Techaisle Analyst Insights

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:

• 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

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

• 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)