In today’s world, data is becoming the most valuable asset of businesses. Following the rapid upward movement in technology, most organizations are choosing to go digital, resulting in the generation of large data volumes. Unlike other organizational assets which can add immediate values, big data’s value comes from the insights that can be extracted from it. Equipped with the knowledge of how data functions, managers can leverage their data to derive value hidden in the data, in form of insights to achieve their business objectives. For instance, these objectives could be shortening the turn-around time for services, creation of new products or services that customers actually want to buy, reduction of organizational cost, improvement of sales, improvement of profit margins. Although the emergence of artificial intelligence can be traced to several decades ago, there is an accord on its importance being critical to the development of our society, especially with recent machines built with the capability to learn, reason and adapt to varying circumstances. It is due to these capabilities that recent machines are able to record unprecedented success when learning to solve complex tasks in our society. Big data analysis, machine learning, and artificial intelligence knowledge can help management make data-driven decisions to achieve these objectives and many more. As great as the values derived from big data sounds, the question arises: why are all businesses not leveraging it? This report aims to explore the world of big data analytics, machine learning and artificial intelligence, and how they can add value to business, its strong-hold and challenges.
Data, Data Analytics, Machine Learning and Artificial Intelligence
These four concepts are not as disconnected as they seem, one concept tie them all together ‘Data’. Data are quantitative or qualitative observations collected for the purpose of information gathering (Statistics, n.d.). Only a couple of decades ago, before the advent of Internet of Things (IOT) and the current pace of technological advancement, these data used to be very minimal and could be managed and stored easily. However, since the early 2000s, data volumes and velocity has soared high. This has given rise to the new name ‘Big Data’. Big data refers to the volume, variability, and velocity with which valuable data is collected and gathered from authentic sources (Walker Richard S. and Brown Irwin, 2019). In today’s business environment, organizations explore the various big data they are able to collect via their day-to-day business transactions (some businesses get some necessary data from outside sources) to get more insights about their customers, what they really want and how those needs can be met at a profit (Provost and Fawcett, 2013). This exploration is known as data analysis. Data can be analyzed to help businesses achieve varying objectives. It can be descriptive, predictive, or prescriptive (Leung et al., 2005). For example, predictive analysis can explore available data to forecast future concurrences, descriptive analysis gives more insights about what the data is intended for, prescriptive analysis is more proactive to guide decision making towards a desirable future. The surge of this big data has created the need for more sophisticated tools to manage the data and derive value from it, at the heart of these tools is database (Taulli, 2019). As this data collections process continues and computers are made to work on this and their output becomes more accurate, this becomes ‘Machine Learning’ (Cielen and Meysman, 2016). For example, the more you watch Netflix, the more accurately the movie suggestions it gives. Machine learning is a subset of artificial intelligence. Artificial intelligence is a stage in computing where machines are able to mirror the human brain to perform simple tasks. However, before a machine can be said to have artificial intelligence, it has to go through the deep learning stage where it is able to spot patterns in data and mimic human behavior (Goodfellow et al., 2016); this process is a continuous one. For example, the face recognition security feature on smart phones is an artificial intelligence that gets better with deep learning. The more the feature is used, the more its able to recognize your face easily. Another use of this in our day-to-day life is reflected in our online activities. Most online platforms like Facebook, Twitter, Google, Instagram have built-in artificial intelligence that is able to give every user personalized suggestions based on their online activities.
Leveraging the Benefits of Artificial Intelligence
In order to gain some competitive advantage in this era of high customer expectation, fierce competition in the business world and the dynamic nature of technology, businesses are worse-off without leveraging technology (Cenamor et al., 2019). For example, imagine a business venture in a technologically advanced country like still insisting on only cash payments. Such venture would automatically put itself at a disadvantage because it has limited itself to only cash-carrying customers, which are usually very few and will purchase a limited amount of goods and services. Artificial intelligence has the capacity of uncovering new knowledge independently by asking and refining intelligent questions (Dhar, 2013). In health care system for instance, if fed the right swath of data set, artificial intelligence can discover if a certain group of people, say young people, are becoming increasingly obese, and then come up with hypothesis to test the reasons for the trend, if it due to specific drugs, lifestyle, diet, income levels, educational status, hereditary and other factors humans may overlook. This is only a fraction of the possibilities of artificial intelligence. Artificial intelligence can also be leveraged to boost the competitive advantage of an organization via competitive intelligence. This process involves the collection, analysis, interpretation and extraction of useful competitive environment data when they are required to make data driven decisions (Cielen and Meysman, 2016). This technique has been in practice for many decades. According to records, its intellectual origins can be traced to Michael Porter. He used the competitive intelligence technique to analyze the competitive intelligence of industries in 1980 (Fourie, 1999).
Downside of Artificial Intelligence
As with everything else with great benefits, artificial intelligence has its downsides. Most of these downsides are surrounded around security and social issues. In health-care Artificial Intelligence systems, for instance, as great as the benefits are, there is danger in creating and using their decision outputs that may not have justifiable, legitimate or even give room to access detailed explanations behind the output (Gunning et al., 2019). In order to get to get the most of artificial intelligence, it is sacrosanct that the supporting explanations are accessible to alleviate doubts, especially in fields like medicine where precision is crucial. Some other fields where this level of supporting explanation is needed is security, transportation, finance among other. Another major challenge of artificial intelligence is the concerning its social acceptance and the accompanying fear of possible unemployment. As artificial intelligence gains momentum around the world, there is growing excitement about machines replacing human input in manufacturing, retail, finance, hospitals, and any other business sector that will one day be replaced by machines. From an economic perspective, organizations are worried about cost and profitability as artificial intelligence does not come cheap. It involves a size-able initial investment (A. Campion et al., 2020). Also, artificial intelligence is not free from its technological challenges, which includes transparency issues in its algorithms, limitations in interoperability, difficulty in processing unstructured data.
Towards Ethical Artificial Intelligence
Efforts are being made to make to alleviate the downsides of artificial intelligence for better sustainability. There exist five measures for the certification of an artificial intelligence model and robots: functionality, significance, rights and responsibilities, downsides, and threats (Tasioulas, 2019). However, focus is usually on the first- functionality. This is what brings about most of the downsides of this technology. In recent years, pressure is being mounted on designers to build artificial models more ethically by making the models more transparent and easier to interpret (McDermid et al., 2021). These ethical pressures will force the designers of artificial intelligent models to pay attention to the other factors, especially the downsides and threats of such models and robots.
Conclusion
Generally, people are not quick to adopt new technologies, especially when there is limited knowledge about them in terms of interpretation, origin and trustworthiness. However, the increase in demand for ethical and sustainable artificial intelligence should encourage business to come on board as the benefits are limitless. For a business looking to grow its sales, revenue, market share, customer base, and also reduce its cost of doing business, it would be to its advantage to explore the possibilities of big data, data analysis and artificial intelligence is offering.