Database Reference
In-Depth Information
to get their job done. In this section we will discuss how to match your
technologies, like Hadoop, to business needs within your organization.
Problem Definition
In order to match big data solutions to a project, take a step back and look
at the different business units in your organization. What are their current
challenges and opportunities? Create a quick chart that maps this out. Talk
to these groups about their data challenges. You will often hear many of the
same statements coming from the groups who may benefit from big data
technologies, such as the following:
• The business can't get access to all the relevant data; we need external
data.
• We are missing the ETL window. The data we needed didn't arrive on
time.
• We can't predict with confidence if we can't explore data and develop
our own models.
• We need to parallelize data operations, but it's too costly and complex.
• We can't keep enough history in our enterprise data warehouse.
Theseareexactly thebusiness unitsyouwanttospeak withaboutleveraging
big data solutions. The next step is to describe the solution to them without
getting too technical. This step is all about the Return on Investment (ROI)
to the business unit.
Many people hear about big data and assume that the first project that they
take on must be an entirely new project solving a new problem with new
data and new infrastructure. Although you can do this, it greatly increases
your chance of failure. The reason for this is that there are too many
unknowns. You are dealing with new technology (Hadoop, Hive, Pig, Oozie,
etc.), new hardware architecture, new solution architecture, and new data.
Every one of those unknowns will slow you down and create opportunities
for mistakes. Instead, look around at existing solutions and ask yourself and
the business group, “What new data elements would make this data and
these reports more statistically accurate and relevant?” Whether you have
current solutions dealing with fraud detection, churn analysis, equipment
monitoring, or pricing analysis, additional data elements can likely be
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