before its true potential can be implemented across a number of domains. It would work the best by leveraging AI to the best possible advantage across the right areas.
Lagging in Accuracy
AI is lagging in provability, and it is difficult to define how AI helps machines make decisions. AI predictions are not accurate as the entities involved would prefer. One is not sure that the decisions made by AI are based upon the right parameters. Hence AI must be presented in a format that is transparent and explaining. A number of enterprises are hence turning to Explainable AI.
Security and data privacy come across as another significant challenge for AI. In order to learn and make reliable decisions, AI applications must have access to a large amount of data. The data may be sensitive in nature. Identity theft and data breach hence come across as a significant concern. European Union or EU has hence come up with General Data Protection Regulation (GDPR). This is driven by a large number of machine-made decisions based upon individuals’ personal data. Federal Learning is another AI technology which is cited to bring about a change in operational methods for AI. It lets data scientists develop AI in a way without risking data security.
AI systems are also subject to the quality of data that they are trained over. There may be ethnic, gender or racial biases in bad data. This becomes a significant issue wherein algorithms make a decision over whose loan is sanctioned, and who gets an interview call. It is these biases that hence need to be toned down. But they will come across even more prominence when machines keep getting trained over bad data. It is hence important that algorithms used to train the machines are explainable. Microsoft is hence working upon the development of a tool that identifies biases across a series of an algorithm. This will help prevent discrimination against a set of people.
Data scarcity is another issue that makes AI difficult to implement effectively. While enterprises have access to much more data than they did earlier, the data sets that are relevant to machine learning are rare. Labeled data is required for the training and is ingestible for machines to learn. It is this labeled data which is hard to find. In the future, as complex algorithms are generated by themselves, as induced by deep learning, this is a problem that will enhance.
There is a positive aspect associated with this nevertheless. Organizations now invest in design methodologies wherein machines are empowered to learn irrespective of a shortage of labeled data. Transfer learning and Active Learning are some ways in which AI algorithms in the next generation can help resolve this issue. The right path forward for AI is hence in a manner that prediction capabilities of machines driven by AI are complemented by human judgment and intuition.
Data science is the superset of which, AI is a part of in many ways. Data science, in general also faces its own set of opportunities and challenges at this point of time. Let us take a look at some of them.
The prime drivers in data science at this point of time are the Internet of things (IoT), Cloud computing and Cyberphysical systems. Computer-generated data, along with collaboration, and connectivity are the trending topics in data science with reference to the present-day scenario. Prospects are bright for the future of data science because we have come across an explosion of data in recent years. This trend is only going to accentuate over the years, and we will come across even more data, which will fuel the growth of data science. The use cases that we will come across will be new and innovative. In the years that follow, IoT will come across even more prominence, primarily in automotive, mining and aerospace sectors. There are presently 7 billion devices connected to the internet, and the numbers will enhance to include 21.5 billion devices across the next 7 years.
Social Media is another source from which a significant amount of data is derived, and every single day, we upload videos that would span 65 years to view individually over YouTube. Similarly, Facebook generates 4 million likes with each passing minute. Apart from this, some primary sources of data are financial, surveillance data and payment transactions. This data will accentuate machine learning and bring new areas to fore in terms of places where this tremendous data can be applied. Data science will be in high demand. With advances in data science and a prominent influx of data, machine learning too will be on a rise. A large amount of data will bring more prominence to deep learning. ML tools are directly powered by data science.
Machine Learning brings to fore capabilities that were difficult to imagine a few years back. We may be in a position to do a search through a million hours of video using a simple search. The surveillance may call for no more than querying the footage. Another example is more basic, and ML could help enable a better analysis of human handwriting. There are numerous cases wherein ML algorithms perform even better than human intervention. This is a trend that will enhance across the years to come. The algorithms that we come across over the years to come will be improved, and this makes ML gain more prominence across new and unforeseen areas. ML would come across a technology that will deliver a competitive advantage to brands that avail it. Data Science Industry faces its own sets of challenges as well. Even while there are the number of challenges faced by the data science industry, the numbers of organizations who are now turning to data and analytics for resolving complex tasks is increasing at a phenomenal rate. They are resolving tasks that seemed difficult earlier, because of the size of data sets, their uneven distribution and the disparity among them. The challenges faced by the data science industry broadly pertain to hiring the right talent, addressing security concerns and getting the data organized.
The talent shortage is an issue that a number of companies have to deal with. There is a shortage of skills in the man force. At this time, there is a high demand for professionals who have a good understanding of complex analytics projects. While they must be possessed with the right kind of analytics skills, they must have good domain knowledge as well. The ideal scenario is when the talent has a business, statistical and programming knowledge in the right mix.
The right data for use
Figuring out the right data for use and sizing the data right are substantial challenges in the data science industry. While the right data is not available or is difficult to select, creating the right model comes across as a natural difficulty. The data available is characterized by a large volume and velocity. It is at times difficult to figure out the ways in which the data would come in a usable form, and help make business decisions.
Actionability of the data is then lost and results in data paralysis. In order to make the analytical model robust, it is of significant importance to capture the data and work upon the noise. Data cleaning keeps the models accurate. Organizations very often come to a juncture wherein they are required to ask themselves about whether or not they are well equipped to make meticulous use of significant amounts of data. They also need to consider if each of the data points is going to be utilized. The problem that arises is that time, money and efforts are utilized generously without recognizing that who is going to consume the data, and in what ways.
Consolidating the information
A feature of data across segregated industries is that it is overflowing and found in a scattered manner. Consolidating the information comes across as a challenge in this case, and a number of organizations are required to have to put up with this challenge. They put internal data systems to avail for the same.
Collecting the data into a single purview is also a challenge. It is hence important to have a way of viewing the data in a unified manner. The information can then be enriched by infusing data elements through analytics.
Communicating with the end-user
Communicating with the end-user comes across as a challenge for the data science industry. While data analytics and data science have gained a significant amount of prominence, the end-user must be informed over aspects such as what is it that the data can do for them. It is only when the end-user stays informed over the capability of data that they can recognize how the data works for them beyond reporting, aggregating and counting. The end-user over here refers to the companies and entities that use the data. It may come across as a challenge to convince these companies to make a shift towards a decision-making process which is driven by data. A right way to go ahead with the same is to provide some use cases, which put a focus over the impact that the data analytics has.
Data analytics solutions deliver results that can be used by businesses to make way for business process transformation. Defining the future process that is driven by data analytics also calls for a great deal of commitment and involvement on the part of domain experts.
Merely defining an analytics roadmap is insufficient. Its implementation is what delivers actual results. It makes sure that a project proceeds in the right direction and brings in success for a brand.
Expressing the use of data science well to the organization that would ultimately be using the model is very important. Whenever an organization chooses to go ahead with an Analytics model, it is going to be intricate and complex to start with. This may be difficult for the end-user to understand.
But to make requisite use of the functionality of the model, such that it delivers measurable returns for the organization is also important. So the end-user can be explained more about the model through the form of storytelling. In order to make sure that the end-user understands the functionality of the model well, the data scientists and related professionals must have fine skills in storytelling. This makes the data and the processes understandable, and one can recognize how he can reap maximum benefits from machine learning models that are available.
Executing a data science model
Keeping the work highly directional is a significant challenge that comes to fore when creating data science models. It is best to have a high degree of clarity over how a data science model stands to benefit a business, and what business problems it would resolve. The process of getting actionable results using a data science model can be a tough deal for an organization. It is best if these results are delivered in real-time.
It is very important that the professionals who oversee the execution of data science model are possessed with the right capabilities for troubleshooting. This is yet another reason why hiring the right talent for data science is very important.
Identifying the right use case
In the data science industry, identification of the right analytics use cases is of significant importance. The numbers of use cases that one comes across are tremendous. Identification of the right alternative amongst them makes all the difference for making sure that the insights that they yield are accurate and actionable. The disruptions may span across the entire chain. If the use cases selected are not proper, and if they deliver insights that do not have the desired levels of accuracy, it implies that the insights will not yield the best possible returns if they are used to make business decisions. While data security of data science models is a concern, so is the implementation in the form of boardroom interactions, such that the organization sees the data materialize into plans that deliver actual results.