Operational Intelligence Why You Needed It Yesterday?
                     Operational Intelligence Why You Needed It 
Yesterday?
Satya K Vivek
Writes for Gadgeon.com, an 
IT outsourcing service from Gadgeon and 
IoT software development company.
 
What is Operational Intelligence?
Operational Intelligence (OI) is a process of creating relevant actionable 
business insights from operational data. Behind your IT infrastructure 
systems are massive streams of machine-generated data. This data is 
incredibly valuable to the overall efficiency of your business. OI is 
designed to enable organizations to:
Gain deeper understanding using all relevant information, especially 
machine data.
Reveal important patterns and analytics by correlating events from 
multiple sources.
Dramatically reduce the time to detect important events.
Leverage live feeds and historical data in order to understand what is 
happening now.
What Drives the Need for Operational Intelligence?
There exist huge amounts of valuable machine-generated data. Here are a 
few examples:
Online businesses providing transaction monitoring 24/7/365.
Web activity data to expand knowledge of customers, capacity, and digital 
asset usage.
Service level monitoring information from Managed Service Providers.
Call and event records to uncover more profitable services for 
Communication Service Providers.
GPS and other data that enriches customer behavior information with 
location data.
Are You Winning the Race Against Time?
Most organizations use a complex mix of business applications / 
reporting / analysis tools. However, many challenges and questions 
remain, such as:
Why does it take so long to get answers re: key business metrics?
Why can’t I see what is actually happening right now?
Why is it so hard to handle exceptions when things go wrong?
Why can’t we capture and preserve knowledge so we can be more 
effective in the future?
Simply put, the speed of business has increased beyond the capacity of 
the previous generation of IT, which focused on the tracking and 
automation of transactional activity. The new generation of IT must not 
only capture what has happened – it must tell us what is happening now 
and facilitate timely action.
Adding Operational Intelligence to Business Intelligence
Business Intelligence (BI) draws on data sources that are historical, batch-
loaded and structured. OI is typically used with time-series, unstructured 
or semi-structured data, e.g., specific machine events or transactions that 
have a timestamp association. The data in OI systems enables you to see 
what is happening now and compare it to what happened in the past. OI 
answers questions that traditional BI systems are not designed to answer.
Rather than thinking of OI as an alternative it is helpful to view BI and OI 
as complementary.  Usually, a successful BI implementation provides one 
with long term direction, while OI implementation provides one with 
short term direction.  With the right tools you can exploit the explosion of 
data and gain new insights for running your business while supplementing 
it with the best available customer analytics along with the knowledge of 
past successes.
Real-time vs. Historical Data
Data doesn’t have to be real-time to offer valuable business insights. Years 
of historical logs can be mined quickly that can reveal trends. There are 
countless ways OI can make an organization more effective, productive 
and secure. Fundamentally, OI helps you take advantage of new 
categories of rich real-time data whose business value you probably have 
not begun to exploit. 
What is Machine Data?
Machine data is a class of data being generated by web servers, apps, 
machines, SaaS systems, etc.  This data includes GPS readings, call logs, 
fleet locators, etc., and is one of the fastest growing categories of ‘big 
data’.
Until now, analysis of this data focused on machines and their operations, 
and not upon what it could tell us about our businesses. Today, massive 
amounts of information can be used for finding and fixing problems, or 
leveraged for strategic business advantage. This machine data explosion 
requires a new way of analysis that sits alongside established practices.
How can you observe machine data? The semantics of machine data are 
complex and CIOs must make clear its value to the organization. Little 
good comes from handing off raw data to your business staff. IT must 
make the data usable so the business staff can analyze operational data in 
order to gain valuable insights.
The Road to Operational Intelligence
The fastest road to OI comes through creating business value from the 
explosion of machine data.
CEOs, CFOs and senior managers are accustomed to seeing historical data. 
They analyze past performance and predict future results based upon 
data points such as sales figures, buying trends, raw material fluctuations, 
etc. It requires experience and intuition to drive business this way, but it is 
a bit like celestial navigation, which tells you where you were and directs 
you toward where you would like to go, but it doesn’t reliably tell you 
where you are. Operational Intelligence is more like GPS. Managers who 
use data in motion can make real-time course corrections or quickly chart 
new directions.
Bringing Operational Intelligence practices to an organization is a gradual 
process. 
Let’s look at each step along this roadmap in greater detail.
Search & Investigation.  The journey begins as IT uses machine data as a 
means to determine what is happening during an incident in a data 
center. To find the root cause IT examines not only each data set of the 
system that produced it, but also for information it offers about 
customers, key events, or performance of business processes.
Proactive Monitoring.  IT proactively monitors data to avoid previously 
identified risks. Simplified forms of predictive models can be created at 
this stage. Events and trends that may lead to trouble are identified so 
failures can be avoided. At this point, IT usually understands machine data 
well enough to start proposing business improvements.
Operational Visibility.  IT starts measuring SLAs and KPIs across the 
organization to engage the business. As business interest grows, users are 
able to answer questions and track consumer behavior in ways previously 
not possible without machine data. Now the conversation begins in 
earnest. IT begins to understand the real business needs and the Business 
staff begins to understand the real value of machine data. More 
sophisticated customer behavior models and business processes begin to 
emerge. At this point, Business staff presents IT with additional questions 
and IT responds with a quick custom dashboard (instead of a pointer to 
raw, unintelligible machine data or a three-month wait for a new report).
Real-Time Business Insights.  The pinnacle of Operational Intelligence 
comes when machine data is used to track and correlate activity in real-
time and to predict behavior. Dashboards are put in place, events are 
recognized that spur activity and predictive models can forestall problems 
and/or identify opportunities. At this stage, Operational Intelligence can 
be used broadly across the organization – often with more Business users 
than IT users. 
Conclusion
Organizations often rely upon today’s leading analytic applications to 
answer questions using static, historical data. In the past, when new 
questions arose, new applications had to be designed.
Organizations now have the tools to examine this new class of data in 
order to understand its raw form. Operational Intelligence presents the 
opportunity to gain new, exciting insights from the massive volumes of 
data machines are creating. 
About Gadgeon
Gadgeon is known for its expertise in Industrial IoT and engineering 
excellence. We connect devices, operations, and processes to create 
business value, and revolutionize enterprises with the power of data. As 
an end-to-end technology services company, we successfully enabled the 
digital journey of customers with critical digital services ranging from 
embedded systems, cloud app development, mobile app development, 
data & analytics, application modernization, emerging technology based 
solutions, and testing & test automation across the industries such as 
connected factory, telecom & datacom, digital healthcare, CSPs, and 
home & building automation.
Thank you for time in reading this article!