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10 Myths About Predictive Analytics

Angela Hausman

Business is tough and competition can be brutal. With many economies still sluggish after the 10 Myths About Predictive Analyticsfinancial Armageddon that caused a mortgage meltdown and the stock market to experience the biggest losses since the Great Depression, it’s never been harder to make a buck. Businesses trying to gain whatever competitive edge they can turned to big data and predictive analytics hoping to “exploit new opportunities and gain the upper hand over competitors” according to TechRadar. In their interview with James Fisher, SAP’s VP of Marketing and Analytics, Fisher talks about the importance of predictive analytics for driving business success:

Predictive analytics technology is the core enabler of big data, allowing businesses to use historical data, combined with customer insight, to predict future events. This could be anything from anticipating customer needs, forecasting wider market trends or managing risk, which in turn offer a competitive advantage, the ability to drive new opportunities and ultimately increase revenue.

No wonder more businesses use predictive analytics today than ever before. Not only is there more data available, but the cost of tools puts them within reach for most businesses [check out predictive analytics solutions by IBM’s SPSS Modeler, SAS Enterprise Miner, SAP Predictive Analytics, and Oracle’s Data Mining ODM]. In the past, you’d need lots of expensive storage, massive computers, and expensive software to run predictive analytics. With the reasonable cost of cloud storage and inexpensive software that runs on the average desktop computer, everyone can harness the power of predictive analytics.

Not true.

It turns out, even though more businesses are trying to make sense of their data, few succeed. Although a recent study by SAP finds more than 85% of surveyed businesses use predictive analytics and 77% believe they’re getting higher revenues because they’re data-driven, it turns out most are leaving a lot of money on the table — at least figuratively.

So, why are businesses large and small failing with predictive analytics?

Maybe they just drank the Koolaid and fell prey to the many myths about predictive analytics.

Now, don’t get me wrong. Anyone who’s read this blog knows I’m a strong advocate of data-driven marketing (and business intelligence, in general). The problem comes when businesses fail to understand how to do predictive analytics the RIGHT way.

Are you falling for any of these 10 myths about predictive analytics?

Let’s see.

Myth #1: Predictive analytics is easy

Sure, new tools, like the ones listed above, make it easy to analyze big data and derive “answers”. In fact, you can throw in data and basically just let the machine run until it spits out something. The problem is, the answers might not be worth the energy it took to make the calculations.

Running analytics programs is easy, doing it right is hard.

That’s because you don’t just turn the computers loose a let them run. Predictive analytics requires some serious training in consumer behavior (at least within the marketing area) as well as alignment with company goals.

I remember when I took a class from IBM on using their software. Within a couple of hours we figured out how to run data and the different options for analysis. Then, they turned us loose on a real data set and I remember staring at the screen, not knowing what to do next. That’s because I didn’t have a theory about how the data might be related — a necessary starting point for running predictive analytics effectively.

Myth #2: Scientific evidence is proof

Just because folks say something, doesn’t mean it’s true.

A great example is the New Coke debacle. Coke’s market research folks went out and asked consumers about their preferences for soft drinks. Coke used their responses to develop a formula that better matched preferences.

It failed.

People were in the streets protesting New Coke (called simply Coke).

People hoarded (Old) Coke so stores quickly ran out of their stock.

It was a public relations nightmare — or maybe not considering the $millions in free publicity Coke got.

Why did New Coke fail?

Simple. Folks didn’t just buy it for the taste. They bought it for the whole brand image — the nostalgia of having grown up drinking Coke.

Coke never asked them about that. They never thought about it. They didn’t really understand their customers.

Myth #3: Only what you can measure matters

Predictive analytics relies on metrics — many of them historical data, some from studies. There’s the prevailing notion that things only matter if you can measure them.

But, that’s not the case.

Sometimes things you can’t measure make a whole lot of difference.

Take trust, for example. Does trust impact whether folks buy your stuff? You better believe it.

You might be able to infer trust because someone buys your product, but you can’t directly measure it. So, it doesn’t show up in your predictions.

Myth #4:Correlation = causation

Predictions are primarily based on correlations (relationships) between the data you have.

But, correlations don’t mean that one factor CAUSED the other factor. Just because 2 things are related doesn’t mean one caused the other.

The best example of this is the correlation between hem lengths and the stock market — the shorter women’s skirts, the higher the stock market. But hem lengths don’t cause stocks to go up any more than high stock prices force skirts up. In fact, both are caused by confidence and a sense of well-being. So, if you try to manipulate stock prices by forcing manufacturers to shorten skirt lengths, you’ll fail.

Myth #5: Predictions are perfect

Predictive analytics produce probabilistic estimates of the future. No one has a crystal ball and predicts with complete accuracy. Take horse racing for example. People, knowledgeable people, place bets on horses using predictive factors like age, bloodlines, prior performance … The odds reflect the combined predictions of all betters. Most of the time, the odds on favorite wins — performs as predicted. But, every once in a while, the long shot surprises everyone and takes the purse. This leads to the next myth….

Myth #6: Predictions are forever

Not so, as we saw with our horse race. More data usually makes predictions better. As time goes on, new data should be added into your model and better predictions made about the future.

But, sometimes, the whole model goes haywire. Cultural shifts, demographic changes, and other events might drastically change the model.

Myth #7: You need a skilled consultant to implement predictive analytics

Not so. Go back to Myth #1 and you’ll see the skill necessary to run accurate predictions. But, hiring an outsider might not be the best way to step up your predictive analytics program. Predictive modeling requires an intimate understanding of what data is available or can be collected, the goals of the organization, insights about the organizations culture, structure and market …

Outsiders rarely have the internal knowledge necessary to run an effective predictive analytics program. Instead, you might have to invest in hiring or training employees.

Myth #8: Predictive analytics is mostly a machine problem

Somewhat related to some earlier myths is the notion that predictive analytics is a black box. You pour data in and something happens in the box (computer) that yields accurate predictions. It’s an appealing notion, but not completely accurate. Pouring in data often generates a lot of spurious correlations that don’t really mean there’s a relationship between factors.

That said, sometimes pouring in data and seeing what comes out is an effective FIRST step in predictive analytics. However, the resulting predictions must be validated before a company uses them for planning.

Myth #9: Predictive analytics are expensive

As I mentioned earlier, predictive analytics doesn’t have to break the bank. New software and cloud storage make it within reach of most businesses.

Myth #10: Insights = action

This may be the grandaddy of all myths about predictive analytics.

Predictive analytics, done effectively, produce insights. Turning those insights into action takes both intuition and managerial skill to gain buy-in from stakeholders and pivot the organization.

Your turn

What are your experiences with predictive analytics like?

Do you have other myths to add to my list?

Need help?

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Data Analysts And Scientists More Important Than Ever For The Enterprise

Daniel Newman

The business world is now firmly in the age of data. Not that data wasn’t relevant before; it was just nowhere close to the speed and volume that’s available to us today. Businesses are buckling under the deluge of petabytes, exabytes, and zettabytes. Within these bytes lie valuable information on customer behavior, key business insights, and revenue generation. However, all that data is practically useless for businesses without the ability to identify the right data. Plus, if they don’t have the talent and resources to capture the right data, organize it, dissect it, draw actionable insights from it and, finally, deliver those insights in a meaningful way, their data initiatives will fail.

Rise of the CDO

Companies of all sizes can easily find themselves drowning in data generated from websites, landing pages, social streams, emails, text messages, and many other sources. Additionally, there is data in their own repositories. With so much data at their disposal, companies are under mounting pressure to utilize it to generate insights. These insights are critical because they can (and should) drive the overall business strategy and help companies make better business decisions. To leverage the power of data analytics, businesses need more “top-management muscle” specialized in the field of data science. This specialized field has lead to the creation of roles like Chief Data Officer (CDO).

In addition, with more companies undertaking digital transformations, there’s greater impetus for the C-suite to make data-driven decisions. The CDO helps make data-driven decisions and also develops a digital business strategy around those decisions. As data grows at an unstoppable rate, becoming an inseparable part of key business functions, we will see the CDO act as a bridge between other C-suite execs.

Data skills an emerging business necessity

So far, only large enterprises with bigger data mining and management needs maintain in-house solutions. These in-house teams and technologies handle the growing sets of diverse and dispersed data. Others work with third-party service providers to develop and execute their big data strategies.

As the amount of data grows, the need to mine it for insights becomes a key business requirement. For both large and small businesses, data-centric roles will experience endless upward mobility. These roles include data anlysts and scientists. There is going to be a huge opportunity for critical thinkers to turn their analytical skills into rapidly growing roles in the field of data science. In fact, data skills are now a prized qualification for titles like IT project managers and computer systems analysts.

Forbes cited the McKinsey Global Institute’s prediction that by 2018 there could be a massive shortage of data-skilled professionals. This indicates a disruption at the demand-supply level with the needs for data skills at an all-time high. With an increasing number of companies adopting big data strategies, salaries for data jobs are going through the roof. This is turning the position into a highly coveted one.

According to Harvard Professor Gary King, “There is a big data revolution. The big data revolution is that now we can do something with the data.” The big problem is that most enterprises don’t know what to do with data. Data professionals are helping businesses figure that out. So if you’re casting about for where to apply your skills and want to take advantage of one of the best career paths in the job market today, focus on data science.

I’m compensated by University of Phoenix for this blog. As always, all thoughts and opinions are my own.

For more insight on our increasingly connected future, see The $19 Trillion Question: Are You Undervaluing The Internet Of Things?

The post Data Analysts and Scientists More Important Than Ever For the Enterprise appeared first on Millennial CEO.

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About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

When Good Is Good Enough: Guiding Business Users On BI Practices

Ina Felsheim

Image_part2-300x200In Part One of this blog series, I talked about changing your IT culture to better support self-service BI and data discovery. Absolutely essential. However, your work is not done!

Self-service BI and data discovery will drive the number of users using the BI solutions to rapidly expand. Yet all of these more casual users will not be well versed in BI and visualization best practices.

When your user base rapidly expands to more casual users, you need to help educate them on what is important. For example, one IT manager told me that his casual BI users were making visualizations with very difficult-to-read charts and customizing color palettes to incredible degrees.

I had a similar experience when I was a technical writer. One of our lead writers was so concerned with readability of every sentence that he was going through the 300+ page manuals (yes, they were printed then) and manually adjusting all of the line breaks and page breaks. (!) Yes, readability was incrementally improved. But now any number of changes–technical capabilities, edits, inserting larger graphics—required re-adjusting all of those manual “optimizations.” The time it took just to do the additional optimization was incredible, much less the maintenance of these optimizations! Meanwhile, the technical writing team was falling behind on new deliverables.

The same scenario applies to your new casual BI users. This new group needs guidance to help them focus on the highest value practices:

  • Customization of color and appearance of visualizations: When is this customization necessary for a management deliverable, versus indulging an OCD tendency? I too have to stop myself from obsessing about the font, line spacing, and that a certain blue is just a bit different than another shade of blue. Yes, these options do matter. But help these casual users determine when that time is well spent.
  • Proper visualizations: When is a spinning 3D pie chart necessary to grab someone’s attention? BI professionals would firmly say “NEVER!” But these casual users do not have a lot of depth on BI best practices. Give them a few simple guidelines as to when “flash” needs to subsume understanding. Consider offering a monthly one-hour Lunch and Learn that shows them how to create impactful, polished visuals. Understanding if their visualizations are going to be viewed casually on the way to a meeting, or dissected at a laptop, also helps determine how much time to spend optimizing a visualization. No, you can’t just mandate that they all read Tufte.
  • Predictive: Provide advanced analytics capabilities like forecasting and regression directly in their casual BI tools. Using these capabilities will really help them wow their audience with substance instead of flash.
  • Feature requests: Make sure you understand the motivation and business value behind some of the casual users’ requests. These casual users are less likely to understand the implications of supporting specific requests across an enterprise, so make sure you are collaborating on use cases and priorities for substantive requests.

By working with your casual BI users on the above points, you will be able to collectively understand when the absolute exact request is critical (and supports good visualization practices), and when it is an “optimization” that may impact productivity. In many cases, “good” is good enough for the fast turnaround of data discovery.

Next week, I’ll wrap this series up with hints on getting your casual users to embrace the “we” not “me” mentality.

Read Part One of this series: Changing The IT Culture For Self-Service BI Success.

Follow me on Twitter: @InaSAP

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The Future of Cybersecurity: Trust as Competitive Advantage

Justin Somaini and Dan Wellers

 

The cost of data breaches will reach US$2.1 trillion globally by 2019—nearly four times the cost in 2015.

Cyberattacks could cost up to $90 trillion in net global economic benefits by 2030 if cybersecurity doesn’t keep pace with growing threat levels.

Cyber insurance premiums could increase tenfold to $20 billion annually by 2025.

Cyberattacks are one of the top 10 global risks of highest concern for the next decade.


Companies are collaborating with a wider network of partners, embracing distributed systems, and meeting new demands for 24/7 operations.

But the bad guys are sharing intelligence, harnessing emerging technologies, and working round the clock as well—and companies are giving them plenty of weaknesses to exploit.

  • 33% of companies today are prepared to prevent a worst-case attack.
  • 25% treat cyber risk as a significant corporate risk.
  • 80% fail to assess their customers and suppliers for cyber risk.

The ROI of Zero Trust

Perimeter security will not be enough. As interconnectivity increases so will the adoption of zero-trust networks, which place controls around data assets and increases visibility into how they are used across the digital ecosystem.


A Layered Approach

Companies that embrace trust as a competitive advantage will build robust security on three core tenets:

  • Prevention: Evolving defensive strategies from security policies and educational approaches to access controls
  • Detection: Deploying effective systems for the timely detection and notification of intrusions
  • Reaction: Implementing incident response plans similar to those for other disaster recovery scenarios

They’ll build security into their digital ecosystems at three levels:

  1. Secure products. Security in all applications to protect data and transactions
  2. Secure operations. Hardened systems, patch management, security monitoring, end-to-end incident handling, and a comprehensive cloud-operations security framework
  3. Secure companies. A security-aware workforce, end-to-end physical security, and a thorough business continuity framework

Against Digital Armageddon

Experts warn that the worst-case scenario is a state of perpetual cybercrime and cyber warfare, vulnerable critical infrastructure, and trillions of dollars in losses. A collaborative approach will be critical to combatting this persistent global threat with implications not just for corporate and personal data but also strategy, supply chains, products, and physical operations.


Download the executive brief The Future of Cybersecurity: Trust as Competitive Advantage.


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Unleash The Digital Transformation

Kadamb Goswami

The world has changed. We’ve seen massive disruption on multiple fronts – business model disruption, cybercrime, new devices, and an app-centric world. Powerful networks are crucial to success in a mobile-first, cloud-first world that’s putting an ever-increasing increasing amount of data at our fingertips. With the Internet of Things (IoT) we can connect instrumented devices worldwide and use new data to transform business models and products.

Disruption

Disruption comes in many forms. It’s not big or scary, it’s just another way of describing change and evolution. In the ’80s it manifested as call centers. Then, as the digital landscape began to take shape, it was the Internet, cloud computing … now it’s artificial intelligence (AI).

Digital transformation

Digital transformation means different things to different companies, but in the end I believe it will be a simple salvation that will carry us forward. If you Bing (note I worked for Microsoft for 15 years before experiencing digital transformation from the lens of the outside world), digital transformation, it says it’s “the profound and accelerating transformation of business activities, processes, competencies, and models to fully leverage the changes and opportunities of digital technologies and their impact across society in a strategic and prioritized way.” (I’ll simplify that; keep reading.)

A lot of today’s digital transformation ideas are ripped straight from the scripts of sci-fi entertainment, whether you’re talking about the robotic assistants of 2001: A Space Odyssey or artificial intelligence in the Star Trek series. We’re forecasting our future with our imagination. So, let’s move on to why digital transformation is needed in our current world.

Business challenges

The basic challenges facing businesses today are the same as they’ve always been: engaging customers, empowering employees, optimizing operations, and reinventing the value offered to customers. However, what has changed is the unique convergence of three things:

  1. Increasing volumes of data, particularly driven by the digitization of “things” and heightened individual mobility and collaboration
  1. Advancements in data analytics and intelligence to draw actionable insight from the data
  1. Ubiquity of cloud computing, which puts this disruptive power in the hands of organizations of all sizes, increasing the pace of innovation and competition

Digital transformation in plain English

Hernan Marino, senior vice president, marketing, & global chief operating officer at SAP, explains digital transformation by giving specific industry examples to make it simpler.

Automobile manufacturing used to be the work of assembly lines, people working side-by-side literally piecing together, painting, and churning out vehicles. It transitioned to automation, reducing costs and marginalizing human error. That was a business transformation. Now, we are seeing companies like Tesla and BMW incorporate technology into their vehicles that essentially make them computers on wheels. Cameras. Sensors. GPS. Self-driving vehicles. Syncing your smartphone with your car.

The point here is that companies need to make the upfront investments in infrastructure to take advantage of digital transformation, and that upfront investment will pay dividends in the long run as technological innovations abound. It is our job to collaboratively work with our customers to understand what infrastructure changes need to be made to achieve and take advantage of digital transformation.

Harman gives electric companies as another example. Remember a few years ago, when you used to go outside your house and see the little power meter spinning as it recorded the kilowatts you use? Every month, the meter reader would show up in your yard, record your usage, and report back to the electric company.

Most electric companies then made a business transformation and installed smart meters – eliminating the cost of the meter reader and integrating most homes into a smart grid that gave customers access to their real-time information. Now, as renewable energy evolves and integrates more fully into our lives, these same electric companies that switched over to smart meters are going to make additional investments to be able to analyze the data and make more informed decisions that will benefit both the company and its customers.

That is digital transformation. Obviously, banks, healthcare, entertainment, trucking, and e-commerce all have different needs than auto manufacturers and electric companies. It is up to us – marketers and account managers promoting digital transformation – to identify those needs and help our clients make the digital transformation as seamlessly as possible.

Digital transformation is more than just a fancy buzzword, it is our present and our future. It is re-envisioning existing business models and embracing a different way of bringing together people, data, and processes to create more for their customers through systems of intelligence.

Learn more about what it means to be a digital business.

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About Goswami Kadamb

Kadamb is a Senior Program Manager at SAP where he is responsible for developing and executing strategic sales program with Concur SaaS portfolio. Prior to that he led several initiatives with Microsoft's Cloud & Enterprise business to enable Solution Sales & IaaS offerings.