Predictive Analytics (PA) is emerging as an important tool in the area of business decision. Predictive Analytics primarily deals with making a forecast based on several inputs. In this and the blogs that follow I will share my experiences with Predictive Modelling (PM), with a view to contributing to the current knowledge base that exists in the Predictive Analytics World.
In the world of business most predictive analytical tools are quantitative where numeric data is used for building an input-output model. The output is the prediction for specific inputs. For example: A 10% increase in advertising in January will result in an increase of 1% sale in May is a typical output from predictive analytics.
Common Mistake of Predictive Modelers: Assumption of linearity
Predictive Models are largely based on statistical techniques. Multiple Linear Regression (MLR) model is what most users will confront when they look at predictive models. This model works in the background whether one is using a multiple time series or multi-level modelling.
Multiple Linear Regression Models are developed based on a crucial assumption: the output is linearly dependent on the inputs. But all experience shows that in most business situations the assumption of linearity is not valid. Hence the statistical models have a poor fit and low predictive capability. In addition, the business world also suffers from Black Swan problems that no modelling can manage with any level of confidence.
The net effect of a linearity assumption, which is ubiquitous in almost all statistical modelling, and the resultant poor fit and low predictive capability has led to frustrated user community. Hence, a business executive looks at models with suspicion and trusts ‘gut’ to make decisions.
Predicament
The predicament of Predictive Modellers’ is: How do we get away from the linearity assumption on which almost all statistical tools are based, but it is known that this assumption is a poor, in fact a very poor, approximation of the real world behaviour?
The story of our approach to modelling starts from this predicament that we have been in, along with all others, and the path that we cut out to get out of it.
Dr. Cooram Ramacharlu Sridhar (Doc)
Managing Director and Advisor, Segmentation & Predictive Modeling
Techaisle Blog
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Whiptail, the first company to successfully commercialize multi-level flash recently announced its second-generation family of NAND Flash storage products, Accela and Invicta. Accela is an enterprise class, single chassis, and standalone flash storage product. On the other hand Invicta is a modular storage array. It has some very impressive specifications:
The products are completely scalable. A mid-market customer can begin with Accela and can add Invicta through InfiniBand as the needs grow. Even within the Invicta chassis, a toup to 6 storage nodes with 6 to 12TB and one router can be added as lego blocks as the data needs evolve.
Analyst Speak
Whiptail’s announcement comes at a time when the buzz about big data has reached a crescendo. And along with big data, vendors and analysts have started to talk about data obesity and therefore need for storage capacity. Granted that storage capacity needs are multiplying but big data poses a bigger challenge – extremely high throughput and read-to-write performance. Traditional storage vendors have tried to make higher-performing storage either by using as many spindles or constricting drives. None of them technically really address the velocity problem – real time streams of high volume information that is both structured and unstructured. Whiptail is taking the conversation away from storage-capacity play to velocity play thereby reducing the cost of transactions.
Even the channel partners wanting to develop or expand their datacenters and offer cloud-based services can use Invicta because of its multi-tennant, multiple addminstrators, and role-based security capabilities.
Invicta is an application acceleration platform that big data purveyors will love to the bane of other other storage vendors.
Anurag Agrawal
Techaisle
- 6-72 TB of NAND Flash capacity
- Up to 650,000 IPOS
- Upto 7 GB/Sec bandwidth
- Asynchronous replication
- VMWare and Citrix ready
The products are completely scalable. A mid-market customer can begin with Accela and can add Invicta through InfiniBand as the needs grow. Even within the Invicta chassis, a toup to 6 storage nodes with 6 to 12TB and one router can be added as lego blocks as the data needs evolve.
Analyst Speak
Whiptail’s announcement comes at a time when the buzz about big data has reached a crescendo. And along with big data, vendors and analysts have started to talk about data obesity and therefore need for storage capacity. Granted that storage capacity needs are multiplying but big data poses a bigger challenge – extremely high throughput and read-to-write performance. Traditional storage vendors have tried to make higher-performing storage either by using as many spindles or constricting drives. None of them technically really address the velocity problem – real time streams of high volume information that is both structured and unstructured. Whiptail is taking the conversation away from storage-capacity play to velocity play thereby reducing the cost of transactions.
Even the channel partners wanting to develop or expand their datacenters and offer cloud-based services can use Invicta because of its multi-tennant, multiple addminstrators, and role-based security capabilities.
Invicta is an application acceleration platform that big data purveyors will love to the bane of other other storage vendors.
Anurag Agrawal
Techaisle
Rapid fire announcements from Intuit, all directed towards the betterment of small businesses. Today Intuit announced the availability of Intuit Small Business Revenue Index, which is based on aggregated data from QuickBooks Online. By the very meaning of the term “aggregated” it should be understood that the data is anonymous, that is democratized across 200,000 small businesses. This index is the first of its kind in the market that provides current information on monthly small business revenue. It complements Intuit’s monthly Small Business Employment Index to provide a more complete picture of the economic health of US’s small businesses based on revenue, hiring and compensation trends.
With its latest announcement, Intuit has demonstrated that it is bringing data-driven insights to small businesses, sole-proprietors; insights that were previously only available to large enterprises. This information should empower small businesses to compare themselves against benchmarks and thereby effect changes in their organizations.
It certainly places in the hands of Intuit’s small business customers, power of the data. Both the Employment and Revenue Indexes are updated monthly by Intuit which is far more often than government stats and take a snapshot that is more targeted and pertinent to small business owners. They could use it as a signal for whether it’s time to hire, cut back or increase employee salaries.
As Techaisle had mentioned in its own press release on big data on April 26, 2012, data analytics is equally relevant for small businesses. 12 percent of small businesses using business intelligence are interested in big data analytics. However, they are looking for an IT vendor or partner to collect, collate, and analyze big data and present to these small businesses as a resource, in other words, democratization of big data. The collected data is an aggregation of information being created by other small businesses within the same vertical segment or employee size category. Intuit to my mind, just did it.
Timing by Intuit could not be more perfect.
Anurag Agrawal
Techaisle
With its latest announcement, Intuit has demonstrated that it is bringing data-driven insights to small businesses, sole-proprietors; insights that were previously only available to large enterprises. This information should empower small businesses to compare themselves against benchmarks and thereby effect changes in their organizations.
It certainly places in the hands of Intuit’s small business customers, power of the data. Both the Employment and Revenue Indexes are updated monthly by Intuit which is far more often than government stats and take a snapshot that is more targeted and pertinent to small business owners. They could use it as a signal for whether it’s time to hire, cut back or increase employee salaries.
As Techaisle had mentioned in its own press release on big data on April 26, 2012, data analytics is equally relevant for small businesses. 12 percent of small businesses using business intelligence are interested in big data analytics. However, they are looking for an IT vendor or partner to collect, collate, and analyze big data and present to these small businesses as a resource, in other words, democratization of big data. The collected data is an aggregation of information being created by other small businesses within the same vertical segment or employee size category. Intuit to my mind, just did it.
Timing by Intuit could not be more perfect.
Anurag Agrawal
Techaisle
The Big Data Analytics' promise: enable “data monetization” through timelier, more accurate, more complete, more granular, more frequent decisions. So, what exactly are the types of business problems big data analytics likely to solve? For this one may need a mini-MBA in Big Data Use Cases.
First let’s define what makes data Big.
Big Data, Little Data
We live in a world of data: transactions, feedback and real-time interaction with customers, partners, suppliers, and employees. In addition to brick, click and mobile transactions, the new variable in the mix is Human generated data – explosive growth of blogs/reviews/messages/emails/pictures. Social graphs such as product recommendations based on circle of friends, jobs you may like, products you have looked at, people who are your contacts etc. also create “second order” data that can be mined for sentiment analytics on products or companies or fact discovery.
Another new variable is computer generated data. Computers generate data as byproduct of interacting with people or with other devices. More the interactions, more is the data and this data comes in a variety of formats from semi-structured log files to unstructured binaries. This “exhaust fumes” of data can be extremely valuable. It can be used to understand and track application or service behavior so that one can find patterns, errors or sub-optimal user experience. One can mine it for statistical patterns and correlations to generate insights.
However, if one listen to the hype, companies can harness this information learn faster, make better decisions, and stay one step ahead of their competitors. Unfortunately, harnessing big data (and separating the signal-from-noise) is trickier than it looks. It takes a lot of skill and superb understanding of use cases.
Big Data Use Cases
The key to exploiting Big Data Analytics is focusing on a compelling business opportunity as defined by a use case — What (What exactly are we trying to do?). Use cases are emerging in a variety of industries that illustrate different core competencies around analytics.
E-tailing/E-Commerce – Online Retailing Use Cases
Retail/Consumer Use Cases
Financial Services Use Cases
Web & Digital Media Services Use Cases
New Applications
Health & Life Sciences Use Cases
Telecommunications Use Cases
So, What’s the Big Deal?
The big deal is that if analytics is done well there is room for margin expansion and additional profit.
Shirish Netke
(Republished with permission)
First let’s define what makes data Big.
Big Data, Little Data
We live in a world of data: transactions, feedback and real-time interaction with customers, partners, suppliers, and employees. In addition to brick, click and mobile transactions, the new variable in the mix is Human generated data – explosive growth of blogs/reviews/messages/emails/pictures. Social graphs such as product recommendations based on circle of friends, jobs you may like, products you have looked at, people who are your contacts etc. also create “second order” data that can be mined for sentiment analytics on products or companies or fact discovery.
Another new variable is computer generated data. Computers generate data as byproduct of interacting with people or with other devices. More the interactions, more is the data and this data comes in a variety of formats from semi-structured log files to unstructured binaries. This “exhaust fumes” of data can be extremely valuable. It can be used to understand and track application or service behavior so that one can find patterns, errors or sub-optimal user experience. One can mine it for statistical patterns and correlations to generate insights.
However, if one listen to the hype, companies can harness this information learn faster, make better decisions, and stay one step ahead of their competitors. Unfortunately, harnessing big data (and separating the signal-from-noise) is trickier than it looks. It takes a lot of skill and superb understanding of use cases.
Big Data Use Cases
The key to exploiting Big Data Analytics is focusing on a compelling business opportunity as defined by a use case — What (What exactly are we trying to do?). Use cases are emerging in a variety of industries that illustrate different core competencies around analytics.
E-tailing/E-Commerce – Online Retailing Use Cases
- Recommendation engines
- Cross-channel analytics
- Event analytics
- Right offer at the right time
Retail/Consumer Use Cases
- Merchandizing and market basket analysis
- Campaign management and customer loyalty programs
- Supply-chain management and analytics
- Event- and behavior-based targeting
- Market and consumer segmentations
Financial Services Use Cases
- Compliance and regulatory reporting
- Risk analysis and management
- Fraud detection and security analytics
- CRM and customer loyalty programs
- Credit risk, scoring and analysis
- High speed Arbitrage trading
- Trade surveillance
- Abnormal trading pattern analysis
Web & Digital Media Services Use Cases
- Large-scale clickstream analytics
- Ad targeting, analysis, forecasting and optimization
- Abuse and click-fraud prevention
- Social graph analysis and profile segmentation
- Campaign management and loyalty programs
New Applications
- Sentiment Analytics
- Mashups – Mobile User Location + Precision Targeting
- Machine-generated data, the exhaust fumes of the Web
Health & Life Sciences Use Cases
- Health Insurance fraud detection
- Campaign and sales program optimization
- Brand management
- Patient care quality and program analysis
- Supply-chain management
- Drug discovery and development analysis
Telecommunications Use Cases
- Revenue assurance and price optimization
- Customer churn prevention
- Campaign management and customer loyalty
- Call Detail Record (CDR) analysis
- Network performance and optimization
- Mobile User Location analysis
So, What’s the Big Deal?
The big deal is that if analytics is done well there is room for margin expansion and additional profit.
Shirish Netke
(Republished with permission)
