CXAIR is a new business intelligence tool that uses search engine technology to allow fast and easy analysis of large datasets. It can search across multiple data sources and provide sub-second responses to natural language queries. Unlike traditional BI solutions, CXAIR does not require data aggregation or knowledge of SQL/MDX. It provides a more user-friendly interface that is optimized for speed without sacrificing the ability to drill down into detail. The document argues that search technology is the future of business analytics as it can better handle the increasing volumes of data available.

1. Redefining Data Analytics
Through Search
Why search technology is the future of business analytics
Whitepaper
info@connexica.comwww.connexica.com +44(0)1785 246777
Search Powered Data Discovery

2. Introduction
With the wealth of technology advancements that have shaped
the way software and internet services are digested, the sheer
volume of data available to businesses is more prevalent than ever
before. The advancements in not only traditional workstations but
in smartphone and tablet technology has ushered in a new era of
data consumption that encompasses speed and portability
without sacrificing power and usability.
Alongside such innovations, data analysis has also progressed,
albeit on a comparatively linear scale, stretching the capabilities
of existing software in an attempt to match the data available.
This process began when existing databases expanded in size.
OLTP (Online Transactional Processing) databases were
established that allowed not only secure storage, but normalised
data entry to aid organisation.
Relational OLTP databases became the norm, with SQL
(Structured Query Language) used to communicate directly with
the embedded data. SQL was used to not only enter data, but to
create new structures by joining data together and retrieve data
from the heart of an organisation for display as reports.
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90%
of all data has been
generated in the last
two years.
source: ScienceDaily
Big Data, Big Problem

3. With the amount of data available steadily increasing, it quickly
became apparent that the rudimentary OLTP database
integration did not operate at a sufficient speed - it was, after all,
designed primarily for data storage, not data retrieval.
Data Warehouses were then developed to allow storage for the
masses of data in both duplicated and de-normalised form,
allowing faster navigation due to the fewer SQL joins that tied
the data together.
This allowed faster access to data but only up until a point. Soon,
using SQL across an entire Warehouse would slow as the data
continued to increase. New ways to express the required data in
summarised form was necessary to reduce access times, leading
to the rise in pre-aggregating key data.
Using OLAP (Online Analytical Processing) to pour over key
information while retaining speed of analysis using MDX
(Multidimensional Expressions) was, and remains, a finely tuned
balance.
Data must first be aggregated, or ‘summarised’, before retrieval,
adding an extra step to the reporting process. While the speed of
retrieval may be faster after the initial wait, the resulting data
cannot always be drilled down into an individual, transactional
level for further analysis or is reliant on generating SQL queries
to drill through to the data which can be slow over large data
volumes.
While this problem can be remedied to an extent with the
adoption of in-memory technology to increase performance, the
process is still based on the dated methodology of previous
solutions.
1.2
trillion Google
searches per year
source: Internet Live Stats
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4. The huge advances in 64bit multi-core processor architecture and
subsequent I/O bandwidth should be pushed to provide cost
effective, innovative solutions that are optimised to take
advantage of this potential performance - not focussed the
short-term solution of adding more and more RAM.
The notion that IT costs must increase linearly alongside the
amount of data being analysed is an astonishingly inefficient
corporate model that only squeezes margins as business
increases.
There is a key theme throughout the history of data analytics –
the data available has, and will, continue to grow at an
exponential rate. Now is the time to take a step back from the
incremental changes of the past and focus on a solution that is
not only future-proof, but can demonstrate an immediate ROI
(Return on Investment) to businesses that require an
improvement in their data analysis today.
To embrace the big data challenges, Connexica have developed a
genuinely innovative solution that encompasses and improves
upon all of the features found in traditional Business Intelligence
(BI) tools while drastically improving usability. Built from the
ground-up with huge volumes of data in mind, it is a move away
from the limitations of outdated technologies, such as OLTP,
OLAP and in-memory technology, towards a far more effective,
modern approach to data analytics, all powered by search engine
technology.
Connexica Ad-hoc Interactive Reporter, CXAIR, is the first
Business Intelligence tool of its kind. Pioneering a new way to
interact with, report, and learn from data, CXAIR represents a
ground-breaking shift in analytic capability, all while
incorporating the key principle of end-user engagement – turning
smart data discovery into actionable information for everyone.
20.8mInternet of Things
(IoT) connected
devices by 2020
source: Gartner
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5. The Technology
From a technology standpoint, the underpinning contents of
CXAIR form a series of highly optimised processes that result in
an extremely versatile and responsive solution.
When combined, they provide fast access to disparate data
sources through a single browser based interface. CXAIR is the
first BI solution to offer integrated storage and analysis over a
search engine with the capability to attain meaningful insight
from the huge amounts of data available.
Continually mining information from multiple data sources, the
data gathering engine stores a copy of the data as encoded index
files. This allows data contained in the index files to be queried
and analysed at high speed using natural language query terms.
Through the implementation of search technology, CXAIR
provides users with sub-second responses to these natural
language queries against both structured and unstructured data
across a vast array of data sources.
The First Search-Powered Solution
200+
implementations of
CXAIR across a
variety of industries
source: Connexica
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6. As the queries are ran against highly optimised CXAIR indexes
and not the original source systems, this rapid performance is
possible without putting additional load on the operational
systems.
Navigating, joining and reporting from data does not require end
users to have any analytical experience or knowledge of writing
complex languages, such as SQL or MDX, and will be familiar to
anyone who has used an internet search engine.
The powerful visualisation engine can then transform search
results into graphics while the analytics and reporting engine
allows users to create visually striking reports and dashboards
from a range of data sources.
Importantly, CXAIR maintains a high speed without
pre-aggregating any of the data. This means that users are able
to drill back down into the underlying records to allow a quick
and easy way of validating the information that is presented
on-screen.
For end users, the search engine technology that powers CXAIR
offers a very different experience that is a fresh approach to the
very notion of analytics, all with a view to take advantage of the
opportunities that the ‘big data’ revolution provides.
600m
number of records
held in the largest
singular CXAIR index
source: Connexica
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7. 3
customers who have
over one billion
records in their
CXAIR instance
The Benefits
of Search
The search engine technology powering CXAIR is a very different
approach to BI, providing a wealth of user friendly features that
are a result of its pioneering application. The subsequent
benefits have yet to be matched by any competing solution
across the analytic landscape.
As search engines are highly optimised for data retrieval across
huge portions of the internet, the speed inherent to the
technology is an observable outcome that has the potential to
drastically reduce analytical waiting times.
CXAIR achieves this speed without any of the pitfalls of
in-memory technology. While in-memory solutions pre-load a
limited pre-aggregated selection of data into volatile memory,
CXAIR analyses all data at transaction level.
For detailed analysis, this methodology ensures there is no
trade-off between speed and accessibility for end users – all data
is available at high speed regardless of database size.
How is CXAIR different?
source: Connexica
6

8. 15+
global partners
reselling CXAIR or
embedding the
technology within
their solution
Furthermore, in-memory solutions rely on high-specification
systems for their relative speed of access, requiring costly
assembly due to the huge amounts of RAM necessary to analyse
larger datasets.
CXAIR has a vastly different approach and is able to run on even
commodity hardware due to the fundamentally optimised nature
of its technology. For businesses looking for a swift ROI, a more
optimised solution represents immediate cost savings for IT
departments.
CXAIR also represents a major turning point for BI by moving
away from traditional solutions, such as OLAP or OLTP, that were
never designed to handle the amount of data that is needed for
organisation-wide analysis.
CXAIR is able to search through millions of rows of data in a
fraction of the time it would take for equivalent searches using
SQL or MDX, with search engines providing results far quicker
than relational database alternatives.
While OLAP was designed as a fast aggregation engine that sits
on top of a number of OLTP or warehouse systems, it does not
provide the integrated reporting and analysis layer that CXAIR
provides.
There is a clear disparity between the data that is available and
the standardised tools that provide analysis. Data analysis is
trailing behind, and CXAIR is the turning point.
source: Connexica
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9. 44
times greater data
production in 2020
compared to 2009
Conclusion
With natural language search, data analysis has a genuine
technological advancement to improve insight. Not only is the
data available for fast access, it is all possible without needing to
use any complex query languages.
Effective data analysis should not be restrictive or difficult.
Search-powered self-service analytics not only encourages a
culture of data-driven decisions, but allows users to engage with
and find accurate answers to their own questions.
Combining elements of business analytics, search engine and
data access technology, CXAIR allows users to satisfy their own
information requirements and combat the unnecessary
overreliance on IT departments that traditional solutions
demand.
Search- Powered Future
source: Wikibon
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