Archive for category Big Data Leadership

P&G CEO Gives Analytics Leadership Advice

Often we talk about how to be an analytics change agent from within: to create a vision and use it to persuade, motivate and recruit others to the cause. This effort helps you inspire the advocacy from senior managers and the executive level. But what if you are the executive level? Well, its the same process in principle. Here is an example of the senior executive leader of the largest CPG company in the world setting the pace for his organization. The reporter picked out several key quotes, that I would like to reiterate:

“We have to move business intelligence from the periphery of operations to the center of how business gets done,”

“I hear a lot about big data,” McDonald said. “It’s not about the data. It’s about how we’re able to use the data.”

Link: P&G CEO Shares 3 Steps To Analytics-Driven Business – Global-cio –.


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The Market Research Tortoise and the Big Data Hare … or a Misguided Analogy

If you are in market research (MR), you may debate the which is tortoise and which is hare, but you probably do see a race and competition for the prize. Or, more realistically, you may fear that others see it as a competition. The prize may be budget, recognition as the source of valuable customer insights, or winning jobs. This may be a limiting perspective. If you are in the Big Data space could see it as a race but MR is probably not on your radar or you consider the outcome a foregone conclusion. You probably believe that MR could be a valuable appendage but ultimately discount MR when viewing the main role and impact of Big Data. There may be some blind spots. However, the two areas do overlap and treating it emotionally or functionally as a competitive situation will be counter-productive and lead to lost opportunity.

Some say that focus group and survey-based market research has reached the end of its life-cycle  and will be replaced by Big Data analytics. Those who fully promote this position usually have an agenda to push or they are not well informed about market research. However, it does seem that these two areas of market intelligence, customer insight, and decision support both offer valuable and maybe even critical decision support. The million, or billion, dollar question is how to integrate them and leverage them together. Will this integration be the basis for the next round of killer market research applications or the means of getting the most value from your big data investments? My thoughts in this area were triggered by the linked article that contains some comments, including one application that combines the two areas of MR and Big Data.

In both areas, those who wish to capitalize the most will figure out how to combine them to leverage value. In Big Data, folks will come to realize that there are many holes in the data, many missing links, many areas where current or past data will not suffice in predicting the future, and many areas where the value of the insights can be magnified through the leaven of custom MR.

On the other hand, MR folks either have or will come to recognize that the in-context reality and pure organic validity of Big Data adds immensely to the precision inquiry possible in MR. Big Data integration will increase MR value by bridge limitations or biases created by interviewer/researcher subjectivity, sampling frames, respondent behavioral self-report inaccuracies, perceived insights credibility, perception of frequent lack of tangible real-world action-ability, to name a few.

Seeing or specifically identifying the areas of combined value will be the first and most important step to making this happen. The next step, and arguably the more difficult one, is to actually combine the functions related to Big Data and MR to bring about a profitable union. Most corporations have these functions silo-ed separately, based on their historical origins and historically separate functions, skill-sets and processes. In fact it goes even deeper in that these two areas may compete for the same budget and in fact may feel quite competitive with each other for recognition in the organization. Executives not only have an organizational issue to resolve but likely an emotional one as well.

On the supplier side the difficulties are multiple for being able to combine these areas. First is that most MR firms do not have an understanding of or a captive skill set that encompasses Big Data analytics. They don’t have the mindset for what it is, how to get it, and what to do with it. Next, most emerging Big Data firms come from a programming, IT, data mining, or highly technical area of strength. They do not have the insights background or training and skills. The foundations of marketing strategy and consumer behavior and psychology is lighter than it needs to be. The softer, more granular or individually based understanding of motivations, attitudes, drivers, etc. and how they mix together to generate action and commitment are usually missing with folks who have been devoted to algorithmic programming or econometric modeling and mining.

The future in this area belongs to people and organization who can bridge these domains. This is a process and the sooner it begins the better and the more likely you can capitalize on it for competitive advantage. Identify the needs, find the relevant gaps in addressing the needs, prioritize and act on filling the holes and showing tangible progress through a series of quick wins.

To motivate these changes, we need to see more examples of how the two domains combine for added value. The referenced article (noted below) provides one lightly sprinkled example. I believe we will begin seeing more.

How will it play out? Will Big Data analytics be the hare? Will the hare win? Maybe fundamentally, is there a race? Maybe, we see the races either being set up or already in process and the question is how do we change the nature of the race so that instead of competitors, the tortoise and the hare are teammates?


Referenced article:

“The general consensus seems to be that “big data” is never going to replace traditional research – that is, specific research methods like surveys and focus groups that deal with particular topics will always be around.  These specific research methods answer the question of “what” – that is, they are concerned with empirical details.  For example, an online survey may indicate that compared to 2011, this year 5% of soda drinkers no longer drink Coca-Cola on a regular basis.  Where “big data” aims to change research is in the “why” – that is, the broad trends and underlying reasons why certain results have been obtained.  Using our previous example, “big data” may be able to tell us that key nutritional influencers have recently been saying that carbonated sugary drinks reduce life expectancy by an average of 4 years in healthy individuals.  By having both the “what” results and the “why” results, researchers can use this combination of data to have a much clearer picture of a particular situation, and potentially be able to advise their clients on how to act to obtain the results they wish to achieve.”

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Garnter Report Shows Huge Big Data Needs: May Seem Overwhelming, Doesn’t Need to Be

Big Data Report: Computer News Middle East Article.

Its an exciting time. But it shouldn’t be intimidating nor paralyzing. Through a series of reports, Gartner and others continue to highlight the business impact of the growing big data phenomena. Leaders should use these kinds of articles as a call to action to examine their own companies and set in motion the processes to ensure they are best positioned for the coming years. For many firms this means outsourcing the necessary resources as the company moves along the learning curve, scopes out the opportunity and generates success through a series of pilot efforts.

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Important Q: If not the CIO, Who? at the

Important Q: If not the CIO, Who? at the end of article: Social Analytics Isn’t Just For Social Networks | Big Data

Openshaw, the author, asks a very important question and then in his answer raises significant issues around the CIO role and ownership when it comes to motivating adoption of insights analytics applications. What should we be requiring of the CIO?

The excerpt reads:

“If Not The CIO, Who?

IT leaders have an untapped asset. As the custodian of the company’s data, it’s the CIO’s job to tap into it.

Start with a one-time deep dive into the company’s social data. See that data warehouse? Dive in. What are you looking for? Patterns of interaction. Make observations about how these patterns influence performance and engage with other business leaders about these insights and about how the patterns identified in social data could be used to create truly predictive indicators.”

It appears that Openshaw is suggesting that the CIO take responsibility for and ownership of actually diving into the data to conduct the analyses and find the insights. That is placing a lot of expectation on the CIO and his team. But I wonder if the CIO should be part of the process, understanding the needs of the other stakeholders to make sure that the infrastructure can support the needed applications with a focus the CIO team’s strengths, while allowing domain experts in marketing or operations to actually describe the needs with the data and do the analyses, in line with their strengths. There is a strong argument that to be most effective, those in the domains of using the insights be the ones to search for the patterns and work with the CIO’s team to have the ability to do that.

Openshaw says that “collectors of data must learn which questions to ask and which hypotheses to test.” This learning should probably take place from others in the organization who own the development of these questions and hypotheses for their areas of responsibility in the organization.

The CIO is probably in the best position to coordinate the stakeholders. The CIO can bring them together in a common cause of data management and analytics tools investment needs, and inspire them to learn what their needs are and to plan out their analytics applications’ agendas. The CIO is in a unique position to have the credibility and the appropriate business interest and political context to facilitate these kinds of vital areas of collaboration.

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Good case study showing 10x time savings

Good case study showing 10x time savings w/Big Data methods:  Why Sears Is Going All-In On Hadoop – Global-cio – Executive

In the article the CTO gives some good, but potentially contradictory advice about making these kinds of changes.

“You have to go fast and be bold without taking stupid risks,” Shelley says. Start with a business need “that causes enough pain that people will notice and they’ll see tangible benefits.”

Shelley, by saying to be fast and bold is not saying to be irrational or recommending taking unnecessary risk. But, I think he is saying to be a leader, create a vision of what can be done, create a plan for application that is bite sized and can create quick value. That allows it to be quick. The impact it promises is bold.

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No Knee-Jerk Reactions: A Reasoned Approach to a “Big Data Strategy”


You have probably seen highly charged atmospheres in companies where the executive team has decided that the company must have a Big Data strategy. Those tasked with then putting it together feel this pressure to prepare an impressive plan that represents a big leap and a lot of investment. It’s similar to what happened in the 90’s with CRM and then what happened right around the 2000 mark with the Internet. Faced with the rising wave of interest and investor questions, companies risk making knee-jerk reactions which all too often result in corporate and personal disaster.  Big Data is on the same trajectory. We see it in our everyday conversations with others, in the press and promotional arena. It has also been shown in the Gartner “Hype Cycle” for 2012 —

Leveraging Big Data in your company does not have to be mysterious, intimidating or expensive. There are different ways to approach the elephant — and as the adage goes, maybe the best way it to take it one piece at a time to digest it properly and align it within the organization.

One approach I’d recommend is doing what I call the 3-V Application Value analysis. This is where you assess the specific Big Data that you have access to and then look at the differences that Big Data offers from what data, analyses and resulting applications you currently use. Do this by each of the V’s that define Big Data: Velocity, Volume and Variety. This leads to an opportunities and costs analysis that will then be the basis of a plan of action. This is a reasoned approach to getting the best value out of your investment in Big Data.

For instance, let’s take Velocity. What is it that is different about Big Data because of Velocity? And, when looking at the form of Big Data you have access to, what does that imply for the applications you could build? A very high level assessment is where you would start and it may look something like this:

  • Opportunities. The opportunities that come from high velocity data include the development of real time or more immediately updating applications. These might be
    • New and more relevant executive dashboards
    • Tools that allow you to make adjustments to engagement campaigns while they are executing
    • Development of individualized recommendation systems
    • Quickly identifying product quality issues
    • Better capitalizing on unforeseen benefits or uses of your product or service
  • Costs. The costs of taking advantage of these opportunities would be driven by a number of factors, including:
    • Instituting new layers of data connectivity
    • Building machine learning and continual statistical processes layers
    • Designing and implementing real time reporting and simulation tools

The benefit of this kind of approach is the creation of a rational framework for advocating specific kinds of Big Data investment. A team can examine the detailed differences between existing data being used and Big Data, link those to potential new analyses or applications, and tie them to specific investments. The contrasting and incremental nature of this approach takes the mystery out of Big Data by relating it to what you have experience with and providing a stepping-stone approach that builds on strengths and ensures investments will be made with confidence and less risk.



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We Can Shape the Value of Our Own Big Data

We Can Shape the Value of Our Own Big Data (

SoLoMo : Nouvelle arme de fidélisation massive ? | Tourisme d'affaires ile-de-France |

In a recently trending LinkedIn article by David Edelman at McKinsey, I commented in a way that bears on Analytics Leadership in Big Data. The article and associated video focus on the great potential of SoLoMo (Social, Local and Mobile) opportunity and data. We can get the most from this data if we are not just reactive to it — but if we assertively acknowledge its use and plan for its application we can multiply that value. Using our knowledge of what value can come from the analytics gives us the power to influence and shape our marketing strategy so that we build, integrate and use SoLoMo data streams as part of how we drive Big Data value. We can help build that vision. Follow me on Twitter @AnalyticMind and view my profile on Linkedin:

My comments on the article:

David’s video and comments really hit the mark. We often talk about the consumer purchase journey in terms of the process that a consumer is wrapped in as they experience a brand and make a purchase in the context of that experience. It used to be quite linear, but now has many strong feedback loops, such that a consumer can experience multiple “stages” at one time and can influence others’ journey’s more strongly and at multiple points.

With the growth of SoLoMo, we can now act on another dimension of the journey with much greater effectiveness. This dimension is defined by geographic distance and frequency. It’s like a whole new take on a gravity model. We can define concentric zones going out away from the product location and for each band of circle for a given level of frequency we can tailor specific interactions and implement appropriate strategies to help the consumer see the value of getting closer to the product. For new customers, in brick and mortar retail, the key is to first have folks physically introduce the behavior or experience of going to a location; then it is in creating familiarity and routine within that location. For existing customers, it’s critical to deepen the value of frequent physical proximity – socially, emotionally and rationally.

SoLoMo data truly is and will become gold to many organizations that not only know how to mine this new mountain, but also in how they mold the mountain in the first place through strategic planning to create the most valuable data streams for analysis. This is a prime example of how Big Data is not a reactive situation and how getting the most from Big Data analytics depends on more than just knowledge discovery analytics. If we bring frameworks of directed analytics to this kind of data, like applying a form of geography/frequency gravity model, in the context of specifically designed communications strategies we can truly uncover revolutionary gains in customer loyalty.

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