With the huge popularity that the data science has achieved in the recent past, it’s high time to take a step back and understand how different organizations fared when they employed ML or Advanced techniques to improve their business operations. Towards the later half of 2015, there was an article published by Gartner where they showed the hype cycle of the emerging technologies.

http://www.gartner.com/newsroom/id/3114217

What is interesting over here is to find that “Machine Learning” falls in the “Peak of Inflated Expectations” in the Hype Cycle which shows that there have been successes (often owing to the initial publicity) accompanied by scores of failures. This may look scary at first since Big Data and Advanced Analytics are supposed to lift up the business. We definitely wanted this to be in the “Plateau of Productivity” on the Hype Cycle. Technologies falling in this plateau region are the ones that are adopted by all the mainstream businesses and have positive ROI. It’s promising to see that both “Machine Learning” and “Advanced Analytics” are expected to reach the plateau in 2-5 years’ time.

Gartner’s Hype Cycle shows that benefits of Machine Learning and Advanced Analytics have not yet reached its true potential. This also shows that it is the best time to be in the space. As shown through the article, the companies who can adopt these technologies in this time and space will get the benefits of it within the next five years.

Neverware’s CloudReady will help you repurpose that laptop into a Chromebook that your young child can use. They have a list of models below that are certified to run CloudReady without any issues here: http://go.neverware.com/certifiedmodels. 

The Verge has a great article below on how Neverware is helping resuscitate these old PCs for schools and other educational institutions.
http://www.theverge.com/2016/2/17/11030406/neverware-google-chromebook-chromium-os-education-microsoft

Have you tried CloudReady already? If so, chime in with your comments and experience. I am planning on trying it out soon myself!

When it comes to making actionable use of data, there is no single playbook or set of common practices that apply universally to all businesses, CIO Journal Columnist Randy Bean says. “Organizations would be well served to break from accepted dogma and apply fresh thinking as they consider how best to align their resources, capabilities, and people to make wise use of their data,” he writes.

Key points in the article:
- Your data combined with those of thousands of other people can tackle bigger problems such as cutting your company’s health care budget or sparing the nearby utility from building another power plant.
- Smart-thermostat maker Nest Labs has quietly built a side business managing the energy consumption of a slice of its customers on behalf of electric companies.
- In wearables, health tracker Fitbit is selling companies the tracking bracelets and analytics services to better manage their health care budgets, and its rival Jawbone may be preparing to do the same.

- These companies are capitalizing on the terabytes of data they collect from consumers and, to an extent, on the largesse of taxpayers. State governments have increased the money–from $1.3 billion in 2003 to $6 billion in 2012–allocated to helping utilities manage energy demand, according to the U.S. Energy Information Administration.

See full article - http://www.forbes.com/sites/parmyolson/2014/04/17/the-quantified-other-nest-and-fitbit-chase-a-lucrative-side-business/

How can we leverage our data either directly or with business partners/customers to generate revenue and solve big problems?

http://www.nytimes.com/2014/07/10/business/smallbusiness/finding-affordable-ways-to-use-big-data.html?_r=1

Gainers: Tableau, Qlik and Spotfire. Losers: Microsoft, MicroStrategy, SAP and Oracle.

We don’t need more tools right now but we do need to keep an eye on the future for when we might want to retire a tool or move more solutions to a tool we already have.

Hope you find the analysis informative

It’s a cliché to say that the world is more uncertain than ever before, but few realize just how much uncertainty has increased over the past 50 years. To illustrate this, consider that patent applications in the U.S. have increased by 6x (from 100k to 600k annually) and, worldwide, start-ups have increased from 10 million to almost 100 million per year.  That means new technologies and new competitors are hitting the market at an unprecedented rate.  Although uncertainty is accelerating, it isn’t affecting all industries the same way. That’s because there are two primary types of uncertainty — demand uncertainty (will customers buy your product?) and technological uncertainty (can we make a desirable solution?) — and how much uncertainty your industry faces depends on the interaction of the two.

Demand uncertainty arises from the unknowns associated with solving any problem, such as hidden customer preferences. The more unknowns there are about customer preferences, the greater the demand uncertainty. For example, when Rent the Runway founder Jenn Hyman came up with the idea to rent designer dresses over the internet, demand uncertainty was high because no one else was offering this type of service.  In contrast, when Samsung and Sony were deciding whether to launch LED TVs, which offered better picture quality than plasma TVs at a slightly higher price, there was lower uncertainty about demand because customers were already buying TVs.

Technological uncertainty results from unknowns regarding the technologies that might emerge or be combined to create a new solution. For example, a wide variety of clean technologies (including wind, solar, and hydrogen) are vying to power vehicles and cities at the same time that a wide variety of medical technologies (chemical, biotechnological, genomic, and robotic) are being developed to treat diseases. As the overall rate of invention across industries increases, so does technological uncertainty.

Consider the 2×2 matrix below. The horizontal axis plots each industry based on technological uncertainty, measured as the average R&D expenditures as a percentage of sales in the industry over the past ten years. The vertical axis plots each industry’s demand uncertainty, measured as an equal weighting of industry revenue volatility, or change, over the past 10 years and percentage of firms in the industry that entered or exited during that same time period. Although these are imperfect measures, they identify the industries facing the highest, and lowest baseline levels of uncertainty.

In today’s big data world, every big business needs a software or service designed to retrieve, analyze, and report data to provide insight into business operations and customer satisfaction. Anything worth collecting is worthy of a report, especially if that report can help improve your business and increase revenues, decrease costs, or both. That report can be a dashboard that shows a 30,000-foot view and lets you drill down to determine the significance of individual factors. And the software and services that provide those kinds of reports are called Business Intelligence (BI) tools.

Naturally, that’s the topic for this edition of the PCMag Business Choice Awards. For more than 25 years, we have been augmenting our hands-on, labs-based product reviews (which receive our Editors’ Choice Award when worthy) with our Readers’ Choice Awards, in which PCMag readers rate the products and services they use the most. The Business Choice Awards extend the Readers’ Choice Awards by garnering feedback about the hardware, software, and services our readers deploy, administer, maintain, and use in a work environment.

Want to participate in future surveys with other PCMag readers? Click here and sign up for the Readers’ Choice survey email list to receive invitations.

Our survey asked respondents to rate their overall satisfaction with the BI products they use or manage and the likelihood they would recommend them to others. In addition, we inquired about their satisfaction with technical support, and the overall reliability of the solution.

If you select, deploy, or administer the products in our Business Choice Awards, or if you advise or manage people in these roles, then you know how critical it is to choose the right products. The results of the PCMag Business Choice Awards survey are invaluable when doing so. And on the next page we’ll reveal just who to turn to when you’re thinking your company needs some serious business

Business Intelligence Tools

Business Intelligence tools are typically developed and sold by big name players in database and business software. This is because it isn’t just about the reporting, it’s also about the ability to find and consume data, most of which is housed in databases built by behemoths.

In this year’s survey, four companies received enough responses to be included. Looking at overall satisfaction, IBM Cognos takes the lead followed by Microsoft BI at 6.7, Oracle at 6.6, and SAP BusinessObjects at 6.4.

Reliability, in this case the ability of the software or service to run every day and provide consistent results (click for the full table of scores below), follows the trend of overall satisfaction. IBM Cognos leads the way with a score of 7.3 followed closely by Oracle at 7.2 and Microsoft BI at 7.1. Trailing well behind is SAP BusinessObjects at 6.8.

Looking at tech support, only Microsoft BI had a reasonable number of customers requiring tech support with 15 percent. IBM Cognos and Oracle trail behind at 21 and 24 percent, respectively. SAP BusinessObjects brings up the rear with a full 30 percent of survey respondents requiring tech support. Tech support ratings are also relatively low, with Microsoft receiving a high of 7.6 out of 10.

Speaking of abysmal, SAP BusinessObjects had the highest tech support needed, the lowest tech support rating, the lowest reliability, and the lowest overall satisfaction.

We asked respondents to answer this critical question: “How likely are you to recommend your business tools provider to a colleague?” Here, we again see that no one is very likely to recommend their business tools provider to a colleague. IBM Cognos again leads with a 6.9 followed by Oracle and Microsoft at 6.7, and SAP BusinessObjects with a 6.4.

That same question is used to calculate the Net Promoter Score (NPS), which is certainly the most fascinating statistic here. The negative numbers tell it all: No one likes their BI tool. Even our winner, IBM Cognos, has a strongly negative NPS at -14, meaning that customers are more likely to steer colleagues away from IBM Cognos than recommend it. It’s unclear from our survey why this is. My 25+ years as an industry analyst tells me it is because no matter how you slice it, business intelligence is tough stuff. There’s a lot of back-end integration required, followed by a lot of report customization. None of these solutions are as turnkey as their marketing materials would lead you to believe and that makes for some pretty unhappy customers.

From these results, we could arguably say that businesses might not need BI tools, but if your office does feel the need, we can say that IBM Cognos is the service to try.

See all survey results for Business Choice 2014: Routers.

Business Choice Winners: BUSINESS INTELLIGENCE

IBM Cognos
IBM says that the Cognos software (which it acquired in 2008) will make any operation “top-performing and data driven” and our readers place IBM Cognos, in all its iterations from Express to Enterprise, at the top of the heap of available BI solutions.

Methodology

For the 2014 Business Choice series, we emailed survey invitations to PCMag.com community members, specifically subscribers to our Readers’ Choice Survey mailing list. The surveys are hosted by SurveyMonkey, which also performs our data collection. This survey was in the field from June 16, 2014 to July 14, 2014.

Respondents are asked to rate their business intelligence tools provider. They are asked multiple questions about their overall satisfaction with the solution, as well as experiences with technical support within the past 12 months.

Because the goal of the survey is to understand how the BI tools compare to one another and not how one respondent’s experience compares to another’s, we use the average of the BI tools’ rating, not the average of every respondent’s rating. In all cases, the overall ratings are not based on averages of other scores in the table; they are based on answers to the question, “Overall, how satisfied are you with your business intelligence tools provider?”

Scores not represented as a percentage are on a scale of 0 to 10 where 10 is the best.

Net Promoter Scores are based on the concept introduced by Fred Reichheld in his 2006 best seller, The Ultimate Question, that no other question can better define the loyalty of a company’s customers than “how likely is it that you would recommend this company to a friend or colleague?” This measure of brand loyalty is calculated by taking the percent of respondents who answered 9 or 10 (promoters) and subtracting the percent who answered 0 through 6 (detractors).

If you would like to participate in PCMag’s monthly Readers’ Choice surveys and to be eligible for our monthly sweepstakes promotion

 

Boston, MA — (SBWIRE) — 07/25/2014 — The term BI refers to applications and technologies that are used for gathering, providing access to, or analyzing information about a company’s operations. BI help organizations to acquire comprehensive knowledge of the factors affecting their business; for example, metrics on sales, production, and internal operations. Some of the BI tools and applications include query reporting, analysis tools, data mining tools, and data warehousing tools. SaaS-based BI refers to the deployment of BI applications as a web-based subscription service. SaaS-based BI is operated by a third-party organization as opposed to the installation and maintenance of an On-premise business system. In the pay-per-use model, BI is hosted by a third party from a remote location, and the outsourcing company pays according to the usage. Hence, enterprises employing SaaS-based BI can shift their focus from frequent software updates, maintenance, continual trouble-shooting, and increasing storage requirements, and concentrate on their core business activities.

Enterprise systems company SAP was demonstrating how an application called Match Insights could gather data before and during a match and use it to influence the coach’s tactical decisions while the game was on.

Most saw the demo as a marketing exercise. But when Germany won the World Cup, systematically outplaying opponents with superior tactics, the data game became very real.

According to SAP, the journey started last year when national team general manager Oliver Bierhoff found that players were happiest communicating with each other on digital platforms. He commissioned SAP to develop an application that could facilitate the exchange of information, including data about opponents. SAP Match Insights was then developed in collaboration with the German team.

“This data can be converted to simulations and graphs that can be viewed on a tablet or smartphone, enabling trainers, coaches and players to identify and assess key situations in a match,” said Manoj Bhoola, a director at SAP Africa.

“SAP Match Insights synchronised data from scouts with the video footage from the pitch to make it easy for coaches to identify key moments in the game.”

The impact on the outcome of the World Cup is not as easy to quantify, but it’s given “big data” its biggest showcase yet.

“Big data is an incredible resource for coaches and players to contextualise information and draw well-informed conclusions to optimise training and tactics,” said Simon Carpenter, chief customer officer at SAP Africa. “It’s high time to make this type of information accessible to sports journalism and the fans as well.”

German soccer may have discovered big data, but it’s a path well worn by large enterprises.

“We have been doing it all along,” said Desan Naidoo, managing director for Southern Africa of global analytics company SAS. “But some of the aspects have changed. If you look at the volume and variety of structured and unstructured data, ranging from social networks to text and video, that has definitely changed. Ninety percent of all data ever created have been created in the past two years.

“This is unbelievable in itself. But now the requirement from clients to have access to this data has moved from running data through models for 18 to 24 hours to wanting access in minutes or seconds.”

And it’s not enough merely to analyse the data that are formally collected in organisational systems. “We’ve had to tap into social media data. We’ve had to restructure the way we do analytics to cope with the volumes. We’ve had to look at hardware changes and infrastructure such as in-memory analysis.”

The latter refers to loading relevant data into live memory so that it can be processed on the fly, providing usable information in seconds. A typical example is a customer going to a bank for a home loan: it can now run a risk profile and give an answer while the individual is waiting.

“In the past, if you based that risk profile on all the data sources the bank has, it would have taken hours,” said Naidoo.

“Having access in-memory means you can click a button and run a risk profile accessing all that data instantaneously. On top of that, analytics today can predict how that customer will behave, rather than being merely reactive, as in the past. That’s what big data means today.”

• Goldstuck is founder of World Wide Worx and editor-in-chief of Gadget.co.za. Follow him on Twitter @art2gee

• This article was first published in Sunday Times: Business Times