Mobile Analytics Form a Two-Way Street Between the Past and the Future

Brad Shimmin
Brad Shimmin

Summary Bullets:

  • There are great advantages to disseminating analytics smarts to mobile users such as sales persons.
  • Real innovation, however, comes when you combine that dissemination with the collection of data points.

I spent a few hours yesterday listening to a number of SAP ISV partners including ExpertIG, Rapid Consulting and Liquid Analytics demonstrate mobile software built to support the wholesale market.  I know, that doesn’t sound incredibly exciting.  Yet, long before the expiration of my admittedly short attention span, I was struck squarely by what was for me a stunning realization.  Big data should be as much about collecting data as it is about gleaning knowledge from that data. Continue reading “Mobile Analytics Form a Two-Way Street Between the Past and the Future”

The Big Data Challenge: Should You Sell It, or Use It?

Joel Stradling
Joel Stradling

Summary Bullets:

  • Services companies need to turn to experts in data warehousing, mining and analytics to seek consultations on how big data can be manipulated.
  • Service providers have fantastic opportunities to both sell big data, for example to advertisers, as well as to use it for strengthening business relations with customer-centric solutions.

There are many questions surrounding the big data phenomenon, such as can services companies (e.g., telcos) sell it and/or use it for their own purposes? The challenges include how to configure already complex billing and IT architectures to capture the information and make sense of it, as well as navigating local regulations. There are several professional software and IT integration companies, such as Amdocs and Accenture that are all vying for business from telecom operators, and other industries, to help capitalize on the big data gold mine. Continue reading “The Big Data Challenge: Should You Sell It, or Use It?”

Big Data and Predictive Analytics Need More People, Not More Data

Brad Shimmin
Brad Shimmin

Summary Bullets:

  • Machine learning, data mining, and advanced analytics coupled with big data seems poised to reshape the way we make business decisions, automating them and making them more effective.
  • However, if our early work with stocks, recruitment and credit scoring are any indications, our algorithmic innovations need human oversight now more than ever.

My Google Nexus tablet knows when I should leave the house in order to keep my daily routine humming along smoothly. Using historical geolocation data, search results and email trafficit knows, for instance, where I like to dine and how long it will take me to get there at the prescribed hour on the customary day of the week. Continue reading “Big Data and Predictive Analytics Need More People, Not More Data”

The New Analytics: Do Android Devices Dream of Electric Sheep?

Brad Shimmin
Brad Shimmin

Summary Bullets:

  • We are already living in the midst of some very smart mobile devices which are capable of capturing the physical, situational, operational and even emotional facets of the human machine.
  • So, why not donate this ‘big data’ to better serve ourselves and the greater good?

Being a hopeful believer in synchronicity (or at least a believer in the potential of coincidence), my ears perked up late last week when the third vendor in as many weeks mentioned the coming ‘Internet of Things’ during three seemingly unrelated discussions around analytics, collaboration and business apps.  Obviously, the idea of smart, interconnected devices has reached some sort of significant meme threshold for major firms IBM, SAP and VMware, helped no doubt by some excellent marketing from Cisco. Continue reading “The New Analytics: Do Android Devices Dream of Electric Sheep?”

Big Data, Big Risk

Michal Halama
Michal Halama

Summary Bullets:

  • Big data solutions are improving management information
  • Big data market growth means buyers need help to recognize where management information is decisive

Organizations in all industries have come to use measurement of more and more aspects of markets and the workplace to provide management with data to make informed decisions. In many cases, the more information, the less room for error, and management decisions improve. For instance, trivial matters or decisions that need to be made innumerable times so that only machines can make them efficiently. Continue reading “Big Data, Big Risk”

Harnessing Big Data in the Contact Center: A Slow but Worthwhile Struggle

K. Landoline
K. Landoline

Summary Bullets:

  • Contact centers have been dealing with the ‘big data’ issue for years as they strive to develop the elusive 360-degree view of the customer across an assortment of structured and unstructured data collected from billions of customer interactions each year.
  • Despite a long history of dealing with big data, there has been little progress in utilizing the information to optimize operations in most contact centers, and thanks to a lack of centralized management capabilities, data silos continue to be a major hindrance to the development of the ‘intelligent enterprise.’

Managers dealing with contact centers on a daily basis are perplexed with the industry’s sudden fixation with big data since they have been obsessed with the issue for more than thirty years in their voice call centers.  Now, the shift to the multichannel contact center, through which recorded phone calls, e-mails, faxes, Web chats, social media, and survey feedback data flow, makes the challenge even more complex and unwieldy.  How to deal with the volume, velocity, and variety of data moving through the multichannel contact center today and use the information to improve enterprise operations is a discussion worth having if we ever hope to reach the dream of the much-discussed ‘intelligent enterprise.’ Continue reading “Harnessing Big Data in the Contact Center: A Slow but Worthwhile Struggle”

Analytics in the Cloud: Making the Right Connections

A. DeCarlo
A. DeCarlo

Summary Bullets:

  • ‘Big data’ analytics could have major implications on the ability of providers to move workloads seamlessly between and among clouds based on processing needs and other requirements.
  • Vendors and providers alike are gearing up for future needs by investing now in the technology to provide the underlying analytics to automate decision support and drive higher-level computing.

‘Big data’ is one of those great nebulous terms that gains traction in part because it is vague enough to be all-inclusive.  In spirit, big data resembles the amorphous nature of the cloud by offering such an undefined scope that its potential seems nearly endless.  Massive volumes of mobile and other data can provide organizations with deep insights into complex pattern phenomena such as consumer behavior, which can be potentially priceless to a company trying to grow market share.  However, without a way to process all this data, the information is practically unintelligible. Continue reading “Analytics in the Cloud: Making the Right Connections”

Stop GIGO Data with Better Information Management

B. Ostergaard
B. Ostergaard

Summary Bullets:

  • The looming GIGO data storm
  • Information management capabilities are more important than cheap storage capacity

Ease of storage expansion as well as lower storage costs per TB, combined with the drive to be more security ‘compliant’, threaten to combine to create a perfect data storm. Present conditions seem to encourage regulators and government agencies to insist that public sector institutions as well as corporations collect and retain even more data that is not required for operational purposes, but might be needed in future, or might be needed for public safety, or might aid future issue handling. Corporate governance, risk, compliance (GRC) policies are going in the same direction. The bottom line is: added operational costs.  Privacy issues aside, from a cost-benefit perspective two facts spring out: first, some 98% of what is stored is never viewed again, and second information management is way behind the curve. To put it bluntly: garbage in, garbage out (GIGO) is a growing problem because duplication, inconsistencies, randomness as well as systemic errors, lead to massive waste. Policy decisions based on such data risk being flawed and misleading, rather than those based on well-informed analysis of timely and reliable data. Clearly, it’s easier to just add more data to storage than to actually create an information management policy and capability that gives some assurance that data used for decision-making is valid to some defined degree. Continue reading “Stop GIGO Data with Better Information Management”

Big Blue and Big Security

A. Braunberg
A. Braunberg

Summary Bullets

  • A “Smarter Planet” is not necessarily a safer planet
  • Analytics will become an increasingly important component of security solutions

I spent an interesting couple of days this week at IBM Software’s Connect event. The yearly analyst event brings together IBM’s software brands to talk strategy and trends. This was the first year that IBM invited security analysts to the event: a nod to the formation of a standalone Security Systems division within IBM Software. IBM has had a checkered past in the security markets (most notably with the poorly-executed ISS acquisition), but I came away from the event with the feeling that the company has a strategy in place that realistically addresses IBM’s strengths and weaknesses. Continue reading “Big Blue and Big Security”