Calling all CFOs: Why connecting with a toothbrush might just be worth it

Until recently, technology vendors and the companies that buy their wares have focused largely on what the technology does – what features it has, the capabilities it can offer. But it’s no longer about what technology can do, it’s about what it can enable, says Tony Kontzer, a business and technology writer.

The fact that a manufacturer can communicate with a toothbrush may represent technological wizardry. However, it’s the knowledge the company can collect from the resulting data stream — such as how many brushings a customer can get out of a single toothbrush — that brings real value.

Looking through the digital transformation lens

Too often, technology buyers have looked to impress with bells and whistles, when what’s really needed are positive financial outcomes. The act of undergoing digital transformation now takes on a completely different context.

We don’t transform so that we can add technological capabilities, we transform so we can become more agile and more efficient. We transform to create better customer experiences, which can lead to increased profitability. We transform to make our businesses stronger, not just to create a splashier IT narrative. All technology investments today should be viewed through this lens.

Enterprise technology vendors should not be in business simply to make great technology, but should help customers tap into the latest technologies to take their businesses to another level. They need to understand that no matter how cool the products and solutions are, if they’re not making an economic contribution that helps an organisation achieve a vision or meet its mission, then the company might as well carry on making cat food in the same way that it always did.

CFOs pivotal in making digital transformation transform the business

This is why ALE, a global communications technology vendor, has increased its effort to speak directly to CFOs. Often tasked with overseeing IT organisations to ensure that technology investments bring real bottom-line value, CFOs are unimpressed with the coolness factor. They are skeptics who look past the hype and go straight to the value a technology can deliver. No value, no purchase.

It is for this reason that CFOs have become integral to the process of digital transformation. It is all too easy for a company looking to inject digital capabilities into its operations to do so merely based on those capabilities themselves. Oooh, look, we can move our infrastructure assets to the cloud and cut our CapEx costs! We can extend our customer-facing processes to mobile platforms! We can invest in IoT sensors for all of our warehouses and keep a closer eye on our products!

Tony Kontzer

While all of these moves may make perfect economic sense, companies frequently jump into them before they’ve properly analysed the impact they will deliver. They fail to determine whether a particular technology, regardless how impressive it is, is the right match for their transformation effort.

And if customers make ill-advised investments in the technologies offered and discover too late that the payoff isn’t there, everyone loses. The customer spends money unnecessarily and ends up with […]

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Your Data Is Worth More Than You Think

Data has become a key input for driving growth, enabling businesses to differentiate themselves and maintain a competitive edge. Given the growing importance of data to companies, should managers measure its value? Is it even possible for a company to effectively measure the value of its data? An increasing number of institutions, academics, and business leaders have begun tackling these questions, leaving managers with many alternatives for assessing the value of data. None are yet generally accepted, nor completely satisfactory, but they can help organizations realize more value from their data.

Why Is Data Valuation Important?

There are three basic reasons organizations want a good way to understand the value of their data. A good sense of value can help guide good decisions around direct monetization, internal investments, and mergers and acquisitions.

Direct Data Monetization

Many organizations are keen to monetize data directly by selling it to third parties or marketing data products. Inability to understand data’s value can result in mispriced products. Understanding the impact of exposing data to third parties on the value of a company’s data for indirect monetization can help guide the decision on whether to pursue explicit monetization. Today, despite an increasing recognition of potential benefit, most organizations are very conservative about what data they expose outside the enterprise. Good valuation approaches could help leaders understand if selling their data would really affect their competitive position or ability to realize their own benefit from it.

Internal Investment

Understanding the value of both current and potential data can help prioritize and direct your investments in data and systems. In our experience, most organizations struggle to articulate the relationship between their IT investments and business value generally. For data systems, the problem is particularly acute. Surveys report that only about 30% to 50% of data warehousing projects are successful at delivering value. Understanding how data drives business value can help you understand where you should be minimizing costs, and where you should be investing to realize potential ROI.

An ability to articulate data’s contribution to an organization’s overall value can transform the relationship between technology and business management. Chief experience officers (CXOs) charged with managing data report that their ability to articulate business value from data investments with rigor supported by the CFO results in more resources available to drive more positive outcomes for their organizations.

Mergers & Acquisitions

Inaccurate valuing of data assets can be costly to shareholders during mergers and acquisitions (M&A). Steve Todd, an EMC fellow, argues that data valuations can be used both to negotiate better terms for initial public offerings, M&As, and bankruptcy, and to improve transparency and communication with shareholders. Did Microsoft Corp.’s purchase price of LinkedIn Corp. include the value of LinkedIn’s data about professionals and companies? Did they survey potential uses of data in the combined company? The assumption that data’s value is captured only by sales and revenue figures may understate the overall value of a transaction to the benefit of the buyer — and to the detriment of the seller.

Current generally accepted accounting practices (GAAP) do not permit data to be capitalized on the balance sheet. This leads to considerable disparity between book value and market value of these companies, and a possible mispricing of valuation premiums. While internationally agreed-upon standards may emerge in the next five years, the Association of Chartered Certified Accountants (ACCA), the global professional accounting organization, is encouraging accounting companies to come forward with approaches. Wilson and Stenson provide an excellent review of accounting approaches that recognize and value intangible assets in general, and information assets in particular.

Existing Approaches Are Useful, But Limited

Methods for valuing data are varied. Most descend from existing asset valuation or information theory. Some attempt to attribute the value of business outcomes directly to data-driven capabilities. Like statistical models, all have limitations, but some are useful.

Dell EMC Global Services Chief Technology Officer Schmarzo developed the so-called “prudent value” approach, which values data sets based on the extent to which they could be used to advance key business initiatives that support an organization’s overall business strategy. This approach has two main advantages:

  • It provides ballpark valuation (or a range of values) for the data set derived from the financial value of the business initiative.
  • More important, it frames the data valuation process around the business decisions that need to be made to drive the targeted business initiative. It quantifies the ways in which different data sets might be utilized and the impact this could have on the success of the targeted business initiative.

Mapping data to valuable outcomes can fulfill many purposes of data valuation. It supports rigorous ROI arguments based on concrete business outcomes for IT investment decisions. It can also guide pricing direct monetization efforts by relating the business value of the decisions third parties use with respect to data to guide the price they might pay for access.

Some of the most comprehensive work on the subject of data valuation comes from Gartner Inc.’s Douglas Laney. Laney, vice president and distinguished analyst, Chief Data Officer Research, proposes “infonomics” as an economic discipline, arguing that information should be treated as an actual corporate asset — measured, managed, and deployed as if it were a traditional asset. Laney describes six different information valuation methods, three foundational and three financial.

The foundational methods are primarily aimed at businesses that wish to prioritize or create an aggregate of data quality characteristics to get a sense of what its relative or intrinsic value is. These methods force businesses to take stock of their data, how they are leveraging it (or not!), and ultimately articulate its value and evaluate what is and isn’t useful. Laney’s financial measures draw on methods to value intangible assets.

The biggest limitation of Laney’s approach is that it does not tie the value of information to its role in supporting business decisions. His approach is more likely to be useful for valuing data in M&A transactions.

Where to Start

While there is still room for significant improvement in how to value data, current methods can still be useful to enterprises. Organizations should begin efforts to:

  • Create management consensus on how to build business cases for IT investments in data, infrastructure, and capabilities.
  • Use data valuation to prioritize data investments.
  • Begin cataloging and estimating value from existing and potential data-driven capabilities to inform valuation on the public markets or in M&A transactions.

Organizations that become more capable of getting value from data will certainly realize benefits and competitive advantage. Developing the ability to understand data’s value, and contribution to outcomes, is an important part of delivering that value.


MIT Sloan Management Review

Are New Advances in AI Worth the Hype?

Almost daily, we’re hit with another breathless news report of the (potential) glories of artificial intelligence in business. Rather than excitement, the fervor can instead kindle a Scrooge-like attitude, tempting executives to grumble “bah humbug” and move on to the next news item.

Why exactly is the “bah humbug” temptation so strong? Perhaps because…

  1. News reports naturally gravitate toward sensational examples of AI. We collectively seem to like it when science fiction becomes science fact. But it may not be clear how humanoids and self-driving cars are actually relevant to most businesses.
  2. Reports tilt toward stories of extreme cases of success. Those managers who have found some aspects of AI that are relevant to their business may be frustrated with the differences between their experiences and the (purported) experiences of others. They may feel that AI is immature and the equivalent of a young child, far from ready for the workplace.

As a result, managers may perceive AI as yet another in a long list of touted technologies that are more fervor than substance. Certainly, the information technology sector is far from immune to getting intoxicated with promising “new” technologies. Still under the influence of the intensity from prior technological shifts (digitization, analytics, big data, e-commerce, etc.), managers may struggle to determine what exactly is new about AI that may be relevant now. After all, AI has been around for decades and is not, actually, new.

Why the attention to AI now? Is there anything new in AI worthy of the hype? Is this vintage of AI just “old wine in new bottles”?

When the web began to garner interest, it was hard to argue that distributed computing was new. We started with centralized processing with mainframes and terminals rendering output and collecting input. Yes, the web promised prettier output than green characters on cathode ray screens, but did the newfangled web differ from prior distributed computing other than cosmetically? In retrospect, it seems silly to ask; it would be hard to argue that the internet didn’t fundamentally change business. Something was new about the web, even if it didn’t look different at first.

More recently, analytics has also seen its fair share of hype. However, statistical analysis, optimization, regression, machine learning, etc., all existed long before attention coalesced around the term “analytics.” Airlines in particular have long used data for revenue management. Yet something was also new about the potential for analytics, starting about a decade ago, that is now affecting businesses everywhere.

Underappreciating the differences between the old periods and new in each of these examples would have been a mistake. Managers who had unfavorable responses to either of these are probably no longer managers. What is different about AI now?

Unlike in earlier incarnations, we now have access to the processing power these AI developments require. What could once be done in theory can now be done in practice. Furthermore, the required processing power is affordable to most organizations. The leap from fervor to value to the business can happen — with investment, experimentation, and tolerance for failure.

Prior vintages of AI also emphasized rules, such as expert systems, formulated to automate reasoning. Now AI relies more often on data-based approaches rather than rule-based approaches. And the analytics boom has stocked the cellars full of the data that AI requires.

Additionally, business ecosystems are now more digital. Previously, an AI system would certainly have had to connect with humans, who would then connect with other humans in other organizations. But now with increasing digitization, links between and within organizations are increasingly digitally native, allowing AI systems to speak directly with digital systems. Network effects will help accelerate AI adoption as the increasing number of potential machine-to-machine interfaces offer opportunities for value creation (and destruction).

While there is AI hype, there is also substance behind recent interest in AI. Both can be true simultaneously. To navigate the drunken AI mob, managers need to embrace a bicameral mindset that allows them to listen to two voices simultaneously.

One voice speaks to the potential that AI now offers. By listening to this voice, managers can investigate promising technologies so that they and their organization do not miss out. They may pursue pilot projects to gain organizational experience, even if the results are not revolutionary. They can test AI approaches along with traditional ones to find their strengths and weaknesses for a particular organization.

The other voice says to question the promises that seem too good to be true, to taste first before swilling. In this way, managers can avoid regrets from overindulging in AI investments and bypass the repercussions from AI investments gone wrong. This voice cautions against binging on AI at the expense of other investments.

Is there anything new in AI worthy of the hype? To find out, managers will have to listen to both voices.


MIT Sloan Management Review

Which Rules Are Worth Breaking?

The 21st-century business world is being built on disruption in industry after industry. The old rules simply no longer apply to the industries being challenged: Thanks to Airbnb, for instance, hotels will never be the same; thanks to Amazon, retail will never be the same.

At one level, disruption is nothing new — simply a more modern way of rephrasing economist Joseph Schumpeter’s theory of “creative destruction.” But at another level, something else is happening. Many innovators are not just building a better mousetrap; they are also trying to articulate and consciously break the rules of the game, sometimes by figuring out ways to accomplish more with less, sometimes by finding ways around legal barriers, and sometimes by taking the low road. And there’s risk in defying too many rules.

Consider Uber. Only a few years ago, the San Francisco-based car service was the poster child for the new sharing economy. As Uber gained popularity, startups in other industries described themselves as the Uber of clothing, or food delivery, or travel reservations. Uber’s business model explored new territory, offering customers the convenience of on-demand ride hailing via a simple app on their mobile phones. Customers got more efficient rides that were digitally integrated into their daily routines, and many of the previous hassles of getting around — from calling car services to pulling out cash or credit cards for each taxi trip to having to make ride appointments ahead of time — became a thing of the past. Uber also provided drivers the benefits of flexible work and additional income.

Uber became extremely successful in a very short time. By 2014, it was providing 1 million rides a day. By 2016, despite increasing competition, it was providing about 5.5 million rides a day globally.

But more recently, Uber has become more of a poster child for bad behavior — to its employees, its users, its communities, and the ride-hailing industry in general — as a series of mistakes and controversies began to litter its path. Earlier this year, because of its surge pricing practice on the day of protests over the Trump administration’s first travel ban, more than 200,000 customers participated in a #DeleteUber campaign to stop using the app and the service. A former Uber engineer wrote a bombshell blog post a few weeks later alleging that the company repeatedly turned a blind eye to sexual harassment and had a culture of gender discrimination. Other sexual harassment issues quickly came to light: A senior vice president of engineering was forced to step down after allegations of harassment at his previous job emerged; a manager was fired for groping women at a company event; a management team in Seoul was reported to have visited escort karaoke bars.

Uber’s problems continued to ripple out. Google, an Uber investor, sued the company for stealing intellectual property from Waymo, Google’s autonomous car program. Uber blamed “human error” for one of its self-driving cars running a red light and then later acknowledged that the fault lay in the self-driving system, which did not recognize the traffic lights. And after a New York Times report called out Uber’s practice of “Greyballing” to deceive authorities in areas where the ride-hailing service was restricted, Uber brashly defended its tactics before later conceding that it would no longer use the tool.

This string of controversies led to a mass exodus of Uber senior executives, including the president, the vice president of products and growth, the head of Uber’s artificial intelligence labs, and CEO Travis Kalanick himself.

Creating and executing innovative products and services that disrupt the status quo require creativity, and creativity involves thinking differently about overcoming constraints. Laws and social norms are important checks on individual and corporate behavior. But there are forces that make it tempting to push aggressively on constraints. Psychology studies have shown a correlation, for instance, between unethical behavior and creativity: Research conducted by Francesca Gino and Scott Wiltermuth demonstrates that there’s a creative upside to cheating. People who cheated in one task were more likely to generate creative solutions in subsequent tasks. The researchers credited a heightened feeling of being unconstrained by rules for the uptick.

Disruptive companies like Uber need to move from a perspective that rules and norms don’t matter to a more specific understanding of which rules are worth breaking and why. When disrupting the status quo, smart startups would do better using a scalpel rather than a hatchet to avoid cutting off vital relationships and essential resources.

A general attitude that “the rules don’t apply to us” paired with a narrow focus on outcomes (particularly shareholder value) creates fertile ground for ethical crises. While an organizational culture geared toward winning can be helpful in many ways, it can also cause unnecessary self-damage because not all rules are worth breaking — even when there is a short-term benefit. For example, society doesn’t see breaking rules about taxi licenses at the same level of importance as breaking norms about respecting gender equality. When key stakeholders perceive that a good rule has been violated, they get upset and can find ways to retaliate. Eventually, the negative impressions from repeated crises accumulate and affect the brand, making it harder to attract and retain high-quality employees, customers, suppliers, and communities, all of which are necessary for the company’s flourishing.

There is more to business than narrow outcomes defined as profit for shareholders. Violating rules and norms has real costs, and you have to pay for what you break.

Our own research demonstrates that employees are more likely to experience meaningful work in companies that they perceive to be pursuing an important purpose. People gain more autonomy and competence, and have better relationships, in companies they think are focused on a purpose beyond profit. This has tangible payoffs, translating to lower turnover, better employee engagement, and better customer service for companies.

Startups and other organizations that want to disrupt the status quo and also be responsible need a fine-grained understanding of which rules they want to break. Organizations need the capability to understand ahead of time the consequences of breaking certain rules, specifically understanding who will be harmed and who will benefit. Involving stakeholders in the process of developing new products and services is essential so that businesses can appreciate the stakeholders’ perspectives and better identify and avoid ethical disasters. Without this capacity to anticipate and circumvent potential problems of its innovations, a company — even one with great and truly innovative ideas — can suffer a death by a thousand cuts.

Yes, we need disruption. But let’s make it responsible disruption.


MIT Sloan Management Review

IoT revenue opportunity worth $1.8 trillion for mobile network operators, says GSMA

Early deployment of commercial low power wide area networks (LPWAN) in licenced spectrum is said to boost IoT revenue to an estimated $ 1.8 trillion (£1.36tn), according to a new report from the GSMA.

The report, containing figures from the IoT Forecast Database Research published by analyst firm Machina Research, underline the huge growth opportunities for mobile operators delivered through new mobile IoT applications and services.

According to the report, 12 mobile operators have launched 15 such commercial services, which includes AT&T, Telstra and Verizon (LTE-M), as well as China Mobile, China Telecom, China Unicom, Deutsche Telekom, KT, LG Uplus, M1, Turkcell and Vodafone (NB-IoT). The Americas region is expected to see an estimated $ 534 billion, or approximately a third of the total revenue.

The findings show that the biggest revenue opportunities for IoT will come from the consumer demand for connected home ($ 441 billion), consumer electronics ($ 376 billion) and connected car technologies ($ 273 billion). Also, other areas such as connected energy are predicted to reach $ 128 billion by 2026 owing to local governments and consumers looking for smarter ways to manage utilities.

Operators are upgrading their licensed cellular networks with NB-IoT and LTE-M technologies that use internationally approved 3GPP standards to scale the IoT. Furthermore, mobile IoT networks are likely to see 862 million active connections by 2022. These new networks are compatible with mass-market IoT applications across various use cases, such as industrial asset tracking, safety monitoring, water and gas metering, smart grids, city parking, vending machines and city lighting, requiring solutions that are low cost, use low data rates, require long battery lives and can operate in remote locations.

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