2018 Technology Predictions: Insights-Driven Enterprises Will Emerge as Winners in 2018

Digital
insights are increasingly being used to transform the business. How fast a
company can react to these insights is the difference between capturing
revenue, customer sentiment or critical national safety information, or not.
Within the digital transformation is the ability for a business to go from
data, to insights, to action, very quickly. I was at a sold out NVIDIA
Singapore AI event this year and heard how their customers are looking for the
ability to consolidate, visualise and simulate multiple scenarios, and make
sense of huge volumes of streaming data from within and outside of the business.
This reinforced what I’ve been hearing from client visits. The need to do this
in an accelerated manner is the key factor and positions companies to be much
more proactive and competitive.

In
the financial services sector, where milliseconds matter and where insights
directly equate to money, organisations have turned to GPU databases to push
conventional computing to its limits. By using the most-up-to-the-moment data
in sub-seconds–instead of hours­­–organisations are able to perform risk
calculations, fraud analysis and algorithmic trading in near real time.

In
retail, one of Asia’s largest conglomerates, Lippo Group, is using Kinetica’s
GPU database to consolidate multiple dimensions of customer attributes from
their diversity of multi industries data sources, including demographics,
location, and cross-channel buying behaviour such as sentiments gained from
social media and interactions in physical stores and online. All generated
insights are then to be encapsulated in the form of API, for any digital touch
point channels to consume and then interact with customers to deliver personalised
offers.

In
the field of life sciences, GlaxoSmithKline’s data science and analytics teams
use GPU-accelerated database by Kinetica for accelerated processing of
transcriptions to run hundreds of thousands of chemical simulations across
complex data sets for drug discovery and research and development.

We
expect more financial services, retail and life sciences organisations as well
as telecommunications, energy and logistics companies to leverage a high
performance, in-memory distributed database to deliver truly real-time,
actionable intelligence on large, complex streaming data sets.

Below
are four technology predictions for insights-driven businesses to succeed in
2018:

2018
will be the year when we see intelligence videos carving out a larger share in
the market. Ranging from surveillance cameras to smartphone cameras, most devices
today are driven by cameras and smart sensors. Organisations will soon realise
the importance of invaluable, real-time data collect from captured videos, be
it by individuals or enterprises. For instance, studying some unique patterns
of movement can indicate and help government and security companies reduce
possible threats.

A
retailer can account for accurate floor space, customer movement patterns, and
adjust their shelf space based on customers’ interactions. It’s using real data
to make decisions, instead of doing so on a hunch.

These
pattern and geospatial insights garnered from video are almost readily
available everywhere and any time – we will simply need to tap into them.

In Korea and Japan, AI has already gone mainstream
across industries such as banking and finance, telecommunications and
retail. SK Telecom, for instance, announced in
early 2017 that it will be investing US$4.2 billion in AI. In other APAC
countries, including Singapore, Indonesia and Australia, AI is still in the
early stage, but forward thinking organisations will adopt AI solutions that
cannot be solved by traditional systems.  The governments are pushing AI
adoption – for instance, Singapore’s AI investment will exceed US$100 million
from 2017 to 2022 in its bid to become both a smart nation and innovation
hub.

As
enterprises operationalise AI, they will look for products and tools to
automate, manage and streamline the entire machine learning and deep learning
lifecycle. Data scientists will need to focus on the code and algorithims and
to deploy these they will require a enterprise class AI & BI GPU database
that is operational ready for their company.

The
amount of data being generated will continue to grow in APAC – consumption is
predicted to surge 30-60 percent per annum between
2015 and 2020. APAC will
lead other regions in the amount of data being created largely from telcos,
banking, e-commerce and social media. With 600 million people alone in ASEAN
and a growing consumer base with wallet share, it creates a perfect storm of data
for organisations to tap. However, the traditional data warehouse is
increasingly struggling with managing how to service these consumers . Being on
the forefront of growth also means being on the forefront of obstacles.

In
2018, organisations will start re-thinking their data warehouse approach to
merge business intelligence (BI) and AI on a single platform. Such
sophisticated platforms will not only help companies accelerate the processing
of parallel data, but also derive highly accurate insights in real time.

AI
is increasingly being deployed for various applications including personalised
medicine and language processing. Enterprises will start investigating how they
can accelerate the delivery of better services and develop more targeted
campaigns to high-value customers.

Conglomerates,
for instance, would be interested to know how to take care of their best
clients and how to drive them to use their other services. Think a regular
consumer of a supermarket, how can a conglomerate influence them to use their
banking and health care services for their family? This is a market that
requires deep understanding on real-time data to move consumers’ behavior.
Responding to a consumer too late is a missed opportunity for them and the
conglomerate.

2018
will be the breakout year in which GPU-powered databases–which are capable of
processing data up to 100 times faster and only at 1/10 of the hardware costs
required–are considered and piloted. Bringing AI and BI analytics together
allows an APAC enterprise the ability to help their consumers get the best
service outcome. It allows the enterprise to monetise and capture outcomes
quickly.

Already,
telecommunications, e-commerce, and financial services enterprises in China,
Korea and Japan are deploying GPU-accelerated applications to counter data and
analytics challenges. Financial services organisations can leverage the service
for consumer and corporate banking. A algorithm output for credit score in a
week to one delivered in sub-seconds changes an outcome for both consumers and
the bank quickly. The GPU professing for AI has changed the IT and business
landscape dramatically and its only starting to be recognised.