Several recent studies have shown that its impact is already tangible in developed Asia–Pacific countries such as Australia, Japan, New Zealand and Singapore, which are all amongst the world's leading 'digital economies'.
This report focuses on DDI services in developing countries, taking India, Indonesia, the Philippines and Vietnam as examples, to explore how innovation with data can help address specific issues in these markets, and how it could flourish in the near future.
Altizon’s headquarters are in India, where it launched the Datonis cloud-based platform in late 2013. Datonis allows enterprises to equip their machines with ‘connectivity kits’ that send data to a device management system. This system is itself connected to a scalable real-time analytics engine with alerting and monitoring services. Altizon serves about 100 customers on the platform, 30% of which are outside India (mostly in developed markets such as the USA, Germany, Australia, Norway and Spain).
As an example, a leading auto-component manufacturer in India uses the Datonis platform to track its overall equipment effectiveness (OEE) automatically and in real time and to improve equipment utilisation. Before it used the Datonis platform, the manufacturer calculated OEE using costly manual and labour-intensive methods, which resulted in inaccurate, untimely data. This made it impossible for the manufacturer to react to efficiency trends in real time, and a lack of historical data meant that future throughput trends could not be predicted. Since using Datonis, machine utilisation has improved by 20%. The platform provides visibility of the top reasons for machine downtime and immediate correction of reduced production. In addition, OEE is now calculated automatically, which provides data on process-improvement initiatives on the assembly line and enables better decision-making.
In India, Altizon reports rapid growth in interest in the offerings it provides, with firms starting pilots of automated data-driven systems and using Altizon solutions in multiple plants. Aside from manufacturing, firms active in logistics, asset management, field operations and supply-chain management are also evaluating and deploying solutions using the Datonis platform. These automated systems will ultimately replace labour-intensive solutions, which are significantly constrained by the scarcity of qualified manpower in high-growth industries. They will also set the foundation for advanced machine learning driven solutions that will benefit from the richer digital data.
Vizury is a start-up based in India, with offices across Asia and the Middle East and operations across the region (including India, Indonesia, the Philippines and Vietnam). The company offers multi-channel marketing insights to companies in four verticals: banking, insurance, airlines and ecommerce. Vizury's main product, 'Engage', is a platform which helps marketers integrate large volumes of data on customer interactions across channels, and derive insight from this data that can then be acted upon through promotion and customer service processes.
Vizury aggregates online data (e.g. information on people's behaviour on the Internet, either on the marketer's website or app, and third-party audience data) and offline data (e.g. customer transaction data not available in an online context). Its proposition for its customers is to improve new customer acquisitions or upselling of products, through this unified view of customer interactions.
As an example, Vizury works with a large Indian bank that has around 30 million customers, a third of whom are active online. The bank brought pseudonymised (i.e. individual records which exclude identifiable information) offline customer data onto Vizury's platform, including transactional data as well as a list of the pre-approved products for which individual customers are eligible (loans, interest rates, etc.). Personalised recommendations can then be offered to the bank's customer next time he/she visits the bank's website or app. Vizury's platform is omni-channel, meaning that the bank could also advertise on other platforms, such as Facebook. Vizury reported that implementing this approach led to a doubling in lead generation for the bank.
Accuster is an Indian company which designs and manufactures innovative healthcare products, including mobile labs which can be used to provide more affordable diagnostics solutions to remote communities. The labs are typically operated by third party healthcare providers, however Accuster offers a service which collects data over a satellite connection and aggregates this data on a cloud-based patient management solution. This data can be accessed and analysed by healthcare providers and used to provide online health profiles for patients.
The data can be used to monitor the activity of the mobile labs and provides a range of opportunity for analytics and innovation. Accuster is already making aggregate, anonymised data available to external experts in the context of health screening programmes in certain districts of India, so that regional variations can be assessed. As diagnostic data is combined with accurate location data close to where people live, it is hoped it can be used in future to map health issues and healthcare profiles, so that regional abnormalities can be analysed in order to identify causes and inform mitigating actions. The availability of real-time data at scale will also enable local health issues to be tracked, enabling more efficient medical inventory management. Accuster also believes there is the potential for aggregated statistical data to be used by medical colleges and other firms to develop innovative new healthcare solutions.
Bima is a mobile-based micro-insurance broker which uses innovative approaches to collect and analyse risk data to bring insurance products to many more people. Bima is part of Swedish firm Milvik, which is active in developing countries around the world, including Indonesia, the Philippines, Bangladesh, Pakistan, Papua New Guinea and Cambodia. Its main focus is a pay-as- you-go life insurance product targeted at lower-income families, which allows policies to be registered through mobile phones and paid for with prepaid airtime credit. As a broker, the company originates policies that are mostly underwritten by established insurers, to which it provides risk profile data to which they did not previously have access. As the customer base expands and policies mature, insurers are able to more accurately price risk and provide fair quotes to people who could not previously be insured, in large part because of a lack of reliable identity and risk-related data.
In addition to insurers, Bima also partners with mobile operators, which provide the payment gateway and a marketing channel, and also data that they have on consumers but are often unable to make use of themselves. Bima uses this data to progressively refine its own risk models, together with data that Bima itself collects (for example, on enquiries and claims). This enables Bima to gain an understanding of consumers based on demographic and geographical characteristics, how they use their mobile phones (e.g. how much they spend, how often they top up their credit) and their claims history.
Overall, the data Bima can get by operating its business has proved much more effective than third-party data that can be purchased or accessed today. Through this iterative approach to risk pricing, Bima has considerably widened access to insurance: 90% of its customers are insured for the first time through Bima. Looking ahead, this approach could be expanded to offer health insurance (which Bima already offers on a small scale) or to offer credit scoring and other financial services to the vast majority of people who are currently unbanked and uninsured.
Lazada is South-East Asia's biggest online retailer, which sells a wide range of consumer goods in Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam. It has made online shopping far more accessible in these markets by offering mobile access and promoting financial inclusion by offering cash-on-delivery payment, thereby reaching new markets for its products.
The digital nature of Lazada's operations provides it with a wealth of data with which to streamline and expand its business. Data is aggregated from a number of sources, such as on operational processes, how customers use the website, deliveries, customer feedback, and customer service interactions. The company uses this data to drive improved sales and targeted marketing techniques such as personalised recommendations and search optimisation, thereby driving growth in sales.
The data is also used to realise cost efficiencies, for example as an input to machine-learning techniques to enable the automation of tasks and processes involved in managing over 15 million products, which were previously highly resource intensive. DDI has enabled Lazada to improve its approach to identifying and combatting fraud, and in future it hopes to use data to drive further efficiency gains in areas such as inventory management.
Launched in 2014, PetaJakarta maps real-time information about floods in Jakarta provided by members of the public and organises this information online to be used by communities and authorities to take appropriate action in the event of flooding.
Flood-management capabilities are necessary due to the city being subjected to severe and frequent life-threatening flooding. Flooding patterns can change by the hour, and accurate live data is therefore crucial for adjusting the disaster response.
PetaJakarta benefits from Jakarta's status as the 'Twitter capital' of the world, by soliciting information about location and severity of floods from the city's residents in the form of tweets. This information is then presented in the form of a map, which allows first responders and emergency services to plan city-wide action and helps citizens avoid flooded areas.
The National Disaster Management Agency (BPBD) uses the social media information from PetaJakarta to augment government-produced population distribution data, as well as socio- economic, weather and topographical data. The result is a comprehensive visualisation of the situation, which is used to guide responders.
Snapcart is an innovative, data-driven alternative to market research panels. It incentivises smartphone users to upload a photo of their shopping receipts on a regular basis, by offering small cash rewards. Snapcart's system automatically logs each line of the receipt in its database, and so is able to provide a large database of purchases across its user base. This is complemented by profile data provided voluntarily by users, information from their device (e.g. location), and from third-party tools such as Facebook Connect.
Snapcart launched in Indonesia in September 2015, and since then its clients (brands and retailers) have been using data from 150 000 monthly active users (MAUs) to gain a better understanding of their customers. For comparison, Snapcart estimates that most offline market research panels only contain around 10 000 consumers, so the ability to reach people through their smartphones enables much greater scale.
Around 40% of Snapcart's MAUs upload receipts at least on a weekly basis. This data provides new insight into offline purchases (which make up 98% of total purchases in Indonesia) and real- time information about consumers that retailers and brands can act upon, for example by offering targeted promotions or pushing surveys and video content through Snapcart's app. The data in Snapcart's system is nearly real-time, as users typically upload their receipts on the day of purchase, whereas there can be a 30-day+ delay in obtaining data from traditional market research panels.
Snapcart sees a large potential for growth in Indonesia, which already has 52 million smartphone users and the number is rising quickly. The company is now working with over 80 brands and has gained recognition through multiple awards (Campaign Innovate for Asia–Pacific, Accenture's global Consumer Innovation Award, and the Disrupt 100 list). Snapcart is planning to expand to other countries in Asia, starting with the Philippines in July 2016.
In Indonesia, Ci-Agriculture aims to improve productivity through DDI by using 'precision farming' (involving sensors, aerial imagery and data analytics). The company has a product which enables analysis of soil conditions, weather and growth progress to inform decisions on when to plant, fertilise or use pest control. Data from these platforms or systems can be used to make predictions about pricing and demand, provide early warnings related to pests or weather, and to determine the best insurance model based on the needs of farmers.
eFishery, another Indonesian startup, is focused on aquaculture and offers an integrated feeding solution for fish and shrimp farming. The machine is able to feed fish automatically, sense the fish’s appetite and adjust the amount of food given to match the appetite. Fish feeding performance can be controlled remotely from a smartphone through the analytics platform, and the product is advertised as being capable of reducing the feeding cost by up to 21%, thereby improving both labour productivity and ultimately profits.
In Vietnam, Mimosa focuses on precision farming using IoT technology. It claims to accurately measure the conditions and needs of crops and animals in real time, to improve predictability and increase yields, whilst minimising costs and risks. Data is available to be analysed on a smartphone at any time or place, allowing remote monitoring and control of various functions, which reduces the workload of farmers.
In the Philippines, CloudFarm has developed a “Heat Stress Analyser”, a sensor connected to a mobile app which measures temperature, light intensity, soil moisture and pH level in the ground. The sensor can help automate the activation of skylights, growing lights, exhaust fans, watering, and other farming components in a greenhouse and thus address the risk of crop heat stress, such as in the early stage of a drought. This technology is especially relevant for weather phenomena such as El Niño, when heat stress may pose a large threat. Research has shown that heat stress was an important reason why crop yields have declined in the Philippines, with over a third of farmers affected.
Other agricultural innovations in terms of automation are also prevalent in the region. For example, Vinteo, has a product which automates the process of sampling and analysing the colour, size and shape of the seeds (coffee beans, rice, etc.) produced, in order to choose the best products.
Developing Asia–Pacific countries are seeing strong interest in DDI applied to healthcare. Although this may seem more relevant to developed countries with ageing populations, in large countries such as India, and in archipelagos (Indonesia and the Philippines), remote monitoring and diagnostics are increasingly important in providing adequate and affordable healthcare to citizens who live in semi-rural/rural areas.
Accuster is an Indian company which designs and manufactures innovative healthcare products, including mobile labs which can be used to provide more affordable diagnostics solutions to remote communities. The labs are typically operated by third party healthcare providers, however Accuster offers a service which collects data over a satellite connection and aggregates this data on a cloud-based patient management solution. This data can be accessed and analysed by healthcare providers and used to provide online health profiles for patients.
The data can be used to monitor the activity of the mobile labs and provides a range of opportunity for analytics and innovation. Accuster is already making aggregate, anonymised data available to external experts in the context of health screening programmes in certain districts of India, so that regional variations can be assessed. As diagnostic data is combined with accurate location data close to where people live, it is hoped it can be used in future to map health issues and healthcare profiles, so that regional abnormalities can be analysed in order to identify causes and inform mitigating actions. The availability of real-time data at scale will also enable local health issues to be tracked, enabling more efficient medical inventory management. Accuster also believes there is the potential for aggregated statistical data to be used by medical colleges and other firms to develop innovative new healthcare solutions.
Medifi, launched in the Philippines in 2014, offers cloud-based health profile data management, two-way video consultations, medical imaging management and messaging services. This helps to address the major imbalance in the ratio of patients to doctors between urban and rural areas of the Philippines. The company is also building in integration with personal health devices and emerging medical sensors (e.g. Fitbit, Jawbone UP, Withings, Apple Watch) to collect data on vital signs automatically and provide a more comprehensive diagnostic experience online. The company has plans to expand into other South-East Asian markets soon.
Konsula is a similar venture based in Indonesia. It offers two products: an online doctor and hospital directory (including information on fees and online appointment bookings) and an online system for medical workers and health facility managers to manage appointments, online patient registration and data analysis. This can improve clinic efficiency, the quality of the service to patients, as well as physician utilisation.
Bima is a mobile-based micro-insurance broker which uses innovative approaches to collect and analyse risk data to bring insurance products to many more people. Bima is part of Swedish firm Milvik, which is active in developing countries around the world, including Indonesia, the Philippines, Bangladesh, Pakistan, Papua New Guinea and Cambodia. Its main focus is a pay-as- you-go life insurance product targeted at lower-income families, which allows policies to be registered through mobile phones and paid for with prepaid airtime credit. As a broker, the company originates policies that are mostly underwritten by established insurers, to which it provides risk profile data to which they did not previously have access. As the customer base expands and policies mature, insurers are able to more accurately price risk and provide fair quotes to people who could not previously be insured, in large part because of a lack of reliable identity and risk-related data.
In addition to insurers, Bima also partners with mobile operators, which provide the payment gateway and a marketing channel, and also data that they have on consumers but are often unable to make use of themselves. Bima uses this data to progressively refine its own risk models, together with data that Bima itself collects (for example, on enquiries and claims). This enables Bima to gain an understanding of consumers based on demographic and geographical characteristics, how they use their mobile phones (e.g. how much they spend, how often they top up their credit) and their claims history.
Overall, the data Bima can get by operating its business has proved much more effective than third-party data that can be purchased or accessed today. Through this iterative approach to risk pricing, Bima has considerably widened access to insurance: 90% of its customers are insured for the first time through Bima. Looking ahead, this approach could be expanded to offer health insurance (which Bima already offers on a small scale) or to offer credit scoring and other financial services to the vast majority of people who are currently unbanked and uninsured.
In Indonesia, Ci-Agriculture aims to improve productivity through DDI by using 'precision farming' (involving sensors, aerial imagery and data analytics). The company has a product which enables analysis of soil conditions, weather and growth progress to inform decisions on when to plant, fertilise or use pest control. Data from these platforms or systems can be used to make predictions about pricing and demand, provide early warnings related to pests or weather, and to determine the best insurance model based on the needs of farmers.
eFishery, another Indonesian startup, is focused on aquaculture and offers an integrated feeding solution for fish and shrimp farming. The machine is able to feed fish automatically, sense the fish’s appetite and adjust the amount of food given to match the appetite. Fish feeding performance can be controlled remotely from a smartphone through the analytics platform, and the product is advertised as being capable of reducing the feeding cost by up to 21%, thereby improving both labour productivity and ultimately profits.
In Vietnam, Mimosa focuses on precision farming using IoT technology. It claims to accurately measure the conditions and needs of crops and animals in real time, to improve predictability and increase yields, whilst minimising costs and risks. Data is available to be analysed on a smartphone at any time or place, allowing remote monitoring and control of various functions, which reduces the workload of farmers.
In the Philippines, CloudFarm has developed a “Heat Stress Analyser”, a sensor connected to a mobile app which measures temperature, light intensity, soil moisture and pH level in the ground. The sensor can help automate the activation of skylights, growing lights, exhaust fans, watering, and other farming components in a greenhouse and thus address the risk of crop heat stress, such as in the early stage of a drought. This technology is especially relevant for weather phenomena such as El Niño, when heat stress may pose a large threat. Research has shown that heat stress was an important reason why crop yields have declined in the Philippines, with over a third of farmers affected.
Other agricultural innovations in terms of automation are also prevalent in the region. For example, Vinteo, has a product which automates the process of sampling and analysing the colour, size and shape of the seeds (coffee beans, rice, etc.) produced, in order to choose the best products.
Lazada is South-East Asia's biggest online retailer, which sells a wide range of consumer goods in Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam. It has made online shopping far more accessible in these markets by offering mobile access and promoting financial inclusion by offering cash-on-delivery payment, thereby reaching new markets for its products.
The digital nature of Lazada's operations provides it with a wealth of data with which to streamline and expand its business. Data is aggregated from a number of sources, such as on operational processes, how customers use the website, deliveries, customer feedback, and customer service interactions. The company uses this data to drive improved sales and targeted marketing techniques such as personalised recommendations and search optimisation, thereby driving growth in sales.
The data is also used to realise cost efficiencies, for example as an input to machine-learning techniques to enable the automation of tasks and processes involved in managing over 15 million products, which were previously highly resource intensive. DDI has enabled Lazada to improve its approach to identifying and combatting fraud, and in future it hopes to use data to drive further efficiency gains in areas such as inventory management.
DRVR is a fleet analytics platform with customers in Thailand and Myanmar and live pilots in Indonesia and Malaysia. It uses standard telemetric devices to collect data from vehicles, and analyses the data to improve the efficiency of fleets (vehicle performance, driver behaviour patterns, fuel efficiency) and deliver a holistic view of a fleet's performance (or underperformance). This helps to identify areas where costs can be reduced, prevent irregular activity (excessive personal use of vehicles, fuel theft) and pre-empt maintenance before breakdowns. Customers report 10–25% fuel savings for vehicles, based on the impact on driver behaviour alone.
Where DRVR differentiates itself from other fleet-management platforms is in its ability to present insights in a very intuitive way. Traditional fleet-management systems are seen as too complex by many companies which do not have the right skills to make full use of their capabilities. DRVR has therefore adjusted the way that data and insights are shown by taking inspiration from mobile games and other apps (e.g. Line), and hiring games developers. The fleet-management platform can be run entirely from a mobile handset.
Despite its success to date, DRVR faces difficulty in sourcing high-quality real-time traffic and road condition data. Commercial mapping software, including Google Maps, is relatively expensive and incomplete outside major cities, and is not sufficiently accurate or reliable for commercial purposes. At the moment, there are no government or private-sector initiatives on a large enough scale to address the commercial logistics barrier, although this would make a huge difference to the sector if implemented. In turn, the ability to access connected fleet data could enable governments to plan infrastructure projects more effectively and reduce road congestion.
Lazada is South-East Asia's biggest online retailer, which sells a wide range of consumer goods in Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam. It has made online shopping far more accessible in these markets by offering mobile access and promoting financial inclusion by offering cash-on-delivery payment, thereby reaching new markets for its products.
The digital nature of Lazada's operations provides it with a wealth of data with which to streamline and expand its business. Data is aggregated from a number of sources, such as on operational processes, how customers use the website, deliveries, customer feedback, and customer service interactions. The company uses this data to drive improved sales and targeted marketing techniques such as personalised recommendations and search optimisation, thereby driving growth in sales.
The data is also used to realise cost efficiencies, for example as an input to machine-learning techniques to enable the automation of tasks and processes involved in managing over 15 million products, which were previously highly resource intensive. DDI has enabled Lazada to improve its approach to identifying and combatting fraud, and in future it hopes to use data to drive further efficiency gains in areas such as inventory management.
Lazada is South-East Asia's biggest online retailer, which sells a wide range of consumer goods in Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam. It has made online shopping far more accessible in these markets by offering mobile access and promoting financial inclusion by offering cash-on-delivery payment, thereby reaching new markets for its products.
The digital nature of Lazada's operations provides it with a wealth of data with which to streamline and expand its business. Data is aggregated from a number of sources, such as on operational processes, how customers use the website, deliveries, customer feedback, and customer service interactions. The company uses this data to drive improved sales and targeted marketing techniques such as personalised recommendations and search optimisation, thereby driving growth in sales.
The data is also used to realise cost efficiencies, for example as an input to machine-learning techniques to enable the automation of tasks and processes involved in managing over 15 million products, which were previously highly resource intensive. DDI has enabled Lazada to improve its approach to identifying and combatting fraud, and in future it hopes to use data to drive further efficiency gains in areas such as inventory management.
In Indonesia, Ci-Agriculture aims to improve productivity through DDI by using 'precision farming' (involving sensors, aerial imagery and data analytics). The company has a product which enables analysis of soil conditions, weather and growth progress to inform decisions on when to plant, fertilise or use pest control. Data from these platforms or systems can be used to make predictions about pricing and demand, provide early warnings related to pests or weather, and to determine the best insurance model based on the needs of farmers.
eFishery, another Indonesian startup, is focused on aquaculture and offers an integrated feeding solution for fish and shrimp farming. The machine is able to feed fish automatically, sense the fish’s appetite and adjust the amount of food given to match the appetite. Fish feeding performance can be controlled remotely from a smartphone through the analytics platform, and the product is advertised as being capable of reducing the feeding cost by up to 21%, thereby improving both labour productivity and ultimately profits.
In Vietnam, Mimosa focuses on precision farming using IoT technology. It claims to accurately measure the conditions and needs of crops and animals in real time, to improve predictability and increase yields, whilst minimising costs and risks. Data is available to be analysed on a smartphone at any time or place, allowing remote monitoring and control of various functions, which reduces the workload of farmers.
In the Philippines, CloudFarm has developed a “Heat Stress Analyser”, a sensor connected to a mobile app which measures temperature, light intensity, soil moisture and pH level in the ground. The sensor can help automate the activation of skylights, growing lights, exhaust fans, watering, and other farming components in a greenhouse and thus address the risk of crop heat stress, such as in the early stage of a drought. This technology is especially relevant for weather phenomena such as El Niño, when heat stress may pose a large threat. Research has shown that heat stress was an important reason why crop yields have declined in the Philippines, with over a third of farmers affected.
Other agricultural innovations in terms of automation are also prevalent in the region. For example, Vinteo, has a product which automates the process of sampling and analysing the colour, size and shape of the seeds (coffee beans, rice, etc.) produced, in order to choose the best products.
Data-driven innovation (DDI) is playing an increasingly important role throughout the world. The OECD defines it as “the analysis of large volumes of data, from transactions, production and communication processes, that results in significant improvements of existing, or the development of new, products, processes, organisational methods and markets”.
Several recent studies have shown that its impact is already tangible in developed Asia–Pacific countries such as Australia, Japan, New Zealand and Singapore, which are all amongst the world's leading 'digital economies'. This report focuses on DDI services in developing countries, illustrating how it addresses specific issues in these markets and asking how it could flourish in the near future.
DDI takes many forms, from the real-time processing of vast amounts of traffic data to manage roads more efficiently, to the analysis of small amounts of data from individual vehicles for insurance or fleet management purposes. Whether big or small, DDI appears in nearly every sector of the economy.
In this report, we explore DDI services in insurance, e-commerce, fleet management, marketing, agriculture and healthcare sectors, and look at five countries in particular (India, Indonesia, the Philippines, Thailand and Vietnam). Its findings are likely to be applicable to developing countries in Asia–Pacific more broadly, and complement the reports that have already been published on developed countries in the region.
Firms in developing Asia–Pacific use DDI for many of the same reasons as firms in developed countries: DDI increases operational efficiency, helping to reduce cost through reduced waste and better planning; it helps companies be more competitive domestically and internationally, improving growth prospects; and it supports new services for customers, from personalised products to more attentive, proactive customer service. For example Lazada, South-East Asia’s largest e-commerce platform, collects and analyses data to improve its sales through personalised recommendations, and to prevent or mitigate the impact of fraud.
The landscape of data-driven services in developing Asia–Pacific countries is extremely varied. Through interviews with stakeholders across the five focus countries (both people working in DDI start-ups and investment professionals from venture capital firms), we found that DDI services can enable firms and governments to overcome specific challenges that currently slow economic and social development in developing Asia–Pacific, such as skills shortages or inadequate information on which to build DDI services. For example, Bima offers life and health insurance, through mobile phones, to consumers who were previously completely excluded from the financial services market and on whom very little risk-related data was previously available.. In the process, it collects real-life data on its customers, their risk profile and their behaviour, which enables it and its insurer partners to price risk more accurately and further expand the number of people who can be insured.
The way in which Bima delivers and improves its services exemplifies a common theme that is recurrent when looking at DDI in developing Asia–Pacific: the critical role of mobile technology in enabling advanced services. To sell insurance, Bima partners with mobile operators who enable it to collect premiums on a daily basis from customers’ prepaid credit. Similarly, DRVR, a start-up in Thailand, provides detailed fleet management data and analytics to help improve the efficiency of transport and logistics fleet. In order to overcome skills limitations, which have hampered traditional data-driven fleet-management solutions, DRVR is developing a user experience that draws from mobile apps and video games.
Mobile phones are becoming ubiquitous and, increasingly, connected to the Internet: they are also highly personal (and personalised) devices, and therefore are a very important source of data. For example, in order to provide near-real-time retail trend data to major brands, Indonesia-based start-up Snapcart incentivises consumers to snap a picture of their supermarket receipts at least once a week and upload it to its cloud platform. Many other apps make use of location data to enable real-time applications, including for the public good: PetaJakarta and the Indonesian National Disaster Management Agency (BPBD) use real-time, location-specific information on floods from mobile social media to inform citizens and disaster relief efforts.
Quantifying the impact of DDI on developing and emerging economies is difficult, but we estimate that currently DDI contributes between 0.5% and 1.3% to gross value added (GVA), roughly between a third and half of estimates in developed countries. This is small but not insignificant: at current prices, in 2015 this contribution was over USD110–210 billion in developing Asia–Pacific, and USD20–40 billion in the five countries we focused on in this report (India, Indonesia, the Philippines, Thailand and Vietnam).
Bringing the DDI environment in developing countries up to the level of the most advanced markets in Asia–Pacific could create significant value: the contribution of DDI to the economy of developing countries in the region could reach USD300–560 billion in 2020.
The challenges in achieving this growth are daunting: more innovation needs to take place, either through start-ups or accelerators within larger companies, to serve the specific needs of each market and country; much more data needs to be collected and processed to build the basis on which these services can be developed; further investment needs to take place to upgrade systems to be properly instrumented to collect this data and act on it; governments and public sector organisations must contribute to the expansion of the amount of useful data available, through open data initiatives including real-time application programming interfaces (APIs); and many more people must be offered education and training that prepares them for jobs in a data-centric economy.
Policy-makers and governments have a fundamental role to play in overcoming these challenges and encouraging DDI. They are providers and users of data, but also can ensure that the legal and regulatory environment is designed in a way that encourages consumers and firms to adopt DDI services, and allows start-ups and larger companies to innovate with data, bring DDI services to market and grow their customer-base domestically and internationally. They also typically have a central role in education and are instrumental to ensuring the right skills are available for DDI to flourish.
Many countries in the region are embarking in fundamental reviews of their privacy and data protection regimes, which are the single most important component of how policy directly affects DDI. In doing so, they must be aware of the inherent tensions that can exist between protecting consumers’ privacy, and enabling them to contribute data that is essential to the development of DDI services they and others will value. These tensions are often articulated as trade-offs, arguing that more innovation requires less privacy and vice-versa. This is not always the case: in some instances, solutions can be found that achieve the desired policy outcomes without harming innovation or competition.
In particular, the specific challenges highlighted in this study suggest the following:
In this crucial period when nearly every government in the Asia–Pacific region is working on setting up modern data protection regimes, it is essential that these regimes are discussed openly and at length, with inputs from civil society and businesses. This will bring to the fore the progress that can be made without trade-offs, as well as informing the trade-offs between the legitimate interests of all parties when they are unavoidable. In turn, this will enable policy makers to make informed decisions that do not unnecessarily hinder DDI.
It is clear that DDI holds substantial potential to help developing countries accelerate their economic and social development. Elsewhere in the world, this development has happened remarkably harmoniously, despite occasional disputes between privacy advocates, governments and businesses. These same economic and social benefits can also be realised in developing Asia–Pacific through clear outcome-oriented thinking, open sharing of expertise and international cooperation.
Data-driven innovation (DDI) has an important role in realising the potential that data collected and processed through information and communication technologies has for creating significant economic and social benefits.
The scope and impact of DDI has been studied in some detail in the OECD and in Asia–Pacific specifically, through country-specific reports focusing on Australia, Japan, New Zealand and Singapore. This report broadens the scope of study to explore DDI in developing Asian countries, focusing on India, Indonesia, the Philippines, Thailand and Vietnam. Through a wide range of case studies, it shows how DDI is addressing specific needs and constraints in developing markets, such as by supporting an expansion of access to insurance, improving the efficiency of retail and logistics, and improving access to healthcare and disaster relief in remote areas.
Many of these innovations are linked to the increasing availability of mobile devices connected to the Internet, and continued growth in their number expected over the next few years. The analysis conducted as part of this study estimates that DDI’s contribution to gross value added (GVA, a key measure of GDP) could grow from at least USD100 billion in 2015 to over USD300 billion in 2020 in Asia–Pacific as a whole (of which the five focus countries could make up 20% to 25%), representing around 2% of total GVA.
Indeed, the true impact of DDI in developing countries is likely to be even greater, as it can help reduce enduring barriers to development, such as difficulties in accessing financial services and healthcare. The challenges in reaping the full value of DDI are significant, however: data, as well as the skills to develop and use DDI services, remain scarce in these countries, requiring investment in machinery, software, training and education.
Governments have a role to play in overcoming these challenges, by making more of their own data available, and ensuring that the legal and regulatory environment surrounding DDI helps firms to develop, adopt and export DDI services. Governments should fulfil these roles in a way that protects consumer privacy and trust, through a clear understanding of the various policy options.
DDI holds substantial potential to help developing countries accelerate their economic and social development. Elsewhere in the world, this development has happened remarkably harmoniously, despite occasional disputes among privacy advocates, governments and businesses. These same economic and social benefits can also be realised in developing Asia–Pacific through clear outcome-oriented thinking, open sharing of expertise and international cooperation.
David Abecassis
Partner
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Richard Morgan
Manager
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This report was commissioned and sponsored by Google, and prepared independently by Analysys Mason, a global consultancy specialising in telecoms, media and technology.
The analysis contained in this document is the sole responsibility of Analysys Mason and does not necessarily reflect the views of Google or other contributors to the research. Lead authors were David Abecassis (Partner), Richard Morgan (Manager) and Hannah de Villiers, with additional input from Elena Korsukova, Paola Valenza and Oliver Kremer (Associate Consultants). The report was edited by Andrea Smith, with graphic design by Julie Bartram.
We would like to thank the following people for their time and valuable inputs during our research: Amit Bhatnagar (Managing Director, Accuster), Dana Blouin (Chief Data Scientist, DRVR), Subra Krishnan (SVP Products, Vizury), Vinay Nathan (CEO and co-Founder, Altizon), Reynazran Royono (Founder and CEO, Snapcart), Yeo Puay Lim (Regional manager SE Asia, Milvik / Bima) and all those who contributed anonymously through interviews. Thanks are also due to Toshiki Yano and Rahul Jain at Google for their comments and feedback during the preparation of this report.