Big Data Year In Review
As 2012 comes to an end and we start looking at 2013, I wanted to share what I learned over the past year.
Organizations Are Starting To Move Down The Big Data Maturity Curve
In 2012, organizations started to look at big data as a business enabler, and not just Business Intelligence 2.0. They spent a good chunk of 2012 educating themselves on Big Data, and in some cases, even experimenting with big data technologies such as Hadoop. 2013 will see organizations starting to spend more time understanding the potential business impacts of big data, and how it can “re-wire” their value creation processes. Their focus won’t be on just trying to advance their existing data warehouse and business intelligence environments, but will be on how to uncover new business or data monetization opportunities (see Figure 1). See the “Big Data Business Model Maturity” blog for more details.
Figure 1: Big Data Business Model Maturity Chart
Link Your Big Data Strategy To Your Key Business Initiatives
As organizations look to create big data strategies, they will realize that their most potent ones will have a direct link to their organization’s business strategy and key business initiatives (see Figure 2). Organizations will identify direct applicability of big data to power their value creation processes, and ensure that big data is relevant and meaningful to the business stakeholders. See my “Most Excellent Big Data Strategy Document” blog for more details.
Figure 2: Big Data Strategy Example With Starbucks
Hadoop Can Enhance Your Existing BI/Data Warehouse Environment
In 2012, Hadoop became synonymous with big data for many organizations. But many struggled with how to use it. One area where organizations were having success was as a universal data store and data prep area. Placed in front of an organization’s existing data warehouse and BI environment (see Figure 3), Hadoop not only provides new capabilities to data preparation processes enabling the integration and management of a wide variety of structured and unstructured data sources, but also provides a layer of abstraction between the data warehouse/BI environments and the advanced analytics environment. See my blog “Understanding the Role of Hadoop In Your BI Environment” for more details.
Figure 3: Hadoop As the Universal Data Store/Data Staging Area
Becoming a Data-driven Organization
Organizations talk about becoming data-driven in their decision-making, but many have huge cultural barriers to overcome. Old school “command and control” structures and the now famous HiPPO (Highest Paid Person’s Opinion) stifle employee creativity in applying analytics to make better business decisions and uncover new data monetization opportunities.
To become a data-driven organization, there are several things that need to happen, including:
- Treating data as an asset and analytics as a form of intellectual property that has financial value
- Building out your analytic capabilities, both in the areas of tools as well as people
But none of these matters if the organization is reluctant to embrace data and analytics as factors in making better decisions. Read my blog “HiPPOs, Presidential Election, and Google…Oh My!” for more details.
Envisioning the Realm of What’s Possible
Business stakeholders know what questions they are trying to answer and what decisions they are trying to make. Unfortunately, these business stakeholders struggle with “envisioning” what’s possible with big data. They don’t fully understand what data is available to them today, or could be made available to them, for answering business questions or making business decisions at a higher-level of fidelity. And they can’t comprehend how advanced analytics could yield new actionable insights out of this tsunami of structured and unstructured data sources (see Figure 4).
Figure 4: Big Data Business Drivers
We have created several “envisioning” techniques to help the business users make this transition, giving them a “creative whack” to envision the realm of the possible and to start to grasp how big data could impact how they run – and even optimize – their businesses. See my blog “Visualizing the Realm of the Possible With the Big Data Envisioning Worksheet” for one example of a technique that we use.
Start Small, But Focus On Enabling The Business
Some organizations struggle to start their big data journeys. Some start with Hadoop. Others start by first building their strategy. And some are struggling to advance beyond the educational stage. Our best practice recommends that organizations “jump into the water” by starting with a business and IT workshop to brainstorm the big data use cases for where and how big data can power their key business initiatives (see Figure 5). Not only does this uncover potential use cases, but it also ensures that all-important alignment and collaboration between the business and IT stakeholders. See my blog “How We Teach Customers to Use Big Data” for more details.
Figure 5: Three-step Process For Starting Your Big Data Journey
Thanks For A Wonderful Big Data 2012!
As we conclude 2012, I want to thank everyone for a wonderful 2012. Most importantly, I want to thank my co-workers and clients who have provided me with the big data insights that I never would have come up with on my own. I love talking to and helping clients with their big data journeys. These organizations are at the forefront of what’s beyond the hype in the big data discussion. Thanks for including me on your journeys!