If you could test all of your decisions, how would that change the way you compete?
Absolutely - if we can test our decisions using bayesian probability; predictive analytics - run scenario planning using big data - the game changes with us having huge competitive advantage.
Predictive analytics is the natural evolution of BI. Predictive analytics technology is designed to enable organizations to mine data and build predictive models that can help them analyze future
business scenarios, such as customer buying behavior or the financial risks of proposed corporate investments.
Recent software is designed to be used by information workers, not just PHD's in math and statistics.
Until now, data mining, predictive analytics and advanced business modeling technology has been used almost exclusively by highly skilled statisticians, mathematicians and quantitative analysts.
But that’s changing as business intelligence (BI) and analytics vendors offer more user-friendly predictive analytics tools.
Predictive analytics can be helpful in day-to-day business operations. If they’re available to information workers – not just to PHD statisticians and professional data miners – predictive modeling tools can help business people continually tweak their plans based on what-if analyses and forecasts that leverage both deep historical data and fresh streams of current-event data.
While there is an impressive new generation of user-friendly predictive analytics tools that are geared to the needs of information workers, traditional predictive analytics tools are still very much the province of a specialized cadre of statistically and mathematically savvy modelers with an academic background in multivariate statistical analysis and data mining.
Although most of the established predictive modeling vendors have made great progress in rolling out more user-friendly visual tooling, the core problem with today’s offerings is that many of them remain power tools with a steep learning curve and a commensurately high price.
A key trend is the move toward user-friendly, self-service, BI-integrated predictive analytics tools that encourage more pervasive adoption.
The trick is training information workers to use predictive analytics and change the culture to use data analysis and evidence to make better decisions.
Changing the culture and training workers to use data analysis is hard and time consuming - but it has been and can be done. If the company is serious and makes the investment in time and resources it will work.
Another trend is the move toward integrating more predictive analytics functionality into the enterprise data warehouse, through in-database analytics.
That’s an approach under which data preparation, statistical analysis, model scoring and other advanced analytics functions can be parallelized and thereby accelerated across one or more data warehouse nodes.
In-database analytics also enables flexible deployment of a wide
range of resource-intensive functions – such as data mining and predictive modeling – to a cluster, grid or cloud of high-performance analytic databases.
Another trend is the growing adoption of open frameworks for building predictive analytics models for data mining, text mining and other applications.
The principal ones are MapReduce and Hadoop, which have been adopted by a wide range of vendors of analytics tools and data warehouse platforms.
Hadoop MapReduce is a programming model and software framework for writing applications that rapidly process vast amounts of data in parallel on large clusters of compute nodes.
Learn more at http://hadoop.apache.org/mapreduce/
Enterprise Hadoop Solutions Wave 2012
A recent trend is an open development framework for inline predictive models that can be deployed to complex event processing (CEP) environments for real-time data streaming applications.
Another trend is the embedding of predictive analytics features in
customer relationship management (CRM) applications to drive real-time “next best offer” recommendations in call centers and multichannel customer service environments.
Business Intelligence Platforms Magic Quadrant 2012
Enterprise Data Warehousing Platforms Wave 2011
Oracle Business Intelligence Applications
Managing Big Data Using Sybase IQ VLDB Option
The goal of business intelligence (BI) is to enable better decision making. By having a full view of sales, customers, information about trends, etc., and by being able to forecast and use predictive models to analyze what is happening both inside and outside the organization, companies can evaluate the appropriate data to make informed decisions.
In addition, individual departments and managers need to understand the implications of their actions and decisions on the company as a whole. Add to this the requirement for intra daily analysis (operational BI), and business intelligence becomes a tool that is essential in the day-to-day running of the business.
Aligning initial data analytics / business intelligence projects to
organizational goals becomes key to ensuring a successful project.
For instance, the ability to show how solving a particular business problem will help an organization attach value to their overall goals helps ensure management buy-in that might be required to get the ball rolling.
To align goals effectively, it becomes important to identify how business intelligence will help or support strategic goals. Examples might be the use of business intelligence through customer analytics to identify consumer buying habits or the identification of why customer retention is dropping, and then tying these to either marketing initiatives or to finding ways to increase retention. It is possible to find links between goals that align with corporate strategy or that are beneficial to the organization as a whole.
Even though there is inherent value in business intelligence solving business pains, in order to get the most worth out of a solution, there need to be components that are broader focused and forward-looking to help tie current initiatives to those that have far-reaching effects.
In-database analytics is analytical processing directly within a data warehouse. In-database analytics tools have the potential to help organizations scale up their data mining activities and other advanced analytics efforts.
For example, as data mining models become increasingly complex, analytic applications have to pull together more data, on a more continuous basis, and from a greater number of data sources than in the past.
You need a powerful platform to do that in an efficient way and in-database analytics can help speed up the process.
Organizations's can't deploy BI / data analytics tech without considering people and process issues.
Whether an analytics project succeeds or fails depends less on tech and more on human factors.
For example, whether an organization's IT department collaborates sufficiently with the business users who will be relying on the data analytics tools, and whether those users feel comfortable with the software once it‟s up and running.
Also, the business users need to have sufficient training to make them feel comfortable.
What business users care about is being able to rapidly and intuitively analyze large amounts of data - and make decisions based on their analysis.
The tech is augmenting human intelligence - not replacing it.
Advanced data analytics tools have to be planned and deployed in close partnership with end users.
You just can't go build an awesome data wharehouse / BI platform with the latest analytics tools and expect business users to figure it out and use it. Won't happen.
Close communication between IT and business users and change management are critical.
A few training programs does not work - from hard experience.
Tech and processes must be planned and deployed with great thought in close partnership with end users.
Change management is a major project with training one part. To get full ROI you need to change the culture - knowledge process and decision making redesign is hard and time consuming.
End users not only need to know how to use the tools but to gain insights and make better decisions via data analysis and evidence.
It don't come easy.
Our mission is to identify, design, customize and implement smart technologies / systems that can interact with the human race faster, cheaper and better.
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