Too often, leaders think of a business case as a one-time checklist item. But the best practice — which will increase your likelihood of success — is to think of a business case as a lifecycle, an iterative process that involves many stakeholders and steps. This lifecycle has five phases:
This is a draft excerpt from a book I’m working on, tentatively titled The Business Bottleneck. If you’re interested in the footnotes, leaving a comment, and the further evolution of the book, check out the Google Doc for it.
The Business Bottleneck
All businesses have one core strategy: to stay alive. They do this by constantly offering new reasons for people to buy from them and, crucially, stay with them. Over the last decade, traditional businesses have been freaked by competitors that are figuring out better offerings and stealing those customers. The super-clever among these competitors innovate entirely new business models: hourly car rentals, next day delivery, short term insurance for jackets, paying for that jacket with your phone, banks with only your iPhone as a branch, incorporating real-time weather information into your reinsurance risk analysis.
In the majority (maybe all) of these cases, surviving and innovating is done well with small business and software development cycles. The two work hand-in-hand are ineffective without the other. I’d urge you think of them as the same thing. Instead of business development and strategy using PowerPoint and Machiavellian meeting tactics as their tool, they now use software.
You innovate by systematically failing weekly, over and over, until you find the thing people will buy and the best way to deliver it. We’ve known this for a long time and enshrined it in processes like The Lean Startup, Jobs to Be Done, agile development and DevOps, and disruption theory. While these processes are known and proven, they’ve hit several bottlenecks in the rest of the organization. In the past, we had IT bottlenecks. Now we have what I’ve been thinking of as The Business Bottleneck. There’s several of them. Let’s start by looking at the first, and, thus, most pressingly damaging one. The bottleneck that cuts off business health and innovation before it even starts: finance.
Most software development finance is done wrong and damages business. Finance seeks to be accurate, predictable, and works on annual cycles. This is not at all what business and software development is like.
Business & software development is chaos
Software development is a chaotic, unpredictable activity. We’ve known this for decades but we willfully ignore it like the advice to floss each day. Mark Schwartz has a clever take on the Standish software project failure reports. Since the numbers in these reports stay the same each year, basically, the chart below shows that that software is difficult and that we’re not getting much better at it:
What this implies, though, is something even more wickedly true: it’s not that these project failed, it was that we had false hopes. In fact, the red and yellow in the original chart actually shows that software is performs consistent to its true nature. Let me rework the chart to show this:
What this second version illustrates is that the time and budget it takes to get software software right can’t be predicted with any useful accuracy. The only useful accuracy is that you’ll be wrong in your predictions. We call it software engineering, and even more accurately “development” because it’s not scientific. Science seeks to describe reality, the be precise and correct – to discover truths that can be repeated. Software isn’t like that at all. There’s little science to what software organizations do, there’s just the engineering mentality of what works with what we have time and budget to do.
What’s more, business development is chaotic as well. Who knows what new business idea, what exact feature will work and be valuable to customers? Worse, there is no science behind business innovation – it’s all trial and error, constantly trying to both sense and shape what people and businesses will buy and at what price. Add in competitors doing the same, suppliers gasping for air in their own chaos quicksand, governments regulating, and culture changing people’s tastes, and it’s all a swirling cipher.
In each case, the only hope is rigorously using a system of exploration and refining. In business, you can study all the charts and McKinsey PDFs you want, but until you actually experiment by putting a product other there, seeing what demand and pricing is, and how your competitors will respond, you know nothing. The same is true for software.
Each domain has tools for this exploration. I’m less familiar with business development, and only know the Jobs to Be Done tool. This tool studies customer behaviors to discover what products they actually will spend money on, to find the “job” they hire your company to solve, and then change the business to profit from that knowledge.
The discovery cycle in software follows a simple recipe: you reduce your release cycle down to a week and use a theory-driven design process to constantly explore and react to customer preferences. You’re looking to find the best way to implement a specific feature in the UI to maximize revenue and customer satisfaction. That is, to achieve whatever “business value” you’re after. It has many names and diagrams, but I call this process the “small batch cycle.”
For example, Orange used this cycle when perfecting its customer billing app. Orange wanted to reduce traffic to call centers, thus lower costs but also driving up customer satisfaction (who wants to call a call center?). By following a small batch cycle, the company found that its customers only wanted to see the last two month’s worth of bills and their employees current data usage. That drove 50% of the customer base to use the app, helping remove their reliance on actual call centers, driving down costly and addressing customer satisfaction.
These business and software tools start with the actual customers, people, who are doing the buying and use these people as the raw materials and lab to run experiments. The results of these experiments are used to validate, more often invalidate theories of what the business should be and do. That’s a whole other story, and the subject of my previous book, Monolithic Transformation.
We were going to talk about finance, though, weren’t we?
The Finance Bottleneck
Finance likes certainly – forecasts, plans, commits, and smooth lines. But if you’re working in the chaos of business and software development, you can’t commit to much. The only certainty is that you’ll know something valuable once you get out there and experiment. At first all you’ll learn is that your idea was wrong. In this process, failure is as valuable as success. Knowing what doesn’t work, a failure, is the path to finding what does work, a success. You keep trying new things until you find success. To finish the absurd truth: failure creates success.
Software organizations can reliably deliver this type of learning each week. The same is true for business development. We’ve known this for decades, and many organizations have used it as their core differentiation engine.
But finance doesn’t work in these clever terms. “What they hell do you mean ‘failure creates success’? How do I put that in a spreadsheet?” we can hear the SVP of Finance saying, “Get the hell out of this conference room. You’re insane.”
Instead, when it comes to software development, finance focuses only on costs. These are easy to know: the costs of staff, the costs of their tools, and the costs of the data centers to run their software. Business development has similar easy to know costs: salary, tools, travel, etc.
When you’re developing new businesses and software, it’s impossible to know the most important number: revenue. Without that number, knowing if costs are good or bad is difficult. You can estimate revenue and, more likely, you can wish-timate it. You can declare that you’re going to have 10% of your total addressable market (TAM). You can just declare – ahem, assume – that you’re chasing a $9bn market opportunity. Over time, once you’ve discovered and developed your business, you can start to use models like consumer spending vs. GDP growth, or the effect of weather and political instability on the global reinsurance market. And, sure, that works as a static model so long as nothing ever changes in your industry.
For software development, things are even worse when it comes to revenue. No one really tells IT what the revenue targets are. When IT is asked to make budgets, they’re rarely involved in, nor given revenue targets. Of course, as laid out here, these targets in new businesses can’t be known with much precision. This pushes IT to just focus on costs. The problem here, as Mark Schwartz points out in all of his books, is that cost is meaningless if you don’t know the “value” you’re trying to achieve. You might try to do something “cheaply,” but without the context of revenue, you have no idea what “cheap” is. If the business ends up making $15m, is $1m cheap? If it ends up making $180m, is $5m cheap? Would it have been better to spend $10m if it meant $50m more in revenue?
IT is rarely involved in the strategic conversations that narrow down to a revenue. Nor are they in meetings about the more useful, but abstract notion of “business value.” So, IT is left with just one number to work with: cost. This means they focus on getting “a good buy” regardless of what’s being bought. Eventually, this just means cutting costs, building up a “debt” of work that should have been done but was “too expensive” at the time. This creates slow moving, or completely stalled out IT.
A rental car company can’t introduce hourly rentals because the back office systems are a mess and take 12 months to modify – but, boy, you sure got a good buy! A reinsurance company can’t integrate daily weather reports into its analytics to reassess its risk profile and adjust its portfolio because the connection between simple weather APIs and rock-solid mainframe processing is slow – but, sister, we sure did get a good buy on those MIPS! A bank can’t be the first in its market to add Apple Pay support because the payments processing system takes a year to integrate with, not to mention the governance changes needed to work with a new clearinghouse, and then there’s fraud detection – but, hoss, we reduced IT costs by $5m last year – another great buy!
Worse than shooting yourself in the foot is having someone else shoot you in the foot. As one pharmacy executive put it, taking six months to release competitive features isn’t much use if Amazon can release them in two months. But, hey! Our software development processes cost a third less than the industry averages!
Business development is the same, just with different tools and people who wear wing-tips instead of toe-shoes. Hopefully you’re realizing that the distinction between business and software development is unhelpful – they’re the same thing.
The business case is wrong from the start
So, when finance tries to assign a revenue number, it will be wrong. When you’re innovating, you can’t know that number, and IT certainly isn’t going to know it. No one knows the business value that you’re going to create: you have to first discover it, and then figure out how to deliver it profitably.
As is well known, the problem here is the long cycle that finance follows: at least a year. At that scope, the prediction, discovery, and certainty cycle is sloppy. You learn only once a year, maybe with indicators each quarter of how it’s going. But, you don’t really adjust the finance numbers: they don’t get smarter, more accurate, as you learn more each week. It’s not like you can go get board approval each week for the new numbers. It takes two weeks just to get the colors and alignment of all those slides right. And all that pre-wiring – don’t even get me started!
In business and software development, each week when you release your software you get smarter. While we could tag shipping containers with RFID tags to track them more accurately, we learn that we can’t actually collect and use that data – instead, it’s more practical to have people just enter the tracking information at each port, which means the software needs to be really good. People don’t actually want to use those expensive to create and maintain infotainment screens in cars, they want to use their phones – cars are just really large iPhone accessories. When buying a dishwasher, customers actually want to come to your store to touch and feel them, but first they want to do all their research ahead of time, and then buy the dishwasher on an app in the store instead of talking with a clerk.
These kinds of results seem obvious in hindsight, but business development people failed their way to those success. And, as you can imagine, strategy and finance assumptions made 12 to 18 months ago that drove businesses cases often seem comical in hindsight.
A smaller cycle means you can fail faster, getting smarter each time. For finance, this means frequently adjusting the numbers instead of sticking to the annual estimates. Your numbers get better, more accurate over time. The goal is to make the numbers adjust to reality as you discover it, as you fail your way to success, getting a better idea of what customers want, what they’ll pay, and how you can defend against competition.
Small batch finance
Some companies are lucky to just ignore finance and business models. They burn venture capital funding as fuel to rocket towards stability and profitability. Uber is a big test of this model – will it become a viable business model (profitable), or will it turn out that all that VC money was just subsidizing a bad business model? Amazon is a positive example here, over the past 20 years cash-as-rocket-fuel launched them to boatloads of profit.
Most organizations prefer a less expensive, less risky methods. In these organizations, what I see are programs that institutionalize these failure driven cycles. They create new governance and financing models that enforce smaller business cycles, allowing business and software development to take work in small batches. Allianz, for example, used 100 day cycles discover and validate new businesses. Instead of one chance every 365 days to get it right, they have three, almost four. As each week goes by, they get smarter, there’s less waste and risk, and finance gets more accurate. If their business theory is validated, the new business is graduated from the lab and integrated back into the relevant line of business. The Home Depot, Thales, Allstate, and many others institutionalize similar practices.
Each of these cycles gives the business the chance to validate and invalidate assumptions. It gives finance more certainly, more precision, and, thus, less errors and risk when it comes to the numbers. Finance might even be able to come up with a revenue number that’s real. That understanding makes funding business and software development less risky: you have ongoing health checks on the viability of the financial investment. You know when to stop throwing good money after bad when you’ve invalidated your business idea. Or, you can change your assumptions and try again: maybe no one really wants to rent cars by the hour, maybe they want scooters, or maybe they just want a bus pass.
Business cases focused on growth, not costs
With a steady flow of business development learning, you can start making growth decisions. If validate that you can track a team of nuclear power plant workers better with RFID badges, thus directing them to new jobs more quickly and reducing costly downtime, you can then increase your confidence that spending millions of dollars to do it for all plant workers with payoff. You see similar small experiments leading to massive investments in omnichannel programs at places like Dick’s Sporting Goods and The Home Depot.
Finance has to get involved in this fail-to-success cycle. Otherwise, business and software development will constantly be driven to be the cheapest provider. We saw how this generally works out with the outsourcing craze of my youth. Seeking to be the cheapest, or the synonomic phrase, the “most cost effective,” option ends up saving money, but paralyzing present and future innovation.
The problem isn’t that IT is too expensive, or can’t prove out a business case. As the Gartner study above shows, the problem is that most financing models we use to gate and rate business and software development are a poor fit. That needs to be fixed, finance needs to innovate. I’ve seen some techniques here and there, but nothing that’s widely accepted and used. And, certainly, when I hear about finance pushing back on IT businesses cases, it’s symptomatic of a disconnect between IT investment and corporate finance.
Businesses can certainly survive and even thrive. The small, failure-to-success learning cycles used by business and software developers works, are well known, and can be done by any organization that wills it. Those bottlenecks are broken. Finance is the next bottleneck to solve for.
I don’t really know how to fix it. Maybe you do!
Crawl into the bottleneck
After finance, for another time, my old friends: corporate strategy. And if you peer past that blizzard of pre-wired slides and pivot tables, you can see just in past the edges of the next bottleneck, that mysterious cabal called “The C-Suite.” Let’s start with strategy first.
Mobile banking is another key area of innovation, including NatWest’s personal finance app Mimo, which uses open banking application programming interfaces (APIs), artificial intelligence and data analytics to create a social feed that helps customers manage their money. Mimo is being beta tested with 5,500 customers. NatWest aims to extend the roll-out later this year.
“These kinds of projects mean the people that work here feel much better about themselves than they did at the outset,” he says.
“I like the fact that our mobile platform is held in high regard by customers, because I see that feedback all the time. And it also irritates me that there are other services we provide that they don’t seem to hold in the same regard. So, that’s an opportunity for us, but also a challenge.”
In the subscription world, you must understand the lifetime relationship with the customer – all the upsells and renewals and how they all build on one another. You also must understand the revenue, billings, and cash derived from those-again, over the entire lifetime of the customer relationship.
In an ASC-606 world, you must track all performance obligations, which is a fancy term for your promises – both the ones explicitly written in your customer agreement and all those pesky side terms that your sales rep slipped into the free-text field on the quote. You must also know all the implicit promises that people make in the deal or that have become ingrained in your business processes.
There’s a customer acquisition piece and then there’s a customer retention piece. For customer acquisition, we can see that new technologies can really add value by looking at all sorts of data sources that can help a financial service company identify who they want to target to provide those services. So, it’s a great place where data science can help find the product market fit, not just at one instance like identifying who you want to target, but also in a continuous form where you can evolve a product and then continuously find the audience that would best fit the product and continue to analyze the audience so you can design the next generation product. … Once you have a specific cohort of users who you want to target, there’s a need to be able to precisely convert them, which means understanding the stage of the customer’s thought process and understanding how to form the narrative to convince the user or the customer that a particular piece of technology or particular piece of service is the current service they need
Making the financial case:
“This is going to sound silly,” he says. “The hardest part isn’t necessarily the refactoring. The hardest part is convincing people to do this. Because, let’s be honest the upfront cost can be very scary, man. It can be frightening. The business is going to say, ‘We just put in X amount of dollars last year to support these kinds of environments.’ You kind of have to ask the question, what’s going to happen five years from now?”
While the legacy application may not be “broken,” forward-looking companies will consider the lost opportunity costs that are inherent when an existing system is not agile enough to support new opportunities and initiatives.
“You’re going to have to have the conversation where you can’t integrate with cloud at all, or you can’t integrate with data analytics, or you’ve failed to do cognitive system and your competitors are because RPG can’t support this stuff?” Kleyman says. “But just because it’s working doesn’t mean necessarily it’s bringing value back to the business.”
It’s easy for an executive to identify problems when servers are down, the application is throwing errors, and the day-to-day business is being impacted. It’s much harder for the executive to be able to identify the ways in which a legacy system could put hamper growth in the future.
“Honestly that’s one of the best approaches, when things aren’t on fire, to start asking some of these difficult questions,” Kleyman says. “It’s kind of like in a relationships. When everything’s going great, you don’t want to bring up any sore points. But realistically speaking, you don’t want to start arguing when everything’s wrong and you start bring up the pain points.”
These low-ball estimates are sometimes provided by consultants working to get their foot in the door, or by executive sponsors working to gain approval for their programs. Excluding low-ball estimates, the primary cause of poor estimates tends to be a lack of experience and background of the leader.
“In Q2 of this year, fintech startups raked in a total of $19 billion in funding, pushing the overall category to well north of $100 billion in total funding raised.”
Original source: New Tech Spotlight: Fintech Investments Reach All-Time High In Q2 2018
A fantastic, quick overview for understanding all those finance numbers and sheets.
Original source: Five Minute Finance
“Fully one in six of the projects we studied was a black swan, with a cost overrun of 200%, on average, and a schedule overrun of almost 70%.”
Original source: Why Your IT Project May Be Riskier Than You Think
“Given the data we shared at the beginning of this post – 75% of the top 20 performing IPOs from the last four years went out at valuations below $1 billion – one would think we’d see this trend picking up steam. Recent sub-$1 billion IPOs by companies like SendGrid, Blackline and TradeDesk have all done very well. Our bet, however, is small cap IPOs will continue to be few and far between until a) the late stage private capital market gets more difficult, b) investment banks decide to focus on smaller deals, or c) the regulatory requirements of public companies are reduced. None of these is likely to happen soon.”
The gate keepers want the biggest pound of flesh possible. It’s how percentages work.
Original source: The Majority of Top Performing IPOs Were Never Unicorns | Jeff Richards | Pulse | LinkedIn
“Accounting drives metrics. Metrics drive culture. Culture eats process for lunch.”
Don’t let accountants drive your innovation.
Original source: The Cost Center Trap – LeanEssays
The capitalist–consumerist ethic is revolutionary in another respect. Most previous ethical systems presented people with a pretty tough deal. They were promised paradise, but only if they cultivated compassion and tolerance, overcame craving and anger, and restrained their selfish interests. This was too tough for most. The history of ethics is a sad tale of wonderful ideals that nobody can live up to. Most Christians did not imitate Christ, most Buddhists failed to follow Buddha, and most Confucians would have caused Confucius a temper tantrum. In contrast, most people today successfully live up to the capitalist–consumerist ideal. The new ethic promises paradise on condition that the rich remain greedy and spend their time making more money, and that the masses give free rein to their cravings and passions –and buy more and more. This is the first religion in history whose followers actually do what they are asked to do. How, though, do we know that we’ll really get paradise in return?
from Sapiens: A Brief History of Humankind by Yuval Noah Harari
How all these unprofitable companies sustaining high valuations:
bending reality today has three elements: a vision, fast growth, and financing.
A few firms other than Amazon have defied the odds. Over the past 20 years Las Vegas Sands, a casino firm, Royal Caribbean, a cruise-line company, and Micron Technology, a chip-maker, each lost $1bn or more for two consecutive years and went on to prosper. But the chances of success are slim. Of the current members of the Russell 1000 index, since 1997 only 37 have lost $1bn or more for at least two years in a row. Of these, 21 still lose money.
Eventually every advisor will be a robo-advisor, which means there will be convergence.
Without some marketshare numbers, it’s tough to tell if the banking startups are making a dent against incumbent banks. Josh Brown suggests that banks are quick to catch-up and have nullified any lead that companies like Weathfront could have made:
It wasn’t long before the weaker B2C robo-advisors folded, the middling players were acquired and the incumbents launched their own competing platforms. The miscalculation on the part of the disruptors may have been the idea that they had years of lead time to scale up their assets before the lumbering giants of the industry would be able to fight back. Turns out they only had months, not years. Charles Schwab and Vanguard launched their own versions of the service and the mindshare / market share battle was joined.
From what I see out there, banks are quick to adapt and adopt new ideas into their businesses. While they’re beset with endless legacy IT and technical debt, they churn ahead nonetheless, e.g.:
While past performance is no guarantee of future results, and even though all the company’s results cannot be entirely attributed to BBVA’s digital transformation plan, so far many signs are encouraging. The number of BBVA’s digital customers increased by 68% from 2011 to 2014, reaching 8.4 million in mid-2014, of which 3.6 million were active mobile users.
Acquisition isn’t always failure, or victory
On the narrative framing side, it’s easy to frame a startup being acquired as “failure” and success for incumbents. That’s not always the case, and suggests a zero-sum view of innovation in industries. Acquisitions can have winners and losers – as with valuing anything, like real estate, the valuation could be wrong and in favor of the buyer or seller.
However, in the ideal case of an acquisition, it makes strategic sense for the buyer to spend their time and money that way instead of trying to innovate on it’s own. For the startup being acquired, they’re usually near the end of their gamble of sacrificing profit in favor of innovation and growth and need someone to bring them to the black.
Breaking up the monolith with good, old fashioned, OO-think:
Instead, Vanguard has begun a journey to break apart our monolithic legacy systems piece-by-piece by replacing them with microservices over time. With a microservices architecture, we remove the business logic and data logic from our applications and replace it with a set of re-usable modules of code that are built and deployed as independent entities. We then compliment this architecture by chunking out our user interfaces into modular purpose-built components.
De-coupling for stability and resiliency, among other things:
This service-based approach to application architecture provides a variety of advantages over the jumble of code that defines a non-modular monolithic application. First, services reduce redundancy by making sure there is only one copy of application logic for a given capability – regardless of how many applications leverage that logic. In the long run, this leads to lower development costs and increases speed to market. Second, since these services are deployed independently and built in a resilient manner, outages in one area of an application are less likely to bring down an entire system. In some instances, several of our services can be down without our clients being aware of a loss in functionality thanks to the ability of our applications to automatically react to a service that isn’t available. Finally, services enable our applications to scale easier. The marriage of cloud and services means we can quickly spin up infrastructure to handle surges in the number of transactions we need to handle without needing to scale up an entire application.
A good, pretty long overview of JPMorgan Chase’s plans for doing cloud with a PaaS focus. Some highlights.
More than just private-IaaS and DIY-platforms:
Like most large U.S. banks, JPMorgan Chase has had some version of a private cloud for years, with virtualized servers, storage and networks that can be shared in a flexible way throughout the organization.
The bank is upgrading its private cloud to “platform as a service” — in other words, the cloud service will manage the infrastructure (servers, storage, and networks), so that developers don’t have to worry about that stuff.
On the multi-/hybrid-cloud thing:
By the second half of 2017, the bank plans to run proprietary applications on the public cloud. At the same time, it’s building a new, modern internal cloud, code-named Gaia.
While “hybrid-cloud” has been tedious vendor-marketing-drivel over the past ten years, pretty much all of the large organizations I work with at Pivotal have exactly this approach. Public, private, whatever: we want to do it all.
Shifting their emphasis innovation:
“We aren’t looking to decrease the amount of money the firm is spending on technology. We’re looking to change the mix between run-the-bank costs versus innovation investment,” he said. “We’ve got to continue to be really aggressive in reducing the run-the bank costs and do it in a very thoughtful way to maintain the existing technology base in the most efficient way possible.” …Dollars saved by using lower-cost cloud infrastructure and platforms will be reinvested in technology, he said.
On appreciating the scale of “large organizations” that drive their very real challenges with adopting new ways of running IT:
The bank has 43,000 employees in IT; almost 19,000 are developers.
Good luck having the “we have no process by design” process with that setup.
On security, there’s a nice, almost syllogistic re-framing of “cloud security here”:
For years, banks have worried about using the public cloud out of security concerns and fears of what their regulators will say. Ever since the 2013 Target data breach, in which hackers stole card information from 40 million customers by breaking into the computers of an air conditioning company Target used, regulators have strongly urged banks to carefully vet and monitor all third parties, with a specific focus on security.
“We’re spending a significant amount of time to ensure that any applications we choose to run on a public cloud will have the same level of security and controls as those run internally,” Deasy said.
Most notable corporate security breeches over the year have involved on-premises IT (like the HVAC example above). The point is not to make sure that “cloud is as secure as [all that on-prem IT that’s been the source of most security problems in the past], but to make sure that all IT has a rigorous approach to security. “Cloud” isn’t the security problem, doing a shitty job at security is the security problem.
Update: or it could be 30,000.
The primary challenge with this blog post on gross margins is to make it interesting enough for people to read.
There’s a pretty good job of it, eventually focusing on tech and software companies.
When Autonomy was negotiating a sale to an end user, but couldn’t close the sale by quarter’s end, Egan would approach the resellers on or near the last day of the quarter, saying the deal was nearly done. Egan coaxed the resellers to buy Autonomy software by paying them hefty commissions. The resellers could then sell the software to a specified end user – but Autonomy maintained control of the deals and handled negotiations with the end user without the resellers’ aid. There’s no way these transactions could be revenue.
By changing its development practices and investing in a private cloud platform as a service, there have been clear benefits to the business. “Historically it would take two or three days for a deployment to go to production, with lots of manual production. Now with the apps in the garages we can do it on the basis of Cloud Foundry within minutes.”
Some interesting ideas to improve debit (and credit, one’d presume) with software-driven features:
CardGuard provides additional protection against fraud, since customers are able turn their debit card “on” or “off.” When the card is “off,” no withdrawals or purchases will be approved, with the exception of previously authorized or recurring transactions. Additionally, transaction controls can be set according to location, meaning transactions attempted outside of the geographic parameters set by the customer will be declined.
With CardGuard, customers are able to better manage their spending by establishing limits for debit card purchases based on the amount of the transaction. Additional controls can be set to manage spending in different categories by enabling or disabling transactions for certain merchant groups, such as gas, grocery or retail stores.
A nice overview of what you’d use analytics fit in investing:
Armed with these advanced techniques, digitally forward asset managers can gain a significant information advantage over peers who rely mainly on traditional data sources and analytical practices. They can crunch through vast quantities of data; scour video and satellite imagery to gauge a retailer’s Black Friday prospects; extract insights from social media, texts, and e-mail to divine market sentiment; and parse a CEO’s comments during an earnings call to estimate the potential impact on the next quarter’s results. They can discern how unexpected weather disruptions might affect their portfolio, and even disprove long-held beliefs about how markets work. Smart, dynamic investment technology also helps managers assess their own performance to see whether they may be making the right decisions at the wrong times, buying too late, or listening to “influencers” who push them in the wrong direction.
There’s also a good overview of how to introduce new approaches like the above into the organization without it be Big Bang projects, likely to fail:
In experimenting with new technologies, firms should prioritize a small number of targeted initiatives that can deliver immediate tangible benefits in a focused, resource-constrained way. In doing so, they should resist two temptations: the “esoteric science experiment,” whose focus is so narrow that the initiative can’t yield scalable results; and the “big bang rollout,” whose scope is so ambitious that it has a daunting price tag and takes too long to demonstrate value.
Estimate of the market-size for companies like Wealthfront: “whilst in the UK robo-advisers currently only cover less than £1 billion assets under management, the US robo-advisory market handled $19 billion AUM in 2014 (a growth of 65% from the previous eight months).”
When I was corporate strategy, this kind of thing drive me up the wall:
As a result, the finance organization spends an inordinate amount of time simply putting the data together and trying to resolve the inconsistencies so that executives can make apples-to-apples comparisons. We estimate that this task consumes roughly 30 percent of the resources in a typical corporate finance function. But the far more serious cost is the negative impact of poor data quality on senior management time and decision making. As one senior executive told us, “Our leadership team spends so much time trying to make sense of the data and debating whether it is right that we never get around to exploring what it really means for the business!”
To speed up development of custom written applications, of course.