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Posts from the ‘ROI’ Category

23
Dec

2014 Year in PreReview for Big Data Analytics


In the movie “Minority Report,” set in 2054,Time Travel Tom Cruise plays the captain of the “PreCrime” police force, which uses “precognitive” abilities of mutants to stop crime before it happens. Silicon Valley futurists have sometimes used this reference in the context of the art of the possible with Big Data. We have another 40 years to go to see how analytics can accurately forecast future events based on human behavior. Meanwhile, imagining the future with some level of accuracy is within our reach today.

Value creation in the data economy made headlines in 2014. While Big Data continued to be the buzzword of the year in 2014, solutions that created economic impact were center stage.  Trending terms such as “predictive analytics” and “advanced analytics” approached the levels of “Big Data” on Google Trends during the year. “ROI,” which was vaguely referenced in the last two years, became the most commonly used term with Big Data in 2014. Here is a cross-section of 2014 events.

Apple announces TopsyTV

This is their next-generation TV appliance that integrates social media engagement with the TV watching experience. Earlier in 2013, Apple acquired Topsy Labs, a reseller for Twitter content for $200M. This was followed by a series of less publicized acquisitions of social media data companies. Apple is characteristically tight-lipped about its plans for monetizing this product with advertising, but speculation is rife that Apple is poised to get a piece of the $600 billion that is spent on advertising today.

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5
Mar

ROI on Analytics – Now We Have Numbers


Return on InvestmentA recent study by the Nucleus Research says that Analytics pays back $10.66 for every dollar spent. The study is based on data from 60 case studies and relates to investments in Business Intelligence, Performance Management and predictive analytics. Not surprising are the areas where they saw ROI increase – revenue, gross margin and expenses.

Enterprises have used various metrics to track the effectiveness of Business Analytics. Cycle Time to Information (CTI) is a metric that measures the elapsed time between the occurrence of a significant event and the time this information is available to a decision maker who has to act on that information. Cycle Time to Action (CTA) is variation of this metric which measures the elapsed time to act on information after an event occurs.  These metrics are useful to track the efficiency of a Business Analytics infrastructure and the elimination of manual processes to increase productivity. As the volume of data increases in an enterprise, automation in data management will become more complex in the future. Read more »

3
Oct

Wanted: CIO – BI/Analytics


In a tough economy, a new tech-fueled BI and analytics arms race is on to create the next competitive advantage.

Everyone is beginning to look beyond the status quo in BI, analytics, Big Data, Cloud Computing etc to fundamentally change how they discover fresh insights, how they can make smarter decisions, profit from customer intelligence and social media, and optimize performance management.

The headache for corporations is not the technology aspects but the leadership side. Who is going to lead this effort, corral the vendors and formalize and execute a more structured program.  

Who is going to lead the effort to create the right toolset, dataset, skillset and mindset necessary for success?

As BI and Analytics moves from “experiment and test” lab projects to commercial deployments, companies are going to need more leadership and program management capabilities.  They need leadership that can provide strategic, expert guidance for using powerful new technologies to find patterns and correlations in data transactions, event streams, and social media.

Some firms are making moves.  In insurance, AIG – Chartis Inc. unit appointed Murli Buluswar to the new post of chief science officer.  This aims to enhance Chartis’ focus on analytics… he “will be responsible for establishing a world-class R&D function to help improve Chartis’ global commercial and consumer business strategies and to deliver more value for customers.”  This focus on analytics involves “asking the right questions and making science-driven decisions about strategies—whether it’s related to underwriting decisions, product innovation, pricing, distribution, marketing, claims or customer experience—with the end result of improving the scope of what Chartis delivers for customers”.

As a result of where we are in the maturity cycle and to support the business units better, we are seeing a new emerging role “CIO – BI” that is dotted lined to the global CIO or a shared services leader.  Let’s look at a representative job posting from GE Capital, which always seems to be a step ahead of most companies.   Read more »

3
Aug

Big Data, Analytics and KPIs in E-commerce and Retail Industry


  • How to convert Lookers to Bookers…
  • How to create unique and effective Digital Experiences that impact probability of purchase or likelihood of return.
  • What offers might result in higher “take rates”

The change in consumer behavior and expectations that e-commerce, mobile and social media are causing is hugely significant – big data and predictive analytics will separate brand/retail winners from losers. This won’t happen overnight but the transformation is for real.

Retail Industry makes up a sizable part of the world economy (6-7%) and covers a large ecosystem –  E-commerce, Apparel, Department Stores, Discount Drugstores, Discount Retailers, Electronics, Home Improvement, Specialty Grocery, Specialty Retailers and Consumer Product Goods suppliers.

Retail is increasingly is looking like a barbell – a brand oriented cluster at the high-end, a very thin middle, and a price sensitive cluster at the low end. The consumerization of technology is putting more downward pricing pressure in an already competitive “middle” retail environment. The squeeze is coming from e-commerce and new “point, scan and analyze” technologies that give shoppers decision making tools — powerful pricing, promotion and product information, often in real-time. Applications in iPhones and Droid, like  Red Laser can scan barcodes and provide immediate price, product and cross-retailer comparisons. They can even point you to the nearest retailer who can give you free shipping (total cost of purchase optimization). This will lead to further margin erosion for retailers that compete based on price (a sizable chunk of the market in the U.S, Europe and Asia).

Data analytics is not new for retailers. Point of sale transactional data obtained from bar-codes first appeared in 1970s. A pack of Wrigley’s chewing gum was the first item scanned using Universal Product Code (UPC) in a Marsh Supermarket in Troy, Ohio in 1974.  Since then, retailers have been applying analytics to get even smarter and speedup the entire industry value chain.

Consumer Goods Value Chains

More recent use cases of retail analytics  include: Read more »

19
Mar

Game changer or Incremental – SAP BusinessObjects 4.0 (and EPM 10)


“The riskier our business problems the more we rely on analytics”
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SAP AG recently released a new 4.0 version of its BI (business intelligence) and EIM (enterprise information management) solutions.  They also released Enterprise Performance Management (EPM 10).

We think this will be a big deal for current customers who are struggling to build a robust enterprise foundation for BI for the diverse business initiatives. Having multiple BI projects with each on a slightly different data hubs leads to chaos and insights where people are unsure about what the numbers mean as they could be interpreted differently along the information chain.

We expect a significant upgrade cycle looming for the SAP community.

BusinessObjects 4.0  is a major release after the merger of SAP and BusinessObjects in 2007 and SAP and Sybase in 2010. It incorporates a significant capability enhancement that business users and CIOs are demanding around analytics – more real-time, better in-memory computing; BI for the masses – powerful BI in users hands, mobile BI capabilities across a wide spectrum of devices; combining structured and unstructured information and providing the tools to govern the information and finally dealing with the growing avalanche of social media data.  Also a common “look and feel” and better data visualization provides a better overall User Experience.

Read more »

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