You might all have heard the term big data and AI by now as these are the hottest trends in Data Science in today’s generation.
Big data refers to a large set of unstructured, semi-structured, and structured data from different sources like a customer database, medical records, business transaction systems, social networks, mobile applications, and even scientific experiments.
Hence, with all this information, big data is getting bigger and thanks to machine learning and AI, scanning these data has been more accessible than ever before.
Using Big Data across Industries
These days big data is used in almost all business industries as the benefits are tremendous.
Big data in business is used to raise profits, promote products, develop better business strategies, reach the target customers, explore new markets and trends, optimize workflow and even lower the cost in general.
However, big data alone is insufficient to gain all these benefits, and that’s when AI, machine learning, and cloud services step in.
From retail, marketing, advertising to banking and finance, e-commerce, telecommunication, resource mining industries, transportation and logistics, and other business management, big data are now in great use.
Business owners are constantly investing in big data solutions to optimize their operations and maintain data traffic.
Likewise, vendors are embracing big data solutions for the better supply chain management.
Finance and Banking
The finance industry widely employs big data and analytics to become more dynamic, customer-centric, and valuable, and reduce risk. As a result, they use Big Data solutions to collect extensive data about their clients, including financial history and other behavioural factors. Analyzing this data enables financial institutions to determine whether or not to extend credit to a specific client or determine what types of deals and services customers require. This also assists banks to instantly respond to customer queries, requests and disputes, and solve their problems quickly and efficiently.
Banks use big data for sentiment analysis and high-frequency trading. Furthermore, Big Data techniques enable banks to implement sophisticated risk management systems that provide real-time risk calculation enhanced by artificial intelligence and machine learning. These complex systems return analysis results quickly after receiving input data, reducing client wait time significantly. As a result, clients learn about loan application decisions almost immediately. Because high risks and even higher competitiveness characterize the banking industry, Big Data software provides invaluable benefits. However, to make the best use of Big Data, a company must also invest in infrastructure that provides adequate computational power, storage capacity, data throughput, and security.
E-commerce and Retail industries
The significance of big data for an e-commerce business is self-evident: the more you know about your customers, the more profits you make from your e-store.
Data on real-time customer behaviour, purchasing history, and preferred products enable tracking of high-demand products and forecasting trends to be the first in the market to introduce best-selling products.
Pricing analysis, inventory management, and customer churn prediction are just a few of the other things that technology can do for businesses.
The key to success is to provide an exceptional customer experience that entices customers to pay a higher price, and this is where technology becomes your business partner.
On the other hand, the primary goal of any retailer, whether online or offline, is to learn how to predict customer behaviour.
A business can handle the increasing rush of competition and hence big data is important in the retail and eCommerce industries.
Yet, as the world grows and gets more globalized, it becomes harder to make sense of the various data floating around.
Minor details like likes, shares, posts and comments can have a significant impact on understanding customers’ behaviour and sensor-generated data can reveal hidden trends, allowing control over the current situation and predicting future ones.
Manufacturing
Manufacturing firms need big data and AI for a variety of purposes. They can integrate the raw material procurement with its production schedules, align orders from retailers.
Having a comprehensive supply chain management system is only possible when the data across all production levels are collected and shared inter-departmentally and inter-organizationally.
This will enable factories to maximize their effectiveness at meeting customer orders on time while minimizing inventory costs.
It is widespread to find Supply chain management systems that place automated orders when inventory reaches the “reorder point” without human involvement.
Big data and AI might also be utilized for quality control because AI can detect quality issues much more accurately and efficiently than human beings, as well as prevent humane errors.
It can save manufacturers significant costs, as potential defects can be identified very early, even before the production of the items, and rectified, reducing wastage.
Besides, complex production procedures can be performed with the help of AI, which might take much longer for humans to perform with lesser accuracy.
Forecasting the future
We can use big data and AI to forecast the future because a larger volume of data, different levels of trends, variables, and constraints can be all analyzed using these tools.
This enables a better quality forecast because the human brain cannot efficiently process so many dimensions scientifically.
Humans are naturally biased and prone to forecasting heuristics.
The use of AI and Big data can mitigate this risk and assist human decision-makers and forecast on their own.
Bangladesh in terms of AI implementation
In Bangladesh, there are some uses of AI, but it is still at the embryonic stage.
Organizations like Gaze have intended to bring AI tools such as face recognition and real-time traffic surveillance into our everyday lives.
Online platforms are growing in Bangladesh, and these platforms often use AI tools, such as chatbots, facial and photo recognition, personalized recommendations, and so on.
As these platforms grow, there will be more scope for integrating AI into business models and processes and increase demand for AI services.
Bangladesh has not effectively integrated AI due to many social, infrastructural, financial, and bureaucratic limitations. It is still a long way from adopting modern technology entirely and implementing it in every aspect of life, let alone artificial intelligence.
People tend to be slightly averse to change, technology and sophistication, so it has to be considered when designing the technology.
Sometimes, great ideas and products do not permeate the hierarchies down to the masses, and massive misunderstanding can be created about the technology, making people uncomfortable with it even before using it.
A more market-oriented approach can bring higher success for AI companies.
However, the Bangladesh government wants to emphasize on the importance of information and communication technologies (ICTs) and in improving efficiency and productivity across all industries. To make public services more transparent, big data analytics can be useful while expanding e-government in all possible sectors, including agriculture, employment, economy, health, education and transportation. Such as, NID biometrics has the potential to change the way government services are digitized.
Passengers, for example, can book tickets online and have NID authentication by integrating with the current railway reservation system; public information can be integrated with ATM security to prevent ATM fraud; e-KYC can be used to link all financial services to a single NID; and also the electronic health record can be linked to modernize the system. In the finance and banking sector, big data sets collected from mobile money services can provide deep insight into spending and saving habits across sectors and regions; additionally, digital payment histories can allow individuals to build credit histories, qualifying them for loans and other credit-based financial services. Hence, the opportunities for using big data in Bangladesh are endless.
Companies such as Abelling, Nascenia, dhakacolo, eGeneration, data edge limited, Atom AP Limited, LightCastle Partners Ltd, Azolution Software & Engineers Limited, etc use AI and big data to improve their client services.
In the Bangladeshi context, there have been concerns around fraudulent e-commerce practices by platforms such as Evaly, E-Orange, Dhamaka, etc. AI and Big Data can also assist regulators in controlling the activities of these platforms, which take advance payments from customers with the promise of hefty, lucrative discounts and cashback. However, eventually, they ended up with enormous debt to customers and suppliers that they were unlikely to meet with existing assets. Such a situation could be avoided with big data as AI enables transparency of the movement of products from the warehouse through the intermediaries to the final customer and handles a large volume of customer requests and queries in a short period. It also would benefit regulators to keep track of the financial situation of these organizations and be warned of potential fraudulentpractices. Besides, taking action quickly according to the data might save investments of thousands of people. For example, requiring a monthly or quarterly update of financial statements in a secure government portal and scanning for any abnormal or suspicious figures and financial ratios could have been helpful for decision-makers to take prompt action.
Can machine learning complement AI?
AI and machine learning are inevitable. AI is a broader term that refers to the creation of intelligent computers that can imitate human learning ability and behaviour. In contrast, machine learning is an application or subset of AI that allows computers to learn from data without being explicitly programmed.
The most straightforward approach to think about their connection is to see them as concentric circles, with AI on the outside and ML on the inside because ML also involves building algorithms that cover every scenario, specifically, of the learning process. The critical issue is that they both rely on computing as a language for intelligent action.
As a subset of AI, ML automates AI-based tasks with minimal human involvement. The learning process is automated and enhanced depending on the observations of the machines all through the procedure. AI is fed high-quality data, and various techniques are employed to construct machine-learning models to train the AI on this data.
The type of data determines the algorithm used and the task to be automated. Machine learning and AI complement each other, and the next innovation will be achieved not just by developing each of them further but also by integrating them.
Will robots replace the human race?
Robots with artificial intelligence can be in place to replace human beings at decision-making levels, so they are interlinked with each other.
Previously, robots often replaced physical labour, making physical jobs redundant. However, the intricacies of the human brain ensured that robots would not be able to replace the analytical and decision-making capabilities.
AI has challenged that notion, and it has brought challenges even for employees with critical thinking roles. If fully developed, this technology could replicate human thinking because the possibilities are endless.
However, one should not forget that AI is also designed by human beings, so are robots. At the end of the day, some human beings will still be in control of AI technology, regardless of its advancements. It is unlikely that robots will replace the human race altogether. A more likely scenario is that the divide between those who can afford advanced technology, and those who cannot, will vastly widen, and one group of the human race will vastly overwhelm the other group with their technological dominance. The symptoms for this have been presented in the response and recovery from the current pandemic.
Big data and AI are becoming inevitable parts of businesses and industries. As big data is used to predict customer behaviour, we can indeed expect that big data and AI will go hand in hand and make our lives a lot easier in the coming days.
