Analytics in 360 Degrees: Looking Back/To the Future

Want to skip around? Jump in your time machine and click over to Analytics Past, Analytics Future, or To 2030, and Beyond.

 

It’s January 2010. The Great Recession is winding down in the United States, and the global economy is still in recovery (as are your spouse’s spending habits). In the tech world, you’ve just heard rumors about a breakthrough tablet from Apple called the “iPad” or the “iPal” — you can’t remember which — but you’re still trying to wrap your head around someone spending $500 for a bigger version of your iPhone 3.

You don’t know it yet, but there’s a historic shift taking place in the data science and analytics industry. Thanks to changes in technology and culture, data is becoming more prevalent than ever, and with it, the growing need to make sense of it — and apply it to solve economic, business, and social problems.

Dean Stoecker, CEO of Alteryx, frames the revolution like this: “The Agrarian Age helped us feed people and resulted in data that tells us how humans are dependent on the earth. The Industrial Age helped us manufacture products that generated data about how humans interact with things not of this earth. The Information Age led to humans creating data sprawl both physically and virtually, and now the 4th age is the Age of Analytics where the human is tasked with making sense of all the data we could ever want to solve any problem we have ever faced. This is going to be fun.”

 

ANALYTICS PAST

Data Becomes a Commodity

Nobody would have called data “rare” in the early 2010s. According to IDC, the world was generating 1 zettabyte (ZB) of data a year in 2010. But compared to the 33 ZB produced in 2018 and the estimated 175 ZB that will be produced in 2025, that little ZB was just a drop in the deluge.

To give you some perspective, one zettabyte is a trillion gigabytes, or one sextillion bytes — more than the amount of human breaths taken in 2019 (77 quadrillion). And as you mental math wizards may have figured out, one to 175 ZB is a 17,400% increase. Even the Starship Enterprise would need a new level of boldness to explore data of that magnitude.

data production in zetabytes graphic showing upward curve

So what caused this crazy growth?

First was the proliferation of data. Several factors contributed, but perhaps the biggest was the arrival of the Internet of Things (IoT). Suddenly, all our IoT devices — devices connected to the internet through Wi-Fi or other frequency networks such as Bluetooth or cellular — were generating data. From our smartphones to our smart refrigerators, these devices were producing data on everything from our exercise habits to the amount of nonfat milk we had left on our shelves. Organizations were, understandably, eager to collect and start using more data on products, people, and transactions than ever before.

As data generation began to skyrocket, we needed to figure out where to put it all. Andrew Brust, CEO/Founder at Blue Badge Insights, explains: “With technology like Hadoop at first, and then cloud object storage, what was a scarce resource [data] became a commodity, and the bias in analytics changed from 'What few things are worthy of analyzing?' to 'Why would we not save and explore this data?'”

“What was a scarce resource [data] became a commodity, and the bias in analytics changed from ‘What few things are worthy of analyzing?’ to ‘Why would we not save and explore this data?’”

— Andrew Brust, CEO/Founder, Blue Badge Insights

While local hardware and servers offered security, on-premises data storage could be costly and hard to scale. Cloud storage offered a cheaper solution where storage could be monitored by third parties, and then, cloud object storage made Big Data storage even more feasible, thanks to data being stored as objects instead of files or blocks.

Massive data production combined with seemingly infinite data storage. The age of Big Data had officially arrived.

 

C-Suite Takes the org for the ride of a Lifetime

The final hurdle was convincing the C-suite that data and analytics was worth their investment. To many organizations, data was a souvenir: something to be collected, but its practical use still had to be proven. Even if organizations could get the specific data they needed in the right format they needed it in, could they analyze it efficiently and quickly enough to positively affect decision-making?

To many organizations, data was a souvenir: something to be collected, but its practical use still had to be proven.

Executive leaders committed to growing their organizations always step up to the plate when it matters. In the 1980s, the role of CFO gained prevalence due to leadership’s initiatives to better manage their assets and investing relationships. Likewise, the CMO became an essential executive thanks to the growing complexity of marketing channels. As the results of the digitally progressive began to speak for themselves, the C-suite turned to the Chief Data Officer to champion this new domain of data and analytics.

While the C-suite saw the value in analytics, standing up this new function was still an undocumented process. Data scientists and analytics teams were expensive and often complicated to set up, and finding the right talent was like mining for gold. It seemed like there was still a critical piece missing.
 
That’s when the decade took a detour it would never look back from. Destination: Data Democratization.

 

Empowered Analysts Take the Driver’s Seat

It was early in the decade when data and analytics software started to gain serious traction  — everything from prep-and-blend to BI and visualization technologies. It was also when a company named SRC rebranded itself to Alteryx. These popular software technologies were solutions to increasingly complex analytics processes.

Ashley Kramer, SVP of Product Management at Alteryx, remembers, “It used to be that you had a separate product or tool for Enterprise ETL, data prep, reporting, data cataloging, visualization, and modeling. Those have now all started to converge into single platforms.”

The cumbersome mess of analytics processes and legacy tools unprepared for Big Data made analytics a headache. The rise of self-service platforms was a saving grace. Complexity was pulled out of the equation, and because the software took care of the coding and back-end, analytics became a “drag-and-drop” process. The barrier to entry for data professionals, as well as the cost for standing up a data team, went to practically nothing, especially when the surprising ROI of quick wins was figured in. 

Paige Bartley, Senior Analyst at 451 Research, explains: “The floodgates of self-service data and analytics within organizations were opened. Formerly the realm of only the most technically-inclined, the leverage of data became democratized in a way that allowed more less-technical individuals to meaningfully derive insight from information that often, in the past, was sequestered and only accessible by a select few.”

“The floodgates of self-service data and analytics within organizations were opened.”

— Paige Bartley, Senior Analyst, 451 Research

Cost and technical know-how were no longer blockers to analytics nirvana. With self-service analytics, IT became a true partner. Not only could analysts get their own data and kick off projects faster, but IT was free of those tiresome ad-hoc data requests that were constantly draining their resources.
 
“IT moved from being the providers of analytics, data, and reporting in the enterprise to facilitators," says Chris Love, Account Manager at The Information Lab. "This allowed analysts and line of business users to do their own data preparation and visualization.”
 
With lower costs and faster turnaround, the C-suite was sold. Digital transformation, spearheaded by data and analytics, swept through organizations.
 
The liberation of the data worker was complete, thanks to simplified, self-service platforms. Undoubtedly, the creation of self-service analytics was the defining event in the last decade of data and analytics.

 

 

The 2010s: Data Democratized

mark-frisch-input.jpg

“Simply put, the biggest change in the past decade has been the democratization of analytics.”

— Mark Frisch, CEO, Marquee Crew

 

 

 

nicole-johnson

The playing field has been leveled because of the accessibility of analytical tools and platforms, which is allowing people to use their non-technical expertise along with the incredible technical improvements of the last decade to make their business and community impacts that much more powerful.”

— Nicole Johnson, Sr. Business Solutions Consultant, T-Mobile

 

 

heather harris

“Data was hidden in the shadows of the centralized, old-school BI team, with few folks who knew how to access and use it. Now, it is a widespread, democratized, key organizational asset that is easy to leverage.”

— Heather Harris, Practice Director, Intelligence & Analytics, ProKarma

 

nick-haylund

Thanks to matured, tested, and ever-improving self-service tools available to data workers today, we are in the golden age of data democratization.”

— Nick Haylund, Director, Tessellation EMEA

 

 

aj-guisande

Democratization of BI & Analytics is the biggest change I can recall in the past decade. The reach to end users is a game-changer.”

— AJ Guisande, Director, Decision Science

 

 

The Decade, Wrapped

By the end of the decade, the value of analytics hit an all-time high. According to IDC, there were now 54 million data workers worldwide, and revenue from Big Data and analytics solutions reached nearly $200 billion

Data was unanimously valuable, and those who could make sense of it saw promotion in their careers and became champions of business insights. All of this, though, was setting the stage for the decade to come.

 

Analytics Future

It’s January 2020. A lot has changed in ten years. The U.S. economy has been on its longest growth trend since 1854, and in tech news, Apple’s iMac Pro just hit shelves. Much like 10 years ago, you’re wondering who would spend $52,400 on a desktop computer with wheels when the iPhone 11 Pro serves your needs just fine.

Over the past decade, you saw a tremendous shift in the value of data, and you rode the wave of self-service analytics. Now, you’re sitting on the top of the world, your self-service skills a scepter, your transformative insights a crown. The only thing that could sweeten the pot is knowing what’s next.

To get a glimpse into the future of data and analytics, we turned to our most credible data sources, aka analytics thought leaders, including industry analysts, Alteryx executives, and our innovative customers. Their top four predictions follow.

 

1. Data Literacy Front and Center

53%

OF BIG DATA AND AI EXECUTIVES ARE NOT YET TREATING DATA AS A BUSINESS ASSET.

— Big Data and AI Executive Survey, NewVantage Partners

 

After the world of BI was captivated by self-service analytics, organizations expected the business results to be immediate, or at the very least apparent. According to Andrew Brust, “The fight is over, and the evangelism is complete. Now that the C-Suite is bought in, the pressure is on to perform and deliver results.”

Nick Haylund, Director at Tessellation EMEA, explains, “If technology is getting easier to use, why are companies not seeing the return on investment that they were expecting? Although having and deploying the best-in-class data and analytics technologies will always be important, many departments and companies often overlook investing time in people and processes.”

“Although having and deploying the best-in-class data and analytics technologies will always be important, many departments and companies often overlook investing time in people and processes.”

— Nick Haylund, Director, Tessellation EMEA

Perfecting a tech stack will always be peripheral to empowering the minds behind the technology. While not a secret, it’s widely understated that the value of data only comes from the application of human intelligence. Until analysts and data scientists become a mouthpiece for it, data will always remain a collection of mute facts and observations.

While not a secret, it’s widely understated that the value of data only comes from the application of human intelligence.

The true power of self-service platforms is not just that they lower the threshold for who can become an analyst or citizen data scientist, but that they empower knowledge workers from every domain of the business to fuel their work with insights. From HR to finance, the prevailing strength of self-service technology is its enablement of a culture of analytics.

Paige Bartley of 451 Research says, “Employees with extremely diverse backgrounds could now add their unique perspectives to the interpretation of data, making for a much more holistic understanding of business performance and potential.”

In the next decade, now that data and analytics has gone to the masses, the companies with the most data-literate employees will beat the curve. Alan Jacobson, Chief Data and Analytics Officer at Alteryx, explains: “A new focus on education within organizations around better transformation is going to increase in 2020 as companies grow faster with a CDO or similar role steering the changing workplace. The digitally savvy company beats non-digital competition, and 2020 will cement this as even more companies leverage their digital assets to solve business problems.”

Ashley Kramer adds, “There will be an impetus for executive management to drive self-service data science and analytics from the top down. Leadership must commit with conviction to evolve beyond the antiquated approach to analytics and propel a cultural shift within their organization.”

Similar to the C-suite’s leadership in adopting analytics for the enterprise, data literacy initiatives will come from the top down, and in order to be effective, these initiatives will have to be democratized throughout the organization.

Andy Uttley, Consulting Manager at the Javelin Group, explains, “Data literacy is no longer a skill required by few; it is required by most, and being able to understand and use data at all levels is critical to businesses being successful.” 

There is a caveat, though. Uttley explains that the democratization of data and the rise of self-service presents new challenges because “putting data in the hands of all stakeholders can increase risk: data governance, overfit or ‘incorrect’ models by unqualified data practitioners, or errors being embedded into data sources used across the business through lack of training or skills.’”

With the power of everyday business practitioners to wield data comes the risk that data could be used incorrectly, inappropriately, or even unethically. As data becomes more pervasive, guidelines for organizations and the protection of consumers will be established. Data literacy must be a priority on the C-suite's agenda for two reasons: business transformation and governance.

Data literacy must be a priority on the C-suite's agenda for two reasons: business transformation and governance.

Paige Bartley adds, “The ethics of appropriate data usage will become a societal zeitgeist. Many say ‘data is the new oil,’ and that metaphor is fitting beyond superficial monetary value of the resource in question. Just as environmental awareness and activism was eventually a response to the exploitation of natural resources that occurred with the industrial revolution, so will privacy and data ethics activism become a response to the exploitation of personal, informational resources that is currently occurring with the digital revolution.”

 

2. The Rise of the Data Native

Along with data literacy and fluency will come a new generation, those who grow up around data, aka the “data native.” Much like the digital native grew up around smartphones and digital technology, the data native won’t know there was a time before Fitbit, Nest thermostat, and Propeller.  

“The younger generations who are adaptable and quick learners will excel as new languages and data analytics skills increase in demand," says Andy Uttley. "I believe we should therefore see a shift from the bottom and expect, if not push for, changes to education systems to help better prepare children for the world they may enter. This should include more problem-solving and, of course, mandatory skills in languages like Python!”

New generations will continue to be integrated into a world of data and analytics. According to Ashley Kramer, SVP of Product Solutions, “New companies have formed that purely focus on data literacy, and we’re seeing analytics become more prominent at universities across the world.” One example is Arizona State University, whom Alteryx just joined forces with to enable smart city initiatives in the Phoenix Metro area.

But what will shift for those of us who are “non-natives,” who learned how to use data the old-fashioned way, walking uphill both ways in piles of snow? While the start of last decade saw data as a souvenir — something to be collected — this decade will see data become its own language. Analytics lingo will become a primary language, integrated into the language of business.

 

3. PREDICTING PREDICTIVE ANALYTICS

If, in the last decade we nailed data prep and blend, in this coming decade, we’ll nail predictive and prescriptive analytics (with the help of our friends Artificial Intelligence and Machine Learning; call them AI and ML for short).

Our Community’s search data bears out the growth in predictive analytics and machine learning interest:

Community Search Growth: 2016 Q1 graphic

As Jarrod Thuener, Chief Analytics Officer of Kristalytics, says, "At the risk of overusing another buzzword, we are getting into the age of machine learning. That is, actually using the data we are collecting. Being able to act on the analytics is key, and soon, we'll have self-monitoring systems where a continuous feedback loop will direct downstream decisions.”

Don’t picture a dystopian 2025 where machines have wrested control from humans. Dean Stoecker believes we should all have a little more faith in ourselves: “If you think artificial intelligence will rule the day, think again. If we amplify human intelligence, singularity will never occur! Never sell out the human.”

Though data feeds into machine-learning models, it will still be humans who guide the selection of data and the applications of its usage. Artificial intelligence — or “artificial stupidity,” as we sometimes think of it when the Alexa or Google Home or Siri doesn’t understand what we want, even though the ask is plain as day — is only as smart as the humans behind it. We can never replace art with math, music with code, or human connection with an algorithm. Rather, AI and ML are an augmentation, not a replacement, Jacobson says.

“AI and ML are an augmentation to humans, not a replacement.”

— Alan Jacobson, Chief Data and Analytics Officer, Alteryx

What will happen, Jacobson continues, is that we’ll shift from a focus on the languages and coding behind ML and AI and predictive analytics to the democratization of those technologies, just as we made that shift in democratizing data accessibility in the last decade. “As of 2019, modeling and language processing technologies are robust, but not packaged accessibly. When everyone from business analysts to data scientists has full accessibility, real improvements will rapidly accelerate.”

Just as the rise of data literacy presents new challenges in governance, so does the rise of accessible advanced analytics. Paige Bartley, Senior Analyst at 451 Research, adds, “Perhaps paradoxically, we need to use data science and analytics to better understand the effects that increasing uses of automation and algorithmic systems are shaping human interaction and behavior in the world. With nearly every aspect of human behavior now quantifiable and being leveraged for insight, we need to better understand the potential ramifications of our systematic technology use before we can use it to move forward as a society.”

Stoecker is confident that even complex problems like these can be solved with the magic of data + human intelligence. “If we can solve one problem using relevant data, we can solve all problems with relevant data,” he says. “The only gating factor to solving the challenges will be amplifying human intelligence so that we know what questions to ask.”

 

4. Analytics For Good

To wrap up this look into the next decade, we asked our community of thought leaders what pressing world problems they’d like to solve with data and analytics. Here are some of their responses:

 

mark-frisch-input.jpg

“I'd like to work on world peace initiatives. Get guns out of the picture and get youth into analytics instead. Make models, not war.”

— Mark Frisch, CEO, Marquee Crew

 

 

 

heather-harris

Climate change remediation, public education analysis and optimization, homelessness alleviation, and social isolation disruption.”

— Heather Harris, Practice Director, Intelligence & Analytics, ProKarma

 

 

 

sharmila-mulligan

“Analysis of girls from the age of 11 on: What stats or paths through life lead them to excel as females in the tech industry — in any role. In addition, nature vs. nurture analysis but from a tech-focus standpoint.”

— Sharmila Mulligan, Chief Strategy Officer, Alteryx

 

 

Sean-adams

Education. We still have a problem where a very large proportion of the world is under-skilled — and the more we can use technology to understand and enable learning, the better for all of us since a more educated planet is more economically stable and makes better decisions.”

— Sean Adams, Senior VP and Executive Director, Multinational Investment Bank

 

andrew-brust

Medical scenarios are a great application of data science and analytics. Aiming higher and applying them to questions of climate change and international affairs should be a goal we strive for, however audacious. We also need to look at ethics; otherwise the opposite will happen: AI will be applied to warfare instead of peace and constructive engagement.”

— Andrew Brust, CEO/Founder, Blue Badge Insights

 

aj-guisande

Poverty distribution worldwide.”

— AJ Guisande, Director, Decision Science

 

 

 

michael-barone

“Well, if I only had to pick one problem to solve, it would be childhood cancer.”

— Michael Barone, Data Scientist, Paychex

 

 

 

adrian-loong

Healthcare and leveraging machine learning to help improve health outcomes, such as cancer research and predictive healthcare alerts.”

— Adrian Loong, Data Science Manager, Datacom

 

 

 

joseph-serpis

Inequality among society and using analytics to improve social mobility.”

— Joseph Serpis, Consultant, Keyrus

 

 

 

To 2030, and Beyond

Self-service analytics revolutionized the past decade, and it’s poised to do the same for the next, but for entirely different reasons. Will things turn out as we’ve predicted? You’ll have to wait and see. That is, unless you’ve already used analytics to build a time machine. That would be pretty cool.

Stay PUT.

READ

A Decade of Inspiration” highlights defining moments in the Alteryx Community over the past decade. 

 

LISTEN

Pop in your earbuds and listen as Libby Duane Adams, co-founder and CCO of Alteryx, and two long-time employees share their thoughts on everything from the first office at Alteryx to the future of analytics in “Alteryx-ing the Decade” on the Alter Everything podcast.

 

Share

Chat with us at @alteryx about your most memorable moments in the past decade of analytics.