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Month: August 2016

Six Characteristics of Data-Driven Rock Stars

As seen in InformationWeek

Rock starData is being used in and across more functional aspects of today’s organizations. Wringing the most business value out of the data requires a mix of roles that may include data scientists, business analysts, data analysts, IT, and line-of-business titles. As a result, more resumes and job descriptions include data-related skills.

A recent survey by technology career site Dice revealed that nine of the top 10 highest-paying IT jobs require big data skills. On the Dice site, searches and job postings including big data skills have increased 39% year-over-year, according to Dice president Shravan Goli. Some of the top-compensated skills include big data, data scientist, data architect, Hadoop, HBase, MapReduce, and Pig — and the pay range for those skills ranges from more than $116,000 to more than $127,000, according to data Dice provided to InformationWeek.

However, the gratuitous use of such terms can cloud the main issue, which is whether the candidate and the company can turn that data into specific, favorable outcomes — whether that’s increasing the ROI of a pay-per-click advertising campaign or building a more accurate recommendation engine.

If data skills are becoming necessary for more roles in an organization, it follows that not all data-driven rock stars are data scientists. Although data scientists are considered the black belts, it is possible for other roles to distinguish themselves based on their superior understanding and application of data. Regardless of a person’s title or position in an organization, there are some traits common to data-driven rock stars that have more to do with attitudes and behaviors than technologies, tools, and methods. Click through for six of them.  [Note to readers:  This appeared as a slideshow.]

They Understand Data

Of course data-driven rock stars are expected to have a keener understanding of data than their peers, but what exactly does that mean? Whether a data scientist or a business professional, the person should know where the data came from, the quality of it, the reliability of it, and what methods can be used to analyze it, appropriate to the person’s role in the company.

How they use numbers is also telling. Rather than presenting a single number to “prove” that a certain course of action is the right one, a data-driven rock star is more likely to compare the risks and benefits of alternative courses of action so business leaders can make more accurate decisions.

“‘Forty-two’ is not a good answer,” said Wolfgang Kliemann, associate VP for research at Iowa State University. “‘Forty-two, under the following conditions and with a probability of 1.2% chance that something else may happen,’ is a better answer.”

They’re Curious

Data-driven rock stars are genuinely curious about what data indicates and does not indicate. Their curiosity inspires them to explore data, whether toggling between data visualizations, drilling down into data, correlating different pieces of data, or experimenting with an alternative algorithm. The curiosity may be inspired by data itself, a particular problem, or problem-solving methods that have been used in a similar or different context.

Data scientists are expected to be curious because their job involves scientific exploration. Highly competitive organizations hire them to help uncover opportunities, risks, behaviors, and other things that were previously unknown. Meanwhile, some of those companies are encouraging “out of the box” thinking from business leaders and employees to fuel innovation, which increasingly includes experimenting with data. Some businesses even offer incentives for data-related innovation.

They Actively Collaborate with Others

The data value chain has a lot of pieces. No one person understands everything there is to know about data structure, data management, analytical methods, statistical analysis, business considerations, and other factors such as privacy and security. Although data-driven rock stars tend to know more about such issues than their peers, they don’t operate in isolation because others possess knowledge they need. For example, data scientists need to be able to talk to business leaders and business leaders have to know something about data. Similarly, a data architect or data analyst may not have the ability to manipulate, explore, understand, and dig through large data sets, but a data scientist could dig through and discover patterns and then bring in statistical and programming knowledge to create forward-looking products and services, according to Dice president Shravan Goli.

They Try to Avoid Confirmation Bias

Data can be used to prove anything, especially a person’s opinion. Data-driven rock stars are aware of confirmation bias, so they are more likely to try to avoid it. While the term itself may not be familiar, they know it is not a best practice to disregard or omit evidence simply because it differs from their opinions.

“People like to think that the perspective they bring is the only perspective or the best perspective. I’m probably not immune to that myself,” said Ravi Ivey, chief data scientist at Ranker, a platform for lists and crowdsourced rankings. “They have their algorithms and don’t appreciate experiments or the difference between exploratory and confirmatory research. I don’t think they respect the traditional scientific method as such.”

The Data Science Association’s Data Science Code of Professional Conduct has a rule dedicated specifically to evidence, data quality, and evidence quality. Several of its subsections are relevant to confirmation bias. Among them are failing to “disclose any and all data science results or engage in cherry-picking” and failing to “disclose failed experiments or disconfirming evidence known to the data scientist to be directly adverse to the position of the client.”

They Update Their Skill Sets

Technology, tools, techniques, and available data are always evolving. The data-driven rock star is motivated to continually expand his or her knowledge base through learning, which may involve attending executive education programs, training programs, online courses, boot camps, or meetups, depending on the person’s role in the company.

“I encourage companies to think about growing their workforce because there aren’t enough people graduating with data science degrees,” said Dice president Shravan Goli. “You have to create a pathway for people who are smart, data-driven, and have the ability to analyze patterns so they have to add a couple more skills.”

Job descriptions and resumes increasingly include more narrowly defined skills because it is critical to understand which specific types of big data and analytical skills a candidate possesses. A data-driven rock star understands the technologies, tools, and methods of her craft as well as when and how to apply them.

They’re Concerned About Business Impact

With so much data available and so many ways of analyzing it, it’s easy to get caught up in the technical issues or the tasks at hand while losing site of the goal: using data in a way that positively impacts the business. A data-driven rock star understands that.

Making a business impact requires three things, according to IDC adjunct research adviser Fred McGee: having a critical mass of data available in a timely manner, using analytics to glean insights, and applying those insights in a manner that advances business objectives.

A data-driven rock star understands the general business objectives as well as the specific objective to which analytical insights are being applied. Nevertheless, some companies are still falling short of their goals. Three-quarters of data analytics leaders from major companies recently told McKinsey & Company that, despite using advanced analytics, their companies had improved revenue and costs by less than 1%.

Pitch Closes That May Not Help You

smiley-1041796_640As a journalist, I’m pitched constantly.  I’d say that 20 percent of the pitches I get are good and perhaps 5 percent are excellent.  How would I know?  Lots of journalism experience and lots of PR experience.

Interestingly, whether a pitch gets a response or not can boil down to a few words.

“If you’re interested in X let me know.”  I don’t need to respond, then, with your permission.  If the close had been different, I probably would have said, “You should try pitching X instead.”  Likely, this person will follow up and ask if I got their pitch.  Yup.

The same close is often posed as a question:  “Are you interested?”  This is an easy one to answer most of the time because the answers are binary (yes or no).  These are so easy to say “no” to without explanation.

I guess my issue with all of this is the PR person doesn’t understand why they’re getting no response or curt responses, neither of which feel good.  I understand.  I spent a lot of years as a PR pro and PR exec, and I know how frustrating pitching can be.   OTOH, when you’re sorting through a pile of pitches, we can and will choose the path of least resistance whenever possible.

What Your PR Client Should NOT Do

Bulldozer

Runaway clients can hurt coverage

Media interviews are an interesting thing to “manage.” There are clients who just want you to set up interviews, clients who value your involvement and guidance, and clients who are like helium balloons that just lost their strings.

Every now and then, even the best clients can get a little out of control, because they’re so passionate.  Passion is fine, but when it gets to the point of bulldozing an interview, it’s time for media training.

Why Bulldozing is a Bad Thing

I’m one of those journalists who prepares for interviews.  I have a set of questions I develop for a story because somebody is going to ask me for one and I need to define the scope of the interviews.  Sometimes I have to improvise when I’m interviewing which is fine, but when the whole interview is off-script, it may cause problems for everyone involved.

Sometimes I can’t get a word in, let alone a question, if it’s a telephone interview (which is very rare these days).  If it’s an email response, I’ll read through it, but…

Why I Have a “Script”

I develop a list of questions for every set of interviews I do.  I’m happy to send them in advance when requested, but I tend not to send them as a matter of course.  Occasionally, whether or not the interviewee has the questions in advance, that person will say, “I know you want to cover this, but…[I’ve decided the angle of the story should be something else]” or “I’ve looked at your questions and [I’m going to ignore them].”  Then they wonder why they’re not included in the story, or why the other guy was quoted multiple times.

There are several answers to to these types of queries which are:

  • The content was irrelevant
  • The content was difficult or impossible to use given its lack of structure
  • The content doesn’t dovetail well with other conversations
  • It’s just too much work to use

An important thing to know is: I write on assignment.  That means an editor says, “write this,” or I pitch an idea, and that’s what I’m expected to deliver.

The Good News

The good news is that most interviewees have figured out that the best way to conduct interviews is to answer the questions asked, directly.  It’s fine to give examples, cite use cases, or use analogies as supplementary material as long as the content is relevant to the angle of the story.  If they respond to questions  in a relevant manner, their chances of being included in a story or getting more coverage than they would otherwise get can improve significantly.

I do my best to include everyone I interview, but it’s not always possible.  I am happy to explain the situation to the PR rep, if asked.  After all, I spent many years sitting on that side of the desk.

Thankfully for all of us, the bulldozers are few and far between.  If your client is one of them, you’re wise to explain why bulldozing isn’t wise.  It will help you better manage client expectations down the line.

How Corporate Culture Impedes Data Innovation

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.”

Why Great Pitches Don’t Make the Cut

I and some very talented PR pros are absolutely anguished that we can’t work together on my latest story.  Do I care?  Yes.  Is there anything I can do about it?  No and yes.

I can get well over 100 responses to a HARO query I post.  I get more than 50 often.  The statistics shouldn’t discourage you; they’re meant to give you some insight into why great pitches sometimes don’t make the cut.

Often, it’s a matter of timing.  My queries are designed to start at 5:35 a.m. ET and end at 7:00 pm ET.  Because I’m on PT, I start receiving pitches while I’m dreaming and throughout my work day.

HARO sends PR pitches in batch, which a lot of PR people don’t know.  That means, I’ll get no emails for an hour or two, or maybe more and than BAM!  I’ll get lots of them, all of a sudden.

The batch process is frustrating for both of us.  I sometimes wonder whether I’m going to get enough of the right sources for a story, and usually I end up with way too many.  Almost without fail, some of the best pitches come later in the day.  The pitch itself may not be top-notch, but the client is.  Alternatively, the pitch is excellent.  It hits all the points, includes interesting information and all that.  Ultimately, we’re both anguished because I have too many sources.  This current story has 16 which is waaay to many.  It takes a lot of creativity to fit that many people into a story, and a lot of fact-checking.  At some point, I just have to say “no,” when I really want to say “yes.”

The good news is, I can say “yes” to something.  That’s explaining the situation to the person and leaving the door open for future pitches.  People seem to appreciate that, which makes it worth the effort.

So, to recap:  Your pitch will have a better chance of succeeding if it’s relevant and timely.

Believe me, I understand why pitches are late:  You’ve been busy with other things, you needed to talk to your client, whatever.  I get it and I don’t fault you for it.

If you have any complaints about HARO, I’d love to hear them.  I’m not out to bash HARO.  I just want to understand what’s driving you nuts on the other side of the system.

Keep up the good work.