Six (Mega)Trends for Deriving Massive Value From Big Data
Learn about six trends that are changing the way we derive value from Big Data, including architecture, the internet of (any)thing, and machine learning.
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“The world is one big data problem.”
Andrew McAfee, associate director of the Center for Digital Business at MIT Sloan
One whole year of almost daily client meetings and discussions with industry leaders have helped me see crystallize my view of an important yet abstract idea into reality. That is, Big Data capabilities or the lack of them will define your enterprise future.
To that end, please find the six megatrends that will continue to drive Big Data into enterprise business and IT architectures for the foreseeable future.
- The Internet of Anything (IoAT) — The rise of the machines has been well-documented but enterprises have just begun waking up to the possibilities in 2016. The paradigm of harnessing IoT data by leveraging Big Data techniques has begun to gain industry wide adoption and cachet. For example, in the manufacturing industry data is being gathered from a wide variety of sensors that are distributed geographically along factory locations running 24/7. Predictive maintenance strategies that pull together sensor data, prognostics are critical to efficiency and also to optimize the business. In other verticals like healthcare and insurance, massive data volumes are now being reliably generated from diverse sources of telemetry such as patient monitoring devices as well as human manned endpoints at hospitals. In transportation, these devices include cars in the consumer space, trucks and other field vehicles, geolocation devices. Others include field machinery in oil exploration and server logs across IT infrastructure. In the personal consumer space, personal fitness devices like FitBit, Home and Office energy management sensors, etc. All of this constitutes the trend that Gartner terms the Digital Mesh. The Mesh really is built from coupling machine data these with the ever growing, social media feeds, web clicks, server logs etc. The Digital Mesh leads to an interconnected information deluge which encompasses classical IoT endpoints along with audio, video, and social data streams. Applications that are leveraging Big Data to ingest, connect, and combine these disparate feeds into one holistic picture of an entity – whether individual or institution – are clearly beginning to differentiate themselves. IoAT is starting to be a huge part of digital transformation initiatives with more usecases emerging in 2017 across industry verticals.
- The Emergence of Unified Architectures — The onset of Digital Architectures in enterprise businesses implies the ability to drive continuous online interactions with global consumers/customers/clients or patients. The goal is not just provide engaging visualization but also to personalize services clients care about across multiple modes of interaction. What Big Data brings to the equation beyond it’s strength in data ingest & processing is a unified architecture. The result is that ANY kind of application processing can be run inside a Hadoop runtime – batch, realtime, interactive or streaming.
- Consumer 360— Mobile applications first begun forcing the need for enterprise to begin supporting multiple channels of interaction with their consumers. For example, banking now requires an ability to engage consumers in a seamless experience across an average of four to five channels – Mobile, eBanking, Call Center, Kiosk etc. Healthcare is a close second where caregivers expect patient, medication and disease data at their fingertips with a few finger swipes on an iPad app. The healthcare industry stores patient data across multiple silos – ADT (Admit Discharge Transfer) systems, medication systems, CRM systems etc. Applications developed in 2016 and beyond must take a 360 degree based approach to ensuring a continuous client experience across the spectrum of endpoints and the platforms that span them from a Data Visualization standpoint.
- Machine Learning, Data Science and Predictive Analytics — Most business problems are data challenges and an approach centered around data analysis helps extract meaningful insights from data thus helping the business. It is a common capability now for many enterprises to possess the capability to acquire, store, and process large volumes of data using a low cost approach leveraging Big Data and Cloud Computing. What we commonly refer to as Machine Learning – a combination of of econometrics, machine learning, statistics, visualization, and computer science – extracts valuable business insights hiding in data and builds operational systems to deliver that value.
- Visualization – Tools such as intelligent dashboards, scorecards, and mashups, are helping change a visualization paradigms that were based on histograms, pie charts, and tons of numbers. Big Data improvements in data lineage, quality are greatly helping the visualization space.
- DevOps — Big Data powered by Hadoop has now evolved into a true application architecture ecosystem as mentioned above. The 30-plus components included in an enterprise grade platform like the Hortonworks Data Platform (HDP) include APIs (Application Programming Interfaces) to satisfy every kind of data need that an application could have – streaming, realtime, interactive or batch Organizations using DevOps are already reaping the rewards as they are able to streamline, improve and create business processes to reflect customer demand and positively affect customer satisfaction. Examples abound in the Webscale world (Netflix, Pinterest, and Etsy) but we now have existing Fortune 1000 companies in verticals like financial services, healthcare, retail and manufacturing who are benefiting from Big Data and DevOps.
The Final Word
Thus, modern data applications are making Big Data ubiquitous. Rather than existing as back-shelf tools for the monthly ETL run or for reporting, these modern applications can help industry firms incorporate data into every decision they make. Applications in 2016 and beyond are beginning to recognize that Analytics are pervasive, relentless, realtime and thus embedded into our daily lives.
Published at DZone with permission of Vamsi Chemitiganti. See the original article here.
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