I recently taught an MBA course at the University of San Francisco titled the “Big Data MBA.” In working with the students to apply Big Data concepts and techniques to their use cases, I came away with a few observations that could be applied by any entrepreneur.
1. Understand the customer's problem.
To ensure that your solution adds value, start by conducting extensive primary and secondary research on both the problem and the value of the solution. To understand your targeted customers, develop personas early in the process that serve as the “face of the customer.” Document the types of questions they ask and decisions they make. Then use the resulting insight to identify and prioritize data sources that you could be capturing about your customers, products and operations based upon business value and ease of implementation.
2. Understand how your product fits in the customer’s environment.
Companies have big investments in their data and technology environments. They will not be easily persuaded to toss out that investment. Instead, figure out how your solution can leverage or extend your targeted customers’ existing data and technology investments. Data, analytics, reports, dashboards tools and even SQL are strategic organizational assets. Explore ways to extend or free up those assets with new big data technologies, products and capabilities. By adding $1 now, they can free up or add $10 of value to their existing investments, such as Business Intelligence and data warehousing. That’s always a winning strategy!
3. Build upon open source and cloud technologies.
There is a compelling suite of open source technologies, many supported by the Apache Foundation, that are free, scalable and that allow organizations to quickly develop and get products to market. These technologies include:
Hadoop, a programming framework that supports the processing of large data sets in a distributed computing environment.
Spark, an in-memory open-source cluster-computing framework that provides performance up to 100 times faster for in-memory analysis and applications.
YARN, which enables multiple data processing engines on top of Hadoop such as interactive SQL, real-time streaming, and advanced analytics, along with the traditional MapReduce batch processing.
Mahout, a suite of scalable machine learning algorithms focused primarily of collaborative filtering, clustering and classification.
HBase, a column-oriented database management system that runs on top of HDFS; very useful for sparse data sets, which are common in many big data use cases.
Hive, an open-source data warehouse system for querying and analyzing large datasets stored in Hadoop files.
R, a free software programming language and software environment for statistical computing and graphics.
The entrepreneur should stand on the shoulders of those who have already built solutions to create your unique and compelling differentiation. Leverage open sources products and the cloud for your development environment. Light your hair on fire to get initial prototypes out to market as quickly as possible. Heavily instrument your product so that you have details about how customers are trying to use your product. Learn and evolve quickly. Speed is everything, with customer service a close second.
4. Provide a compelling, short payback ROI.
Help organizations find new ways to monetize their data and analytic assets. Focus on the business stakeholders by providing products and solutions that help them optimize their key business processes. To accomplish this, develop an initial ROI for your product and use it as compelling evidence for your customers to test, try and buy your product. Empowering front-line employees to deliver new services to customers is a great way to monetize your data and analytic assets and drive that ROI. If you don’t know how you product makes your targeted customers money, then don’t expect them to figure it out on their own.
It’s very encouraging to see MBA students empowered to be bold and brave in creating new business opportunities. And as I told my MBA class, the days of the MBA and business leaders delegating data and analytic decisions to IT are over. Business leaders (and MBA students) need to start owning these new sources of monetization, and the time is now.