INTELLIGENT BRANDS // Software for Business
AI is more
important
to IoT than
big data
insights, says
GlobalData
F
or enterprise software,
operational efficiency trumps
superfluous features. When
it comes to optimising these
applications, however, particularly
for the Internet of Things (IoT),
enterprises need to focus their efforts
on the basics of business optimisation
rather than insight-driven innovation.
A recent GlobalData survey of 1,000
IoT professionals revealed a heavy
reliance on traditional business
intelligence (BI) software. 40% of those
surveyed ranked business intelligence
platforms well above all other means
of analysing data. Unfortunately, with
the broad market trend toward the
democratisation of data now well-
established, such do-it-all BI software
platforms have already given way to
numerous smaller, more discrete ways
of deriving value from enterprise data,
be that a direct SQL query, a predictive
data modeller, an auto-generated
data discovery visualisation, or a live,
interactive executive dashboard.
The reasons for this are simple: business
intelligence software is reactionary and
static. Its users rely heavily upon basic
reporting mechanisms that, in turn,
rely heavily on laborious queries and
reports – a very costly venture to both
build and maintain. This reluctance to
follow the broader market away from BI
platforms within IoT is concerning, given
a subtle shift noted in the same survey
concerning when, during its lifecycle,
an IoT deployment fails. In 2016, no
failures were noted post-deployment.
www.intelligentcio.com
In 2017, however, that number shot
up to 12%. “With deployment and
maintenance costs also topping our
survey as the number one reason IoT
deployments fail or are abandoned
prior to deployment, it becomes clear
that IoT practitioners should emphasise
tactical benefits over strategic analytical
insights, at least at the outset of a
project as a means of proving ROI and
securing future investment from the
business” noted Brad Shimmin, Service
Director for Global IT Technology and
Software at GlobalData.
Artificial intelligence (AI), however,
can do far more than inform. It can
immediately prove the value of IoT as
a means of optimising existing business
processes. With even the simplest AI
machine learning (ML) framework and
“A recent
GlobalData
survey of 1,000
IoT professionals
revealed a
heavy reliance
on traditional
business
intelligence (BI)
software.”
model at the ready, for example, IoT
practitioners can solve two pressing
problems: detecting anomalies and
predicting desired outcomes.
GlobalData’s survey shows that
enterprise buyers are eager to do just
that, with 43% indicating that the best
role for AI is to centrally automate and
optimise business processes. The problem
lies within the idea of centralisation.
Centralisation is part and parcel to
traditional BI analysis and reporting and
traditional ideas like predictive modelling.
Where AI is most valuable, however is
at the edge. IoT deployments need to
employ tools like ML, not centrally, but at
the edge, close to the device itself. And
like today’s enterprise software, those
analytics endeavours should be brief
and to the point, and focused on solving
specific challenges.
This is the same idea espoused by
some branches of AI itself such as deep
learning (DL), where the combination
of numerous, smaller decision-making
algorithms can give rise to a larger,
seemingly intelligent system. The idea
is simple – avoid building an expensive
monolithic analytics system centrally.
Our advice, therefore, is that IoT
buyers not only seek centralised, global
visibility of the business but also local
optimisation via discrete, AI-driven
outcomes. This approach will not solve
the full set of potential problems, but
it is affordable and will have a direct
and immediate impact on businesses,
helping to prove the value of IoT one
problem at a time . n
INTELLIGENTCIO
63