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What’s next for disaster recovery?

Companies are now less concerned with data backup times, but more with the integrity and time taken to restore a backup. It is for this reason that recovery times and objectives are becoming more precise than ever, writes MARK BENTKOWER.

When it comes to modern data protection, not all data should be treated the same way. Long gone are the days of just dumping a bunch of files onto a tape overnight and sending it to the vault. Today’s organisations are less concerned about data backup times, than they are about ensuring a quick and easy recovery of application data and business services due to a natural or human-induced disaster. Recovery time and recovery point objectives are becoming more precise and demanding as Service Level Agreements (SLA’s) begin to cover larger amounts of data.

A recent IDC survey of small and medium-sized business users revealed that 67 percent of these firms have a recovery time requirement of less than four hours, while 31 percent have a recovery time requirement of less than two hours. Recovering from multiple mediums, such as Storage Area Network (SAN) snapshots, hypervisor guests and virtualised applications is critical to maintain productivity and avoid the legal risks and hefty financial penalties that come with broken SLA’s. Rapid application recovery is fast becoming the only option, providing organisations with new levels of agility that are critical in today’s information era.

Recognising DR challenges

In a region where serious outages and natural disasters are not uncommon, the lack of a comprehensive Disaster Recovery (DR) plan has the very real potential of threatening the continued existence of some organisations. Many companies in Southeast Asia do not have a cohesive DR strategy, or have implemented DR strategies which cannot sufficiently safeguard them from these business crippling risks. Below are some of the key DR challenges identified in the region.

Lack of automation: The manual management of information requires a significant investment of time and burdens technical teams to simply manage backups and address issues as they arise.  There is no time to take a nuanced approach based on mission criticality. Manual systems create greater risk around human error, confidential data exposure and information loss. With automated information lifecycle systems, today’s IT teams should focus more on individual SLA’s, and should prioritise automation to free up administrators to fulfil more difficult tasks.

Use of tape: While tape is fine for slow archival storage, it is too inefficient and slow for the rapid pace of DR restores, especially at the application level. Think about the rapid pace of change at play here. In terms of global data growth, the world generated over 90 percent of extant data in the last two years alone. That’s a game changing statistic. Yet, many organisations in Asia Pacific still rely on tape as a key source of backup, which is hindering their ability to be agile, flexible and react quickly to both crises and market opportunities.

Redundant data: The proliferation of data silos within Asia Pacific organisations are hindering the ability for IT managers to make insight-based decisions and effectively manage large pools of data. This results in increased IT costs, hindered innovation and a segmented view of the business. A Commvault-commissioned survey by IDC found that 40 percent of IT decision makers across APAC report that backup, recovery, data protection and analytics strategies are still managed at a departmental level .

Network bottlenecks: Asia and the Pacific are amongst the world’s most natural disaster-prone areas. Of the world’s reported natural disasters between 2004 and 2013, 41.2 percent or 1,690 incidences, occurred in the Asia-Pacific region alone. Compounding this, Southeast Asia is made up of predominantly under-developed and developing economies with slow and unreliable network connections. For example, in Thailand, businesses have lost US$297 million in revenue from network downtime over the past year.

Defining the new state of recovery

So how can companies move past these challenges and adopt a modern approach to DR? Organisations can consider using block-level methods with orchestrated snapshot and streaming recovery across backup data with incremental change capture. This technology captures regular snapshots of only time incremental changes in information (rather than entire environment every time), which dramatically reduces network impact during data protection operations. Incremental change capture also provides downstream efficiencies in network and storage utilisation by reading and moving the delta blocks, and storing only the unique changed blocks. This reduces bandwidth and storage requirements for ongoing recovery operations, and speeds Recovery Point Objective (RPO) and Recovery Time Objective (RTO).

Additionally, organisations can drive the benefits below from including incremental change capture in their checklist as they seek to advance their data management strategy.

– Lower impact on the business as full backups are not required – as much as 90 percent less impact, compared with streaming backup

– Workload computing capacity typically required for backup will be available for other business needs

– An hourly recovery point minimises risk by reducing RPO

– Reduction of data storage space as a single copy of the data can be used for multiple purposes

– Faster data recovery as data is stored in an open format instead of a proprietary format

Innovating to address evolving needs

As mega trends like migration to the cloud, anywhere computing, and the explosive growth of data sweep across all industries, business expectations have also evolved. Businesses have become increasingly intolerant of data loss and services downtime.  Redefining traditional DR strategies assures continued availability of information, which is fundamental to maintaining competitive edge and enabling innovation.

* Mark Bentkower, CISSP, Director of Systems Engineering, ASEAN, Commvault.

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Now IBM’s Watson joins IoT revolution in agriculture

Global expansion of the Watson Decision Platform taps into AI, weather and IoT data to boost production

IBM has announced the global expansion of Watson Decision Platform for Agriculture, with AI technology tailored for new crops and specific regions to help feed a growing population. For the first time, IBM is providing a global agriculture solution that combines predictive technology with data from The Weather Company, an IBM Business, and IoT data to help give farmers around the world greater insights about planning, ploughing, planting, spraying and harvesting.

By 2050, the world will need to feed two billion more people without an increase in arable land [1]. IBM is combining power weather data – including historical, current and forecast data and weather prediction models from The Weather Company – with crop models to help improve yield forecast accuracy, generate value, and increase both farm production and profitability.

Roric Paulman, owner/operator of Paulman Farms in Southwest Nebraska, said: “As a farmer, the wild card is always weather. IBM overlays weather details with my own data and historical information to help me apply, verify, and make decisions. For example, our farm is in a highly restricted water basin, so the ability to better anticipate rain not only saves me money but also helps me save precious natural resources.”

New crop models include corn, wheat, soy, cotton, sorghum, barley, sugar cane and potato, with more coming soon. These models will now be available in the Africa, U.S. Canada, Mexico, and Brazil, as well as new markets across Europe and Australia.

Kristen Lauria, general manager of Watson Media and Weather Solutions at IBM, said: “These days farmers don’t just farm food, they also cultivate data – from drones flying over fields to smart irrigation systems, and IoT sensors affixed to combines, seeders, sprayers and other equipment. Most of the time, this data is left on the vine — never analysed or used to derive insights. Watson Decision Platform for Agriculture aims to change that by offering tools and solutions to help growers make more informed decisions about their crops.” 

The average farm generates an estimated 500,000 data points per day, which will grow to 4 million data points by 2036 [2]. Applying AI and analysis to aggregated field, machine and environmental data can help improve shared insights between growers and enterprises across the agriculture ecosystem. With a better view of the fields, growers can see what’s working on certain farms and share best practices with other farmers. The platform assesses data in an electronic field record to identify and communicate crop management patterns and insights. Enterprise businesses such as food companies, grain processors, or produce distributors can then work with farmers to leverage those insights. It helps track crop yield as well as the environmental, weather and plant biologic conditions that go into a good or bad yield, such as irrigation management, pest and disease risk analysis and cohort analysis for comparing similar subsets of fields.

The result isn’t just more productive farmers. Watson Decision Platform for Agriculture could help a livestock company eliminate a certain mold or fungus from feed supply grains or help identify the best crop irrigation practices for farmers to use in drought-stricken areas like California. It could help deliver the perfect French fry for a fast food chain that needs longer – not fatter – potatoes from its network of growers. Or it could help a beer distributor produce a more affordable premium beer by growing higher quality barley that meets the standard required to become malting barley.

Watson Decision Platform for Agriculture is built on IBM PAIRS Geoscope from IBM Research, which quickly processes massive, complex geospatial and time-based datasets collected by satellites, drones, aerial flights, millions of IoT sensors and weather models. It crunches large, complex data and creates insights quickly and easily so farmers and food companies can focus on growing crops for global communities.

IBM and The Weather Company help the agriculture industry find value in weather insights. IBM Research collaborates with start up Hello Tractor to integrate The Weather Company data, remote sensing data (e.g., satellite), and IoT data from tractors. IBM also works with crop nutrition leader Yara to include hyperlocal weather forecasts in its digital platform for real-time recommendations, tailored to specific fields or crops. IBM acquired The Weather Company in 2016 and has since been helping clients better understand and mitigate the cost of weather on their businesses. The global expansion of Watson Decision Platform for Agriculture is the latest innovation in IBM’s efforts to make weather a more predictable business consideration. Also just announced, Weather Signals is a new AI-based tool that merges The Weather Company data with a company’s own operations data to reveal how minor fluctuations in weather affects business.

The combination of rich weather forecast data from The Weather Company and IBM’s AI and Cloud technologies is designed to provide a unique capability, which is being leveraged by agriculture, energy and utility companies, airlines, retailers and many others to make informed business decisions.

[1] The UN Department of Economic and Social Affairs, “World Population Prospects: The 2017 Revision”

[2] Business Insider Intelligence, 2016 report: https://www.businessinsider.com/internet-of-things-smart-agriculture-2016-10


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What if Amazon used AI to take on factories?

By ANTONY BOURNE, IFS Global Industry Director for Manufacturing

Amazon recently announced record profits of $3.03bn, breaking its own record for the third consecutive time. However, Amazon appears to be at a crossroads as to where it heads next. Beyond pouring additional energy into Amazon Prime, many have wondered whether the company may decide to enter an entirely new sector such as manufacturing to drive future growth, after all, it seems a logical step for the company with its finger in so many pies.

At this point, it is unclear whether Amazon would truly ‘get its hands dirty’ by manufacturing its own products on a grand scale. But what if it did? It’s worth exploring this reality. What if Amazon did decide to move into manufacturing, a sector dominated by traditional firms and one that is yet to see an explosive tech rival enter? After all, many similarly positioned tech giants have stuck to providing data analytics services or consulting to these firms rather than genuinely engaging with and analysing manufacturing techniques directly.

If Amazon did factories

If Amazon decided to take a step into manufacturing, it is likely that they could use the Echo range as a template of what AI can achieve. In recent years,Amazon gained expertise on the way to designing its Echo home speaker range that features Alexa, an artificial intelligence and IoT-based digital assistant.Amazon could replicate a similar form with the deployment of AI and Industrial IoT (IIoT) to create an autonomously-run smart manufacturing plant. Such a plant could feature IIoT sensors to enable the machinery to be run remotely and self-aware; managing external inputs and outputs such as supply deliveries and the shipping of finished goods. Just-in-time logistics would remove the need for warehousing while other machines could be placed in charge of maintenance using AI and remote access. Through this, Amazon could radically reduce the need for human labour and interaction in manufacturing as the use of AI, IIoT and data analytics will leave only the human role for monitoring and strategic evaluation. Amazon has been using autonomous robots in their logistics and distribution centres since 2017. As demonstrated with the Echo range, this technology is available now, with the full capabilities of Blockchain and 5G soon to be realised and allowing an exponentially-increased amount of data to be received, processed and communicated.

Manufacturing with knowledge

Theorising what Amazon’s manufacturing debut would look like provides a stark learning opportunity for traditional manufacturers. After all, wheneverAmazon has entered the fray in other traditional industries such as retail and logistics, the sector has never remained the same again. The key takeaway for manufacturers is that now is the time to start leveraging the sort of technologies and approaches to data management that Amazon is already doing in its current operations. When thinking about how to implement AI and new technologies in existing environments, specific end-business goals and targets must be considered, or else the end result will fail to live up to the most optimistic of expectations. As with any target and goal, the more targeted your objectives, the more competitive and transformative your results. Once specific targets and deliverables have been considered, the resources and methods of implementation must also be considered. As Amazon did with early automation of their distribution and logistics centres, manufacturers need to implement change gradually and be focused on achieving small and incremental results that will generate wider momentum and the appetite to lead more expansive changes.

In implementing newer technologies, manufacturers need to bear in mind two fundamental aspects of implementation: software and hardware solutions. Enterprise Resource Planning (ERP) software, which is increasingly bolstered by AI, will enable manufacturers to leverage the data from connected IoT devices, sensors, and automated systems from the factory floor and the wider business. ERP software will be the key to making strategic decisions and executing routine operational tasks more efficiently. This will allow manufacturers to keep on top of trends and deliver real-time forecasting and spot any potential problems before they impact the wider business.

As for the hardware, stock management drones and sensor-embedded hardware will be the eyes through which manufacturers view the impact emerging technologies bring to their operations. Unlike manual stock audits and counting, drones with AI capabilities can monitor stock intelligently around production so that operations are not disrupted or halted. Manufacturers will be able to see what is working, what is going wrong, and where there is potential for further improvement and change.

Knowledge for manufacturing

For many traditional manufacturers, they may see Amazon as a looming threat, and smart-factory technologies such as AI and Robotic Process Automation (RPA) as a far off utopia. However, 2019 presents a perfect opportunity for manufacturers themselves to really determine how the tech giants and emerging technologies will affect the industry. Technologies such as AI and IoT are available today; and the full benefits of these technologies will only deepen as they are implemented alongside the maturing of other emerging technologies such as 5G and Blockchain in the next 3-5 years. Manufacturers need to analyse the needs which these technologies can address and produce a proper plan on how to gradually implement these technologies to address specific targets and deliverables. AI-based software and hardware solutions will fundamentally revolutionise manufacturing, yet for 2019, manufacturers just have to be willing to make the first steps in modernisation.

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