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The Mid-Market Big Data Call to Action By @Schmarzo | @BigDataExpo #BigData

Where are you in the process of integrating Big Data with your existing data warehouse environment?

I recently had the opportunity to do a webinar titled "The Data Lake: Empowering your Data Science Team" with Senturus. (Note: Select the [webinar] button on the upper right of the page to view the video.) Senturus is a leading consulting company that focuses on the Business Intelligence (BI) and data warehouse markets, where they have impressive market penetration and deep content credibility. We had over 220 people in attendance (over 500 signed up), which is quite impressive.

During the webinar, we conducted a poll and got some surprising results.

Poll Question: Where are you in the process of integrating big data with your existing data warehouse environment?

Screen Shot 2015-12-02 at 11.34.03 AM

Over 80% of the attendees still do NOT have any meaningful Hadoop plans, and these numbers come from an audience that was mainly BI and data warehouse practitioners. Let me put his another way: with an audience that was heavily skewed to being intimately aware of the intrinsic value of data and analytics, over 80% were NOT doing anything meaningful in the way of leveraging Hadoop.

My key takeaway from this is that in spite of the hype about big data, Hadoop and data science, that the big data market is still very immature and is still looking for a leader that can guide these organizations to the big data world. And in particular, the BI and data warehouse audience is not getting that leadership from the existing BI and data warehouse vendors.

This provides a significant business opportunity for a very unlikely market - mid-market organizations.

The Big Data Mid-market Opportunity
Small organizations seem to have this inferiority complex when it comes to big data. Yes, the large organizations have much more data with which to work. And yes, the large organizations have much larger budgets to invest in new big data technologies and these new fangled data scientists. And yes, many of these large organizations have decades of experience dealing with data from a data warehouse perspective. It would seem that the deck is stacked against the small organizations that lack the technology resources to invest or the data experience upon which to leverage to compete with the large companies in the area of big data.

However, I think the opposite is true, that small organizations have a HUGE advantage over many of their larger counterparts with respect to integrating data and analytics into their business models. Here are a few of my reasons why I think small organizations have a compelling advantage over the large organizations:

  • Smaller organizations have fewer data silos. As you know from my previous blog "Data Silos!? Danger Will Robinson!!", data silos are a big data killer. Data silos hinder the sharing of data across different business units and jeopardize creating a holistic view of the business opportunities of big data. Smaller organizations, without the data silos, gain a much clearer view of their customers, products, operations and markets.
  • It is easier for smaller organizations to drive cross-organizational collaboration and sharing. Large organizations tend to be business function focused; trying to create the very best in class marketing or sales or manufacturing organizations. But what's the good of having a world-class marketing organization if your sales organization sucks? Or what's the advantage of having an industry-beating manufacturing capability if your logistics and distribution functions can't deliver? The big payoff of big data is on enabling an organization's key business initiatives, and key business initiatives tend to be cross-organizational in nature (i.e., an "improve customer acquisition" business initiative likely would involve marketing, sales, service and finance).
  • Smaller organizations have a smaller number of HIPPO's with which to deal. HIPPO's (the Highest Paid Person's Opinion) is a creativity killer. No better way to kill the creative thinking of a big data project than to have the HIPPO walk into the room and declare the "right" answer. The HIPPO is the anti-data scientist. While all organizations have HIPPOs, smaller organizations at least have a fewer number. Word of advice: when you work with larger organizations, get ready to dance with the HIPPO's (wish I had a picture of that!).
  • Smaller organizations can unlearn faster. Because many smaller organizations do not have such a huge investment in data warehousing, they never learned some of the data warehousing bad practices. So these smaller organizations have an opportunity to learn the modern data warehousing practices (data governance lifecycles, granular data warehouses, SQL on Hadoop) as well as incorporating some of the new-world data science capabilities (predictive analytics, prescriptive analytics, analytic sandboxes, data lakes, etc.).
  • Smaller organizations are less fixated on technology for technology's sake. Because smaller organizations have less spare resources just hanging around, they are unlikely to randomly acquire Hadoop, throw some data on it, hire a couple of data scientists and wait for magic to happen. To quote a recent customer meeting who has found themselves in this situation, "Our data scientists find insights in the data that they want to present at conferences, but these insights don't help our business."
  • Smaller organizations have a better focus on delivering business results; there seems to be less concern with the organizational politics that come from larger organizations protecting their turf. Smaller organizations need to ensure that their data and technology investments are focused on delivering meaningful business value. Consequently, smaller organizations have much fewer science experiments! And while bigger organizations have more resources to invest in these types of science experiments, larger organizations struggle to translate the science experiment into business benefits; they've created a solution in search of a problem. Best to thoroughly understand upfront what the problem is before deciding what the solution needs to look like.
  • But the most important reason why small organizations have an advantage over large organizations is that it is easier for small organizations to institute the organizational and cultural change necessary to actually act on the analytic insights. In order to drive the cultural change necessary for organizations to act on the customer, product, operational and market insights requires a level of trust and collaboration between the business stakeholders and the supporting Information Technology (IT) organization. These two groups need to collaborate to ensure that not only are they focused on delivering meaningful business results, but that there is a sense of urgency to get it done quickly. For small organizations, the 3 V's of big data are not nearly as important as the 4 M's of big data: make me more money!!

So if you are a small organization, don't be afraid of big data. In fact, embrace the natural advantages that your small organization has over your larger competition. Let the larger organizations learn to dance with the HIPPO's!!

Read the original blog entry...

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.

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