Riding the Information Tsunami: IoT and Big Data Analytics

by Max Chan, Vice President, Global Information Solutions, Avnet Technology Solutions Asia Pacific

 

Max LJ Chan 25percent

There has been a number of exciting developments around the Internet of Things at Avnet lately.  First, we kicked off the calendar year 2016 announcing a newly created role of vice president, Internet of Things in the person of Eric Williams who will steer the company’s global IoT strategy. The year prior, Tim FitzGerald who previously led Avnet’s Cloud Solutions business has been appointed vice president of digital transformation. These key appointments underscore Avnet’s steadfast commitment to investing in the right resources to capitalize on the opportunities in the rapidly growing IoT market.

In February, Avnet and IBM announced joining forces to accelerate IoT innovation. Having been solid partners for 30 years, the two companies teamed up to help customers develop IoT solutions built on the IBM Watson IoT Platform to help create new revenue streams and operational efficiencies. The collaboration builds on Avnet’s successful IoT development practices, one of the recent success stories being the water safety improvement project with SPICA Technologies. Avnet played a role in building a solution that reduces the risk of Legionnaire’s disease by using connected devices attached to the water pipes that provide real-time information and analysis on water temperature and flow, improving accuracy and reducing monitoring costs by up to 60 percent.

And the excitement continues! Worldwide IoT spending reached more than $643.8 billion in 2015, and it is expected to account for approximately $998 billion in 2018.[1] For the individual, what IoT really means is information at one’s fingertips, more embedded intelligence, smarter use of resources, and simply put, improved life through technology. For companies, the implication of IoT is the creation of a tremendous amount of data that could be mined not only to analyse the past, but also to predict future behaviour or actions.

Avnet’s insights on data analytics

It’s not just about technology

Today’s technology allows companies to handle complex queries in much less time. Our priority, though, is to help businesses convert their data into manageable value their employees and customers can use. Avnet helps organisations implement strong data governance policies and ensure the smooth execution of big data analytical tools that translate their data into insights.

Data is meaningless if not understood and acted upon. When insights are required for critical decision making, we must begin by asking the right questions. This has given rise to a new demand for data scientists, as more people start recruiting talent to aid their pursuits in intelligence gathering and interpretation. Employees also need to be aware of the changes analytics can bring to the organisation. Existing employees should be receiving training and education to help them acclimate to the realities of the new technological environment.

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Analysed data needs to reach the right people – in many cases, the decision-makers

The role of the data scientists is to work with the data they have, and churn it out into business intelligence, to be brought to management who will be reliant on the insights. Something that we have observed to be very successful is the transportation of this business intelligence from data scientists to decision-makers via mobile channels. In this way, they can acquire, in real-time, important information such as, where the biggest opportunities lie, which customers have the most potential, and what are the best-performing product lines. At the same time, these decision-makers need to get used to change on their end.

Challenges in adoption  

The common impediment for big data and analytics adoption is the price of the technology. However, this has changed in the last few years, with the total cost of ownership for this technology on the decline, making it within reach for all enterprises, large or small. Although, even if the cost remains the top barrier to adoption, I’d say, in most cases that that is just an excuse.

From another perspective, there is the concern around having in-house talent as not all can afford data scientists, and they are talents which aren’t easy to find. The truth is that the organisations aren’t going to see progress simply by doing what they have been doing all along, generating more and more data – there has to be someone to work with that data.

The availability of data internally and externally is another challenge. Data is being generated from a wide variety of sources constantly, but not everyone knows how to find it. This is particularly so in Asia-Pacific, due to the diversity across the region. You can’t just go onto Facebook and think that it will provide you with the intelligence you need. You also have to look at China, where majority of the users are on Weibo. You have to consider the different platforms that your target market is on, and this can be area-specific.

Current and future adoption in APAC

What we are seeing today is that a few industries are getting ahead of the rest when it comes to big data analytics. Retail is one of the few industries that is succeeding, both online and offline. This is because in retail, it’s no longer just about what the customer views or purchases – it goes to the extent of what they are saying on social networks, their preferences and psyches. All of this information is readily available across their social media accounts.

There is also geo-fencing, where advertisers, marketers and retailers specifically look at where the individual has been, and what he or she has done. They then leverage that information by placing advertisements along those historical geographical locations. It’s clear to see here that retail is an industry leading the way forward for data analytics.

Utilities and buildings are also areas where analytics adoptions are increasing, primarily to drive efficiency in power and water usage. Within the utilities and buildings space, sensors are used to collect data on things such as room temperature, humidity and energy consumption. With a clear view of their electrical consumption and costs, building management can then take appropriate steps to reduce unnecessary costs. Being able to track utilities consumption will also create identifiers of higher usage, thus possible higher-value customers. From here, new business models can be crafted, such as shorter billing cycles where it better suits the customers’ needs. The utilities sector is a great example of the versatility and effectiveness of IoT technologies working together with analytics to solve problems.

Geographically, we see countries such as Singapore, Hong Kong and Australia as being far ahead of the curve, from a technological standpoint and in terms of analytics usage. China and India look to be promising and viable markets as well.

There will be differences in adoption across industries, but if you look at sectors such as telecommunications, retail, government and public utilities, analytics is something that cannot be an option anymore; it has to be standard. It is becoming increasingly hard for these sectors to make real-time decisions to respond, either to customers or the public. Look at the retail industry, which is characterized by fierce competition. If you can get closer to the consumer and uncover their buying power, brand preferences and preferred shopping locations, you can leverage the intelligence to bring to them what they want. This is critical for businesses to stay ahead of their competitors. Then there is also the comparison between online and offline shopping, where measurements can only be acquired via extensive data analytics. Increasingly, many of these companies will be operating in a big data environment.

I would say that analytics has become an inevitable part of any organisation, especially those where the public is involved, and there is strong demand for products or services. Again with utilities, they will want to find out how to use the M2M data generated to drive efficiency in their power and water supply operations. Real-time data for the utilities sector is made possible through on-demand metering, enabling more accurate insights as to how customers are being billed.

All these activities are driven by analytics, made possible as a result of all the technology that is available today. IoT as well as M2M will be responsible for a large part of the adoption of analytics.

Final thoughts

To remain ahead of emerging technology trends, Avnet consistently builds upon and aggregates our collective resources, capabilities and multiple elements of technology from the edge to the enterprise. As our CEO Rick Hamada puts it: “The pace of technology in today’s global business environment demands agility and imagination.” I believe Avnet is perfectly positioned to develop and optimize IoT as well as data analytics, among other solutions, because we have a 360-degree view of market from the heart of the technology supply chain. We will continue to invest in our organic growth initiatives in next generation technologies.

[1] Source:  Avnet Business Intelligence Office estimates based upon Gartner and IDC industry data. Excludes modules and sensors.

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March 29, 2016 5:37 pm



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