Is Big Data The New Term For Business Intelligence?

Timo Elliott

The term “business intelligence” was first coined by IBM researcher Hans Luhn in 1958, and then used in its modern sense in 1989 by then-Gartner analyst Howard Dresner.

Earlier this year, Gartner joined other analyst firms such as IDC and started using “business analytics” as an umbrella term for solutions for turning data into value :

Business analytics is comprised of solutions used to build analysis models and simulations to create scenarios, understand realities and predict future states. Business analytics includes data mining, predictive analytics, applied analytics and statistics, and is delivered as an application suitable for a business user. These analytics solutions often come with prebuilt industry content that is targeted at an industry business process (for example, claims, underwriting or a specific regulatory requirement).

There’s still lots of disagreement about the differences between the terms “business intelligence” vs “business analytics” (read the comments), but it now increasingly looks like the battle of the semantics has been lost to a newcomer: “big data”.


Google Trends lets us take a look at the dramatic rise of the term in searches, while “business intelligence” continues its slow, steady decline, and “business analytics”, while still growing, doesn’t look like it will ever catch up…

The term “big data” is used just as nebulously as its rivals. The reality is that new terms are created because of new technology — in this case ‘big data” was first used (this time around) to describe the new opportunities afforded by Hadoop, Map Reduce, etc.

But, of course, everybody in the market knows that they are supposed to talk about the business benefits not the features: “the hole, not the drill bit!” — but the business benefits are, of course, the same as they’ve always been: better decisions, insight, lower costs, etc. So everybody, even those not using Hadoop etc, started using it in conjunction with whatever was new in the industry (technologies like in-memory, mobile, cloud, visualization, predictive; data sources such as sensors, social, etc… )

This meant that the definition of “big data” quickly became another industry squabbling match that has generated liters of ink (and nostalgia: The Google Books Ngram Viewer seems to show the term was first used in the 1930s and that the term was clearly used  in roughly the same sense as today even  in 1969: “”Datamec has made some headway outside the field of big data processors”)

What will the future hold? Will we all end up using “big data” as the new umbrella term? Will something else come along to take its place?

Time will tell, but in the meantime, I’ll stick to my position in the BI vs analytics post: “real people” shouldn’t care very much. Let the analysts and marketers bicker — instead, you should concentrate on pragmatically solving the business needs you face with the best technology available, whatever it’s called…


About Timo Elliott

Timo Elliott is an innovation evangelist and international conference speaker who has presented to business and IT audiences in over forty countries around the world. A 23-year veteran of SAP BusinessObjects, Elliott works closely with SAP development and innovation centers around the world on new technology directions. His popular Business Analytics blog at tracks innovation in analytics and social media, including topics such as big data, collaborative decision-making, and social analytics. Prior to Business Objects, Elliott was a computer consultant in Hong Kong and led analytics projects for Shell in New Zealand. He holds a first-class honors degree in Economics with Statistics from Bristol University, England.

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13 Scary Statistics On Employee Engagement [INFOGRAPHIC]

Jacob Shriar

There is a serious problem with the way we work.

Most employees are disengaged and not passionate about the work they do. This is costing companies a ton of money in lost productivity, absenteeism, and turnover. It’s also harmful to employees, because they’re more stressed out than ever.

The thing that bothers me the most about it, is that it’s all so easy to fix. I can’t figure out why managers aren’t more proactive about this. Besides the human element of caring for our employees, it’s costing them money, so they should care more about fixing it. Something as simple as saying thank you to your employees can have a huge effect on their engagement, not to mention it’s good for your level of happiness.

The infographic that we put together has some pretty shocking statistics in it, but there are a few common themes. Employees feel overworked, overwhelmed, and they don’t like what they do. Companies are noticing it, with 75% of them saying they can’t attract the right talent, and 83% of them feeling that their employer brand isn’t compelling. Companies that want to fix this need to be smart, and patient. This doesn’t happen overnight, but like I mentioned, it’s easy to do. Being patient might be the hardest thing for companies, and I understand how frustrating it can be not to see results right away, but it’s important that you invest in this, because the ROI of employee engagement is huge.

Here are 4 simple (and free) things you can do to get that passion back into employees. These are all based on research from Deloitte.

1.  Encourage side projects

Employees feel overworked and underappreciated, so as leaders, we need to stop overloading them to the point where they can’t handle the workload. Let them explore their own passions and interests, and work on side projects. Ideally, they wouldn’t have to be related to the company, but if you’re worried about them wasting time, you can set that boundary that it has to be related to the company. What this does, is give them autonomy, and let them improve on their skills (mastery), two of the biggest motivators for work.

Employees feel overworked and underappreciated, so as leaders, we need to stop overloading them to the point where they can’t handle the workload.

2.  Encourage workers to engage with customers

At Wistia, a video hosting company, they make everyone in the company do customer support during their onboarding, and they often rotate people into customer support. When I asked Chris, their CEO, why they do this, he mentioned to me that it’s so every single person in the company understands how their customers are using their product. What pains they’re having, what they like about it, it gets everyone on the same page. It keeps all employees in the loop, and can really motivate you to work when you’re talking directly with customers.

3.  Encourage workers to work cross-functionally

Both Apple and Google have created common areas in their offices, specifically and strategically located, so that different workers that don’t normally interact with each other can have a chance to chat.

This isn’t a coincidence. It’s meant for that collaborative learning, and building those relationships with your colleagues.

4.  Encourage networking in their industry

This is similar to number 2 on the list, but it’s important for employees to grow and learn more about what they do. It helps them build that passion for their industry. It’s important to go to networking events, and encourage your employees to participate in these things. Websites like Eventbrite or Meetup have lots of great resources, and most of the events on there are free.

13 Disturbing Facts About Employee Engagement [Infographic]

What do you do to increase employee engagement? Let me know your thoughts in the comments!

Did you like today’s post? If so you’ll love our frequent newsletter! Sign up here and receive The Switch and Shift Change Playbook, by Shawn Murphy, as our thanks to you!

This infographic was crafted with love by Officevibe, the employee survey tool that helps companies improve their corporate wellness, and have a better organizational culture.


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Supply Chain Fraud: The Threat from Within

Lindsey LaManna

Supply chain fraud – whether perpetrated by suppliers, subcontractors, employees, or some combination of those – can take many forms. Among the most common are:

  • Falsified labor
  • Inflated bills or expense accounts
  • Bribery and corruption
  • Phantom vendor accounts or invoices
  • Bid rigging
  • Grey markets (counterfeit or knockoff products)
  • Failure to meet specifications (resulting in substandard or dangerous goods)
  • Unauthorized disbursements

LSAP_Smart Supply Chains_graphics_briefook inside

Perhaps the most damaging sources of supply chain fraud are internal, especially collusion between an employee and a supplier. Such partnerships help fraudsters evade independent checks and other controls, enabling them to steal larger amounts. The median loss from fraud committed
by a single thief was US$80,000, according to the Association of Certified Fraud Examiners (ACFE).

Costs increase along with the number of perpetrators involved. Fraud involving two thieves had a median loss of US$200,000; fraud involving three people had a median loss of US$355,000; and fraud with four or more had a median loss of more than US$500,000, according to ACFE.

Build a culture to fight fraud

The most effective method to fight internal supply chain theft is to create a culture dedicated to fighting it. Here are a few ways to do it:

  • Make sure the board and C-level executives understand the critical nature of the supply chain and the risk of fraud throughout the procurement lifecycle.
  • Market the organization’s supply chain policies internally and among contractors.
  • Institute policies that prohibit conflicts of interest, and cross-check employee and supplier data to uncover potential conflicts.
  • Define the rules for accepting gifts from suppliers and insist that all gifts be documented.
  • Require two employees to sign off on any proposed changes to suppliers.
  • Watch for staff defections to suppliers, and pay close attention to any supplier that has recently poached an employee.

About Lindsey LaManna

Lindsey LaManna is Social and Reporting Manager for the Digitalist Magazine by SAP Global Marketing. Follow @LindseyLaManna on Twitter, on LinkedIn or Google+.


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What If Chelsea Manager Jose Mourinho Could Be Proved Right In Medical Staff Row?

Mark Goad

Big Data and the Internet of Things brings new level of insight to sports medicine

With the 2015-16 European football (soccer) season underway, we are already seeing the impact of the huge pressure to succeed. In some cases, it is boiling over even this early on, with Chelsea manager Jose Mourinho getting involved in a very public row with his medical staff over the treatment of Eden Hazard during a match. As the season builds momentum, all clubs know one of the most vital aspects of winning trophies is keeping the best players fit so they can play at the top of their game as often as possible.

Last season, just like in every season, we saw injuries that affected teams’ results and possibly their final standings at the end of the season, while other teams capitalized. Arsenal manager Arsene Wenger blamed injuries for the team’s failed title bid, while Real Madrid suffered injuries to players like Gareth Bale and Luka Modric at a crucial stage of the season and lost the title to Barcelona.

There’s no doubt that football clubs, especially the bigger teams, employ first-rate medical staff – physiotherapists, doctors, sports scientists, and so on – but they can only do so much to keep players off the treatment table. Players are human, after all, and keeping them injury-free for such long and grueling campaigns is a big ask. This season again will see players on the end of crunching tackles, over-exerting their bodies, and over-stretching.

What’s less talked about than lost games and league titles when discussing injuries is the salaries paid to injured players. The estimated average cost of player injuries in the top four professional football leagues in 2015 was $12.4 million* per team. Remarkably, every year teams lose an equivalent of 15%-30%** of their player payroll to injuries.

As salaries continue to rise, injuries are becoming just as much of an off-the-pitch boardroom issue as they are an on-the-pitch issue. Consider that if Barcelona’s Lionel Messi, the world’s highest-paid player, spends just a week out injured, the club still has to pay his weekly salary of around $1 million. Not only that, but there’s the huge potential for lost revenue from missing out on UEFA Champions League progress or domestic success because key players are out.

Just as winning seems to mean more than ever, so does football as a business. So with the spotlight firmly on “sweating the assets” – extracting maximum value from the entire squad – clubs are looking to Big Data and Internet of Things technology to consider how player injuries can be prevented with new levels of insight.

Prevention is better than cure

In July this year we saw what could be a huge landmark in the potential of monitoring the risk of injuries, when football’s international governing body FIFA announced its approval of wearable electronic performance and tracking systems during matches. As well as collecting data on statistics like distance covered and heart rate to determine decisions like substitution timings, this also paves the way for wearable satellite devices that keep medical staff updated on the likelihood of a player picking up an injury from over-exertion.

Emerging injury-risk monitoring software uses the concepts of Big Data and wearable technology to pull in and apply mathematical formulas to an exhaustive range of relevant data about players: fitness levels, recent levels of exertion, opponents, age, technique, hydration, even weather. This could help medical staff predict the risk of future injuries with much greater accuracy, allowing them to run simulations and take corrective actions in real time. Imagine a seemingly non-injured key player being substituted during a tightly contested match, only to find out afterwards that monitoring software had indicated he was at a high risk of pulling a muscle. This could very much be a part of the future of professional football.

Going back to Jose Mourinho and his reaction to the Chelsea medical staff running onto the pitch to treat Eden Hazard, it’s interesting to consider how in the future this kind of technology could either support or discredit his position in the dispute. It could help managers work more closely with physiotherapists, as they can visualize the data that shows the risk of injury to players. Although the pressure to win will likely keep on rising, the risk of expensive players injuries could see a big reduction.

SAP’s own injury risk monitoring software is currently in the proof-of-concept phase and will be entering development in the near future. The goal is to build IRM on the SAP Sports One platform as an additional component, and to provide integration to the existing modules of SAP Sports One solution. SAP Sports One was launched earlier this year and is the first sports-specific cloud solution powered by the SAP HANA platform, providing a single, unified platform for team management and performance optimization.

*Statistic calulated using 2015 Global Sports Salaries Survey

**Bleacher Report “Inside the 2014 Numbers of Each MLB Team’s Regular-Season Injury Impact” and NBA Injury Analysis


Mark Goad

About Mark Goad

Mark Goad, Value Advisory Associate, SAP Canada, is an experienced business analyst with industry coverage spanning telecommunications & retail, with a focus on digital business models. He specializes in synthesizing industry trends with a detailed analysis of client-specific data to help customers build out high-impact business & IT strategies. Outside of work, Mark volunteers as a lead management consultant for Junior Achievement of Central Ontario and contributes to a range of thought leadership publications.


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Ambient Intelligence: What's Next for The Internet of Things?

Dan Wellers

Imagine that your home security system lets you know when your kids get home from school. As they’re grabbing an afternoon snack, your kitchen takes inventory and sends a shopping list to your local supermarket. There, robots prepare the goods and pack them for home delivery into an autonomous vehicle – or a drone. Meanwhile, your smart watch, connected to a system that senses and analyzes real-time health indicators, alerts you to a suggested dinner menu it just created based on your family’s nutritional needs and ingredients available in your pantry. If you signal your approval, it offers to warm the oven before you get home from work.

This scenario isn’t as futuristic as you might think. In fact, what Gartner calls “the device mesh” is the logical evolution of the Internet of Things. All around us and always on, it will be both ubiquitous and subtle — ambient intelligence.

We’ll do truly different things, instead of just doing things differently. Today’s processes and problems are only a small subset of the many, many scenarios possible when practically everything is instrumented, interconnected, and intelligent.

We’re also going to need to come up with new ways of interacting with the technology and the infrastructure that supports it. Instead of typing on a keyboard or swiping a touchscreen, we’ll be surrounded by various interfaces that capture input automatically, almost incidentally. It will be a fundamental paradigm shift in the way we think of “computing,” and possibly whether we think about computing at all.

The Internet of not-things

The foundation will be a digital infrastructure that responds to its surroundings and the people in it, whether that means ubiquitous communications, ubiquitous entertainment, or ubiquitous opportunities for commerce. This infrastructure will be so seamless that rather than interacting with discrete objects, people will simply interact with their environment through deliberate voice and gesture — or cues like respiration and body temperature that will trigger the environment to respond.

Once such an infrastructure is in place, the possibilities for innovation explode. The power of Moore’s Law is now amplified by Metcalfe’s Law, which says that a network’s value is equal to the square of the number of participants in it. All these Internet-connected “things” — the sensors, devices, actuators, drones, vehicles, products, etc.  — will be able to react automatically, seeing, analyzing, and combining to create value in as yet unimaginable ways.  The individual “things” themselves will meld into a background of ambient connectedness and responsiveness.

The path is clearly marked

Think of the trends we’ve seen emerge in recent years:

  • Sensors and actuators, including implantables and wearables, that let us capture more data and impressions from more objects in more places, and that affect the environment around them.
  • Ubiquitous computing and hyperconnectivity, which exponentially increase the flow of data between people and devices and among devices themselves.
  • Nanotechnology and nanomaterials, which let us build ever more complex devices at microscopic scale.
  • Artificial intelligence, in which algorithms become increasingly capable of making decisions based on past performance and desired results.
  • Vision as an interface to participate in and control augmented and virtual reality
  • Blockchain technology, which makes all kinds of digital transactions secure, verifiable, and potentially automatic.

As these emerging technologies become more powerful and sophisticated, they will increasingly overlap. For example, the distinctions between drones, autonomous vehicles, and robotics are already blurring. This convergence, which multiplies the strengths of each technology, makes ambient intelligence not just desirable but inevitable.

Early signposts on the way

We’re edging into the territory of ambient intelligence today. Increasingly complex sensors, systems architectures, and software can gather, store, manage, and analyze vastly more data in far less time with much greater sophistication.

Home automation is accelerating, allowing people to program lighting, air conditioning, audio and video, security systems, appliances, and other complex devices and then let them run more or less independently. Drones, robots, and autonomous vehicles can gather, generate, and navigate by data from locations human beings can’t or don’t access. Entire urban areas like Barcelona and Singapore are aiming to become “smart cities,” with initiatives already underway to automate the management of services like parking, trash collection, and traffic lights.

Our homes, vehicles, and communities may not be entirely self-maintaining yet, but it’s possible to set parameters within which significant systems operate more or less on their own. Eventually, these systems will become proficient enough at pattern matching that they’ll be able to learn from each other. That’s when we’ll hit the knee of the exponential growth curve.

Where are we heading?

Experts predict that, by 2022, 1 trillion networked sensors will be embedded in the world around us, with up to 45 trillion in 20 years. With this many sources of data for all manner of purposes, systems will be able to arrive at fast, accurate decisions about nearly everything. And they’ll be able to act on those things at the slightest prompting, or with little to no action on your part at all.

Ambient intelligence could transform cities through dynamic routing and signage for both drivers and pedestrians. It could manage mass transit for optimal efficiency based on real-time conditions. It could monitor environmental conditions and mitigate potential hotspots proactively, predict the need for government services and make sure those services are delivered efficiently, spot opportunities to streamline the supply chain and put them into effect automatically.

Nanotechnology in your clothing could send environmental data to your smart phone, or charge it from electricity generated as you walk. But why carry a phone when any glass surface, from your bathroom mirror to your kitchen window, could become an interactive interface for checking your calendar, answering email, watching videos, and anything else we do today on our phones and tablets? For that matter, why carry a phone when ambient connectivity will let us simply speak to each other across a distance without devices?

How to get there

In Tech Trends 2015, Deloitte Consulting outlines four capabilities required for ambient computing:

  1. Integrating information flow between varying types of devices from a wide range of global manufacturers with proprietary data and technologies
  2. Performing analytics and management of the physical objects and low-level events to detect signals and predict impact
  3. Orchestrating those signals and objects to fulfill complex events or end-to-end business processes
  4. Securing and monitoring the entire system of devices, connectivity, and information exchange

These technical challenges are daunting, but doable.

Of course, businesses and governments need to consider the ramifications of systems that can sense, reason, act, and interact for us. We need to solve the trust and security issues inherent in a future world where we’re constantly surrounded by connectivity and information. We need to consider what happens when tasks currently performed by humans can be automated into near invisibility. And we need to think about what it means to be human when ambient intelligence can satisfy our wants and needs before we express them, or before we even know that we have them.

There are incredible upsides to such a future, but there are also drawbacks. Let’s make sure we go there with our eyes wide open, and plan for the outcomes we want.

Download the Executive Brief: Enveloped by Ambient Intelligence

Ambient Intelligence thumb

To learn more about how exponential technology will affect business and life, see The Digitalist’s Digital Futures.


About Dan Wellers

Dan Wellers leads Digital Futures for SAP Marketing Strategy.

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