With the turn of decade we saw how business analytics has become analogous to
business intelligence, and how every BI solution incorporates analytics
capabilities to help enterprises make better business decisions. The data
warehouses of organizations are growing at incredible rate, and as organizations
perform highly complex and interactive queries against large volume of data and
do granular level of data analysis against that to gain insights for better
business decisions. They do not want to rely on historic data, but also on the
current streaming data. That would help organizations to make more relevant
decisions which are based on information of prevailing business scenario. Hence,
to do real-time data analysis and avoiding latency in query executions,
organizations require high performance analytics.
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What is high performance analytics?
High performance analytics can be defined as the technology that enables
complex analysis of data, phenomenon or information using integrated
computational methods on massive amounts. That would support critical reasoning
leading to proper insights and understanding towards the context of applied
challenge. Such advanced analytics incorporates methodologies for answering
future-oriented, proactive and predictive questions, as well as streaming data;
so that consequent business decision is based on real-time questions about
what's going on now. Advanced analytics leverages the same core features of
typical analytics solution, i.e. reporting, dashboards, visualizations etc. but
takes the analytics power to several steps further. Such powerful analytics
capabilities would be required by enterprises where they generate bulk of data
and there is a need to analyze that in real-time. For instance, large investment
or trading houses that invest large amount on shares they purchase daily in
stock markets. These trading houses rely on algorithmic trading that allows
these investors to obtain best possible price. Depending on these methods, these
investors buy large quantity of stocks and hold those momentarily for minutes or
even seconds and then sell them off to gain benefit. For this they constantly
monitor the fluctuating stock prices and compare those with the specified
parameters and constraints along with past performance of that particular
company. With buying or selling decision to be taken instantaneously before the
price of stock changes require real-time analysis of the stock market data with
minimal latency required in analysis process, and for such purposes high
performance analytic solutions like Sybase RAP are required. Such high
performance analytics isn't required by every organization, but companies which
are in domains of banking or telecom, where data is in bulk and output has to be
in real-time require tools having capability for high performance analytics.
Need for advanced analytics
For an enterprise implementing a Business Intelligence solution is not a
destination to arrive at, but rather a journey to begin with. With each passing
milestone being the series of processes in which the enterprise incorporates
various business processes and data-silos to achieve seamless flow of
information across the enterprise and thus have a “one view” of information.
That means throughout the enterprise it acts as a single data pipeline that is
accessed by all business processes and departments. Enterprises need variety of
analytics capabilities for addressing diverse decisions that need to be made by
the range of users, varying from top-notch business heads to sales personnel who
all would be resorting to the BI solution for that purpose. With advances in
data collection and storage technologies, large data sources have become
ubiquitous with every enterprise. The enterprise does want to do data analysis
on that, and that too speedily. Whereas, the current process includes
data-cleansing, extractions, transformation and loading of data into the
data-warehouses, and then through data-mining granular information can be
obtained from that data. This results in latency in the data relevance, and also
as data volume is huge the query execution time is also high. This is a cause of
concern for organizations that are in financial dealing, stock trading etc.
where data relevance is momentarily.
High performance platforms
Since the enterprises are collecting terabytes of data on a daily basis, it
has become imperative that the data mining and machine learning techniques also
scale up to such proportions. Such scaling can be achieved through design of
faster algorithms for query analysis and through employment of parallelism. The
emerging processor architecture, like multi-core, have made it possible to
achieve performance gains through parallelism. To address to these scales of
information management and processing, have seen the emergence of platforms like
Hadoop from Apache and Microsoft's Dryad. These platforms can support
implementation and deployment of large-scale analytics, and can support
execution on the cloud. Thus, offering to draw out more information out of
available data with higher efficiency and lower costs for the enterprises.
Thanks to the advancements in processor technology, data-mining and data
storage methodologies and techniques, the challenge to build platforms for
supporting high performance analytics is no more technical. Rather, road-blocks
are faced in form of enterprises still having silos of business information and
lack of enterprise information management procedures to streamline data flow
across business processes. The ever-increasing volume of data from heterogeneous
data sources has pushed the need for high-performance solutions so that
organizations can gain proper insight and competitive advantage from all the
data they posses.
Sybase RAP — The Trading Edition Edition
Capital markets industry today is facing a challenge to have better It provides a real-time cache and historical repository with The combination of Sybase RAP and CEP then can analyze through the |
Social Network Analytics
Social network had been the buzzword for the past year from an enterprise
perspective. Enterprises were monitoring their brand's reputation in the online
space and taking necessary steps to maintain that. According to Forrester's
report, the year 2010 will emerge as the year when social network analysis will
attain to new frontier in advanced analytics, which would incorporate behavioral
and attitude analysis of individuals. For social media analysis organizations
would be able to listen to all the tweets and updates in Facebook and what
people or consumers are talking about their products, the kind of interactions
going about their services or brands in forums. Then, this stream of event
information would be brought to data warehouses for analysis, and compared that
with historical information on those customers or cluster of people. This ways
an organization would be able to sense how people would respond to new offers,
campaigns or products, and what would be the best manner to offer those to their
consumers.
It's a predictive model, and involves complex event processing to analyze the
behaviors of networks of people on Internet. Even, they can change or modify
their marketing campaigns on the fly by getting users' views about it online and
prevent the campaign from failure. Thus, Internet being a huge source, it will
enhance an enterprise if its social network analysis supports real-time customer
segmentation, target marketing, churn analysis, and anti-fraud features, which
can be achieved through high-performance analytics tools.
Enhanced analytics solutions
The information does't necessarily has to be in a textual format, it can be
in form of images, audio, video etc. Some of the analytics solutions that are
coming up have phonetic search and recognition engines. For example, TelStrat's
Engage Analyze can index and mine audio words and phrases buried in the calls.
Unlike other speech to text approaches, the phonetic speech search is quicker
and can index content at 60-80 times faster. And these new systems learn on
their own as they record different phonetic for words. In a scenario where
customer calls have to be attended to, this type of system can comprehend user's
query and on its own find the most accurate solution for that to the caller.
Thus, automating the minor trouble-shooting problems of the users/callers.
There are other analytic engines that support multiple language support as
well. UTOPY's SpeechMiner can support multiple languages like Italian, Chinese,
Japanese, German besides English.
It allows processing of unlimited customer calls from across geographies.
Such high performance analytics is being deployed to reduce costs as interaction
costs with self-service are a fraction when compared to a person attending those
customer calls.