Incorporating big data analytics and business intelligence into organization strategy in Nepal

Big Data Analytics has been coined the new paradigm of knowledge assets in the 21st century – a fourth paradigm of science. It has triggered a shift from theory-driven models based on hypothesis, experiments and simulations to a model of data-intensive exploratory science. This concept of analyzing trends and patterns in data to deduce insights has been facilitated by the exponential increase in the generation of data. The digital universe is expected to have reached 44 zettabytes in 2020 – 40 times more bytes than there are stars in the observable universe![1] Over 2.5 quintillion (1018) bytes of data are currently being generated per day.[2] The universal adoption of technology that boosts the already large production of data such as cloud computing and the Internet of Things (IoT) is expected to further increase the traffic of data being produced. As a result, it is estimated that 463 exabytes(1 billion GB) of data will be created globally each day by 2025.[3] Much like how natural resources spearheaded the first industrial revolution in the beginning of the 18th century, data will be the largest commodity of the 21st century. Big Data Analytics and Business Intelligence will revolutionize how corporations, governments, academic institutions and research facilities function, ushering a new era of human exploration. Failing to incorporate analytics into the everyday functioning will be equivalent to keeping oneself out of the microcosm of the internet.

In this article, I look to discuss ways in which Big Data Analytics and Business Intelligence are being incorporated in various sectors across the globe, which might serve as case studies for the implementation of similar technologies in Nepal.

What is Big Data Analytics and Business Intelligence?

Big Data Analytics is the interpretation of the data collected; examining large amounts of data to uncover hidden patterns, correlations and other insights such as market trends and customer preferences that can help organizations make informed business decisions. Big Data Analytics allows for organizations to generate insights from various sources including consumer transactions, inventory monitoring, store-based video, consumer preferences, sales management and financial data.

Business Intelligence (BI) meanwhile, is more specific and focuses on insights around business operations and performances. It encompasses a range of areas such as competitor analysis, customer intelligence, product intelligence, strategic intelligence, technological intelligence and business counterintelligence.

The use of Big Data Analytics and BI to make data driven decisions has been found to have increased more accurate reporting, improved business decision making, improved customer service and increased organization revenue.[4] It helps businesses and organizations optimize everything from loss prevention programs to better order management systems to streamlining product innovation processes. A retailer that can use big data efficiently has the potential to increase operating margins by as much as 60 percent. (Tankard, 2012)

Big Data Analytics and Business Intelligence in Nepal

The concepts of data analytics and business intelligence are yet to be fully incorporated into business colloquialism in Nepal. Two decades into the twenty-first century, most of the businesses and firms in Nepal have been observed to still use basic analytics – essentially manually examining numbers in basic spreadsheets to uncover insights and trends. The potential of data to drive high growth in several sectors of the Nepalese economy remains high yet untapped. Strategic deployment of data can reinvigorate the struggling manufacturing sector in the country, facilitating better asset performance and reducing supply chain costs through optimization. Similarly, it can boost the country’s ailing health sector by reducing costs for lifesaving diagnostics and providing efficient customized diagnostics at a low fee – compensating for the weak infrastructure in the sector in the country. Big Data can also be leveraged by the government to predict and identify potential threats to the country – including natural disasters and illegal activities. This allows for the government for better resource planning and mobilization.

But with Nepal’s low rate of digital adoption – a fact underscored by the World Economic Forum’s Global Competitive Index 2019, which highlighted Nepal’s poor performance in areas of innovation capability as well as Information communication and technology adoption, will make incorporation of Big Data Analytics and BI into organizational processes a herculean task. This preferably needs to be tackled with an all-encompassing collaboration between the public and private sectors since it requires large investments in computational infrastructure in addition to a framework for adoption through policies and laws around data sharing and security.

The incorporation of Data Analytics and Business Intelligence into organization strategy has introduced highly innovative changes to a plethora of different sectors across the global economy. But in the context of Nepal, positive disruption on a larger scale is likely to be experienced through strategically incorporating analytics and BI into the most established sectors like banking, healthcare, retail & trade, and manufacturing.

1. Manufacturing

Using big data analytics and BI allows manufacturers to identify patterns to improve the manufacturing processes and alter variables to increase the production processes’ efficiency and also increase supply chain efficiency. Big Data has been used in manufacturing in areas of predictive maintenance, predictive quality, anomaly detection and tool life-cycle optimization. Business Intelligence meanwhile, has been used for production forecasting and supply chain management.

Leveraging the use of analytics and Internet of Things (IoT), the manufacturer’s asset performance can be significantly improved upon. Real time monitoring of asset performance can help identify asset breakdown in addition to continuously optimizing asset performance to ensure minimum inefficiency and loss reduction. Furthermore, using predictive analytics, manufacturers can also schedule predictive maintenance. This allows manufacturers to prevent asset breakdown and avoid unexpected downtime in the future. An automatic continuous monitoring system that allows for manufacturers to identify bottlenecks and reveal underperforming processes and components in real time also perpetuates the concept of a connected factory, allowing for an optimized factory floor in which the functioning of separate units can be streamlined. Leveraging analytics and IoT also allows for higher product checks and surveillance, ensuring manufacturing quality. This serves to increase product quality, enhancing customer satisfaction and also minimizing product recalls.

Business Intelligence is also allowing manufacturers to accurately predict changes in customer behavior and ultimately predict the demand for customized products. By detecting changes in customer behavior, data analytics provides manufacturers with more time to design and produce customized products. The leveraging of data analytics also allows for demand prediction, ensuring that inventory is kept at a minimum. BI and Big Data Analytics have also been increasingly used for collaboration along the supply chain through transaction integration, warehouse optimization, asset rerouting, route mapping and management of supplier inventory- quality and quantity monitoring.

Zara – the fast fashion retail brand with USD 21.9 billion in sales per year collects data from its e-commerce websites, Personal Digital Assistance (PDA) devices, Radio Frequency Identification (RFID) tags and Point-of-Sale (POS) terminals and customer surveys to capture data on the specifications of cloths being bought, inventory levels of different items to understand their customers fashion sense and needs. Analysts then process these data to initiate new design releases. The incorporation of analytics into its functioning has allowed Zara to retail 11,000 diverse items every year as compared to 2,000 to 3,000 items of its competitions. What is the most impressive out of all this is that Zara also has the least year-end inventory among all its competitors. While other competitors are forced to sell their excess inventory at heavy discounts, Zara’s quick refill cycles create a sense of shortage which in turn has found to create more demand and stronger brand value.

2. Banking

Banks, unlike its traditional role do not only provide services of deposit, withdrawal and loan processing through its brick-and-mortar infrastructures anymore. The sector has been revolutionized through the use of internet banking and mobile banking services to pay for online purchases, buy insurance, process loans and make peer to peer transfers. With a large pool of structured transactional data available with banks, it allows for customer profiles to be built on top of data points such as monthly income, payment history, and mortgage information and spending habits. This understanding of customer habits and customer history allow for banks to create a comprehensive profile of the clients and offer them auxiliary services such as insurance or mutual fund services, credit or debit cards and safe deposit services in addition to providing personalized assistance such as financial advising and fraud detection. Highly customized banking services allows banks to diversify their portfolio offerings while also ensuring customer retention.

The use of Business Intelligence in the banking landscape has allowed Financial Institutions(FI) and banks to improve customer experience considerably – an area that has spiked interested from concerned stakeholders. The use of big data to better understand customer habits through behavioral analysis allows for the use of AI chat bots for quick and efficient resolution of query and problem that a customer might have. It also allows for banking customers to have an Artificial Intelligence(AI) backed financial advisor that gives recommendations based on analytical models run on customized income levels, spending and desired financial goals.

Big Data Analytics is also increasingly being used in banking for fraud detection. Monitoring consumer’s spending patterns can help in identifying unusual activity in an account and decrease likelihood of fraud. Furthermore, banks can also leverage big data analytics for risk management, to monitor their investments and make decisions on loan screening, mortgage evaluation and the cross selling of products like insurance.

Bank of America uses analytics to assess consumer sentiments extensively in order to retain its customers. The bank analyzes its customer data by running assessments on the consumers economic value, mortgage history, transaction history, behavior pattern and, preferred service channel to identify dissatisfied consumers and creates strategies and offers to ensure consumer retention. It utilized this strategy to a great extent in 2008 when it was losing many of its customers to smaller banks. It was discovered through the data of the customers that the bank’s end-to-end cash management system was too stiff for its user base. This quick problem identification allowed the bank to act swiftly and address the issue by releasing a new one stop website with more flexible online products within a year.

3. Health Care

The digitalization of clinical exams and medical records has allowed for not only an electronic report on past physical/mental medical conditions to but has also opened avenues for diagnosis of possible ailments in the future. Use of analytics equips doctors with detailed medical healthcare history reports and overview of the patient which allows for detection of warning signs of serious illness as they arise.

In addition, the combination of IoT and data analytics also allows for real-time monitoring of essential functioning of vital organ of people. We see this in use, albeit in its nascent stages through the use of smart watches where basic information on heart beat rate, calories burned is provided to users. With further advancement, this service might be extended to provide detailed and comprehensive reports on the critical functioning of various organs and parts of the body, facilitating real time alerts and even predictive alerts – alerting heart patients of an impending stroke as for instance. Adopting Big Data Analytics in Healthcare also allows for doctors and medical field experts to draw a comprehensive picture of a patient and will allow insurance to provide a tailored package to the patient.

Big Data Analytics is also facilitating groundbreaking research on lifesaving diagnostics and treatment options. Treatment of cancer is one great example of this. Medical researchers are using the information on tumor samples in the biobanks(databases) – to see how certain mutations and cancer proteins interact with different treatments and find trends that will lead to better patient outcomes.

IBM’s Watson has the capacity to process 500 GB of data per second, which allows it to refer 600,000 pieces of medical evidence and more than two million pages from medical journal in its repository in a matter of seconds. In addition to this, it can also refer to over 1.5 million patient records to cross verify its analysis and findings. This has been found to equip the Medical AI with expert-level accuracy in diagnosing diseases. As for instance, according to IBM, Watson’s successful diagnostic rate for lung cancer is 90 percent, compared to 50 percent for human doctors. Medical AIs have also consistently outperformed its human counterparts in detecting heart diseases, eye diseases and different forms of cancer.

4. Retail and Trade

Business Intelligence and Big Data Analytics can be leveraged in retail for pricing optimization, supply chain movement and improving customer loyalty. Big Data Analytics allows for a greater understanding of customers spending habits to customize the shopping experience for each customer and improve on customer service.

Analytics also allows for forecasting demand by analyzing social media and website searches to predict the next big thing or trend in the retail market. Companies like Zara which are known for their fast fashion business model are known to utilize this very suavely. Even in brick-and-mortar retail stores, business use analytics to understand the differences in demand for their products across various geographical regions and based on demand, make real time changes to their supply chains to redirect their shipments to regions which are experiencing high demand for a particular product.

Amazon is known to predict items to its customers based on their past searches and history. The company reportedly generates 29 percent of their sales through their recommendations engine which analyzes more than 150 million accounts.

Challenges for adoption:

Big Data Analytics and Business intelligence undoubtedly possess an unharnessed potential to possibly disrupt several industries and even change the way we gather, process and analyze information as we know it. However, there are still some challenges to its universal adaptation at this point in time,

  1. Lack of scalable real-time analytics capabilities.
  2. The lack of network resources for running high bandwidth applications.
  3. Concerns on data privacy and information security.
  4. Lack of cost effective storage subsystems of high performance.
  5. Requirement of huge computational infrastructure with high capital overlay.
  6. Lack of intelligent big data sources – Fragmented, Unmanaged and Inaccurate data.

Conclusion and Recommendations:

Incorporating Big Data Analytics and Business Intelligence into organizational strategies allows for countries like Nepal to reorient their cumbersome and inefficient practices to reinvigorate its economy. However, it comes with its set of challenges, with a lot to be done in terms of infrastructure investments and policy orientation. The successful adoption of Big Data Analytics in Nepal hinges the level of institutional and financial support from the public sector actors, the willingness of private corporations and academic teams to collaborate with them by sharing data, technology and analytical tools and the development and implementation of laws and policies around sharing of data, backed by efficient networking and computational architectural development.






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