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Why Privacy Is a Corporate Responsibility Issue

February 11, 2018 /

Many organizations have Corporate Responsibility programs that focus on social issues and philanthropy. Especially in today’s Big Data era, why is privacy not part of the program?

Today’s companies are promising to lower their carbon footprints and save endangered species. They’re donating to people in developing countries who have far less than we do, which is also noble. But what about the fact that American citizens are a product whose information is bought, sold, and obtained without consent? In light of recent events, perhaps the privacy policies deserve more consideration than just two linked words at the bottom of a website home page.

“Privacy is a big issue for a host of reasons — legal, ethical, brand protection and moral,” Mark Cohen, Chief Strategy Officer at consultancy and technology service provider Elevate. “[Privacy] is an element of corporate culture [so what goes into a privacy policy depends on] your values and priorities.”

Problems with Privacy Policies

There are three big problems with privacy policies, at least in the US: what’s in them, how they’re written, and how they’re ignored.

One might think that privacy policies are tailored to a particular company and its audience. However, such documents are not necessarily original. Rather than penning a privacy policy from scratch, some are literally cutting and pasting entire privacy policies regardless of their contents. In fact, the people who are simply grabbing another company’s privacy policy might not even bother to read the content before using it.

The boilerplate language is also a problem. In-house counsel often uses freely available forms to put together a privacy policy. They may use one form or a combination of forms available to lawyers, but again, they’re not thinking about what should be in the document.

In addition, the documents are written in legalese, which is difficult for the average person to read. Businesses are counting on that because if you don’t know what’s in a privacy policy, what you’re giving away and what they intend to do with your information, you’ll probably just hope for the best. Even better, you’ll click an “I agree” button without knowing what clicking that button actually means. It’s a common practice, so you’re not alone if that’s the case.

Oh, and what’s stated in the documents may or may not be true, either because the company changed the policy since you last read it or they’re ignoring the document itself.

“After May 2018 when the new GDPR [General Data Protection Regulation] goes into effect, it’s going to force many companies to look at their privacy policies. their privacy statements and consents and make them more transparent,” said Sheila Fitzpatrick, Data Governance & Privacy counsel and chief privacy officer at data services for hybrid cloud company NetApp. “They’re going to have to be easily understandable and readable.”

Businesses Confuse Privacy with Security

Privacy and security go hand-in-hand, but they’re not the same thing. However, the assumption is, if you’re encrypting data then you’re protecting privacy.

“Every company focuses on risk, export control trade compliance, security, but rarely you find companies focused on privacy,” said Fitzpatrick. “That’s changing with GDPR because it’s extraterritorial. It’s forcing companies to start really addressing areas around privacy.”

It’s entirely possible to have all kinds of security and still not address privacy issues. OK, so the data is being locked down, but are you legally allowed to have it in the first place? Perhaps not.

“Before you lock down that data, you need the legal right to have it,” said Fitzpatrick. “That’s the part that organizations still aren’t comprehending because they think they need the data to manage the relationship. In the past organizations thought they need the data to manage employment, customer or prospect relationships, but they were never really transparent about what they’re doing with that data, and they haven’t obtained the consent from the individual.”

In the US the default is opt-in. In countries that have restrictive privacy policies, the default is opt-out.

The Data Lake Mentality Problem

We hear a lot about data lakes and data swamps. In a lot of cases, companies are just throwing every piece of data into a data lake, hoping it will have value in the future. After all, cloud storage is dirt cheap.

“Companies need to think about the data they absolutely need to support a relationship. If they’re an organization that designs technology, what problem are they trying to solve and what data do they need to solve the problem?” said Fitzpatrick.

Instead of collecting massive amounts of information that’s totally irrelevant, they should consider data minimization if they want to lower privacy-related risks and comply with the EU’s GDPR.

“Companies also need to think about how long are they’re maintaining this data because they have a tendency to want to keep data forever even if it has no value,” said Fitzpatrick. “Under data protection laws, not just the GDPR, data should only be maintained for the purpose it was given and only for the time period for which it was relevant.”

The Effect of GDPR

Under the GDPR, consent has to be freely given, not forced or implied. That means companies can’t pre-check an opt-in box or force people to trade personal data for the use or continued use of a service.

“Some data is needed. If you’re buying a new car they need financial information, but they’d only be using it for the purpose of the purchase, not 19 other things they want to use it for including sales and marketing purposes,” said Fitzpatrick.

Privacy may well become the new competitive advantage as people become more aware of privacy policies and what they mean and don’t mean.

“Especially Europeans, Canadians, and those who live in Asia-Pacific countries that have restrictive privacy laws, part of their vetting process will be looking at your privacy program,” said Fitzpatrick. “If you have a strong privacy program and can answer a privacy question with a privacy answer as opposed to answering a privacy question with a security answer, [you’ll have an advantage].”

On the flip side, sanctions from international countries can destroy a company from reputational, brand and financial points of view. The sanction under the new GDPR regulation can be as high as 4% of a company’s annual turnover.

Your Data Is Biased. Here’s Why.

January 11, 2018 /

Bias is everywhere, including in your data. A little skew here and there may be fine if the ramifications are minimal, but bias can negatively affect your company and its customers if left unchecked, so you should make an effort to understand how, where and why it happens.

“Many [business leaders] trust the technical experts but I would argue that they’re ultimately responsible if one of these models has unexpected results or causes harm to people’s lives in some way,” said Steve Mills, a principal and director of machine intelligence at technology and management consulting firm Booz Allen Hamilton.

In the financial industry, for example, biased data may cause results that offend the Equal Credit Opportunity Act (fair lending). That law, enacted in 1974, prohibits credit discrimination based on race, color, religion, national origin, sex, marital status, age or source of income. While lenders will take steps not to include such data in a loan decision, it may be possible to infer race in some cases using a zip code, for example.

“The best example of [bias in data] is the 2008 crash in which the models were trained on a dataset,” said Shervin Khodabandeh, a partner and managing director of Boston Computing Group (BCG) Los Angeles, a management consulting company. “Everything looked good, but the datasets changed and the models were not able to pick that up, [so] the model collapsed and the financial system collapsed.”

What Causes Bias in Data

A considerable amount of data has been generated by humans, whether it’s the diagnosis of a patient’s condition or the facts associated with an automobile accident.  Quite often, individual biases are evident in the data, so when such data is used for machine learning training purposes, the machine intelligence reflects that bias.  A prime example of that was Microsoft’s infamous AI bot, Tay, which in less than 24 hours adopted the biases of certain Twitter members. The results were a string of shocking, offensive and racist posts.

“There’s a famous case in Broward County, Florida, that showed racial bias,” said Mills. “What appears to have happened is there was historically racial bias in sentencing so when you base a model on that data, bias flows into the model. At times, bias can be extremely hard to detect and it may take as much work as building the original model to tease out whether that bias exists or not.”

What Needs to Happen

Business leaders need to be aware of bias and the unintended consequences biased data may cause.  In the longer-term view, data-related bias is a governance issue that needs to be addressed with the appropriate checks and balances which include awareness, mitigation and a game plan should matters go awry.

“You need a formal process in place, especially when you’re impacting people’s lives,” said Booz Allen Hamilton’s Mills. “If there’s no formal process in place, it’s a really bad situation. Too many times we’ve seen these cases where issues are pointed out, and rather than the original people who did the work stepping up and saying, ‘I see what you’re seeing, let’s talk about this,’ they get very defensive and defend their approach so I think we need to have a much more open dialog on this.”

As a matter of policy, business leaders need to consider which decisions they’re comfortable allowing algorithms to make, the safeguards which ensure the algorithms remain accurate over time, and model transparency, meaning that the reasoning behind an automated decision or recommendation can be explained.  That’s not always possible, but still, business leaders should endeavor to understand the reasoning behind decisions and recommendations.

“The tough part is not knowing where the biases are there and not taking the initiative to do adequate testing to find out if something is wrong,” said Kevin Petrasic, a partner at law firm White & Case.  “If you have a situation where certain results are being kicked out by a program, it’s incumbent on the folks monitoring the programs to do periodic testing to make sure there’s appropriate alignment so there’s not fair lending issues or other issues that could be problematic because of key datasets or the training or the structure of the program.”

Data scientists know how to compensate for bias, but they often have trouble explaining what they did and why they did it, or the output of a model in simple terms. To bridge that gap, BCG’s Khodabandeh uses two models: one that’s used to make decisions and a simpler model that explains the basics in a way that clients can understand.

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BCG also uses two models to identify and mitigate bias.  One is the original model, the other is used to test extreme scenarios.

“We have models with an opposite hypothesis in mind which forces the model to go to extremes,” said Khodabandeh. “We also force models to go to extremes. That didn’t happen in the 2008 collapse. They did not test extreme scenarios. If they had tested extreme scenarios, there would have been indicators coming in in 2007 and 2008 that would allow the model to realize it needs to adjust itself.”

A smart assumption is that bias is present in data, regardless.  What the bias is, where it stems from, what can be done about it and what the potential outcomes of it may be are all things to ponder.

Conclusion

All organizations have biased data.  The questions are whether the bias can be identified, what effect that bias may have, and what the organization is going to do about it.

To minimize the negative effects of bias, business leaders should make a point of understanding the various types and how they can impact data, analysis and decisions. They should also ensure there’s a formal process in place for identifying and dealing with bias, which is likely best executed as a formal part of data governance.

Finally, the risks associated with data bias vary greatly, depending on the circumstances. While it’s prudent to ponder all the positive things machine learning and AI can do for an organization, business leaders are wise to understand the weaknesses also, one of which is data bias.

How to Teach Executives About Analytics

January 4, 2018 /
If your data is failing to persuade executives, maybe it’s not the data that is the problem. Here’s how to change your approach to fit the audience.

One of the biggest challenges data analysts and data scientists face is educating executives about analytics. The general tendency is to nerd out on data and fail to tell a story in a meaningful way to the target audience.

Sometimes data analytics professionals get so wrapped up in the details of what they do that they forget not everyone has the same background or understanding. As a result, they may use technical terms, acronyms, or jargon and then wonder why no one “got” their presentations or what they were saying.

They didn’t anything wrong, per se, it’s how they’re saying it and to whom.

If you find yourself in such a situation, following are a few simple things you can do to facilitate better understanding.

Discover What Matters

What matters most to your audience? Is it a competitive issue? ROI? Building your presence in a target market? Pay attention to the clues they give you and don’t be afraid to ask about their priorities. Those will clue you in to how you should teach them about analytics within the context of what they do and what they want to achieve.

Understand Your Audience

Some executives are extremely data-savvy, but the majority aren’t just yet. Dialogs between executives and data analysts or data scientists can be uncomfortable and even frustrating when the parties speak different languages. Consider asking what your target audience would like to learn about and why. That will help you choose the content you need to cover and the best format for presenting that content.

For example, if the C-suite wants to know how the company can use analytics for competitive advantage, then consider a presentation. If one of them wants to understand how to use a certain dashboard, that’s a completely different conversation and one that’s probably best tackled with some 1:1 hands-on training.

Set Realistic Expectations

Each individual has a unique view of the world. Someone who isn’t a data analyst or a data scientist probably doesn’t understand what that role actually does, so they make up their own story which becomes their reality. Their reality probably involves some unrealistic expectations about what data-oriented roles can do or accomplish or what analytics can accomplish generally.

One of the best ways to deal with unrealistic expectations is to acknowledge them and then explain what is realistic and why. For example, a charming and accomplished data scientist I know would be inclined to say, “You’d think we could accomplish that in a week, right? Here’s why it actually takes three weeks.”

Stories can differ greatly, but the one thing good presentations have in common is a beginning, a middle, and an end. One of the mistakes I see brilliant people making is focusing solely on the body of a presentation, immediately going down some technical rabbit hole that’s fascinating for people who understand it and confusing for others.

A good beginning gets everyone on the same page about what the presentation is about, why the topic of discussion is important, and what you’re going to discuss. The middle should explain the meat of the story in a logical way that flows from beginning to end. The end should briefly recap the highlights and help bring your audience to same conclusion you’re stating in your presentation.

Consider Using Options

If the executive(s) you’re presenting to hold the keys to an outcome you desire, consider giving them options from which to choose. Doing that empowers them as the decision-makers they are. Usually, that approach also helps facilitate a discussion about tradeoffs. The more dialog you have, the better you’ll understand each other.

Another related tip is make sure your options are within the realm of the reasonable. In a recent scenario, a data analyst wanted to add two people to her team. Her A, B, and C options were A) if we do nothing, then you can expect the same results, B) if we hire these two roles we’ll be able to do X and Y, which we couldn’t do before, and C) if we hire 5 people we’ll be able to do even more stuff, but it will cost this much. She came prepared to discuss the roles, the interplay with the existing team and where she got her salary figures. If they asked what adding 1, 3, or 4 people looked like, she was prepared to answer that too.

Speak Plainly

Plain English is always a wise guide. Choose simple words and concepts, keeping in mind how the meaning of a single word can differ. For example, if you say, “These two variables have higher affinity,” someone may not understand what you mean by variables or affinity.

Also endeavor to simplify what you say, using concise language. For example, “The analytics of the marketing department has at one time or another tended overlook the metrics of the customer service department” can be consolidated into, “Our marketing analytics sometimes overlooks customer service metrics.”

5 Things to Know About IT Candidates

November 2, 2017 /

Hiring and retaining IT talent is difficult. Part of the problem is that some companies don’t understand what IT professionals want and why they want it.

Manpower Solutions Group recently published a survey-based report that sheds some light on the matter. More than 14,000 currently-employed individuals between the ages of 18 and 65 participated, across industries. Some of the results are specific to IT professionals and they may surprise you.

#1: Expect turnover

IT professionals who change jobs frequently do it for two reasons: to increase their compensation (43%) and to advance their careers (60%). Employers should appeal to those desires.

“Candidates within the IT space shouldn’t be measured solely on their time spent within a specific role,” said Stephen Rees, Director of Program Delivery at Manpower Group Solutions, in an interview. “A review of a project’s purpose, the candidate’s role and [her] accomplishments within the timeframe of the project should be the key areas of focus. Seasoned recruiters and hiring managers will need to account for the time needed to ramp up performance in order to understand the value of work delivered.”

Technology is constantly changing which impacts what IT does and what IT professionals must know. Those who learn the newest must-have skills, whether it’s DevOps or virtualized IT infrastructures, tend to be in high demand. When skills are in high demand and there’s a “skills shortage,” companies will pay handsomely for the right talent.

IT professionals have to acquire those new skills somewhere, however. If they can’t learn those skills at their present companies or their present company doesn’t invest education or training, they may seek opportunities at a company that provides such benefits.

#2:  Monetary compensation isn’t everything

IT professionals weigh several factors before making a decision. The top three of seven options are compensation (23%) opportunity for advancement (22%) and benefits (21%). Schedule flexibility, type of work, geography and the company’s brand reputation rank lower. Of those, schedule flexibility ranks the highest.

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Interestingly, opportunity for advancement is almost twice as important to IT professionals than individuals who work in financial services, healthcare/pharmaceuticals and retail. Benefits are more important to IT individuals than others too and not just traditional benefits, such as a 401K program or health and dental insurance. They tend to value non-traditional benefits such as game areas, rest areas and perhaps a healthy drink on tap. Although benefits hold some value in themselves, more importantly, they tend reflect a company’s culture.

“Today’s benefits are becoming more lifestyle/non-work specific,” said Rees. “The emphasis is shifting from the immediate short-term benefits that tie employees to the office and are instead focusing on the broader impact on an individual’s life such as PTO, sabbaticals, learning and development, diversity and inclusion, etc. While the specific role, project or product is still important, the company the work is being done for is increasing in importance as candidates increasingly want to align themselves with an organization that shares their values.”

#3: Your digital presence and industry associations matter

Most survey respondents, including IT professionals, use company websites and search engines to research career opportunities. However, IT professionals are more likely to rely on social media (55%) and industry associations (33%) than the U.S. average of 38% and 18%, respectively.

In the IT world, associations are where standards are defined. Defining standards involves a lot of intellectual banter and collaboration among individuals who work at competing companies. The comradery can result in very compelling career opportunities that don’t appear on a job site or a company’s website.

Manpower notes that some of these IT associations have emerged around certification, training programs and hacking events. Within those groups knowledge exchange and mentoring happen.

“Networking has always been a core component of the IT space. For IT professionals, their work is typically their passion,” said Rees. “This participation is also seen as a way of giving back and helping others develop – there is a true desire to share experiences and knowledge, helping others to learn instead of keeping information to themselves.”

Companies can create their own hubs for interaction, whether that’s offering training or certification at an event or hosting informational sessions that enable IT professionals to meet with some of the company’s engineers.

#4:  They want you to reach out to them

More than half (55%) of IT professionals said they prefer weekly emails from potential employers of interest, which is considerably more than retail (37%), financial services (37%) and healthcare/pharmaceuticals (33%). Manpower equates this finding with the fact that 65% of IT professionals are always looking for the next job opportunity.

If you’re going to reach out to IT professionals and you’re truly interested in maintaining a dialog, don’t send out a general email blast. Instead, engage in a meaningful conversation.

#5: They’re more willing to relocate than others

IT professionals are more likely to relocate to a new city (38%) or a new state (40%) than the U.S. average of 30% and 29%, respectively, but less willing to move to a different country (8%) than the U.S. average (10%). Manpower attributes the greater degree of mobility to the lure of California locations.

While Skype interviews are common, be ready and willing to reimburse top candidates for their travel to and from an on-site interview. It demonstrates a willingness to invest in your employees.

Conclusion

Companies should avoid cookie-cutter approaches to IT recruitment because they tend to overlook some of the important things andidates value. What they value changes with time.

Manpower’s report can provide more insight into what IT professionals really want. It also includes some great advice. Happy reading.

Want to Succeed at Data-Driven Transformation? Start Slow

September 21, 2017 /

Data-driven transformation efforts often fail because companies are moving too fast or too slow. If they’re racing into it, they’re probably not getting the details right. If they’re trying to get everything into a perfect state before doing anything, they’re wasting precious time. Either way, ROI suffers.

If you want to strike the right balance, you have to think carefully about what you want to do and why you want to do it. You also have to move at an appropriate speed which means balancing short-term and long-term goals.

Start small

Boston Consulting Group recently published an in-depth article about data-driven transformation. In it, the authors raise a lot of good points, one of which is taking small steps rather than giant leaps.

Taking small steps enables businesses to succeed more often, albeit on a smaller scale. If the project is a success, the money saved or earned can be used to fund subsequent steps. Small successes also help keep teams motivated. However, executing too many small projects simultaneously can be counterproductive.

“The first mistake I see is doing 200 proof of concepts. The second thing I see is people start to do a pilot, even at scale, but [they think] first we need a developer to reinvent the system and then we’ll get the value at the end,” said Antoine Gourévitch, a Senior Partner and Managing Director at BCG. “I’d rather find a quick and dirty way to connect the IT systems and get [some immediate value] rather than doing a full transformation. You can have a transformation over three to five years, but at least I need to do the connection between my pilot at scale and the dedicated systems for the data platform that’s needed to be of value as we go.”

The third challenge is prioritizing pilots or projects. Value is the criteria there. Without a prioritized roadmap, “cool” projects may take precedence over projects that deliver business value.

Three steps to better ROI

BCG offers a lot of good advice in its article, not the least of which is breaking short-term and long-term goals into a three-step process that enables quick wins while paving the way to data-driven transformation. The three steps are:

  • Use quick wins fund the digital journey and learn
  • Design the company-wide transformation
  • Organize for sustained performance
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Within those three steps, BCG specified actions companies should take. Upon close review, it’s clear that some of the recommended actions, such as “fail fast,” apply to more than one step. If you read BCG’s article and ponder the graphics, it will get you thinking about how to scale success in your organization.

BCG also presents a five-level transformation model that includes vision, use cases, analytics, data governance and data infrastructure. Gourévitch said data governance tends to be the most problematic because data isn’t viewed as a corporate asset, so data owners may hesitate to share it.

Bottom line

Companies often move too fast or too slow when becoming data-driven organizations. When they move too fast, they can overlook important details that cause initiatives to fail. When they move too slow, they risk losing competitive advantages.

Somewhere between 200 pilots and one massive transformation effort is a balance of short-term and long-term goals, investment and ROI. The challenge, as always, is finding the balance.

Why IT is in Jeopardy

July 22, 2017 /

ome IT departments are struggling to prove their relevance as the pace of change continues to accelerate. On one hand, they’re responsible for their own predicament and on the other hand they’re not.

IT has been the master of change. On the other hand, what department wants to be responsible for its own its own demise?  IT as a function isn’t dead and it’s not going to be dead any time soon. However, IT is changing for good. Here’s why:

IT overpromised and under-delivered

Lines of business no longer want to wait for IT. They can’t. The competitive pressures are just too great to ignore. But, when something goes wrong with their tech purchases, who do they call?  IT.

“IT is in jeopardy because of the agreements or promises they’ve made to the business,” said David Caldwell, senior IT solutions consultant at Kaiser Permanente. “You can’t deliver on time, you can’t deliver what you promised and you can’t deliver reliable systems.”

What the business really wants is a dependable, enabling service that delivers what it promises.

Business expectations are too high

IT can’t be successful if the business leadership is viewing IT as a cost rather than an investment, which seems a bit strange, given the fact that today’s companies depend on technology for survival. Nevertheless, some businesses still have legacy cultural issues to overcome, one of which is realizing how value in their company is actually produced in this day and age.

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Worse, even C-level information and technology executives may not be viewed as equals among business leaders, so they’re left out of important meetings. Rather than having a partnership between IT and the business, the business may tell IT what it wants when without understanding the entire scope of the problem and how difficult or complex the solution to the problem may be.

“They don’t consider that IT leadership can help you decide how you’re going to strategically differentiate your business,” said Caldwell. “If you don’t let them in, you’re missing out on a lot of valuable input.”

A related issue is budget. If IT isn’t given enough budget to be successful, who can pin failures on IT?  Yet, over the past couple of decades IT has been told to do more with less to the point where the model itself breaks down.

IT has enabled its own demise

IT had a specific role to play before the cloud, SaaS and Shadow IT were fashionable. They were the keepers of hardware, software and networks.

“IT brought the wave of innovation in, [and yet,] IT is under the same assault of things they were the perpetrators of,” said Greg Arnette, CTO at cloud information archiving company, Sonian. “IT is going through a metamorphosis that reduces the need to have as many in IT as in previous history.”

The adoption of cloud architectures and SaaS were fueled by the economic downturn of 2008 and 2009 which forced companies to view IT in terms of operating expenses rather than capital expenses.

“It was a perfect storm,” said Arnette. “Shadow IT was driven by business unit managers frustrated with their IT departments [so they] used their credit cards to sign up for Salesforce.com or go buy ZenDesk or any of these popular SaaS apps that have become the new back office systems for most companies.”

Never mind who purchased what with which purchasing method — purchase order or credit card — when things go wrong, it’s IT’s job is to fix it. That’s one way to provide the business with services, but probably not the model IT had in mind.

The CIO/CTO role is changing

There are plenty of CIOs and CTOs, but some of them are being moved into new roles such as Chief Data Officer, Chief Analytics Officer or Chief Innovation Officer. Whether these roles are a reflection of The Brave New World or whether they’re ultimately too narrow is a debatable point.

“It’s not such a focus on information. It’s now analytics, data wrangling and a focus on innovation as a key way IT can help customers do more,” said Arnette. “I think that’s where IT will come back, but it won’t be the same type of IT department.”

Indeed. Traditional hardware and enterprise software management are being usurped by IaaS and SaaS alternatives. It’s true that a lot of companies have hybrid strategies that combine their own systems with virtualized equivalents and that some companies are managing all of their own technology, but the economics of the virtual world (when managed responsibly) are too attractive to ignore over the long term.

Why Marketing Is So Smart, Yet So Dumb

May 27, 2017 /

Marketing is considered the most analytically advanced function in most companies. Yet, consumers and businesses are still bombarded with irrelevant promotional messages.

It’s true that marketers have had access to “modern” analytics tools longer than most others in an organization. It started with web analytics and then grew to encompass other digital channels and even offline channels.

In the last decade, there has been a push toward “multi-channel” and “omnichannel” analytics. Multi-channel analytics is designed to optimize marketing effectiveness within and across channels. Omnichannel analytics focuses on improving a continuous user experience across channels.

Marketing analytics is difficult, in other words, despite the availability of more and better tools.

“What are the exact ads, the exact conversations, and the exact place that drove someone to make a purchase on my site or in my store?” said Chris Madden, co-founder of digital marketing agency Matchnode. “I think in 0% of the cases does the super smart, data-driven marketer, CMO, or CEO know what drove the sale.”

It’s Complicated

The number of online channels has exploded over the past couple of decades with the rise of Search Engine Marketing (SEM), social media, and mobile, to name a few. Anyone familiar with even one of those channels knows that change is constant, and if you don’t keep up, you’ll slip up eventually.

“We’ve seen Facebook come out twice in the past year claiming that their method for measuring engagement metrics [was] wrong. There will be more growing pains as these platforms stabilize,” said Mitul Jain, vice president at data science platform provider r4 Technologies.

Meanwhile, brick and mortar entities are tracking what’s happening in stores using kiosks, digital point of sale (POS) systems, customers’ smartphones, and security cameras. They also have e-commerce sites. Their big challenge is to understand the relationship of online and offline channels.

We’re Tracking Activities, Not People

Activities are being monitored in every channel whether posts, clicks, downloads, foot traffic, or credit card swipes, but not all of that information is being stitched together into a coherent, accurate picture.

“Analytics does not do a very good job of knowing that the person on my phone is the same person on my tablet and desktop,” said Matchnode’s Madden. “The marketers who are doing well are those that start with the person.

Attribution is Difficult

Most of the time, there isn’t a 1:1 relationship between a message and an outcome (e.g., a sale, download, or donation). Usually, the final outcome is influenced by several factors that may include search-based research, search or social media advertising, product reviews, direct mail and email offers, apps, and websites.

The natural and incorrect thing to do is to attribute the last interaction to the outcome. The shopper visited the site and bought something, so the ecommerce site gets full credit. However, since several other factors likely influenced the decision, what percentage the sale should be attributed to each? That’s the burning question.

Data Quality Could Improve

Marketing tends to use several different systems and platforms, each of which may differ enough to affect data quality. Perhaps fields or tags are implemented differently, or there are five instances of a customer record, all of which are inconsistent.

“Large sites may not have the code in the right places or double instances of code. For example, an .edu site with multiple departments may have different tracking codes on the same site, which can create a lot of confusion,” said Max Thomas, CMO at fintech startup YayPay.

Thomas audits a client’s data points to make sure they’re correct early in the relationship. Quite often he discovers that the client hasn’t set up website analytics correctly or they haven’t set up conversion tracking correctly. If either or both of those things are true, the client is referencing faulty data.

We’re Biased

Humans are biased creatures. What we perceive is based on beliefs and experience, most of which is subjective. Our subjective view or bias causes us to do many things that skew analytical results such as selecting non-random samples or cherry-picking data.

“The mistake people are making is they don’t let the data talk to them. They’re looking for something in the data that’s not there,” said D. Anthony Miles, CEO and founder of consulting firm Miles Development Industries Corp. “You have to ask what the data is telling you and what it isn’t telling you.”

Marketers tend not to look at analytical results critically, however. They tend to accept analytical at face value unless it’s out of sync with their beliefs. If they were looking at analytical results critically, they’d ask why a particular analytical result occurred or didn’t occur.

“The data can and should tell the story, but we make up our own story and look for data to support it, so we may miss the most important thing because we were looking for something else,” said Matchnode’s Madden.

Pesky PII

Finally, marketing can only be so accurate without a critical mass of Personally Identifiable Information or PII, some of which consumers do not want to give and some of which is illegal. Without the missing data points, it’s difficult to reach consumers with the right message at the right time for the right reason.

The lack of that “last mile” data is the reason why some people think marketing will never be 100% accurate. What do you think? Before you answer, think of a compromising message that might be sent to you, just at the wrong moment.

What A Chief Analytics Officer Really Does

May 17, 2017 /

As analytics continues to spread out across an organization, someone needs to orchestrate it all. The “best” person for the job is likely a chief analytics officer (CAO) who understands the business, understands analytics, and can help align the two.

The CAO role is a relatively new C-suite position, as is the chief data officer or CDO. Most organizations don’t have both and when they don’t, the titles tend to be used interchangeably. The general distinction is that the CAO focuses more on analytics and its business impact while the CDO is in charge of data management and data governance.

“The new roles are really designed to expand the use of data and expand the questions that data is used to answer,” said Jennifer Belissent, principal analyst at Forrester. “It’s changing the nature of data and analytics use in the organization, leveraging the new tools and techniques available, and creating a culture around the use of data in an organization.”

Someone in your organization may already have some or all of a CAO’s responsibilities and may be succeeding in the position without the title, which is fine. However, in some organizations a C-suite title and capability can help underscore the importance of the role and the organization’s shift toward more strategic data usage.

“The CAO needs to be able to evangelize the use of data, demonstrate the value of data, and deliver outcomes,” said Belissent. “It’s a role around cultural change, change management, and evangelism.”

If you’re planning to appoint a CAO, make sure that your organization is really ready for one because the role can fail if it is prevented from making the kinds of change the organization needs. A successful CAO needs the support of senior management, as well as the authority, responsibility, budget, and people skills necessary to affect change.

One mistake organizations make when hiring a CAO is placing too much emphasis on technology and not enough emphasis on business acumen and people skills.

The making of a CAO

When professional services company EY revisited its global strategy a few years ago, it was clear to its leadership that data and analytics were of growing importance to both its core business and the new services it would provide to clients.

Rather than hiring someone from the outside, EY chose its chief strategy officer, Chris Mazzei, for the role. His charter as CAO was to develop an analytics capability across EY’s four business units and the four global regions in which it operates.

[Want to learn more about CAOs and CDOs, read 12 Ways to Connect Data Analytics to Business Outcomes.]

Part of his responsibility was shaping the strategy and making sure each of the businesses had a plan they were executing against. He also helped expand the breadth and depth of EY’s analytical capabilities, which included acquiring 30 companies in four years.

The acquisitions coupled with EY’s matrixed organizational structure meant lots of analytics tools, lots of redundancies, and a patchwork of other technology capabilities that were eventually rationalized and made available as a service. Meanwhile, the Global Analytics Center of Excellence Mazzei leads was also building reusable software assets that could be used for analytics across the business and for client engagements.

Mazzei and his team also have been responsible for defining an analytics competency profile for practitioners and providing structured training that maps to it. Not surprisingly, his team also works in a consultative capacity with account teams to help enable clients’ analytical capabilities.

“The question is, ‘What is the strategy and how does analytics fit into it?’ It sounds obvious, but few organizations have a clear strategy where analytics is really connected into it across the enterprise and at a business level,” said Mazzei. “You really need a deep understanding of how the business creates value, how the market is evolving, what the sources of competitive differentiation are and how those could evolve. Where you point analytics is fundamentally predicated on having those views.”

Mazzei had the advantage of working for EY for more than a decade and leading the strategy function before becoming the CAO. Unlike a newly-hired CAO, he already had relationships with the people at EY with whom he’d be interfacing.

“Succeeding in this role takes building really trusted relationships in a lot of different parts of the organization, and often at very senior levels,” said Mazzei. “One reason we’ve seen CAOs fail is either because they didn’t have the skills to build those relationships or didn’t invest enough time on it during their tenure.”

Big Data: The Interdisciplinary Vortex

September 28, 2016 /

As seen in  InformationWeek.

vortexGetting the most from data requires information sharing across departmental boundaries. Even though information silos remain common, CIOs and business leaders in many organizations are cooperating to enable cross-functional data sharing to improve business process efficiencies, lower costs, reduce risks, and identify new opportunities.

Interdepartmental data sharing can take a company only so far, however, as evidenced by the number of companies using (or planning to use) external data. To get to the next level, some organizations are embracing interdisciplinary approaches to big data.

Why Interdisciplinary Problem-Solving May Be Overlooked

Breaking down departmental barriers isn’t easy. There are the technical challenges of accessing, cleansing, blending, and securing data, as well as very real cultural habits that are difficult to change.

Today’s businesses are placing greater emphasis on data scientists, business analysts, and data-savvy staff members. Some of them also employ or retain mathematicians and statisticians, although they may not have considered tapping other forms of expertise that could help enable different and perhaps more accurate forms of data analysis and new innovations.

“Thinking of big data as one new research area is a misunderstanding of the entire impact that big data will have,” said Dr. Wolfgang Kliemann, associate VP for research at Iowa State University. “You can’t help but be interdisciplinary because big data is affecting all kinds of things including agriculture, engineering, and business.”

Although interdisciplinary collaboration is mature in many scientific and academic circles, applying non-traditional talent to big data analysis is a stretch for most businesses.

But there are exceptions. For example, Ranker, a platform for lists and crowdsourced rankings, employs a chief data scientist who is also a moral psychologist.

“I think psychology is particularly useful because the interesting data today is generated by people’s opinions and behaviors,” said Ravi Iyer, chief data scientist at Ranker. “When you’re trying to look at the error that’s associated with any method of data connection, it usually has something to do with a cognitive bias.”

Ranker has been working with a UC Irvine professor in the cognitive sciences department who studies the wisdom of crowds.

“We measure things in different ways and understand the psychological biases each method of data creates. Diversity of opinion is the secret to both our algorithms and the philosophy behind the algorithms,” said Iyer. “Most of the problems you’re trying to solve involve people. You can’t just think of it as data, you have to understand the problem area you’re trying to solve.”

Why Interdisciplinary Problem-Solving Will Become More Common

Despite the availability of new research methods, online communities, and social media streams, products still fail and big-name companies continue to make high-profile mistakes. They have more data available than ever before, but there may be a problem with the data, the analysis, or both. Alternatively, the outcome may fall short of what is possible.

“A large retail chain is interested in figuring out how to optimize supply management, so they collect the data from sales, run it through a big program, and say, ‘this is what we need.’ This approach leads to improvements for many companies,” said Kliemann. “The question is, if you use this specific program and approach, what is your risk of not having the things you need at a given moment? The way we do business analytics these days, that question cannot be answered.”

One mistake is failing to understand the error structure of the data. With such information, it’s possible to identify missing pieces of data, what the possible courses of action are, and the risk associated with a particular strategy.

“You need new ideas under research, ideas of data models, [to] understand data errors and how they propagate through models,” said Kliemann. “If you don’t understand the error structure of your data, you make predictions that are totally worthless.”

Already, organizations are adapting their approaches to accommodate the growing volume, velocity, and variety of data. In the energy sector, cheap sensors, cheap data storage, and fast networks are enabling new data models that would have been impossible just a few years ago.

“Now we can ask ourselves questions such as if we have variability in wind, solar, and other alternative energies, how does it affect the stability of a power system? [We can also ask] how we can best continue building alternative energies that make the system better instead of jeopardizing it,” said Kleinman.

Many universities are developing interdisciplinary programs focused on big data to spur innovation and educate students entering the workforce about how big data can affect their chosen field. As the students enter the workforce, they will influence the direction and culture of the companies for which they work. Meanwhile, progressive companies are teaming up with universities with the goal of applying interdisciplinary approaches to real-world big data challenges.

In addition, the National Science Foundation (NSF) is trying to accelerate innovation through Big Data Regional Innovation Hubs. The initiative encourages federal agencies, private industry, academia, state and local governments, nonprofits, and foundations to develop and participate in big data research and innovation projects across the country. Iowa State University is one of about a dozen universities in the Midwestern region working on a proposal.

In short, interdisciplinary big data problem-solving will likely become more common in industry as organizations struggle to understand the expanding universe of data. Although interdisciplinary problem-solving is alive and well in academia and in many scientific research circles, most businesses are still trying to master interdepartmental collaboration when it comes to big data.

How Corporate Culture Impedes Data Innovation

August 5, 2016 /

As seen in InformationWeek

Floppy disk

Corporate culture moves slower than tech

Competing in today’s data-intensive business environment requires unprecedented organizational agility and the ability to drive value from data. Although businesses have allocated significant resources to collecting and storing data, their abilities to analyze it, act upon it, and use it to unlock new opportunities are often stifled by cultural impediments.

While the need to update technology may be obvious, it may be less obvious that corporate cultures must also adapt to changing times. The necessary adjustments to business values, business practices, and leadership strategies can be uncomfortable and difficult to manage, especially when they conflict with the way the company operated in the past.

If your organization isn’t realizing the kind of value from its big data and analytics investments that it should be, the problem may have little to do with technology. Even with the most effective technologies in place, it’s possible to limit the value they provide by clinging to old habits.

Here are five ways that cultural issues can negatively affect data innovation:

1. The Vision And Culture Are At Odds

Data-driven aspirations and “business as usual” may well be at odds. What served a company well up to a certain point may not serve the company well going forward.

“You need to serve the customer as quickly as possible, and that may conflict with the way you measured labor efficiencies or productivity in the past,” explained Ken Gilbert, director of business analytics at the University of Tennessee Office of Research and Economic Development, in an interview with InformationWeek.

[ What matters more: Technology or people? Read Technology Is A Human Endeavor. ]

Companies able to realize the most benefit from their data are aligning their visions, corporate mindsets, performance measurement, and incentives to effect widespread cultural change. They are also more transparent than similar organizations, meaning that a wide range of personnel has visibility into the same data, and data is commonly shared among departments, or even across the entire enterprise.

“Transparency doesn’t come naturally,” Gilbert said. “Companies don’t tend to share information as much as they should.”

Encouraging exploration is also key. Companies that give data access to more executives, managers, and employees than they did in the past have to also remove limits that may be driven by old habits. For example, some businesses discourage employees from exploring the data and sharing their original observations.

2. Managers Need Analytics Training

Companies that are training their employees in ways to use analytical tools may not be reaching managers and executives who choose not to participate because they are busy or consider themselves exempt. In the most highly competitive companies, executives, managers, and employees are expected to be — or become — data savvy.

Getting the most from BI and big data analytics means understanding what the technology can do, and how it can be used to best achieve the desired business outcomes. There are many executive programs that teach business leaders how to compete with business analytics and big data, including the Harvard Business School Executive Education program.

3. Expectations Are Inconsistent

This problem is not always obvious. While it’s clear the value of BI and big data analytics is compromised when the systems are underutilized, less obvious are inconsistent expectations about how people within the organization should use data.

“Some businesses say they’re data-driven, but they’re not actually acting on that. People respond to what they see rather than what they hear,” said Gilbert. “The big picture should be made clear to everybody — including how you intend to grow the business and how analytics fits into the overall strategy.”

4. Fiefdoms Restrict Data Sharing

BI and analytics have moved out from the C-suite, marketing, and manufacturing to encompass more departments, but not all organizations are taking advantage of the intelligence that can be derived from cross-functional data sharing. An Economist Intelligence Unit survey of 530 executives around the world revealed that information-sharing issues represented the biggest obstacle to becoming a data-driven organization.

“Some organizations supply data on a need-to-know basis. There’s a belief that somebody in another area doesn’t need to know how my area is performing when they really do,” Gilbert said. “If you want to use data as the engine of business growth, you have to integrate data from internal and external sources across lines, across corporate boundaries.”

5. Little-Picture Implementations

Data is commonly used to improve the efficiency or control the costs of a particular business function. However, individual departmental goals may not align with the strategic goal of the organization, which is typically to increase revenue, Gilbert said.

“If the company can understand what the customer values, and build operational systems to better deliver, that is the company that’s going to win. If the company is being managed in pieces, you may save a dime in one department that costs the company a dollar in revenue.”

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