Role of Big data in Cloud Computing
Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.
This blog explains what we mean by Big Data, the cloud, different kinds of cloud applications, the main stakeholders in the cloud ecosystem, and how the cloud and Big Data are related.
Cloud computing involves hosting applications on servers and delivering software and services via the Internet. In the cloud model, companies can access computing power and resources on the “cloud” and pay for services based on usage.
This contrasts cloud computing from the traditional, ownership-based model of IT assets and resources. One way of describing cloud services is that they are services that are provided and used by clients “on demand at any time, through an access network, using any connected devices [that use] cloud computing technologies.”
There are different categories of cloud services and applications. The three types most commonly referred to are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).
Infrastructure as a Service (IaaS): In IaaS, compute power and storage space are offered on demand. IaaS can provide server, operating system, disk storage, and database, among other things. One of the main advantages of IaaS is that it offers rapid elasticity and flexibility.
Amazon is one of the biggest IaaS providers. Its Elastic Compute Cloud (EC2) allows subscribers to run cloud application programs. IBM, Vmware, and HP also offer IaaS. China’s Huawei provides IaaS solutions for operators, governments, and enterprises with data center IT infrastructure.
Platform as a Service (PaaS): Applications are developed and executed through platforms provided by cloud vendors. This model allows quick and cost-effective development and deployment of applications.
PaaS is often used by application developers working on mobile applications. Some well- known PaaS vendors include Google (Google App Engine), Salesforce.com, and Microsoft (Windows Azure platform).
As another example, the US-based start-up, Cumulux, specialized on building, operating, and managing PaaS-based solutions, which was acquired by India’s Aditi Technologies.
Some facilities provided under the PaaS model include database management, security, workflow management, and application serving.
Software as a Service (SaaS): It is a software distribution model, in which applications are hosted by a vendor and made available to customers over a network.
The provider licenses an application to customers for use as a service on demand. With the SaaS, users do not need to hire professional IT staff to install and use the software.
SaaS is the most widespread form of cloud service. Some well-known examples include Google email, Google Apps, and Salesforce.com’s customer relationship management software. As a developing world-based example, MTN offers SaaS applications for micro-finance institutions.
SIGNIFICANCE OF MOBILE PHONES IN BIG DATA AND THE CLOUD
The use of mobile devices, rather than personal computers, to access Big Data- and cloud-based applications stand out as a particularly appealing and promising choice for developing economies, especially the least developed countries (LDCs).
Among the most compelling reasons is that less than a third of the population in developing countries owned a PC in 2015, compared with a cell-phone subscription penetration of 92 per 100 inhabitants.
In India, for instance, mobile phone subscriptions reached one billion in 2015, which is 83 percent of the country’s population. On the other hand, India’s PC penetration in the same year was estimated at 10 percent.
Indeed, more people worldwide have a mobile subscription than have access to a clean toilet, electricity, or clean drinking water.
Unsurprisingly, there are already many successful examples of projects involving Big Data- and cloud-based mobile computing solutions in developing economies. This blog focuses on more general issues of Big Data, the cloud, and mobile phone combination to deliver value to end-users.
In order to illustrate the above point, consider the use of mobile computing solutions to deal with the shortage of clean drinking water.
It is estimated that women and girls in developing countries spend between three minutes to three hours per day in collecting water for drinking, cooking, cleaning, laundering, etc.
Reducing this time would give them more time to spend on productive economic and social endeavors such as agriculture and farming-related activities, focusing on maternal and child- health-related issues and attending school.
Various approaches have been reported involving the cloud and mobiles in combination with mapping applications and other technologies to facilitate information flow and to improve water supply availability and reliability.
Innovative use of Big Data and cloud-based mobile computing solutions has been in minimizing water loss due to leaky pipes. One estimate suggested that 60 percent of water worldwide is lost due to leaky pipes.
It should, therefore, come as no surprise that improving water management is a key target under the Sustainable Development Goals.
Target 6.4 says: “By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity.”
Other applications involving Big Data- and cloud-based mobile computing solutions have helped to deal with developmental, political, and social issues such as improving health outcomes, fighting corruption, and reducing poverty.
For instance, such solutions have transformed health care by driving down costs, changing workflow, improving business continuity, and making an adequate provision for disaster recovery.
Big Data- and cloud-based mobile computing solutions have boosted the productivity of traditional sectors such as agriculture and farming as well as that of enterprises in the modern sector.
For instance, the mobile application, iCow, which helps small-scale Kenyan dairy farmers track and manage cows’ fertility cycles, has helped to increase milk production by 2–3 liters per cow per day.
No less important is the role of the data and information generated through Big Data- and cloud-based mobile computing solutions in the creation and functioning of the market.
The data and information play an especially important role in developing countries because factors such as underdeveloped intermediary institutions and informational opaqueness of small and young firms have acted as a barrier to the functioning of markets in these countries.
For instance, as of September 2013, the information created by the cloud- mobile platform, Agri-Life, which provides financial institutions and suppliers “near-real-time information” on farmers’ ability to pay for services, facilitated over US$2 million in revolving credit lines to about 120,000 small farmers in Kenya and Uganda.
The Deployment of Big Data- and Cloud-Based Mobile Computing Solutions
The cloud reduces infrastructure costs, which can be illustrated with the deployment of PaaS. For instance, the South African start-up, Nomanini sells a “business in a box,” a cloud-based mobile prepaid airtime machine, to small informal entrepreneurs, which allows them to set up a “mini-business.”
It is called Lula, which is especially useful for providing services to support individuals engaged in small business and informal economic activities such as taxi drivers and “on the go” vendors. Lula generates and prints codes which people purchase to add minutes to their mobile phones.
Lula runs on Google App Engine, which is the same infrastructure that powers Google’s own applications such as Google Calendar, Gmail, and Google Docs.
That is, Google provides the framework and storage and manages servers for Lula. Google also provides services to software applications associated with Lula beyond those that are available from the operating system (known as middle-ware).
In addition, Google provides runtime- related services such as supporting the execution of programs required to print vouchers using Lula.
Nomanini’s goal is to reach one million informal market merchants by 2020. That also allows the company to build up a huge amount of data about the informal markets.
An IaaS model allows delegating functions such as storage and computing to the cloud provider. For instance, according to biNu’s developers, by moving much of the processing to the cloud, biNu works ten times faster than regular mobile web browsers. In this way, biNu creates a virtual “smartphone in the cloud” for a user.
It makes graphics and text on the cloud and the data is sent back to the phone as tiny images.
An advantage of sending the data as images is that the text can be displayed in any language irrespective of the language a phone is programmed to handle. Each image consists of only one or two packets of data of less than one kilobyte (KB) each.
Information is not sent twice. The servers remember the information that is sent before and provides only new instructions that are needed to change the content on the screen. The cloud also provides the flexibility of scaling up uses when the demand increases.
Appropriateness, Effectiveness, Feasibility, and Worthwhileness of Big Data and Cloud-Based Mobile Computing Solutions in the Developing World The high penetration of mobile phones in developing countries makes Big Data- and cloud-based mobile computing solutions among the most appropriate and useful technologies.
The least gap between developed and developing countries is observed for mobile subscriptions compared to most ICTs, which makes Big Data-and cloud-based mobile computing solutions particularly promising.
The cloud has increased the effectiveness and enriched the uses of cell phones. New technological developments, related to cellphones and the cloud, are contributing to more rapid diffusion of Big Data- and cloud-based mobile computing solutions.
First, less sophisticated cell phones are now “cloud- ready” as a result of recent developments. A cell phone capable of running a browser can access a number of mobile cloud applications. Low-cost phone users can thus tap into applications that are currently accessible only through smartphones.
Second, consumers in developing countries are using increasingly sophisticated devices facilitating the diffusion of Big Data- and cloud-based mobile computing solutions. According to the NPD Group, in the third quarter of 2013, China’s smartphone penetration rate was 55 percent.
In the fourth quarter of 2013, smart-phone sales in India grew by 167 percent. Handset makers, meanwhile, are offering ultra-cheap smartphones. To take an example, China’s mobile chip supplier, Spreadtrum Communications and Mozilla, announced that they have teamed up to launch a US$25 smartphone for the world’s emerging markets.
The unavailability and high costs of bandwidth can be the major challenges in offering bandwidth-intensive applications in developing countries. In countries such as South Africa, whereas bandwidth is available, the price of broadband is a critical barrier for using cloud-based services.
Thus most mobile phones used in developing countries are only adequate for non-bandwidth intensive applications such as text messaging.
In order to demonstrate the usefulness, appropriateness, and effectiveness of Big Data- and cloud-based mobile computing solutions in developing countries, we discuss an application to enhance transparency and fight corruption in Guatemala.
In 2010, a national survey of the beneficiaries of Mi Familia Progresa (Mifapro) was administered with low- cost mobile phones and EpiSurveyor (free software) for data collection. Mifapro was the then President Alvaro Colom’s flagship social program.
It was a conditional cash transfer (CCT) aimed at improving the quality of life of poor families. Before 2010, similar surveys were carried out using paper-based data collection methods, which suffered from drawbacks such as frequent errors, storage burdens, and the high costs of double data entry.
While handheld devices not connected to the cloud, such as personal digital assistants (PDAs) are sometimes used to replace paper-based methods, they have their own shortcomings.
The data need to be downloaded to laptops in frequent intervals are not available in real-time, and may be corrupted or even lost if PDAs are damaged, misplaced, or stolen.
In this way, mobile-based clouds perform well from the standpoint of disaster recovery. Big Data- and cloud-based mobile computing solutions helped to overcome these limitations.
The 2010 CCT survey relied on EpiSurveyor installed on entry-level mobile phones to collect information from 500 Mifapro beneficiaries, mainly indigenous women. It was funded by the United Nations Foundation, the Vodafone Foundation, and a World Bank Development Marketplace Grant.
It drastically reduced the cost and enhanced survey accuracy. The results accelerated the implementation of a nationally representative beneficiary survey of the CCT program.
EpiSurveyor was arguably the first cloud application for data collection in international development. The process and results were compared with the 2009 paper-based survey conducted with 200 beneficiaries.
It was reported that Big Data- and cloud-based mobile computing solutions reduced average interview cost by 71 percent and average interview length by 3.6 percent.
The data quality was reported to improve and 89 percent of the interviewers preferred Big Data- and cloud-based mobile computing solutions methods compared to the paper-based survey. In contrast to paper- or PDA-based approaches, the use of Big Data- and cloud-based mobile computing solutions resulted in no data loss.
Other worthwhile applications of Big Data- and cloud-based mobile computing solutions concern financial services such as stock trading.
In October 2010, Intel announced an agreement with an alliance of seventy companies, including the Bombay Stock Exchange (BSE) and CtrlS to develop hardware and software for an open and interoperable cloud.
The Open Data Centre Alliance (ODCA) works to address security, energy efficiency, and interoperability.
The BSE expects that the new trading platforms supported by Big Data- and cloud-based mobile computing solutions will broaden the participation of younger Indians, deepen and widen asset classes traded in pension funds, insurance, and mutual funds and others.
As noted above, as of 2015, only 10 percent of Indians owned a computer, but more than a billion had mobile subscriptions.
Key Areas of Deployment and Impacts
Impact on the Healthcare System
Some of the most dramatic impacts of Big Data- and cloud-based mobile computing solutions in developing economies have been documented in health care.
M2M digitizes patient records and shares with doctors, nurses, mentor mothers, and counselors across its networks of over 700 sites.
The records contain information on treatment plans, and advanced reporting tools, which allow a quick response. Women in villages authenticate children’s medication with text messages.32 As of 2011, M2M has served more than 1.5 million women in nine SSA countries. It is estimated that the cost is less than US$75 per beneficiary.
As a further example, in Malawi, the cloud-based Mother Infant Pair (MIP) mobile application was developed by D-Tree International and designed for Health Surveillance Assistants (HSAs) working with pregnant HIV-infected women.
The MIP was developed according to the Community-Based Maternal and Neonatal Care (CBMNC) guidelines, which aims to regulate community infant care until the child is two years old. All HSAs are properly trained on CBMNC guidelines as well as on MIP.
During the home visits before and after the birth, the MIP-installed mobile phone takes the HSA over the questions that need to be asked to the client, as well as the associated advice that needs to be communicated. MIP uses the General Packet Radio Service (GPRS).
Data transmission using the GPRS costs US$0.00003/KB, which is significantly lower than the cost of an SMS, which is US$0.03/KB. The data is sent to a cloud server and can be downloaded from the Internet. The average size of submitted forms is 3–5 KB.
Moving to a different topic, the reduction of maternal and infant mortality is a key priority in many countries. For example, the SDG target is to reduce the global maternal mortality ratio to less than 70 per 100,000 live births by 2030.
As of 2010, about 2,500 midwives were working in 652 primary health care centers serving 10.7 million people in Nigeria.37 Big Data- and cloud-based mobile computing solutions are well positioned for contributing towards this goal.
In order to collect data from midwives, Nigeria’s Mobile Application Data Exchange (MADEX) has developed an SMS- based data collection platform.
Data sent by midwives is forwarded to the central cloud-based server. Due to the simplicity of the processes, cloud-based systems such as MADEX are appropriate for technologically less savvy users.
The data can also be tagged with a GPS coordinate, pictures, video, and audio. If there are signals of an outbreak of malaria, Ministry of Health officials and other health workers in the area receive a real-time notification via text message.
An innovative application in the early phases of development can help to identify counterfeit or sub-standard drugs. Individuals find it difficult to make decisions in the face of inadequate information as to whether a drug they are buying in order to save a life is genuine or not.
While buying a drug at a pharmacy store, a customer can find a twelve-digit code by scratching a sticker on the surface of the package and then send a text message to a given number.
The code sent by the customer is matched with the one registered by the pharmaceutical company in HP’s cloud database. The customer then receives a response back that tells whether the drug is counterfeit or genuine.
Although this application provides obvious commercial benefits to drug manufacturers and patients, one of the most important ones is that it helps save lives by enabling customers to check the authenticity of life-saving drugs.
Impacts on Business Processes, Productivity, and the Creation of Markets
In this section, we discuss the impacts of Big Data- and cloud-based mobile computing solutions on business processes and productivity and the creation of markets.
Big Data- and cloud-based mobile computing solutions allow for better workflow by saving time and allowing data to flow automatically into the clouds.
Let us now consider such possibilities in small- and medium-sized enterprises (SMEs). The South Africa- based telecommunications company MTN launched pilot cloud computing services targeting SMEs in Uganda, Cameroon, Côte d’Ivoire, Ghana, Nigeria, and South Africa.
Following the pilot projects, it launched a suite of cloud services for SMEs in Ghana and Nigeria in December 2012.
For instance, MTN MyOffice, a suite of SME-focused ICT solutions designed to enhance the way businesses work and collaborate is available in Nigeria. The subscribers of MTN’s SME Closed User Group (CUG) can pay a flat monthly fee and enjoy zero- and call services among their staff.
The service is available to SMEs with 2–199 employees. According to MTN, this offering has already been adopted by SMEs in manufacturing, hospitality, microfinance, and advertising.
The MTN Cloud offers packages to manage the infrastructure and platforms to support business functions such as accounting, human resource, customer relations management, email, and video conferencing, storage and back- up for small businesses.
The business software and data are stored on a cloud which ends- users can access through mobile phones as well as a PC.
The roles of Big Data- and cloud-based mobile computing solutions in market creation are much less appreciated but may be quite important. Kenya’s Musoni Systems established the world’s 100 percent mobile microfinance institution in 2009. Its cloud-based core banking solution has a huge amount of information on small-scale financial service providers as well as low-income consumers.
Among the key functions of markets are to match buyers and sellers, facilitate the exchange of information, goods, services, and payments and to provide an institutional infrastructure, such as a legal and regulatory framework.
Factors such as transportation and communication infrastructures and intermediary institutions are essential for the creation and functioning of markets.
Regarding buyer/seller matching services, more broadly, we discuss how Big Data- and cloud-based mobile computing solutions have facilitated the dialogue, interaction, and coordination between producers and users of goods and services in developing countries.
Market creation can occur through various mechanisms and with varying effects on the market participants.
HP’s cloud database was designed to provide information as to whether or not a drug is genuine. Lula owners act as intermediaries, matching buyers with sellers of airtime.
Developing world-based farmers are often paid low prices for their products. On the other hand, the poorly informed farmers pay higher prices in order to obtain the needed inputs that market conditions dictate.
Big Data- and cloud-based mobile computing solutions have made it possible to fight these problems.
In Mauritius and Ghana, Esoko, a mobile-enabled cloud service, collects and provides information to farmers on such topics as current market prices, bids and offers, weather, and tips.
Advisories are also sent by voice messages and a live call center of agricultural experts is made available.
SMEs are especially likely to benefit from the data and information created by Big Data- and cloud-based mobile computing solutions because developing economies are characterized by the lack, or poor performance, of credit rating agencies providing information about the creditworthiness of SMEs.
A national credit bureau would collect and distribute reliable credit information and hence increase transparency and minimize the banks’ lending risks. Many emerging economies lack such an agency and some have a poorly functioning one.
This situation puts SMEs in a disadvantaged position in the credit market. SMEs tend to be more informationally opaque than large corporations because they often lack certified audited financial statements and thus it is difficult for banks to assess or monitor their financial conditions.
Discussion and Concluding Comments
Developing economies’ broad political, economic, and social contexts have given rise to the growth of Big Data- and cloud-based mobile computing solutions.
Unsurprisingly, a number of creative approaches to the deployment, operation, and use of Big Data- and cloud-based mobile computing solutions have already been reported in the developing world.
While initial investment costs may be relatively high in some cases, possible savings due to efficiency gains, improved quality, and other advantages are obtained, which often exceed the initial investment. The simple fact that the data are created digitally as opposed to being digitized manually dramatically reduces the costs.
The cost savings that could be achieved through the immediate digitization and data transmission and automated data aggregation were much greater than the purchasing and data transmission costs of mobile phones.
One advantage of the cloud is that if a device fails or is lost, the information is still securely saved in the cloud. There is no need to worry about data loss in issues such as theft and burglary involving cellphones and computer crash, which are not uncommon.
We noted earlier that health organizations in the USA suffer from the lack of skills related to high- level project management. The shortage of skills and talent is a more serious problem in developing countries.
Since clients can access resources from a web browser, they are not required to install or download anything on their devices. The cloud is thus a more appropriate choice.
The low penetration rates of computers, the Internet, and smartphones, and the fact that a large proportion of the population lacks a documented home address make this an attractive option economically and technologically.
Mobile prepaid services do not require Internet access or a bank account and can be purchased in small or large amounts. A further reason for the popularity of such vouchers is that it is difficult to distribute physical vouchers because of theft and fraud risks.
The lack of availability and quality of cloud-related infrastructure have hindered cloud adoption in developing countries. In SSA, only 53 percent of the urban population and 8 percent of the rural population have electricity.
Developing countries perform relatively poorly compared to developed countries in most factors related to cloud computing. On the other hand, some constraints that have hindered the diffusion of the cloud in developed countries are less restrictive in developing ones.
For instance, due to the requirement of stringent security measures in order to comply with the HIPAA requirements, healthcare organizations in the USA are reluctant to use the cloud despite significant potential efficiency gain.
The Roles of Big Data and the Cloud in Overcoming Economic Barriers
The main attractiveness of the cloud is that it leads to a reduction in upfront investments and addresses constraints related to organizations’ human, business, and technological resources.
For instance, India’s Apeejay Stya and Svran Group, which is a US$1 billion-plus conglomerate, moved the entire IT functions including the mission-critical ones to AWS. The company reported that by doing so, it reduced IT staff from twenty-three to three and cut costs by over 80 percent.
A large number of firms and individuals in developing economies tend to use technologies that lack web compatibilities and have a limited number and variety of web functionalities. For instance, it is not possible to use low- end mobile phones to perform most e-commerce activities.
In this regard, note that in 2011, India had more than 700 million mobile phone users, which was about forty times the number of Internet users in the country.
Over 50 percent of these phones had only voice and text messaging capabilities. Technology companies have come up with innovative solutions in order to overcome obstacles associated with accessing the cloud-based applications with technologies with limited functionalities such as low-end mobile phones.
To take one example, to cater to the needs of low- end mobile phone users, Hewlett- Packard has developed cloud-based SiteOnMobile to deliver Internet experience over low-end phones.
Many SMEs in India that have invested in e-commerce websites but they have not been able to benefit from them. SiteOnMobile enables them to define specific tasks on their websites.
One example of a task could be “Buy Solar Stoves.” SiteOnMobile makes those tasks accessible to their customers from mobile phones. For this purpose, HP introduced the concept of “Tasklets,” which are special types of “task-based widgets” that can be created by a user by “showing the action once on the browser.”
For the first time, the user performs the tasks on multiple websites using HP’s special browser. SiteOnMobile then automatically generates a widget- like interaction for that task for the user.
Let us assume that a user has created a tasklet of “book a Taxi.” In order to blog a taxi, the user does not have to go through several web pages to reach the blogging page.
SiteOnMobile will perform all the steps for the user for subsequent uses once the interactions are created in HP’s special browser.
The task-lets can be accessed by users via SMS or voice services. SiteOnMobile performs all the needed tasks on the cloud and sends back the result to the user over SMS.
For example, if an SMS “book a Taxi” is sent to a predefined SMS access code, a taxi will be booked and the user will receive back details about the taxi that has been booked.
Regarding the barriers associated with a low penetration of mainstream online payment methods such as credit cards, it was reported that foreign CSPs require cloud users to pay with credit cards. Among the encouraging developments is the emergence of local CSPs, which sell to non- credit card users.
In Africa, for instance, CSPs accept payments by the mobile payment system, MPesa. Likewise, given the unavailability of mainstream payment methods such as credit cards and the risks of existing mechanisms such as CoD, cloud-based payment mechanisms such as Alipay possess a high degree of attractiveness and utility.
Alipay Wallet’s features include payments at brick-and-mortar stores, transportation, and appointment at hospitals.
Merchants can also offer discounts, coupons, and special membership privileges to Alipay Wallet users. Users of Alipay can also make e- payments at brick-and-mortar shops.
In June 2014, the Chinese online grocery store Yihaodian, which is majority-owned by WalMart, launched a storefront on Alipay Wallet. It allowed customers to buy groceries from their Alipay Wallet app.
The Roles of Big Data and the Cloud in Overcoming Socio-Political Barriers
The cloud’s transformative potential has resulted in significant national political decisions to utilize this technology for key economic activities.
To some extent, such decisions addressed issues such as the lack of appropriate government regulations related to privacy and security, and the lack of institutional trust.
For instance, Brazil’s federal representative Ruy Carneiro presented a bill for the creation of a cloud computing framework and the introduction of data protection laws. The bill is intended to address the lack of regulations around data privacy protection.
It also addresses interoperability and standards for services provided. Likewise, India’s industry body, National Association of Software and Services Companies, has issued best practice security standards, procedures, and guidelines for its member companies. However, compliance is voluntary.
The Roles of Big Data and the Cloud in Overcoming Cognitive Barriers
Unlike client-based computing, cloud-based software is easier to install, maintain, and update. For instance, in the above examples, Demandware makes new features available to Clarins on a rolling basis.
Thus in order to expand e-commerce activities, Clarins is not required to undergo any major upgrade project or add staff for maintenance.
The cloud’s key features such as the ability to reduce upfront investments and handle business processes and transaction without the intervention of an organization have greatly increased the confidence of firms in developing economies.
According to a survey conducted by Vmware and Forrester in 2012, 80 percent of respondents in India believed that the cloud would help reduce IT costs, and 82 percent believed that it will help optimize IT management and automation capabilities.
Measures taken by global CSPs have also helped overcome cognitive barriers associated with e-commerce adoption.
Global CSPs such as Amazon and Google hold seminars for start-ups in India, Indonesia, and many other developing economies. There are also consultants, who teach businesses how to use the clouds of Amazon and other CSPs.
In addition, Google has teamed up with Endurance International Group to help African and Southeast Asian SMEs to launch online businesses.
Endurance helps SMEs set up Google My Business, which is a free service and helps customers find a business online. SMEs can pay for add-ons such as the creation of a full-blown website and launch of e-commerce.
In this way, firms’ e-commerce adoption constitutes a gradual process instead of a quick change.
Barriers That Still Remain and New Barriers That Have Emerged in the Big Data and Cloud Environment
Obviously, it is not possible for Big Data and the cloud to overcome all the barriers facing developing economies. These economies still face severe market and infrastructure constraints.
Most cloud services, for instance, rely on bandwidth, which is the most glaring shortcoming of most developing countries, especially in rural areas. Likewise, the cloud is not of much use to firms who lack access to basic needs such as electricity.
Cloud-based e-commerce solutions in these economies are provided using cloud servers located at distant data centers in foreign countries. For instance, India’s nationwide cab-booking service Getmecab uses Amazon servers. The closest servers are in Singapore.
Likewise, India’s Tata Communications opened a data center in Singapore to offer cloud services to foreign clients due to Singapore’s cutting-edge IT infrastructure, the availability of IT experts, and government incentives.
Due primarily to poor bandwidth in India, Tata Communications decided to locate the facility in Singapore, despite Singapore’s expensive land and high temperatures, which increase the costs of keeping the servers cool.
As of 2014, India’s national 3G coverage in India was 9 percent. In those cities where 3G coverage is available, the services perform poorly due to congestion.
Many governments have viewed the cloud as a disruptive technology with the potential to change the national competitive performance. They are also preoccupied with national security concerns regarding the sensitive data and information stored in the cloud.
Consequently, many governments in developing economies have pursued a somewhat protectionist agenda regarding the development of the cloud industry and market.
These barriers are especially problematic for global efficiency because technologies such as cloud are cross-border in nature.
The Indian government proposed a measure which requires companies operating in the country to locate part of their IT infrastructure in the country. This requirement was for providing law enforcement agencies ready access to encrypted data on servers in India.
This measure also prohibits businesses to move data related to Indian citizens and government organizations out of the country.
Failure to comply will be a criminal offense and responsible people will face prosecution. The guidelines of the central bank, the Reserve Bank of India (RBI), prohibit storing customer data outside India, which limits cloud adoption by financial services companies.
Similarly, due to concerns related to national security, currency control, and industrial policy, China has local data server requirements. The Chinese government has also expressed a desire to see the cloud industry dominated by local companies.
In addition, it wants Chinese companies to control a higher share of world cloud markets. To fulfill this desire, it has taken a number of measures. According to the NDRC, the government has viewed cloud computing as a strategic industry.
It is worth noting that companies in other strategic sectors are eligible for market protection. China has classified cloud computing as a value-added telecom service (VATS).
Under the Chinese regulatory regime, and in accordance with China’s WTO commitments, foreign- company ownership is capped at 50 percent in VATS ventures and 49 percent for basic telecom services. This means that foreign CSPs are required to find local partners in order to operate in China.
While the central government defines cloud computing as a value-added service, there is no specific “cloud computing” category in the license list of value-added services.
To deploy its Microsoft Office 365 in China, it is hosted by a Chinese domestic company by license requirement. Other foreign cloud providers such as Amazon, IBM, and SAP have followed similar models.
In the same vein, in September 2013, the then Brazilian President Rousseff asked the Congress to introduce regulations to require foreign companies to store data generated by Brazilians on local servers.
Among foreign ICT companies, there is widespread and growing disenchantment with the adequacy of the Brazilian regulatory system for the cloud industry. In the 2012 ranking of the Business Software Alliance of twenty-four countries, in terms of the cloud environment, Brazil finished last.
In the 2013 BSA Global Cloud Computing Scorecard, Brazil improved slightly and ranked twenty-second of the twenty-four economies considered.
The scorecard considered Data Privacy, Security, Cybercrime, Intellectual Property Rights, Support for Industry-Led Standards, and International Harmonization of Rules Promoting Free Trade and ICT Readiness, Broadband Deployment.
Brazil was among the countries showing the most improvement in their infrastructure score for 2013. Brazil also lacks an appropriate framework for the development of ICT standards and gives domestic service providers preferential treatment in public procurement.
Regarding cognitive institutions, the cloud involves outsourcing IT functions. Some organizations hold the view that the adoption of the cloud model would result in the loss of control.
For instance, China’s state-owned enterprises (SOEs) exhibit a higher level of concern regarding data security: they have a tendency to distrust CSPs and are reluctant to outsource IT needs and move to the cloud.
Indeed, until not long ago, SOEs were not allowed to use outsourced data center services. There also have been concerns related to data security, which has made businesses cautious in spending on cloud services. A further limitation of cloud-based e-commerce solutions concerns the reduction in control over customer experience.
Case Studies of Diffusion and Impact of Big Data and Cloud Computing
Big Data and the cloud have become increasingly pervasive in SSA economies. According to the undersea cable operator, MainOne, the African cloud market was US$35.6 billion in 2015. The market research company, IDC estimated that South African cloud services market reached US$230 million in 2014.
According to IBM, as of 2014, about half of the medium and large businesses in South Africa and Kenya had adopted the cloud.
A survey of World Wide Worx and Cisco revealed that 44 percent of Nigerian businesses had plans to adopt the cloud in 2014. Another study found that at least 40 percent of businesses in Nigeria and Kenya were in the planning stages of Big Data projects in 2015.
Some economic sectors are more advanced than others in the adoption of Big Data analytics and the cloud. For instance, SSA- based firms in retail, telecoms, oil, and gas, and banking are most likely to use the cloud to handle large volumes of data transactions.
Competitively priced fast and ultra-fast internet access is becoming widely available in SSA economies. The region’s telecom companies are using this as a selling point to attract customers.
The resulting latency has a visible effect on video streaming and other bandwidth consuming applications. Such services and applications also cost more on the continent.
The Current State of Big Data and Cloud Market
A number of creative Big Data- and cloud-based apps have been developed that are unique to SSA economies.
The Higher Education Alliance for Leadership through Health, a consortium of seven universities (in Kenya, Ethiopia, the Democratic Republic of Congo, Tanzania, and Uganda), works with industry experts to extend education through virtual labs that students access remotely.
South Africa’s Center for Higher Education Transformation lets African universities access and manipulate its performance data stored on a Google platform. Also, Google has provided its cloud-based Apps for Education to universities in Rwanda, Kenya, and Mauritius.
In 2009, Microsoft donated 250,000 laptops to teachers in Ethiopia. The software in the laptop is managed by the US company FullArmor through Microsoft’s Azure cloud platform. Teachers can download curriculum; maintain, view, and track academic records; and transfer student data securely.
Farming and Agriculture
Improving agricultural productivity is likely to be among the most positive effects of Big Data in SSA economies. It is estimated that the average African small-holder farmer produces only a quarter to half of the productive potential.
Big Data can help them make full use of their productive potential. Big Data can also help farmers reduce costs by providing access to detailed information about irrigation and reduce wastage in resources.
A number of efforts and initiatives are being undertaken to digitize SSA’s agricultural activities. A promising area of Big Data and cloud utilization to increase agriculture productivity is soil infrared spectroscopy.
Data about soil characteristics such as texture, organic matter, and fertility help determine fertilizer needs and can be used as a basis for precise prescriptions.
One way to get information about soil characteristics is to use orbiting satellites, which can collect data in a cost-effective manner by measuring electromagnetic radiation reflected from farmlands. Efforts have been made to develop national and regional databases for soil properties.
Green Dreams’ iCow helps farmers track and manage cows’ fertility cycles. It informs them about important days of cow gestation periods, collects and stores milk and breeding records, and finds the nearest veterinarian and other service providers. A simple system involving Google Docs is also used.
If Green Dreams and the vet can’t answer a question, the question is uploaded to the iCow system and vets send messages among themselves to come up with the best answer, which is then forwarded to the farmer. As of 2012, 42,000 farmers were using iCow, which increased milk production by 2–3 liters per cow per day.
A National Geographic article reported the story of a Farmer Thuo, who experienced a substantial increase in his yields and improvement of his animals’ health thanks to the adoption of iCow.
By applying the knowledge (e.g., fodder production, hygiene, and animal diseases) that he received from iCow, he was able to double the production of milk from his cows.
The iCow system also helped him manage challenges such as food shortages that he had faced in the past. Before he started using iCow, Thuo lacked the knowledge and skills required to measure the cost per liter of milk.
He started keeping records of his farming activities. Thanks to the confidence that iCow gave him as a farmer and a businessman, he was considering to expand into pig farming.
Big Data and Cloud Deployment in Modern Versus Traditional Sectors
Nedbank teamed up with South Africa’s Intersect Technologies to develop a secure system based on digital certificates and push notifications. Users approve or deny transactions on phones by entering PINs.
However, financial institutions such as Nedbank might face fluctuations in demand. For example, the end of the month is a high transaction period, because that’s when employees are paid. Likewise, the end of the year and holiday shopping seasons face high demands.
The system scales up during high transaction periods and scales down in slower periods. Compared to the more traditional iCow application, Nedbank’s application is more costly. For example, digital certificates must be installed on the phone to authenticate transactions.
Key Driving Factors of Big Data and Cloud Industry and Market
The growth of SSA economies’ Big Data and cloud industry and the market is driven by a number of factors, which include government initiatives, local entrepreneurial activities, foreign multinationals’ entry into the region’s cloud sector, improving international bandwidth, international agencies’ roles, and philanthropic and charitable causes.
Governments in SSA economies are making efforts to strengthen the Big Data and cloud ecosystem. In May 2016, the government of Rwanda announced its plans to make government data accessible online in order to allow interested parties to use them for business, research, education, and other purposes.
By doing so, Rwanda will be the fourth East African country after Kenya, Tanzania, and Uganda to have data portals. The National Institute of Statistics will implement the policy.
Rwanda has also a plan to build a US$1.9 billion innovation city in the Kigali Special Economic Zone. The city will comprise key elements of a typical urban center such as corporate buildings, retail, leisure, sports, accommodation, and a healthcare center.
Privacy and Security Issues Associated With Big Data and the Cloud
Privacy and security issues associated with Big Data and the cloud have been a top concern for firms in developing as well as developed countries.
According to a study conducted by EMC in 2014, data loss and associated business downtime cost the global economy over US$1.7 trillion annually.
China alone is estimated to have more than 1.5 million hackers, who have created an underground industry of more than US$15 billion by exploiting security flaws in Big Data, the cloud, and the IoT.
Likewise, a study conducted by Compfix Data indicated that over 45 terabytes of data are lost every year in Kenya alone. The study also found that a company that experiences an outage of ten days or more due to data loss cannot fully recover and 50 percent of such companies go bankrupt.
Big Data and cloud security issues are related to a number of important organizational outcomes. For instance, cloud services in some countries are experiencing a negative country-of-origin effect due to security issues.
For instance, according to the 15th annual Business Climate Survey of the Amer can Chamber of Commerce in China conducted with companies operating in China (most of which were from the USA) (“AmCham China”), which was released in March 2013, only 10 percent considered using China-based cloud computing.
The respondents expressed concerns about data security. Twenty- six percent of the respondents stated that their proprietary data or trade secrets related to China operations had been stolen.
A top concern for consumers has been privacy and security practices of companies handling their data. For instance, online credit services in China that use Big Data analytics in extending loans are being accused of abusing personal data to collect debts.
Alibaba’s Ant Check Later, which allows users to delay payments and pay in installments, allegedly misused personal data.
Finally, it is important to compare the privacy and security issues associated with Big Data and the cloud in developing and industrialized countries.
In the industrialized world, cloud and Big Data users are becoming educated and are bringing more holistic perspectives to incorporate all the relevant issues that are important to them such as cost saving, productivity gain, security and privacy issues and, control over data.
They have also changed their behavior in response to changing perceptions of the benefits and risks and their potential and realized power. Over time, this may give venders a better assessment of clients’ needs and power, which may lead to effective tailoring of services and improvements in privacy and security issues.
Such conditions are less likely to be found in developing countries due primarily to the newness of the cloud and Big Data.
Characteristics of Big Data and the Cloud in Relation to Security and Privacy
A significant gap remains between CSPs’ claims and users’ views of the cloud’s security, privacy, and transparency. The cloud industry’s response has been: “Clouds are more secure than whatever you’re using now.” But many users do not agree.
Issues such as security, privacy, and availability are among the topmost concerns in organizations’ cloud adoption decisions rather than the total cost of ownership.
Due primarily to concerns related to security, privacy, and confidentiality, critics have argued that its perceived costs may outweigh the benefits.
Organizations worry about hidden costs associated with security breaches or lawsuits tied to a data breach. Many businesses and consumers are cautious in using clouds to store high- value or sensitive data and information.
Security and Privacy Issues Associated with Big Data
The various characteristics or dimensions of Big Data For instance, in order to create highly customized offerings, a company may need to mine a huge amount (volume) of structured and unstructured (variety) data from multiple sources (complexity).
In some cases, this process may also involve the use of high-velocity data. Big Data may challenge the Fair Information Practices (FIPs).
If consumer data is not handled appropriately by organizations, in addition to privacy and CS issues, there are also possibilities of civil rights violations such as discrimination.
For this reason, regulators such as the Federal Trade Commission (FTC) are showing concerns that Big Data may “perpetuate and even amplify” societal biases by screening out certain groups, often disadvantaged ones, from opportunities for employment, credit, or other forms of advancement.
Developing world-based organizations are experiencing a huge amount of data flows from various sources.
For instance, Etisalat’s operating brand, Mobily, in Saudi Arabia noted that 1.3 petabytes of data flow daily through the company’s network. Organizations such as Etisalat are often required to store all data in one location in order to facilitate analysis.
The higher volume and concentration of data make a more appealing target for hackers. Moreover, a higher data volume increases the probability that the data files and documents may contain inherently valuable and sensitive information. Information stored for the purpose of Big Data analytics is thus a potential goldmine for cybercriminals.
A huge amount of data means that security breaches and privacy violations are likely to lead to more severe consequences and losses via reputational damage, legal liability, ethical harms, and other issues, which is also referred to as an amplified technical impact.
The availability of a huge amount of data also increases the possibility that personal data can be put to new uses to create value. The US FTC Commissioner pointed out the possibility that firms, “without our knowledge or consent, can amass large amounts of private information about people to use for purposes we don’t expect or understand.” Such uses often violate the transparency principle of FIPs.
A huge data volume is also related to the demand or even the necessity of outsourcing. An issue of more pressing concern is determining relevance within large data volumes and how to use analytics to create value from relevant data. Firms may thus rely on CSPs for analytic solutions.
The quickly degrading quality of real-time data is noteworthy. In particular, clickstream data (click paths), which constitute the route chosen by visitors when they click/navigate through a site, is typically collected by online advertisers, retailers, and ISPs.
The fact that such data can be collected, stored, and reused indefinitely poses significant privacy risks. Some tracking tools can manipulate clickstreams to build a detailed database of personal profiles in order to target Internet advertising.
An important use of Big Data is real-time consumer profile-driven campaigns such as serving customized ads. For instance, location- tracking technologies allow marketers to serve SMS and other forms of ads based on real-time location. This process often involves passive data collection without any overt consumer interaction.
The lack of individual consent for the collection, use, and dissemination of such information means that such a practice violates the individual participation principle of FIPs.
Big Data initiatives have led to an increase in both the supply and demand for location-based real-time personal information. Data created and made available for use in the implementation of Big Data initiatives also have negative spillover effects. Particularly, the availability of location information to third parties may have some dangerous aspects.
One example is the use of location data for stalking people in real-time. For instance, the iOS app Girls Around Me, which was developed by the Russian company I-Free, leveraged data from Foursquare to scan and detect women checking into a user’s neighborhood.
The user could identify a woman he liked to talk, connect with her through Facebook, see her full name, profile photos, and also send her a message. The woman being tracked however would have no idea that someone was “snooping” on her. As of March 2012, the app had been downloaded over 70,000 times.
There is also a physical risk of (near) real-time data. For instance, a China Daily article commented that in China, illegal companies buy databases from malicious actors and provide services to their clients, which include private investigation, illegal debt collection, asset investigation, and even kidnapping.
By combining structured and unstructured data from multiple sources, firms can uncover hidden connections between seemingly unrelated pieces of data. In addition to the amount, a high variety of information in Big Data makes it more difficult to detect security breaches, react appropriately, and respond to attacks.
One estimate suggested that only about 10 percent of available data is in a structured form (e.g., transactional data on customers, time-series data from statistical agencies on various macroeconomic and financial indicators) which can be presented in rows and columns.
Especially because of the relative newness, most organizations lack the capability to manage unstructured data, which arguably contains more sensitive information.
Processes and technology solutions for securing unstructured data are still in the nascent phase and governance issues are not addressed.
For instance, organizations often lack mechanisms to ensure that permanent and temporary employees and third parties have appropriate access to unstructured data and that they are in compliance with data protection regulations.
According to a survey released in 2013 by Ernst & Young Global Limited, which was conducted among Indian firms, 43 percent of the respondents were concerned about data privacy and the security risks associated with the handling of unstructured data as among the major challenges.
The variability characteristic is related to the time-variant nature of security and privacy risks. The volume of data collected and stored, which need protection, will grow during the peak data collection and flow periods.
It is during such periods that organizations may lack the internal capacity and tools to manage and protect information. A related point is that the attractiveness as a prime target is high during such periods.
The variability characteristic of Big Data may also necessitate the outsourcing of hardware, software, and business-critical applications to CSPs.
Applications such as ERP and accounting systems are required to be configured for peak loads during daily and seasonal business periods or when quarterly and annual financial statements are prepared.
Big Data often constitutes aggregated data from various sources that are not necessarily identifiable. There is thus no process to request the consent of a person for the resulting data, which is often more personal than the set of data the person would consent to give. Related privacy risk involves re-identification.
It is possible to use a data aggregation process to convert semi-anonymous or certain personally non-identifiable information into non-anonymous or personally identifiable information.
Health-related data is of special concern. Based on a consumer’s search terms for disease symptoms, online purchases of medical supplies, and RFID tagging of drug packages can provide marketers with information about the consumer’s health.
Access to such information would enable an insurance underwriter to predict certain disease and disorder probabilities, which would not be possible using information voluntarily disclosed by consumers.
Many of the innovations involving Big Data use multiple data sources and involve transferring data to third parties.
Many organizations believe that making data anonymous before sharing with third parties would make it impossible to identify. This is often a convenient but possibly false assumption.
Researchers have presented a variety of methods and techniques that can be used to de-identify personal data and reassociate with specific consumers. Big Data processes can generate predictive models that have a high probability of revealing PII40 and thus make anonymization impossible.
Failure to protect PII and unintended or inappropriate disclosure violate the security provision of FIPs. In some cases, the identified person may suffer physical, psychological, or economic harm.
The Security and Privacy Situation in Developing Countries in the Context of Big Data and the Cloud
Most of the current discussion on Big Data and the cloud in relation to security and privacy has been focused on industrialized countries.
One commentator noted that about 90 percent of the discussion at the 2013 Internet Governance Forum (IGF ) held in Bali, Indonesia referred to Big Data as a surveillance tool.
At the same time, the debate focusing on developing countries treated Big Data as a means to observe people to fight poverty.
The argument provided by IGF participants was that data can help provide access to clean drinking water, health care, and other necessities. Some have challenged this view and noted that poor people have no less reason than rich people to be worried about surveillance.
Privacy violation is most likely to occur in countries with poor protection of civil liberties. The worst case is that the authoritarian government may see and use detailed, real-time data on citizens.
However, due to the secrecy in the way information is handled, the affected individuals may not have a way to know that their information is inappropriately accessed and used.
Likewise, some businesses may obtain, use, and sell personal data for their own gain. There may not be any restrictions on such activities and people whose information is used or sold may lack control over how their personal data is handled.
CS and privacy issues in developing countries need to be seen in the backdrop of the rapid diffusion of modern technologies such as the IoT. Banks and retailers are using customers’ real-time geo-location technologies for authentication and other purposes.
New types of biometric data such as fingerprints, iris scans, voice-recognition software, and facial recognition are also being used for authentication.
Location and financial data and other information can be attacked by cybercriminals. Such new developments require new security measures and user authentication to make sure that customers are happy and transmitted data is secure.
It is important for users to understand that they can fully trust the connected devices that are tied to bank accounts.
Most developing world-based organizations which collect consumer data perform extremely poorly in protecting privacy. Consider the 2015 Corporate Accountability Index of the Washington DC-based non-profit research initiative, Ranking Digital Rights.
With regard to user privacy, Tencent ranked thirteen out of the sixteen tech companies surveyed.
The survey did not include Baidu and Alibaba. The companies included in the survey were evaluated using a number of criteria such as the existence of privacy policies, how user information is collected and shared, and security practices.
Measures Taken at Various Levels to Strengthen Security and Privacy in Developing Countries International organizations, governments, NGOs, and private sector actors in developing countries are directing more efforts towards strengthening data privacy and CS issues.
A 2016 survey by the UN Conference on Trade and Development found that more than half of all developing countries still lacked national legislation to protect data and privacy online. In addition, many countries have legislation that needs to be updated and/or better enforced.
There is no globally accepted instrument to address the concerns related to data protection and privacy.
The current system for data protection is highly fragmented, with diverging global, regional, and national regulatory approaches. At the same time, various measures are being taken at the international level to address this situation.
For instance, the African Union Convention on Cyber Security and Personal Data Protection requires member states to establish a legal framework for the “protection of physical data and national Data Protection Authorities” (DPA).
Other regional initiatives include agreements by the European Union, the Asia-Pacific Economic Cooperation and the Commonwealth.
As a response to domestic and international pressures to build CS measures, in July 2013, the Government of India released the National Cyber Security Policy (NCSP). The NCSP outlines the basic policies and strategies “to build secure and resilient cyberspace for citizens, businesses and government.”
It set forth fourteen objectives that included enhancing the protection of critical infrastructure and developing 500,000 skilled CS professionals in the next five years. The development of public-private partnership (PPP) efforts towards enhancing the CS is a key component of the NCSP.
Note that PPPs are especially well-suited and justified for areas that require diverse types of expertise and knowledge in order to address complex problems. This condition fits squarely with CS.
Professional and Trade Associations
As important sources that shape institutional structures in an economy, professional and trade associations play significant roles in bringing and legitimating institutional changes in the areas of security and privacy in Big Data and the cloud environments.
In order to understand professional and trade associations’ roles in shaping a nascent industry in a developing economy (e.g., Big Data and the cloud), it is important to examine such associations in relation to other institutional ele-ments—most notably, the state.
Note that the state is arguably the most important institutional actor. With regard to the state’s role in influencing industry behavior in emerging economies, however, it is important to note that the rule of law is “often weakly developed” or sometimes “ignored with impunity” in such economies.
Second, in the nascent and formative sectors such as Big Data and the cloud, there is no developed network of regulatory agencies comparable to established industrial sectors. In such settings, professional and trade associations may emerge to play unique and important roles in shaping the industry.
India’s Data Security Council of India (DSCI), an industry body, entered into a partnership with Pearson VUE to deliver a data privacy credentialing program: DSCI Certified Privacy Professional (DCPP).
The certification is expected to address the need for privacy professionals in the country. DCPP equips individuals with the necessary privacy-related skill sets.
Individuals with the DCPP certification will be aware of privacy-related concepts and principles and the data privacy landscape in India and other economies. They will also learn about contemporary global developments in privacy-related matters, data protection regulations, and trans-border data flows.
In India, a report by the Internet & Mobile Association of India (IAMAI), a not-for-profit industry body, argued against a mandatory requirement to set up data centers in India.
It maintained that the data location requirement is likely to harm Indian companies. The report cited many examples of Indian companies that rely on international data centers.
For instance, Zoho’s data centers are in California and New Jersey. Myntra and Redbus host servers with CSPs such as AWS. Likewise, in the beginning, Flipkart relied on data centers in Canada.
In some countries, Internet companies are directing more efforts towards strengthening CS. In China, an e-commerce union has been formed, whose members comprise of major online firms. The union analyses vendor data such as those related to transactions and other sales activity in order to identify rogue online vendors.
Providers of Big Data and the Cloud
Developing world-based providers of Big Data and the cloud is taking measures to strengthen security. For instance, as of 2014, China’s Aliyun was reported to have a 100-plus personnel team to provide cloud security.
Organizations and Consumers Using Big Data and the Cloud
CIOs are increasing their orientation towards security and privacy. According to a survey released in 2013 by Ernst & Young Global Limited, which was conducted among Indian firms, 69 percent of respondents were planning to strengthen CS.
Likewise, as noted earlier, China Youth Daily’s poll showed that over three-quarters of respondents believed there was an abuse of Big Data.
From the privacy and security standpoint, Big Data and the cloud are likely to affect the welfare of unsophisticated, vulnerable, and technologically unsavvy consumers more negatively.
Such consumers may lack awareness of multiple information sources and are less likely to receive up-to-date and accurate information about multiple suppliers in a manner that facilitates effective search and comparisons.
They are also not in a position to assess the degree of sensitiveness of their online actions and are more likely to be tricked by illicit actors. Consumers in developing countries are more likely than those in developed countries to exhibit these features and profiles.
Big Data- and Cloud-Related Investments in the Primary Sector
Investments in Big Data and cloud technologies are flowing from the public as well as private sectors. For instance, India’s meteorology office uses a statistical method introduced in the 1920s under the British colonial rule.
In June 2016, India announced a plan to spend US$60 million on a new supercomputer system in order to improve the accuracy of weather forecasts.
The new system generates three-dimensional models to predict the monsoon’s development. Some experts think that better forecasting could help India boost farm output by as much as 15 percent by helping farmers make decisions regarding the best time to sow, irrigate, or apply fertilizer.
“Enterprise Knowledge Graph,” data analysts, business analysts, data scientists, and enterprise architects in an organization can collaborate in a single, integrated system.
The Roles of the Transnational Corporations (TNCs)
In one way, transnational corporations (TNCs) are likely to be a driving force behind the diffusion of cloud and Big Data in the primary sector of developing countries.
For instance, large food and biotechnology TNCs such as Monsanto and Syngenta already have a notable presence in developing countries, which is a positive factor from the standpoint of Big Data led productivity growth in these countries.
Monsanto reportedly has control on over 95 percent of the Indian cotton seed market. TNCs, which are often producers, processors, or traders of agricultural products or sellers of inputs or machinery, engage in a contracting system in which they assume a variety of responsibilities including providing technical assistance and marketing to developing world-based small farmers.
NCs such as Monsanto and Syngenta, which have become a driving force behind the utilization of Big Data in the industrialized world, are thus likely to act as a key channel in the international technology transfer of Big Data.
A typical offshore oil production platform is estimated to have over 40,000 data tags. However, not all of them are connected or used.
In order to convert to making better business and operating decisions using the datasets, new and carefully designed capabilities for data manipulation, analysis, and presentation are required. In addition, tools to support decision- making are needed.
One estimate suggested that if production efficiency of a brownfield oil and gas service company is improved by 10 percent, profitability will increase in the US$220 million to US$260 million range.
Digitization can also extend field life. Even higher potential could be achieved in greenfield assets by including instrumentation from the start.
Global technology providers have become an even more important force in driving the adoption of Big Data and cloud in the primary sectors in the developing world. In mid-2016, Microsoft opened its biggest center of excellence for the oil and gas industry in Dubai.
The center seeks to help organizations in the oil and gas sector to utilize the latest technologies such as the IoT, Big Data analytics, and cloud computing using Microsoft Azure and Office 365.
The Roles of International Agencies
In some countries, international agencies have been the driving force behind the adoption of Big Data and the cloud.
For instance, Haiti’s Ministry of Agriculture has strengthened its knowledge management system through its adoption of cloud technology. This project is funded by the World Bank and makes Haiti’s agricultural infrastructure, which was destroyed by an earthquake in 2010, resilient to natural disasters.
Some encouraging signs have emerged to suggest that the adoption of Big Data and the cloud are likely to get a big boost in the primary sectors of some developing countries.
Among the most encouraging factors is the rapid increase in investment in this sector. For instance, as noted earlier, agtech is one of the sweet spots for VC investors.
The VC investments are likely to lead to the development of technologies that are likely to benefit smallholder farmers. As noted above, policy and decision makers in these sectors are gaining a better understanding of the transformative potential of Big Data and the cloud.