4
As typical of any analysis, the decision to apply different types of models is dependent on the data as well
as the business problem the analysis solves. More often than not, IoT analytic solutions require
multiphase analytics, that is models defined in the traditional, stored data paradigm, and scoring for new
analytical insight, as well as in-stream model derivation/calculation, and analytics applied to the edge.
SAS
®
does this. With the same integrated code, and with over one-hundred and fifty (at last count)
adapters and connectors linking streaming data, SAS Event Stream Processing is used to define the
complete continuous query that can be as simple or complex as the business problem itself. Moreover,
built into SAS Event Stream processing is the ability to automatically issue alerts and notifications for real-
time situational awareness and understanding of event status.
When we consider the IoT we are describing, an analytically driven network of objects that communicate
with each other. When we automate actions between objects, especially when there is no human
intervention, the risks associated with rogue actions, as well as the technical debt that accumulates from
both machine learning algorithms (Sculley et al., 2015) and from any unmanaged advanced analytics
environments, will outweigh the advantages. As such, the IoT demands a governed, reliable and secure
environment for streaming analytics and associated prescribed analytic actions. SAS® Decision Manager
is a prescriptive analytics solution. With fully traceable workflows, versioning and audit trails to assure
command control over streaming analytics for real-time, reliable, and accurate IoT applications.
BUILDING STREAMING DECISIONS WITH SAS®
Decision Construction
When designing, building, and testing decisions it’s best to begin with a description of what is included
within a decision. For the discussion within this paper, we consider the types of decision-making that
organizations use, that is strategic and tactical decisions, and the ways that organizations leverage
analytical models and their output to make decisions that meet businesses goals.
Strategic and Tactical Decisions
Organizations are required to address decisions at both the strategic and the tactical levels since both are
required for a business to run effectively. Strategic decisions typically represent the less common
decisions that an organization makes such as creating new product lines or expanding into new territories
or merging with another firm. These decisions, while important, are not typically made on a frequent basis
and therefore businesses can take the time and effort needed to create specific processes that aren’t
required to be repeatable nor require automation.
Tactical decisions, on the other hand, and which can include operational decisions, are made frequently
and often (often thousands of decisions), made in a single day or even in minutes or seconds. Loan
underwriting, fleet maintenance operations, point of sale operations, fraud detection, and remediation are
examples of the decisions that process high volumes of rapidly moving information. Tactical decisions like
these are numerous, require short timeframes, high rates of data ingestion, as well as automation and
analytics, and of course ways to prescribe an appropriate action based on the analytic model output.
Analytics, an important element for tactical decision making, has become pervasive within organizations
due to a couple of factors. First, the accessibility of analytics has increased due to the rise of tools that
assist and guide users through the analytical process to suggest relevant algorithms based on the
available data. This data-driven, guided approach includes better visualizations to identify patterns in the
data and recommendations, as to which is the best model to use. Second, analytics is being applied to a
wider set of problems, ranging across industries from retail to manufacturing, to health care and drug
development. Organizations have come to recognize that the application of analytics can help with a vast
array of problems. The emergence of the data scientist and the citizen data scientist represents the wider
number of users that are using the new and powerful analytical tools, applying them in a variety of ways
to solve difficult and complex business problems within a single business.