Recognizing Relevant Big Data and How to Use It

Big Data is everywhere you look, and we have seen how useful it can be. Among billions of terabytes of data gathered, there is a treasure of marketing data that businesses need to understand in order to know what is relevant and how to use it to get better business results. Businesses waste 40 percent of their marketing budget on the inadequate usage of big data.

Big data is naturally all-encompassing and can be difficult to manage or use effectively. It comprises of both unstructured and structured data that comes from numerous devices and channels across geographies.

With this massive scope of Big Data, company marketers have to focus on high-impact information – the data that can drive strategic decisions in the company. The only way marketers can harness the right form of data is by knowing what is relevant and what isn’t. This helps a company to differentiate itself from its competitors and begin to realize real business success and growth. So how can you understand relevant data? Here are some tips:

Use Your Goals to Create Meaning

Better marketing data analytics can boost ROI by 10% to 20%. But to make the most out of this, you will have to determine the data that is most relevant to your company goals and then focus accordingly.

Data can only be useful to a company when it is used to solve essential business problems and drive strategy. About 50 percent of the businesses McKinsey surveyed showed that businesses struggled to know the effects that digital marketing had…

Leveraging analytics to see the whole story

Leveraging analytics to see the whole story

The race to unlock the potential of data is on. According to IDC, the volume of data created worldwide will increase tenfold by 2025. This means we will have even greater access to a wealth of information, enabling more powerful business insights than ever before. Most organisations already have metrics in place to understand their data. But many are merely scratching the surface and are yet to uncover true data-driven possibilities.

The idea of data creating business value is not new. Business leaders have been making decisions based on data reports for years. In today’s hyper-connected digital economy however, the ability to access data visualisation and intelligent analytics in real-time is vital to organisations looking to gain a competitive advantage. Data-driven enterprises that have access to information at their fingertips have been found to outperform their industry peers by up to 6%. For business leaders, data analytics and visualisation can make or break key conversations with potential investors, partners and shareholders.

What does the data-driven enterprise look like?

As with most enterprises, data resides across a broad ecosystem of sources. The enterprise that can leverage all data, irrespective of its source or location, will be best equipped to act on insights now and into the future.

Data-driven businesses provide a framework for users to see the whole story when it comes to data. They offer the ability for users to input and analyse all their data. Analysis is not limited to preconceived notions of how data should be structured. They recognise that it is often within combinations of seemingly disparate data that innovations occur in today’s digital era.

Data-driven possibilities

With most companies collecting vast amounts of data from their business operations, and the growth of publicly available data, the time is now to leverage data analytics to make better decisions and realise strategic goals. For example, e-commerce retailers, such as Lazada in South East Asia, are leveraging business intelligence to effectively compete against global online retail giants, optimise their supply chain, increase operational efficiency and better support merchant and…

What is the Name of My Game?

By: Daniel T. Bloom

Every day our organizational management is confronted with the rush to Big Data and its impacts on organizational metrics. However, this rush is failing to understand one critical factor in making a decision.



Consider this:  It is a dreary, overcast day and so you decide to go to the mall to do some shopping. As you enter your favorite big box store, you see an 18-year-old, blonde, blue-eyed girl head directly to a particular display. I am not trying to create a stereotype but rather to demonstrate the basis of big data.

Marketing has spent large sums of money to create an experience based on big data to create a vision of the ‘why that 18-year-old would head to that particular display.’ Their models extensively study the correlation behind the demographics and desires of certain population groups and how they result in purchases by these groups.



In the readings on the implications of big data in HR, one article suggested the use of a tool called predictive analysis. The example they provided was that big data told an organization that every time a certain manager interviewed a candidate for an open position, the hire resulted in a failed hire. The extended logic was that if this hiring manager was the next manager up for an opening, the odds were that the hire would not last. Correlation is great for certain aspects of the organization, but HR needs to look at the causality of the human capital management issues are clearly understood.

Return to our predictive analysis example we discussed above. It is critical that when we have a problem with a process, it is almost never a people problem. If this is correct, then the fact that a particular manager is interviewing failed hires is not the grounds for a valid correlation. Rather, it’s a sign that something is wrong with the process. Is the reason that the hires fail due to the wrong cultural fit? Is the reason the hires fail due to the wrong skills for the position? The use of the continual process improvement methodology provides you with the tools to discover the root causes of the process problems that a concentration on correlations does not and cannot.



When we determine that in order to correct the obstacles to the hiring process, we need to find a driven method to empower change in our organizations. Cause and effect determination is method to drive that change. The TLS Continuum (Theory of Constraints- Lean- Six Sigma) provides a roadmap to discover the causes of the process problems.

We are not suggesting that Big Data does not have a place within our organizations. It certainly does in areas like sales or marketing. But when the success of our organizations is dependent on knowing why we are experiencing process errors there is a better route to go with the TLS Continuum and the Continuous Process Improvement tools.

The TLS Continuum combines the tools of critical thinking with those evidence-based tools of Lean and Six Sigma to produce a congruent system which identifies the obstacles (TOC) and then removes the obstacle (Lean) and then concludes with the application of six sigma to create the standard of work and remove variations.


About the Author

Daniel T. Bloom SPHR, SSBB, SCRP is a well-respected author, speaker and human resource strategist, who during his career has worked within a wide variety of industries. He has been an educator, a contingency executive recruiter, a member of a Fortune 1000 divisional HR staff and the Corporate Relocation Director for several real estate firms in the Tampa Bay area. He is an active member of the HR social media scene since 2006 with contributions to Best, WordPress, Human Capital League, and Recruiting Blogs.

He has also published three books—Just Get me There in 2005 which is documented history of the Corporate Relocation Industry, Achieving HR Excellence through Six Sigma published in 2013 and the Field Guide to Achieving HR Excellence through Six Sigma in 2016. He has also written over 40 articles which have appeared both in print and online on various HR issues.

Help Wanted: The Demand for Data Scientists and How to Tap Big Data

by Bobby Koritala


Data Scientist. It’s one of the most in-demand professions in the market, with major organizations clamoring to put one on their staff. Unfortunately, so few of them even exist. That’s according to statistics from the National Center for Education and the Wall Street Journal, both reporting that chief information officers are seriously struggling to fill the coveted role to analyze big data.

McKinsey Global Institute consultant Michael Chui told WSJ reporter Clint Boulton that his study estimates between 140,000 and 180,000 data scientist positions will remain unfilled by 2018. Another Wall Street Journal article states a data scientist can command up to $300,000 annually with little experience.

It’s understandable knowing there aren’t many data scientists to go around. A 2014 study by the National Center for Education Statistics revealed just 1,669 people graduated in 2012 with a PhD in statistics or a related math degree. Of those, only 323 are true statisticians.

And it’s not going to get better, according to Michael Rappa, founding director of the Institute for Advanced Analytics at North Carolina State University, one of the first data science programs in the nation:

“Although about 70 higher-education institutions, including Northwestern University, New York University and Columbia University, are offering comparable analytics programs, [Rappa] estimates that matriculations will only yield 1,000 new data scientists this year —  hardly enough to fill businesses requirements at the position.”

It looks like there’s no end in sight. Fortunately, one new trend has CIOs seeing a light at the end of the tunnel. It’s the idea of “No PhD required,” using new technology to put the power of data science in the hands of organizations that may not have one on staff. The idea that anyone can become a data scientist is becoming a reality.

Could the future of data science mean not having a scientist on staff at all?

As data scientists continue to be in short supply, companies must, nevertheless, respond to growing amounts of data – from customer information to operational data and everything in between. Future competitive advantage relies on a strong knowledge of business data and, even more importantly, the ability to make decisions based upon it.

In the absence of a full bench of data scientists, primed to take on any and all input laid in front of them, companies are seeking out expertise — outsourcing certain analyst functions to a trusted source, as well as deploying business intelligence programs that make sense of data without rigorous application of statistical analysis by the user. The dearth in data scientist talent requires sophisticated solutions that are easy to use, empowering staff with limited analytical knowledge to assess business scenarios equipped with the data available to them.

Fortunately, nimble technology can support companies with their vision to be data-driven — no PhD required.

Bobby Koritala, Chief Product Officer at Infogix, joined the company in 2009 and leads the Marketing, Product Management and Development Group. Prior to this, Bobby served as the Director of Risk Technology Solutions at Protiviti, COO of Spark Biotech, Vice-President of Investments at Open Prairie Ventures, Director of Applied Technology at Blue Cross Blue Shield, Director of Product Development at Lexis Nexis, and Senior Manager, Software Development at SPSS. Bobby has a Bachelor of Arts degree in Computer Science and Physics from Coe College, a Master of Science degree in Computer Science from the University of Wisconsin, and an MBA from the Kellogg School of Management, Northwestern University.

Data-Driven Marketing | Step Five: Process is the New Black

by Lisa Arthur


Process is the last step in my five-step series about data-driven marketing. I know, I know, process is not the most popular topic among marketers, and it’s certainly not something marketers usually find “sexy.” However, when process helps you gain an edge over your competitors or increases brand relevance, process can be very sexy. Here’s how you can master process to improve performance and the customer experience, as well as generate more sales wins:

Agree on the definition of process. Make sure your team knows you’re not talking about the old ways of doing business. Processes today are much more sophisticated; they’re purposeful marketing activities that include advances in technology and automation. Process can give you more control in today’s marketing environment. To gain that control, though, you’ll need to identify a process for your company and figure out what analytics are needed for the individual customer, the overall customer and operational performance views.

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Data-Driven Marketing | Step Three: Untangle the Data Hairball

by Lisa Arthur


Previously, I’ve blogged about how to create a strategic plan for data-driven marketing and how marketers can tear down the silos that prevent change. I’m now ready to share my third step to data-driven marketing covered in more detail in my “Big Data Marketing” book – untangle the data hairball! First you might be asking, “What exactly is a big data hairball?”

I use the term “hairball” as a metaphor to define a complicated jumble of interactions, applications, data and processes that can easily accumulate in a company without proper sources and methods for handling big data. It’s different than the “data deluge” or the “sea of data” — other terms you might have heard before — because the data hairball would be the shoreline after a tsunami, prior to reconstruction.

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The Digital Odyssey | How Homer, 8-Tracks and Led Zeppelin Took Me Home

by Jim McNamara8track

For the past six months I’ve been reading my classic books to my son. Two nights ago we started “The Odyssey.” Like that wild 10-year adventure, life in the C-Suite — especially for marketers — is full of adventure, story twists and danger. At some point you sort of have to wonder, shouldn’t doing what we are supposed to be doing — marketing, selling and running our business — be simpler?

In 1993, while living in Kansas, I embarked upon a business Odyssey of my own, a yellow-brick-road adventure to find “the next big thing.” I discovered the Web. I don’t claim I actually started the Information Superhighway (Al Gore did that somewhere, I believe, in the smoky hills of Tennessee). But reading in the back pages of a small business newspaper I had that EUREKA! moment, and like Saul on the road to Damascus, my worldview was instantly changed. This thing will change the world, I thought.

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Data-Driven Marketing | Step One: Get Smart, Get Strategic

by Lisa Arthur

via Getty Images

The ability for your team and the C-Suite to align behind a shared strategic vision is the first step in my process to successful data-driven marketing.

Once you have a shared vision you can build a case to your larger organization that will:

  • Connect the dots between projects
  • Show how those projects drive value
  • Create alignment if changes spark turf wars

After you’ve created this strategic vision, other things will start to fall into place. Once you have a solid vision, you can truly get smart and strategic about data-driven marketing. Because every business is unique, starting points, visions and plans will vary from team to team. Once you’re ready to put data-driven marketing into action,  break down your strategy into smaller components. This is a good first step to take because you need to give each component some attention while still staying true to your overall vision.

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How Big Data Can Help CMOs’ Budgeting Process

by Jeff Winsper

big data in marketing
According to the American Association of Advertising Agencies, more than 1 trillion dollars are spent globally on advertising alone. This does not account for any human capital or other media channels. From all the research conducted, at best, 50 percent of CMOs claim they can measure their ROI.*

According to Bill Zengel at the Association of National Advertisers, accountability has been the No. 1 issue facing CMOs for the 7th year in a row. If CMOs want to be held accountable, and yet are challenged to measure and monitor their marketing investments, perhaps it’s time to start the journey so there is a livelihood destination.

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