by Fronetics | Jul 6, 2015 | Big Data, Blog, Data/Analytics, Internet of Things, Logistics, Marketing, Strategy, Supply Chain
The battle for competitiveness in the cloud.
In this age of radical transformation for supply chains, top companies are tying together prevailing concepts, like big data and the Internet of Things (IoT), with cloud-based computing. Supply chains are being reimagined as digital networks that track not only physical goods, but also people, data, and money. As such, global technology giants continue to invest heavily in cloud computing.
The Chinese e-commerce company Alibaba Group Holding, Ltd. recently announced that it is launching its first international venture – a cloud computing hub in Silicon Valley, proving the fierce competition for market share is stiffening both globally and on the home turf. Notably, all five top-ranked companies listed by the 2013 Strategy & Global Information, Communications and Technology 50 study bet their future on cloud technologies. The companies (IBM, Microsoft, SAP, Oracle, and Cisco Systems) could not, until recently, be perceived as direct competitors, and now they all wield cloud-based portfolios as their competitive weapon of choice.
Analysts note:
The industry leaders are seeking dominant positions, wanting to become the kings of the cloud. As a group, they are putting distance between themselves and the second tier of followers.
For further proof data analytics is driving competition for cloud customers, we can look to a new report by Market & Research that shows data analytics and cloud computing are expected to record a combined growth of 26% annually over the next five years. The implication is that demand is hardly going to lessen as an increasing number of organizations need cloud solutions to manage and store the huge amounts of data that they use to transform manufacturing processes, fine-tune supply chains, forecast customer behavior, and optimize inventories – to name a fraction of potential applications.
And cloud-based computing is even moving the needle of interest in industries that seem inherently averse to making data and information more easily accessible. For example, concerns about data encryption, auditing controls, and transparency have stymied the adoption of cloud-based computing in the financial industry. According to a report by the Cloud Security Alliance, only 28% of American financial institutions have a cloud-based strategy in place, but as a sponsor of the report, Dr. Chenxi Wang, vice president of Cloud Security and Strategy at CipherCloud points out:
Cloud has made solid inroads in this industry with many firms looking to harnessing the power of cloud. There’s plenty of room for growth, particularly for providers who can fill the void for the auditing and data protection controls that are at the top of respondents’ cloud wish list.
Meanwhile, the public cloud services market alone could grow into a $100 billion industry by 2017, according to researcher IDC. Is your business prepared to leverage cloud computing for its supply chain activities? It’s coming, ready or not.
Fronetics Strategic Advisors is a leading management consulting firm. Our firm works with companies to identify and execute strategies for growth and value creation.
Whether it is a wholesale food distributor seeking guidance on how to define and execute corporate strategy; a telematics firm needing high quality content on a consistent basis; a real estate firm looking for a marketing partner; or a supply chain firm in need of interim management, our clients rely on Fronetics to help them navigate through critical junctures, meet their toughest challenges, and take advantage of opportunities. We deliver high-impact results.
We advise and work with companies on their most critical issues and opportunities: strategy, marketing, organization, talent acquisition, performance management, and M&A support.
We have deep expertise and a proven track record in a broad range of industries including: supply chain, real estate, software, and logistics.
by Fronetics | Jun 25, 2015 | Big Data, Blog, Data/Analytics, Internet of Things, Marketing
From coffee makers to urban design, the Internet of Things (IoT) is affecting change in virtually all aspects of daily life. And even though the IoT is still at the early-adopter stage, in just five years 50 billion devices are projected to be connected to the Internet, generating an estimated $2 trillion to $14 trillion in value. Expectations are running high, so high, in fact, that Gartner ranked IoT at the top of its 2014 Hype Cycle for Emerging Technologies.
Companies are naturally eager to get a piece of the action with as many as three out of four exploring how IoT technologies could fit into their business operations. Too many times, however, companies end up with mountains of data and no actionable information. Entry into the IoT should come with warnings: analyze the data or prepare to be disappointed. Or, data without analytics is nothing but noise.
But unless businesses turn the focus away from pure data collection to data analysis, investments in IoT technologies are doomed to produce disappointing results. To be truly useful, the data should do more than “look pretty on a dashboard,” as Steven Sarracino, founder of Activant Capital Group, LLC, in Greenwich, Conn., pointed out.
Vendors of sensor technologies would simultaneously be wise to take their services beyond singing the virtues of amassing data to showing their clients how to make sensor-driven decisions. In fact, the need for more guidance was underscored in a fleet report by Tracking Automotive Technology: TU Automotive (previously Telematics Update). Vendors should, according to the report, present the data in a digestible format to assist overwhelmed end-users. While purely monitoring the performance of a forklift, for example, provides value, it is not until the data is analyzed and acted upon that maximum ROI is achieved. In the case of a forklift fleet, it might entail optimizing routes in the warehouse or performing preventive maintenance. As another example, the retail sector can apply analytics to data collected by security cameras and Wi-Fi beacons to help retailers understand what types of displays catch customers’ attention.
The adoption of IoT technologies will likely come easier to industries such as manufacturing and supply chain which already connect machinery and fleets with Internet-enabled sensors or devices. Smart grid technologies also hold a lot of promise for public utilities based on current industry trends, connecting countless data points for continuous monitoring and proactive management of the power supply. However, until companies are able to adequately apply analytics to squeeze value out of their investments, it may be a while before IoT technologies reach critical mass.
by Fronetics | Jun 15, 2015 | Blog, Content Marketing, Data/Analytics, Marketing
It’s impossible not to recognize that the business world is changing. Whether it’s the fall of the travel agent as people migrate towards online booking, or the irrelevance of the compact disc as Spotify and iTunes changed the music industry, or how advertising is done. Outbound marketing, such as print ads, TV ads, banner ads, trade shows, telemarketing, and direct mail are no longer what consumers are requiring. According to Forbes, many brands are moving their advertising budgets from television to online videos. The Content Marketing Institute reports that 8 out of 10 people identify themselves as blog readers, and 23% of all time spent online is spent on social media sites. With the rise of the blog, companies have gotten smart about how to reach their current and potential consumers.
In the B2B world things are changing, too, with many executives wanting to gain information through other mediums. The Content Marketing Institute also reports that a majority (80%) of business decision-makers prefer to get information from articles rather than through advertisements.
We know that inbound marketing is effective in garnering consumers’ attention. It’s aligned with a generation of people who want to be educated about the products they’re buying and who are willing to search for those products online. Even with all of this known, it’s important to ask: what is the ROI when it comes to content marketing?
According to Search Engine Journal, inbound leads cost 60% less than outbound leads. In the Harvard Business Review article, How to Profit from “Lean Advertising”, the shoe company DC Shoes is profiled as a model for inbound marketing. In an industry where star athletes are profiled in big-production advertisements via TV commercials, billboards, and magazine ads, the skateboard shoe company decided to take a different route. According to the 2013 HBS article, “Over the past four years they have gotten more than 180 million views—and in 2011 alone, sales jumped 15%. One was YouTube’s most-shared video of 2011; another garnered a million views in its first 24 hours. Paying online media for this type of exposure would cost upward of $5 million.”
Like any new tack in business decisions, relying on case studies from other businesses is helpful, but cost needs to be considered. In order to calculate ROI the cost of content marketing needs to be assessed:
- salaries (if going in-house)
- marketing agency or contractor services
- additional overhead
- distribution costs
- design and publication software
After those costs have been calculated, the next step is to subtract that number from the revenue generated. The Guardian has put forth its simple content marketing ROI calculator:
(Revenue Generated – Cost of Content Marketing) / Cost of Content Marketing = ROI
According to the newspaper, “A simple calculation could say that you drove 1000 visits through a piece of content, and Google Ads would have cost £1 per click, e.g. £1000 to equal the same. If the content only cost £500, you have a saving!”
But with most seemingly simple things, there’s complexity underneath. Dig deeper and ask more questions. Is the money you’re spending on inbound marketing deterring other, less obvious, costs? Would it have cost you more through outbound marketing methods to achieve that same level of visibility than through inbound marketing solutions? Is inbound marketing bringing in customers or closing a deal more quickly than alternative methods (time is money, after all)? Is inbound marketing cutting down the need for staffing in other areas, such as support staff to manage inquiries or support calls?
Some incalculable values from inbound marketing, like consumer preferences, content intelligence, customer relationship strategies, and branding can be hard to tie to a number, but over time you will see that your ROI will become more clear to you as you generate leads, turn leads into customers, and see the result in the form of money gained (American dollars or British pounds!).
by Jennifer Hart Yim | May 12, 2015 | Big Data, Blog, Data/Analytics, Strategy
This article is part of a series of articles written by MBA students and graduates from the University of New Hampshire Peter T. Paul College of Business and Economics.
Josh Hutchins received his B.S. in Business Administration from the University of New Hampshire in 2005. He is currently pursuing his MBA at the Peter T. Paul School at the University of New Hampshire; on course to graduate in May 2016.
“Working with data is something I do every day as a financial analyst. I enjoy crunching the data and experimenting with various data analytic techniques. I’ve found that my love for playing with the data and thinking in unconventional ways has led me to be efficient and successful at work. The way data is being used is revolutionizing the way we do business. I’m glad I can be part of this wave of the future.”
The responsibility of big data
Data is coming into businesses at incredible speeds, in large quantities, and in all types of different formats. In a world full of big data it’s not just having the data – it’s what you do with it that matters. Big data analysis is becoming a very powerful tool used by companies of all sizes. Companies are analyzing and using the data in order to create sustainable business models and gain a competitive advantage over their competition. However, as one company would come to learn – with big data comes big responsibility.
Solid Gold Bomb T-Shirt Company
In 2011, Solid Gold Bomb, an Amazon Marketplace merchant based out of Australia, thought of an ingenious way to create fresh slogans for t-shirts. The main concept behind Solid Gold Bomb’s operation was that by utilizing a computer programming script, they could create clever t-shirt slogans that no one had thought of previously. The company created various t-shirt slogans that played off of the popular British WWII era phrase Keep Calm and Carry On. Under the systematic script method, Solid Gold Bombs was able to create literally millions of t-shirt offerings without the need to have them on hand in inventory. With the substantial increase in product offerings, the chances of customers stumbling upon a Solid Gold Bomb shirt increased dramatically. By utilizing this new on-demand approach to t-shirt printing, Solid Gold Bomb was able to reduce expenses, while simultaneously increasing potential revenue by offering exponential products at little additional marginal cost.
Use of ‘Big Data’
The computer script relied on the following to operate: a large pool of words, associated rule learning, and an algorithm.
Large Pool of Words – Solid Gold Bomb gathered a list of approximately 202,000 words that could be found in the dictionary. Of these words, they whittled it down to approximately 1,100 of the most popular words. Some of the words were too long to be included on a t-shirt, so the list was further culled. They settled on 700 different verbs and corresponding pronouns. These words would be used by the computer script to generate t-shirt slogans.
Associated rule learning – Associated rule learning is the degree to which two variables in a given list relate to each other. The first step in associated rule learning is to identify and isolate the most frequent variables. The second step is forming rules based on different constraints on the variables – assigning an “interestingness” factor. In the case of Solid Gold Bomb, the associated rule learning assigned an interestingness factor to verb-pronoun combinations.
Algorithm – The algorithm designed by Solid Gold Bomb was very simple. Each shirt would begin with “Keep calm and”. The algorithm script would then search through the word pool and pull back the most highly associated verb and pronoun combinations. The words would then be put into the typical format of the Keep Calm and Carry On. An image of each individual combination would be mocked up and posted to their Amazon merchant account. The process would continuously loop, creating millions of combinations.
The Big Data Blues
With one innocent mistake, Solid Gold Bomb fell apart in the blink of an eye. Amazon started getting complaints about offensive slogans on Solid Gold Bomb’s t-shirts. Images of t-shirts with phrases such as “Keep Calm and Rape Her” and “Keep Calm and Hit Her” were being sold on their Amazon merchant account. Their typical weekend orders for around 800 shirts were reduced to just 3 – few enough to count on one hand. Amazon ended up pulling their entire line of clothing, essentially putting Solid Gold Bomb out of business.
What went wrong?
While Solid Gold Bomb had a good handle on how to use data that they had gathered and how to use it to their advantage, they neglected to consider the potential hidden consequences of unintentional misuse of the data. When culling 202,000 words down to 700 useable words, words such as “rape” should have been eliminated from the useable word pool. From a high level perspective, the human mind is incapable of naming all the potential combinations of the 700 useable words without the assistance of a computer program. However, the end user needs to be aware that the computer program logic will create every potential combination based on the word pool.
Moral of the Story
Big data by itself is not beneficial to a company. The real value is in the analytics that are applied to the data. The results of the analytics can be utilized in numerous ways – to make more informed decisions, create new revenue streams, and create competitive advantages, to name a few. When a company makes the decision to utilize big data analytics, each process needs to be mapped out to have an intimate understanding of how the data will be used. In the case of Solid Gold Bomb, they failed to have this intimate understanding of how the data would be used throughout the process. As a result, they paid the ultimate price; they were not able to sustain themselves through this debacle. The morale of the story: With big data comes big responsibility. Know your data and know the potential uses of the data better. Don’t be afraid to think outside the box, but know the potential consequences.
For information about another big data faux pas, learn how Target predicted that a 16 year old girl was pregnant before her father knew.
by Jennifer Hart Yim | May 12, 2015 | Big Data, Blog, Data/Analytics, Strategy
This article is part of a series of articles written by MBA students and graduates from the University of New Hampshire Peter T. Paul College of Business and Economics.
Josh Hutchins received his B.S. in Business Administration from the University of New Hampshire in 2005. He is currently pursuing his MBA at the Peter T. Paul School at the University of New Hampshire; on course to graduate in May 2016.
“Working with data is something I do every day as a financial analyst. I enjoy crunching the data and experimenting with various data analytic techniques. I’ve found that my love for playing with the data and thinking in unconventional ways has led me to be efficient and successful at work. The way data is being used is revolutionizing the way we do business. I’m glad I can be part of this wave of the future.”
The responsibility of big data
Data is coming into businesses at incredible speeds, in large quantities, and in all types of different formats. In a world full of big data it’s not just having the data – it’s what you do with it that matters. Big data analysis is becoming a very powerful tool used by companies of all sizes. Companies are analyzing and using the data in order to create sustainable business models and gain a competitive advantage over their competition. However, as one company would come to learn – with big data comes big responsibility.
Solid Gold Bomb T-Shirt Company
In 2011, Solid Gold Bomb, an Amazon Marketplace merchant based out of Australia, thought of an ingenious way to create fresh slogans for t-shirts. The main concept behind Solid Gold Bomb’s operation was that by utilizing a computer programming script, they could create clever t-shirt slogans that no one had thought of previously. The company created various t-shirt slogans that played off of the popular British WWII era phrase Keep Calm and Carry On. Under the systematic script method, Solid Gold Bombs was able to create literally millions of t-shirt offerings without the need to have them on hand in inventory. With the substantial increase in product offerings, the chances of customers stumbling upon a Solid Gold Bomb shirt increased dramatically. By utilizing this new on-demand approach to t-shirt printing, Solid Gold Bomb was able to reduce expenses, while simultaneously increasing potential revenue by offering exponential products at little additional marginal cost.
Use of ‘Big Data’
The computer script relied on the following to operate: a large pool of words, associated rule learning, and an algorithm.
Large Pool of Words – Solid Gold Bomb gathered a list of approximately 202,000 words that could be found in the dictionary. Of these words, they whittled it down to approximately 1,100 of the most popular words. Some of the words were too long to be included on a t-shirt, so the list was further culled. They settled on 700 different verbs and corresponding pronouns. These words would be used by the computer script to generate t-shirt slogans.
Associated rule learning – Associated rule learning is the degree to which two variables in a given list relate to each other. The first step in associated rule learning is to identify and isolate the most frequent variables. The second step is forming rules based on different constraints on the variables – assigning an “interestingness” factor. In the case of Solid Gold Bomb, the associated rule learning assigned an interestingness factor to verb-pronoun combinations.
Algorithm – The algorithm designed by Solid Gold Bomb was very simple. Each shirt would begin with “Keep calm and”. The algorithm script would then search through the word pool and pull back the most highly associated verb and pronoun combinations. The words would then be put into the typical format of the Keep Calm and Carry On. An image of each individual combination would be mocked up and posted to their Amazon merchant account. The process would continuously loop, creating millions of combinations.
The Big Data Blues
With one innocent mistake, Solid Gold Bomb fell apart in the blink of an eye. Amazon started getting complaints about offensive slogans on Solid Gold Bomb’s t-shirts. Images of t-shirts with phrases such as “Keep Calm and Rape Her” and “Keep Calm and Hit Her” were being sold on their Amazon merchant account. Their typical weekend orders for around 800 shirts were reduced to just 3 – few enough to count on one hand. Amazon ended up pulling their entire line of clothing, essentially putting Solid Gold Bomb out of business.
What went wrong?
While Solid Gold Bomb had a good handle on how to use data that they had gathered and how to use it to their advantage, they neglected to consider the potential hidden consequences of unintentional misuse of the data. When culling 202,000 words down to 700 useable words, words such as “rape” should have been eliminated from the useable word pool. From a high level perspective, the human mind is incapable of naming all the potential combinations of the 700 useable words without the assistance of a computer program. However, the end user needs to be aware that the computer program logic will create every potential combination based on the word pool.
Moral of the Story
Big data by itself is not beneficial to a company. The real value is in the analytics that are applied to the data. The results of the analytics can be utilized in numerous ways – to make more informed decisions, create new revenue streams, and create competitive advantages, to name a few. When a company makes the decision to utilize big data analytics, each process needs to be mapped out to have an intimate understanding of how the data will be used. In the case of Solid Gold Bomb, they failed to have this intimate understanding of how the data would be used throughout the process. As a result, they paid the ultimate price; they were not able to sustain themselves through this debacle. The morale of the story: With big data comes big responsibility. Know your data and know the potential uses of the data better. Don’t be afraid to think outside the box, but know the potential consequences.
For information about another big data faux pas, learn how Target predicted that a 16 year old girl was pregnant before her father knew.
by Elizabeth Hines | Apr 6, 2015 | Big Data, Blog, Data/Analytics
Concurrent with the extraordinary rise of the Internet of Things (IoT), predictive analytics are gaining in popularity. With an increasing number of companies learning to master the precursors to developing predictive models — namely, connecting, monitoring, and analyzing — we can safely assume the art of gleaning business intelligence from foresight will continue to grow rapidly.
Indeed, Gartner analysts put forward that “few technology areas will have greater potential to improve the financial performance and position of a commercial global enterprise than predictive analytics.” And executives seem eager to jump on the bandwagon; in a survey of executives conducted by Accenture a full 88% indicated big data analytics is a top priority for their company. Amid the promises of predictive analytics, however, we also find a number of pitfalls. Some experts caution there are situations when predictive analytics techniques can prove inadequate, if not useless.
Here, we dissect three problems most commonly encountered by companies employing predictive analytics.
Past as a poor predictor of the future
The concept of predictive analytics is predicated on the assumption that future behavior can be more or less determined by examining past behaviors; that is, predictive analytics works well in a stable environment in which the future of the business is likely to resemble its past and present. But Harvard Business School professor Clayton Christensen points out that in the event of a major disruption, the past will do a poor job of foreshadowing future events. As an example, he cites the advent of PCs and commodity servers, arguing computer vendors who specialized in minicomputers in the 1980s couldn’t possibly have predicted their sales impact, since they were innovations and there was no data to analyze.
Interpreting bias
Consider also the bias in favor of a positive result when interpreting data for predictive purposes; it is one of the most common errors in predictive analytics projects. Speaking at the 2014 Predictive Analytics World conference in Boston, John Elder, president of consulting firm Elder Research, Inc., made a good point when he noted that people “‘often look for data to justify our decisions,’ when it should be the other way around.”
Collecting and analyzing unhelpful or superfluous data
Failure to tie data efforts to operational processes can lead to an unnecessary drain of staff resources. Mining big data will do little good if the insights are not directly tied to functional procedures. More companies than we probably realize are wasting precious time and manpower on big data projects that are not adequately understood, producing trivia rather than actionable business intelligence.
To overcome these common pitfalls of predictive analytics, spend some time reviewing the sources of your data and the basic assumptions on which your predictive analytics projects are based. Because the major principle of predictive analytics is that the past behavior can forecast future behavior, keep your ear to the ground for growing industry trends or any other factors that might influence consumer behavior. Plan to revisit the source of your data frequently to determine if the sample set is representative of your future set and should continue to be used. Most importantly, regularly evaluate how your predictive analysis relates and contributes to your company’s overall goals and objectives.
What else do you think organizations can do to ward off the snags of predictive data analysis and use foresight more effectively?
