Analytics Trends 2015   |   A below-the-surface look

Analytics Trends 2015

A below-the-surface look

More

Analytics Trends 2015

A below-the-surface look

Close

If some of the hype around business analytics seems to have diminished, it's not because fewer companies are embracing the discipline. On the contrary, analytics momentum continues to grow, moving squarely into the mainstream of business decision-making worldwide. Put simply, analytics is becoming both the air that we breathe—and the ocean in which we swim.

Analytics innovators continue to push the edge, looking for new ways to gain advantage over slower-moving competitors. In some cases, that advantage comes through sweeping discoveries which can upend entire business models. In other cases, more modest insights may emerge that unleash cascading value. For 2015, leading companies are working on both fronts to strengthen their competitive positions. These significant trends are in play—and in 2015, one supertrend is the context for everything that follows.

Supertrend:

Quadruple down on data security

In 2014, the business world got walloped on the issue of data security. Looking ahead, business leaders and tech leaders alike are deeply anxious about this issue—with good reason.

Exponential growth in the areas of mobile data generation, real-time connectivity, and digital business have made the job of protecting data assets and securing the gates in a "big data" world an altogether different—and more challenging—undertaking. In areas ranging from intrusion detection and differential privacy to digital watermarking and malware countermeasures, analytics is already having a huge impact.

Read more about this trend

#AnalyticsTrends2015

The Analytics of Things

The So What

The Internet of Things generates massive amounts of structured and unstructured data, requiring a new class of big data analytics to uncover and capture value. Analytics tools and techniques are already finding their way around the Internet of Things, but the integration of systems is lagging. Both consumer and industrial applications could potentially benefit from industry standards that help avoid the massive programming investments that would otherwise be required. Also, because sensor data tends to be noisy, analog, and high-velocity, there are major challenges that traditional analytics architectures and techniques don't handle well.

Read more about this trend

#AnalyticsTrends2015

So what?

The Internet of Things is becoming a day-to-day reality. And analytics capabilities are finally strong enough to make sense of it in terms of data and insights. The combination can be tempting—just don't bite off more than you can chew. Maintaining a laser focus will be key.

Monetize this?

The So What

Many analysts and researchers are insisting that data not only should be managed as an asset but should be valued as one. They see a future where companies can routinely monetize their own data for financial gain.

Data monetization initiatives clearly make sense in some sectors and are already fueling new products and service approaches. The potential of data as an asset is so great that some companies are rebuilding their strategy around this asset. Some—first online businesses, and now industrial firms as well—are already beginning to prosper, but others are likely underestimating the great responsibilities that come with this potential power—responsibilities not only to the business but to society at large. If "data ethics" is an unfamiliar term, it probably should be playing a bigger role in your data strategy.

Read more about this trend

#AnalyticsTrends2015

So what?

Have you ever seen a movie in which the hero is given a weapon that is far more powerful than the hero expects? Keep that in mind when considering monetizing your data. There's an emerging perception that the more data you have, the better. In reality, capturing, storing, and protecting data come with real costs.

Bionic brains

The So What

The convergence of machine and human intelligence is disrupting traditional decision-making by equipping people with knowledge that was almost unimaginable just a few years ago. With the rise of big data and machine-to-machine communications, analytical models and algorithms are increasingly being embedded into complex event processing (CEP) and other automated workflow environments. Automated decision-making is probably here to stay, enhanced by a host of cognitive analytics applications.

Cognitive analytics is still in its early stages, and it is by no means a replacement for traditional information and analytics programs. However, industries wrestling with massive amounts of unstructured data or struggling to meet growing demand for real-time visibility are taking a closer look.

Read more about this trend

#AnalyticsTrends2015

So what?

Cognitive computing and analytics appear to be capable of improving virtually any knowledge-intensive undertaking. At the same time, this raises some big questions about the respective roles of humans and knowledge workers. Both individual workers and organizations need to consider how these systems can augment the work of talented humans rather than fully automating it.

The rise of open source

The So What

Once restricted to Silicon Valley, open source solutions such as Hadoop are finding their way into the enterprise and being used by mainstream firms around the world as data storage and processing engines. Open source can have a distinct role, but it generally has to be part of a broader, overall strategy. For example, Hadoop can be effective when you have "real" big data that is multi-structured, volume-heavy, and slow to process. It's a case of finding the right tool for the job.

Risk management must also be part of the equation when an open source tool is used. What happens if the army of volunteer open-source developers moves on to the "next big thing"—or simply wants to be paid? What if the quality of the solutions declines along with the quality of talent working on them? It's easier to calculate your risk exposure if you have a clear picture of the portion of your infrastructure that relies on, or is built on, open source solutions.

Open source solutions come with unique benefits, not least of which is economic value. That said, companies have to keep in mind the cost and availability of people who can work with these emerging technologies. Those people are getting harder and harder to find.

Read more about this trend

#AnalyticsTrends2015

So what?

Many tech leaders would say the rise of such solutions has been a long time coming—and they're hungry to put these capabilities to work. They could be rushing too fast. Leaders must be sure that the open source solutions they are putting in place today will sit comfortably alongside their overarching technology strategies. They must also make sure their reliance on open source solutions doesn't leave the organization exposed to more risk than anticipated. Open source solutions have earned their place in today's technology strategies. The key is knowing their place.

Tax analytics: Striking gold?

The So What

Despite being focused on numbers, tax leaders within companies have generally been slower to adopt analytics.1 Historically, companies have not generally captured their tax situations and outcomes in structured formats—but today, companies have an increasing number of common data sets that tax leaders can leverage to bring more fact-based insights to the organization. Some of the most interesting work in tax analytics today is on simulation models that explain or predict tax levels under particular circumstances: If the tax rate was 32 percent last year, and 34 percent last quarter, executives want to know why the rate is changing.

The future of tax planning will likely be more analytical than it is today. Tax executives should be preparing now by working on data infrastructure, assembling the right people and skills, and acquainting managers with the art of what’s possible.

Read more about this trend

#AnalyticsTrends2015

So what?

"Be prepared" is a good motto for leaders taking on tax analytics. Many governments around the world are requiring more and more tax data to be submitted in standard electronic formats so that they may perform their own analytics from both technical and industry perspectives. They are also changing the ways that they conduct audits. It may be important to understand what trends your organization's detailed data might reveal before extending access to large amounts of data.

Universities step up

The So What

The marketplace is looking for a supply of true data scientists, not just button pushers. Many universities are working to serve this need, beginning to churn out thousands of data scientists and quantitative analysts. But as universities find themselves facing increased expectations to support the new data economy, the pressures will build. There's likely to be a shakeout. Some new programs won't turn out good data scientists in sufficient numbers, for a variety of reasons. For the longer term, it will be essential to have an abundance of students with solid quantitative prerequisites entering and succeeding in these programs.

"STEM"—shorthand for the academic disciplines of science, technology, engineering, and math—has been one of the hottest buzzwords on college campuses for years. Today, some are beginning to talk about "STEAM" instead, adding an "A" for Art. This is good news for the business world, which is looking to such programs to deliver the analytics talent they need—and are increasingly on the hunt for people who can balance quantitative analysis skills with an ability to tell the story of their data in compelling, visual ways. Design thinking, visualization, and storytelling are increasingly important.

Read more about this trend

#AnalyticsTrends2015

So what?

Core STEM disciplines provide the necessary foundation for new data science talent entering the workplace—but on their own, they are no guarantee of analytics success. Liberal arts skills are also needed to identify the right questions, think critically, collaborate with experts in other fields, and explain technical assumptions and results to non-technical audiences.

As universities continue to hone their analytics and data science programs, a strong feedback loop is needed to make important connections between technical expertise, organizational context, communications excellence, and user experience.

Accuracy quest

The So What

The data brokerage business has only grown hotter as analytics capabilities have experienced exponential growth. That's unlikely to change for the near future. But as those who purchase and use this data grow more familiar with it, they're applying more scrutiny to the product they're being sold.

Today, data scientists and other informed buyers of data are generally aware of a fundamental paradox at work in big data: It is directionally accurate but often individually inaccurate. A company may know quite a bit about buyers like you, which is valuable, but they may not actually have as much accurate information about you in particular as you might expect. Which in turn means that the data is simultaneously inaccurate and valuable.

The ability to gather and leverage detailed and accurate information about current and potential customers could allow marketers to better tailor specific advertisements and offers to consumers, target customers for optimized offers, and reduce offers to disinterested customers. That's why we anticipate increased pressure on data brokers to improve the accuracy of their data over the next year.

Read more about this trend

#AnalyticsTrends2015

So what?

Micromarketing and micro-segmentation at the consumer level can provide a big competitive advantage for companies looking to break away. Today, many marketers continue to settle for "better than nothing" improvements to their current targeting approaches, or, perhaps even worse, a so-called "spray and pray" approach to mass marketing. Data inaccuracy is one of the most significant obstacles, and in a world where companies increasingly rely on external sources for their marketing data, the pressure is on for data brokers to deliver the goods.

Bubbles to watch

Which trends-in-the-making are we likely to be talking about a year from now? Keep your eyes on these.

Facial recognition and geospatial monitoring. From tagging friends in photographs and recognizing customers to catching criminals by tracking their movements in society, there are many reported successes of these types of technologies. Data from inexpensive cameras and cellphones is now widely available to train machine learning systems. Expect to see plenty of innovation in this field.

Citizen backlash. Between government monitoring, data breaches, and well-intentioned commercial efforts that cross the "creepy" line, people are starting to realize just how much can be learned about them from the data they unintentionally produce. It may not be long before we see public demands for enforceable accountability on those who collect or disseminate personal data.

Analytics driving the physical world. Technology that controls physical activities (think of the Google self-driving car, or even the Nest thermostat) has received a significant amount of media attention. Many consumers seem to be eager to use these analytics-enabled capabilities. In the rush to serve consumer appetites, it will be important for businesses to thoroughly plan for the potential consequences—good and bad—of these capabilities.

#AnalyticsTrends2015

Sources

1. Davenport, Tom. "Tax Analytics: From the Inside Out." 2014.

Meet our Trend Watchers

Forrest Danson

Forrest Danson

Principal
US Leader, Deloitte Analytics
Deloitte Consulting LLP

fdanson@deloitte.com
Tom Davenport

Tom Davenport

Independent Senior Advisor
Deloitte Analytics

tdavenport@babson.edu
Nick Gonnella

Nick Gonnella

Partner
Deloitte Tax LLP

ngonnella@deloitte.com
Jim Guszcza

Jim Guszcza

Senior Manager
Deloitte Consulting LLP

jguszcza@deloitte.com
Vivek Katyal

Vivek Katyal

Principal
Deloitte & Touche LLP

vkatyal@deloitte.com
John Lucker

John Lucker

Principal
Deloitte Consulting LLP

jlucker@deloitte.com
David Steier

David Steier

Director
Deloitte Consulting LLP

dsteier@deloitte.com
Greg Swinehart

Greg Swinehart

Partner
Deloitte Financial Advisory Services LLP

gswinehart@deloitte.com

#AnalyticsTrends2015