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Riding the Wild Waves of Analytics Industry Change

What are the seven seas of analytics? Jen Underwood, Founder, Impact Analytix, covers future trends in analytics in this in-depth article. Meet Jen and attend her session "Data Science with Excel, Open Source R, and Python for Data Analysts" at BA Day Atlanta on June 21, 2017. 

Riding the Wild Waves of Analytics Industry Change

By Jen Underwood

What an exciting time for analytics professionals! Accelerated technology innovation, infinite cloud scale, ubiquitous connectivity, and an internet of smart things powered by artificial intelligence is enabling a fourth industrial revolution—the digital transformation. 

Reinvent and Thrive

Digital business is an overarching concept that refers to the blending of physical and virtual worlds. As digital organizations transform, new business models, industries, and markets emerge, think about the impact Amazon has had in the retail industry, Airbnb in the hotel industry, Lyft in taxi business, or Kickstarter crowd funding in lending. Expect much more market disruption to happen.

Like many changes in our lifetime, digital transformation is an opportunity to reinvent. It is literally empowering a radical re-imagination of our world as we know it. Looking ahead, we will be digitally engaging customers, interacting with digital channel ecosystems, proactively sensing with intelligent things, and automating decisions. We are entering a world where data is gold. 

Seven Seas of Analytics

Today, we are seeing unprecedented levels of opportunity for data savvy analytics professionals that can skillfully navigate oceans of data to help with digital transitions. To prevent wiping out while riding the wild waves of analytics industry trends, here seven areas to master. 

  1. Cloud and Hybrid Analytics
    The move to cloud is accelerating. Although most analytics applications today still leverage older data warehouse and OLAP technologies on-premises, the pace of the cloud shift is significantly increasing. Infrastructure is getting better and is almost invisible in mature markets. Cloud fears are subsiding as more organizations witness the triumphs of early adopters. Instant, easy cloud solutions continue to win the hearts and minds of non-technical users. Cloud also accelerates time to market allowing for innovation at faster speeds than ever before. As data and analytics professionals, be sure to make time to learn a variety of cloud and hybrid analytics tools.   

  2. Embedded Analytics
    Another key area of digital transformation is embedded analytics. In the analytics market, this segment claims the top spot in self-service analytics growth. Data-driven cultural shifts are bringing analytics much closer to the user—in the app, when and where decisions are made. Cognitive, predictive and prescriptive analytics are increasingly being embedded into line-of-business apps. As more decisions are automated, analytics will invisibly be embedded into apps or processes. Key technologies to learn for embedding includes but is not limited to REST APIs, JSON, basic HTML and JavaScript concepts. Yes, you need to know just a little about development to stay relevant in analytics.  

  3. Smart Data Discovery, Predictive and Prescriptive Analytics
    In a natural progression of analytics maturity, organizations will advance towards smart data discovery, predictive and prescriptive. Smart data discovery has the potential to disrupt the analytics industry much like data discovery did to traditional BI approaches. A solid foundation in understanding how “automated” smart data discovery works, limitations, strengths and weaknesses will become more important soon. If you love analytics, predictive and prescriptive analytics that can help you optimize data-driven decisions should also be at the top of your list to learn. Thousands of your peers have already started on this path in 2017.

  4. Cognitive Computing
    Cognitive computing technologies are progressing from successful early adoption. In 2016, we saw cognitive, deep learning and natural language technologies take on increasingly prominent roles in apps while capturing news headlines around the world. Cognitive computing algorithms can make sense of many types of structured and unstructured data sources. Unstructured data (files, images, email, audio, video, etc.), aka “dark data” sources, account for most of the world’s data. Thus, this cool technology that continually trains itself to get smarter will eventually become a must-have in enterprise analytics roadmaps. Consider learning how cognitive computing works and how to embed cognitive intelligence output into your analytics applications.

  5. Real-time Analytics
    In an interconnected, omnichannel digital world, timely intelligence becomes a need versus a want to prosper. The use cases for batch reporting are declining. In automated channels, you don’t have the luxury to pull a report or wait for a data refresh from last night, week, or month. Marketing, sales, operations, support and many other areas of the business will be leveraging real-time analytics apps that contain predictive algorithms to automatically detect exceptions and provide proactive alerts. We are already seeing these capabilities in leading analytics vendor offerings. Streaming data analytics and extremely fast GPU-powered databases are also easier than ever to spin up and deploy for delivering real-time analytics.

  6. Data Monetization
    Most organizations already appreciate the value of data internally. The next step is maximizing the economic benefit of that collected data externally together with customers, partners and suppliers. The ability to derive monetary value from data is an essential skill in a digital business ecosystem. Keep in mind that this area of our industry is not well regulated yet. For early adopters, data monetization is a bleeding edge area of analytics. 

  7. Data Security and Privacy
    Along with conversations about how to maximize the value of data externally, you should expect to hear related concerns about the proper handling of data from legal and ethical perspectives. Every analytics professional should take data security and privacy seriously. Per Verizon’s 2016 Data Breach Report, most data breaches happen internally, are unintentional, get found externally and were the result permissions misuse. To prepare yourself for the digital transformation, be sure to ramp up minimally on data security basics. 

Everyone Needs to Master Data

The challenges we face as data and analytics professionals right now are not technology related. We are fortunate to live in an era of amazing innovation. Our challenges today are related to maintaining relevant skills and keeping up with rapidly changing analytics technologies. Too many of us are not looking beyond the comfortable, older technologies that we already know. 

I challenge you to expand your horizons by exploring novel technologies across various ecosystems in the cloud world. It is incredibly simple to launch a cloud image or service to get started. As you explore a new world of data, you will find common analytics architectures, design patterns and technologies being used to solve problems. Once you become more skilled in contemporary technologies, you should be able to successfully ride out the big wave of analytics change as an enabler of digital intelligence. 

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