We live in an incredible era of extremely rapid, disruptive innovation. Globalization, accelerated technology change, infinite cloud scale, ubiquitous connectivity, and an internet of smart things powered by artificial intelligence is enabling a fourth industrial revolution — the digital transformation. In this article, I will highlight a few areas to explore as you prepare for the future of analytics.

I also have a little surprise for someone. Keep reading…

Natural Language, Search, Analytics Automation and Smart Data Discovery

The challenges data and analytics professionals are facing right now are not technology related. Our challenge are related to staying relevant. One of the benefits of monitoring the analytics market daily is getting an early glimpse into the future of analytics. Right now I already see disruptive natural language interfaces, search, analytics automation and smart data discovery solutions that will alter the landscape once again much like data discovery did to traditional BI practices.

I already see disruptive analytics solutions that will alter the landscape once again

It is more important than ever to invest a little time each week to look beyond the comfortable, older and current technologies that you already know or might be assigned to work on. If you do work in an organization that is not embracing newer technologies, you’ll likely need to burn the midnight oil to keep your skills updated. The good news is that the latest and greatest tech is fun to learn.

Cloud and Hybrid

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.

Exploring novel technologies across various ecosystems in the cloud world is usually as simple as spinning up a cloud image or service to get started. There are literally zillions of free and low cost resources for learning. As you dive into a new world of data, you will find common analytics architectures, design patterns, and types of technologies (hybrid connectivity, storage, compute, microservices, IoT, streaming, orchestration, database, big data, visualization, artificial intelligence, etc.) being used to solve problems.

Think Digital

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 like that to happen…also to our jobs. The machines are coming!


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.

entering a world where data is gold

Data Science

If you are able to master extracting intelligence from massive volumes of data, you should enjoy unprecedented levels of opportunity in the realm of digital transformation. Data science demand continues to grow with no signs of slowing down anytime soon.

My little surprise…one complimentary Data Science Day pass

If you know of someone in the Atlanta, Georgia area that really wants (or needs) to learn more about data science that is unemployed, doesn’t know where to start, might be working but gets held back keeping the lights on at your organization taking care of old technology or for some other reason, please nominate them for one complimentary pass to my upcoming PASS Data Science Day on June 21. Self nominations are welcome…I know many of us are introverts.


Please share why your nominee might want this training and what is holding them back. I will contact you if your nominee is chosen to make arrangements. Everyone else is welcome to sign up for this enjoyable day diving into the science of data science per se and playing with data.

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.

Cognitive Computing

Cognitive computing technologies are progressing from successful early adoption. 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 arsenals. Consider learning how cognitive computing works and how to embed cognitive intelligence output into your analytics applications.

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.

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.

Data Security and Privacy

Along with conversations about how to maximize the value of data externally, you should expect more concern around the proper handling of data from legal and ethical perspectives. Every analytics professional should take data security and privacy seriously. Be sure to ramp up minimally on data security basics.

Every analytics professional should take data security and privacy seriously

Last winter I shared findings from Verizon’s 2016 Data Breach Report. Most data breaches originate internally and are unintentional. Those breaches were most often the result of permissions misuse. For self-service BI administrators, check out my article on that topic that includes crucial tips and a white paper on best practices. Even if you don’t care about any of my other tips to stay relevant, do care about this one since it does affect other people.


Note: This article was adapted from my recent May 11, 2017 PASS News feature: Riding the Wild Waves of Analytics Industry Change.