Article Blog Featured - Blog IoT Partners November 6, 2015 3 minute read

Preparing for the Windfall of Data

In Maria Deutscher’s article in SiliconANGLE, Cisco Predicts Internet of Things will Generate 500 Zettabytes of Traffic by 2019, she covers Cisco’s predictions for traffic created by a connected universe. Essentially, more devices are getting connected to the Internet, which translates to much more unstructured data being generated over the next five years. The prediction is over 500 zettabytes annually. At the same time, cloud consumption is expected to quadruple in the next five years, as more apps and data move to the cloud.

While this is definitely interesting, it leaves many of us wondering what companies can do to harness insights hidden in this data and create business value. Technology is not the limiter – we’re seeing amazing advances in computing, like massively parallel processing (MPP), machine learning, automation, and other enabling technologies, such as networking and storage. In my mind, there are several fundamental questions that we need to consider in order to be better prepared to handle and analyze this massive volume of data.

 

Data Governance

With so much data being generated, we need to consider a number of questions about the data, including: Will it be freely available and accessible to all players? Who owns it? Who will safeguard it against misuse? Will consumers be responsible for opting-in to restrict how it will be shared and used? How will it be sourced and consolidated from multiple sources? As we see more zettabytes generated, these are the basic issues that need to be addressed around data governance, management, information security, and privacy.

 

Regulations and Rules

The European Union recently decided to invalidate a transatlantic data pact that will prevent Europeans’ personal data from being stored in U.S. data centers. The issue is how this and other regulations will impact the ability of businesses to innovate using Big Data now and in the future. Beyond just governance and management, there is the challenge of how companies will access, index, and search that data in a timely and compliant manner. How do we tackle the persistent challenge of the 4 V’s framework: volume, variety, velocity, and veracity? If a company wants to create products based on the data, how do they retrieve it from various sources (e.g., supply chain risk analytics based on various internal and external global sources, consumer sentiment analysis based on social media, web analytics based on clickstream and other data) in a consistent form that they can then analyze? Again, technology is not the limiting factor; the main challenge for companies is the cost and effort required to keep up with the rules and regulations governing access to Big Data in order to create valuable products.

 

Distributed Analytics

We can now move all forms of raw structured, semi-structured, and unstructured data into centralized, massively scalable data stores, perform advanced data science on it, and run machine learning algorithms to extract business insights from it. The learning algorithms used are getting a lot smarter and improving all the time, but extracting meaningful insights requires the timely preparation and integration of data from multiple, distributed sources. Data federation and distributed analytics will need to be addressed at an architectural level. Cloud-based solutions will definitely be part of the answer – the challenge will be to federate across multiple clouds (public, private, government, service provider, etc.). Regardless of the setting, many applications will require insights to be consolidated into a coherent whole (e.g., in Financial Services, Supply Chain, and other domains).

 

These are the important questions that need to be addressed. Cisco’s prediction is a very real trend. No doubt that the opportunity for innovation and value creation is compelling, and the technology keeps advancing by leaps and bounds. The more critical questions should be: what are the business applications and what data is required? Who will have access to it; how will they use it; and how can we create lasting value from the imminent windfall of data?

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