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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Diving Deep Into Digital Experiences

Kai Goerlich

 

Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

Link to Sources


From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.


What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”


Download the executive brief Diving Deep Into Digital Experiences.


Read the full article Swimming in the Immersive Digital Experience.

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Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

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Blockchain: Much Ado About Nothing? How Very Wrong!

Juergen Roehricht

Let me start with a quote from McKinsey, that in my view hits the nail right on the head:

“No matter what the context, there’s a strong possibility that blockchain will affect your business. The very big question is when.”

Now, in the industries that I cover in my role as general manager and innovation lead for travel and transportation/cargo, engineering, construction and operations, professional services, and media, I engage with many different digital leaders on a regular basis. We are having visionary conversations about the impact of digital technologies and digital transformation on business models and business processes and the way companies address them. Many topics are at different stages of the hype cycle, but the one that definitely stands out is blockchain as a new enabling technology in the enterprise space.

Just a few weeks ago, a customer said to me: “My board is all about blockchain, but I don’t get what the excitement is about – isn’t this just about Bitcoin and a cryptocurrency?”

I can totally understand his confusion. I’ve been talking to many blockchain experts who know that it will have a big impact on many industries and the related business communities. But even they are uncertain about the where, how, and when, and about the strategy on how to deal with it. The reason is that we often look at it from a technology point of view. This is a common mistake, as the starting point should be the business problem and the business issue or process that you want to solve or create.

In my many interactions with Torsten Zube, vice president and blockchain lead at the SAP Innovation Center Network (ICN) in Potsdam, Germany, he has made it very clear that it’s mandatory to “start by identifying the real business problem and then … figure out how blockchain can add value.” This is the right approach.

What we really need to do is provide guidance for our customers to enable them to bring this into the context of their business in order to understand and define valuable use cases for blockchain. We need to use design thinking or other creative strategies to identify the relevant fields for a particular company. We must work with our customers and review their processes and business models to determine which key blockchain aspects, such as provenance and trust, are crucial elements in their industry. This way, we can identify use cases in which blockchain will benefit their business and make their company more successful.

My highly regarded colleague Ulrich Scholl, who is responsible for externalizing the latest industry innovations, especially blockchain, in our SAP Industries organization, recently said: “These kinds of use cases are often not evident, as blockchain capabilities sometimes provide minor but crucial elements when used in combination with other enabling technologies such as IoT and machine learning.” In one recent and very interesting customer case from the autonomous province of South Tyrol, Italy, blockchain was one of various cloud platform services required to make this scenario happen.

How to identify “blockchainable” processes and business topics (value drivers)

To understand the true value and impact of blockchain, we need to keep in mind that a verified transaction can involve any kind of digital asset such as cryptocurrency, contracts, and records (for instance, assets can be tangible equipment or digital media). While blockchain can be used for many different scenarios, some don’t need blockchain technology because they could be handled by a simple ledger, managed and owned by the company, or have such a large volume of data that a distributed ledger cannot support it. Blockchain would not the right solution for these scenarios.

Here are some common factors that can help identify potential blockchain use cases:

  • Multiparty collaboration: Are many different parties, and not just one, involved in the process or scenario, but one party dominates everything? For example, a company with many parties in the ecosystem that are all connected to it but not in a network or more decentralized structure.
  • Process optimization: Will blockchain massively improve a process that today is performed manually, involves multiple parties, needs to be digitized, and is very cumbersome to manage or be part of?
  • Transparency and auditability: Is it important to offer each party transparency (e.g., on the origin, delivery, geolocation, and hand-overs) and auditable steps? (e.g., How can I be sure that the wine in my bottle really is from Bordeaux?)
  • Risk and fraud minimization: Does it help (or is there a need) to minimize risk and fraud for each party, or at least for most of them in the chain? (e.g., A company might want to know if its goods have suffered any shocks in transit or whether the predefined route was not followed.)

Connecting blockchain with the Internet of Things

This is where blockchain’s value can be increased and automated. Just think about a blockchain that is not just maintained or simply added by a human, but automatically acquires different signals from sensors, such as geolocation, temperature, shock, usage hours, alerts, etc. One that knows when a payment or any kind of money transfer has been made, a delivery has been received or arrived at its destination, or a digital asset has been downloaded from the Internet. The relevant automated actions or signals are then recorded in the distributed ledger/blockchain.

Of course, given the massive amount of data that is created by those sensors, automated signals, and data streams, it is imperative that only the very few pieces of data coming from a signal that are relevant for a specific business process or transaction be stored in a blockchain. By recording non-relevant data in a blockchain, we would soon hit data size and performance issues.

Ideas to ignite thinking in specific industries

  • The digital, “blockchained” physical asset (asset lifecycle management): No matter whether you build, use, or maintain an asset, such as a machine, a piece of equipment, a turbine, or a whole aircraft, a blockchain transaction (genesis block) can be created when the asset is created. The blockchain will contain all the contracts and information for the asset as a whole and its parts. In this scenario, an entry is made in the blockchain every time an asset is: sold; maintained by the producer or owner’s maintenance team; audited by a third-party auditor; has malfunctioning parts; sends or receives information from sensors; meets specific thresholds; has spare parts built in; requires a change to the purpose or the capability of the assets due to age or usage duration; receives (or doesn’t receive) payments; etc.
  • The delivery chain, bill of lading: In today’s world, shipping freight from A to B involves lots of manual steps. For example, a carrier receives a booking from a shipper or forwarder, confirms it, and, before the document cut-off time, receives the shipping instructions describing the content and how the master bill of lading should be created. The carrier creates the original bill of lading and hands it over to the ordering party (the current owner of the cargo). Today, that original paper-based bill of lading is required for the freight (the container) to be picked up at the destination (the port of discharge). Imagine if we could do this as a blockchain transaction and by forwarding a PDF by email. There would be one transaction at the beginning, when the shipping carrier creates the bill of lading. Then there would be look-ups, e.g., by the import and release processing clerk of the shipper at the port of discharge and the new owner of the cargo at the destination. Then another transaction could document that the container had been handed over.

The future

I personally believe in the massive transformative power of blockchain, even though we are just at the very beginning. This transformation will be achieved by looking at larger networks with many participants that all have a nearly equal part in a process. Today, many blockchain ideas still have a more centralistic approach, in which one company has a more prominent role than the (many) others and often is “managing” this blockchain/distributed ledger-supported process/approach.

But think about the delivery scenario today, where goods are shipped from one door or company to another door or company, across many parties in the delivery chain: from the shipper/producer via the third-party logistics service provider and/or freight forwarder; to the companies doing the actual transport, like vessels, trucks, aircraft, trains, cars, ferries, and so on; to the final destination/receiver. And all of this happens across many countries, many borders, many handovers, customs, etc., and involves a lot of paperwork, across all constituents.

“Blockchaining” this will be truly transformational. But it will need all constituents in the process or network to participate, even if they have different interests, and to agree on basic principles and an approach.

As Torsten Zube put it, I am not a “blockchain extremist” nor a denier that believes this is just a hype, but a realist open to embracing a new technology in order to change our processes for our collective benefit.

Turn insight into action, make better decisions, and transform your business. Learn how.

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Juergen Roehricht

About Juergen Roehricht

Juergen Roehricht is General Manager of Services Industries and Innovation Lead of the Middle and Eastern Europe region for SAP. The industries he covers include travel and transportation; professional services; media; and engineering, construction and operations. Besides managing the business in those segments, Juergen is focused on supporting innovation and digital transformation strategies of SAP customers. With more than 20 years of experience in IT, he stays up to date on the leading edge of innovation, pioneering and bringing new technologies to market and providing thought leadership. He has published several articles and books, including Collaborative Business and The Multi-Channel Company.