Archive for category Big Data Leadership

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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Article: Peta, Exa, Yotta And Beyond: Big Data Reaches Cosmic Proportions

Sometimes it’s hard to visualize the amazing things we are doing with Big Data. To lead we want to inspire and educate – and motivate. This is a great example of educating with interest. Peta, Exa, Yotta And Beyond: Big Data Reaches Cosmic Proportions

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7 top tools for taming big data | Business Intelligence – InfoWorld

“Big Data” Etymology from Peter Wayner:

The industry now has a buzzword, “big data,” for how we’re going to do something with the huge amount of information piling up. “Big data” is replacing “business intelligence,” which subsumed “reporting,” which put a nicer gloss on “spreadsheets,” which beat out the old-fashioned “printouts.” Managers who long ago studied printouts are now hiring mathematicians who claim to be big data specialists to help them solve the same old problem: What’s selling and why?

via 7 top tools for taming big data | Business Intelligence – InfoWorld.

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