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As a market research practitioner and a technology industry analyst covering big data and business analytics, I enjoyed listening to other analysts discuss the market research industry in a webinar.  My own research augments and sometimes contrasts with that of the webinar participants.

In the webinar Simon Chadwick spoke about data mining and the need to focus on the analytic skills gap. I’d maintain that businesses need to focus on traditional hypothesis-driven statistics in addition to data mining, especially when we start talking about predictive analytics. While data mining is directed from the data itself, statistics may be thought of as more hypothesis-driven. If you’re familiar with SPSS tools that I recently assessed, you can see the difference by comparing SPSS Statistics and SPSS Modeler. Market research practitioners have traditionally been trained in latter, but not in the former. I go into more detail in a joint webinar I did with IBM on predictive analytics.

Folks who know data mining are in limited supply. They often come from the data warehouse or business intelligence worlds, which have not traditionally churned out deep analytical expertise. Data warehouse and business intelligence is often the domain of database administrators or folks who have a good understanding of structured query language (SQL). Some technology companies are trying to fill the skills gap related to big data (read unstructured data) by taking advantage of these SQL skillsets. Teradata is moving in this direction with its Aster Data integration, and Karmasphere with its toolset, but SQL is a declarative language, and while it fills some gaps in organizations’ ability to access big data, it has own limitations.

vr_predanalytics_usage_of_predictive_analyticsTo bring the point home of how important advanced analytic skillsets are to an organization, our benchmark research shows that companies are more satisfied (70% versus 59%) when their predictive analytics initiatives are led by analytics professionals than by the data warehouse team. (As an aside, our benchmark research into predictive analytics shows preditive to be a key area where marketing and sales are focusing their efforts right now, with social media analytics, attrition, response and attribution modeling as key components of the strategy.)

Since neither the technology industry nor the market research industry has trained analytics professionals especially well, we have a big shortage of folks who can lead big analytics initiatives. This skills gap is driving significant funding for companies such as Mu Sigma, Absolute Data and GoodData. Not surprisingly, these companies target the marketing and market research client-side professional. It’s not a big leap for analytics professionals in the market research industry who already know hypothesis-driven statistics to move into data mining and data modeling, but these skills generally exist on the market research supplier side, not inside client organizations themselves.

Technologists and market research practitioners have long lived in parallel universes.  In technology, we deal with tables, joins and the ETL process. In market research and analysis, we deal with datasets, merges and data preparation. When you think about it, these are the same things! The subtle difference is that technologists have had a data mining mindset and market researchers have had a hypothesis-driven mindset.

As analytics and data environments come together, market researchers need to get more into data mining and more comfortable with data modeling. At the same time, technologists need to get more into hypothesis-driven analytics. In the webinar, Chadwick mentioned the massive advances in technologies, which are apparent when we look at trends in in-database analytics and embedded analytics. We’re seeing a rise in the availability of complex algorithms and open source languages such as R; nevertheless the three most used algorithms in enterprises today are still the simpler ones –  logistic regression, linear regression and decision trees. These should sound very familiar to those of you in the market research industry.

vr_predanalytics_top_predictive_techniques_usedAnother part of the discussion focused on web analytics. I’d extend that to digital analytics and a new class of tools beyond cookie-driven web analytics pulling from machine data, which is exposed in a variety of ways. The net impact is that rather than just looking at numbers of hits or click-through rates or transactions, we’re beginning to be able to see into the customer journey – sort of like doing shop-along in retail, but in a digital space. This gives us great insight into the purchase funnel and competitive dynamics, the likes of which we just didn’t see before. As we marry this technology with analysis of offline behavior, things get even more interesting, but also more complex. We start to deal with privacy issues unless we invoke faceless types of analysis, but that limits us in our ability to market at an individual level. At the same time, attribution modeling becomes more complex given the increases in the number of both promotional and transaction channels.

Finally, my one disagreement with the webinar speakers is their assertion that there’s not enough investment in education going into schools. On the contrary, every major vendor in the technology space I speak with highlights the schools they are aligned with, and most of these folks talk about investing in these schools with respect to analytics training. I agree with webinar participant Lenny Murphy that academia is often slow to change; I often compare market research with academia, in fact. But schools are getting more private funding as the government pulls back its spending, and for this reason schools are becoming more responsive to the needs of private enterprise. It’s here where the skills gap will begin to be filled as schools move away from classical education underpinnings to be more aligned with the needs of the 21st century.

Regards,

Tony Cosentino

VP & Research Director

Hello! I’m excited to be the newest member of the Ventana Research leadership team to bring research insights and education to the business analytics and technology industry. I’d like to start by telling you a bit about who I am, why I’ve chosen to join this company and what I hope to contribute.

For more than 15 years I’ve been studying businesses and their buying behaviors in technology markets. I have a long-time passion for technology, which led me early in my career to systems design and integration at General Electric. I’ve led technology initiatives across marketing, sales and customer service and brought to market one of the first global deployments of a Web-based architecture for Voice of the Customer (VOC). Over the years, I’ve worked with some of the largest technology vendors, including Cisco, Hewlett-Packard, IBM, Microsoft and Oracle, on strategic initiatives in the areas of market segmentation, offer optimization and stakeholder management. Through my predictive analytics work I’ve come to understand how companies can use the new generation of tools to look into the future rather than just analyze the past. My book, Into the River: How Big Data, The Long Tail, and Situated Cognition are Changing the World of Market Insights Forever discusses the revolutionary changes in the way innovative companies use data to effect change and gain competitive advantage. I appreciate that Ventana Research has the most in-depth and new benchmark research in big data and predictive analytics that came out in 2012 building on top of its research on business analytics and Hadoop in 2011.

At Ventana Research I’ll focus on the expanding world of business analytics. Businesses increasingly are looking at past and present behaviors in order to be able to predict future ones. While we’ve done this for a long time in select areas such as financial forecasting, it’s only been in the past few years that the amount of data available, married with massive computing power, has made it possible for the newest generation of business intelligence systems to provide decision support that goes beyond the “what” to begin to provide the “so what” and the “now what.” Including social media for contextual inquiry and attitude analysis, it’s now possible to build a solid, powerful decision support system.

My interest in being part of a team that works hard at accumulating and analyzing reliable data to be able to help organizations move ever closer to “the truth” is a large part of why I came to this company. Its research and advisory services model has kept Ventana Research going strong through two recessions and has made it the go-to choice to advise both technologists and business professionals. Its prolific work, all grounded in research data, puts Ventana Research in a unique position to help companies navigate their way.

A second reason I joined the company is because I share its conviction that technology categories cannot be analyzed in a vacuum. Facing the dynamic interactions today of cloud computing, mobile technology, social media, analytics, business collaboration and big data, to look at markets as silos is to proceed with blinders on. Our ongoing benchmark research, maturity analysis and Value Index work allow us to look across the spectrum of technologies and understand both their interactions and their roles in the business.

As I’ve suggested, I’m a firm believer that knowledge evolves – that we approach the truth at an uneven pace, though hopefully moving ever closer. I learn from everyone around me – including, I hope, you. If you have a thought about something I write, please don’t hesitate to let me know.

I will be posting regularly to report on the exciting research we have going on now, trends in the industry and my views on market developments and directions. I look forward to hearing from you and working with you to help create effective, forward-looking business strategies.

Regards,

Tony Cosentino – VP & Research Director

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