Jul
29
2021
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4 key areas SaaS startups must address to scale infrastructure for the enterprise

Startups and SMBs are usually the first to adopt many SaaS products. But as these customers grow in size and complexity — and as you rope in larger organizations — scaling your infrastructure for the enterprise becomes critical for success.

Below are four tips on how to advance your company’s infrastructure to support and grow with your largest customers.

Address your customers’ security and reliability needs

If you’re building SaaS, odds are you’re holding very important customer data. Regardless of what you build, that makes you a threat vector for attacks on your customers. While security is important for all customers, the stakes certainly get higher the larger they grow.

Given the stakes, it’s paramount to build infrastructure, products and processes that address your customers’ growing security and reliability needs. That includes the ethical and moral obligation you have to make sure your systems and practices meet and exceed any claim you make about security and reliability to your customers.

Here are security and reliability requirements large customers typically ask for:

Formal SLAs around uptime: If you’re building SaaS, customers expect it to be available all the time. Large customers using your software for mission-critical applications will expect to see formal SLAs in contracts committing to 99.9% uptime or higher. As you build infrastructure and product layers, you need to be confident in your uptime and be able to measure uptime on a per customer basis so you know if you’re meeting your contractual obligations.

While it’s hard to prioritize asks from your largest customers, you’ll find that their collective feedback will pull your product roadmap in a specific direction.

Real-time status of your platform: Most larger customers will expect to see your platform’s historical uptime and have real-time visibility into events and incidents as they happen. As you mature and specialize, creating this visibility for customers also drives more collaboration between your customer operations and infrastructure teams. This collaboration is valuable to invest in, as it provides insights into how customers are experiencing a particular degradation in your service and allows for you to communicate back what you found so far and what your ETA is.

Backups: As your customers grow, be prepared for expectations around backups — not just in terms of how long it takes to recover the whole application, but also around backup periodicity, location of your backups and data retention (e.g., are you holding on to the data too long?). If you’re building your backup strategy, thinking about future flexibility around backup management will help you stay ahead of these asks.

Jul
20
2021
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How we built an AI unicorn in 6 years

Today, Tractable is worth $1 billion. Our AI is used by millions of people across the world to recover faster from road accidents, and it also helps recycle as many cars as Tesla puts on the road.

And yet six years ago, Tractable was just me and Raz (Razvan Ranca, CTO), two college grads coding in a basement. Here’s how we did it, and what we learned along the way.

Build upon a fresh technological breakthrough

In 2013, I was fortunate to get into artificial intelligence (more specifically, deep learning) six months before it blew up internationally. It started when I took a course on Coursera called “Machine learning with neural networks” by Geoffrey Hinton. It was like being love struck. Back then, to me AI was science fiction, like “The Terminator.”

Narrowly focusing on a branch of applied science that was undergoing a paradigm shift which hadn’t yet reached the business world changed everything.

But an article in the tech press said the academic field was amid a resurgence. As a result of 100x larger training data sets and 100x higher compute power becoming available by reprogramming GPUs (graphics cards), a huge leap in predictive performance had been attained in image classification a year earlier. This meant computers were starting to be able to understand what’s in an image — like humans do.

The next step was getting this technology into the real world. While at university — Imperial College London — teaming up with much more skilled people, we built a plant recognition app with deep learning. We walked our professor through Hyde Park, watching him take photos of flowers with the app and laughing from joy as the AI recognized the right plant species. This had previously been impossible.

I started spending every spare moment on image classification with deep learning. Still, no one was talking about it in the news — even Imperial’s computer vision lab wasn’t yet on it! I felt like I was in on a revolutionary secret.

Looking back, narrowly focusing on a branch of applied science undergoing a breakthrough paradigm shift that hadn’t yet reached the business world changed everything.

Search for complementary co-founders who will become your best friends

I’d previously been rejected from Entrepreneur First (EF), one of the world’s best incubators, for not knowing anything about tech. Having changed that, I applied again.

The last interview was a hackathon, where I met Raz. He was doing machine learning research at Cambridge, had topped EF’s technical test, and published papers on reconstructing shredded documents and on poker bots that could detect bluffs. His bare-bones webpage read: “I seek data-driven solutions to currently intractable problems.” Now that had a ring to it (and where we’d get the name for Tractable).

That hackathon, we coded all night. The morning after, he and I knew something special was happening between us. We moved in together and would spend years side by side, 24/7, from waking up to Pantera in the morning to coding marathons at night.

But we also wouldn’t have got where we are without Adrien (Cohen, president), who joined as our third co-founder right after our seed round. Adrien had previously co-founded Lazada, an online supermarket in South East Asia like Amazon and Alibaba, which sold to Alibaba for $1.5 billion. Adrien would teach us how to build a business, inspire trust and hire world-class talent.

Find potential customers early so you can work out market fit

Tractable started at EF with a head start — a paying customer. Our first use case was … plastic pipe welds.

It was as glamorous as it sounds. Pipes that carry water and natural gas to your home are made of plastic. They’re connected by welds (melt the two plastic ends, connect them, let them cool down and solidify again as one). Image classification AI could visually check people’s weld setups to ensure good quality. Most of all, it was real-world value for breakthrough AI.

And yet in the end, they — our only paying customer — stopped working with us, just as we were raising our first round of funding. That was rough. Luckily, the number of pipe weld inspections was too small a market to interest investors, so we explored other use cases — utilities, geology, dermatology and medical imaging.

Jul
13
2021
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Build a digital ops toolbox to streamline business processes with hyperautomation

Reliance on a single technology as a lifeline is a futile battle now. When simple automation no longer does the trick, delivering end-to-end automation needs a combination of complementary technologies that can give a facelift to business processes: the digital operations toolbox.

According to a McKinsey survey, enterprises that have likely been successful with digital transformation efforts adopted sophisticated technologies such as artificial intelligence, Internet of Things or machine learning. Enterprises can achieve hyperautomation with the digital ops toolbox, the hub for your digital operations.

The hyperautomation market is burgeoning: Analysts predict that by 2025, it will reach around $860 billion.

The toolbox is a synchronous medley of intelligent business process management (iBPM), robotic process automation (RPA), process mining, low code, artificial intelligence (AI), machine learning (ML) and a rules engine. The technologies can be optimally combined to achieve the organization’s key performance indicator (KPI) through hyperautomation.

The hyperautomation market is burgeoning: Analysts predict that by 2025, it will reach around $860 billion. Let’s see why.

The purpose of a digital ops toolbox

The toolbox, the treasure chest of technologies it is, helps with three crucial aspects: process automation, orchestration and intelligence.

Process automation: A hyperautomation mindset introduces the world of “automating anything that can be,” whether that’s a process or a task. If something can be handled by bots or other technologies, it should be.

Orchestration: Hyperautomation, per se, adds an orchestration layer to simple automation. Technologies like intelligent business process management orchestrate the entire process.

Intelligence: Machines can automate repetitive tasks, but they lack the decision-making capabilities of humans. And, to achieve a perfect harmony where machines are made to “think and act,” or attain cognitive skills, we need AI. Combining AI, ML and natural language processing algorithms with analytics propels simple automation to become more cognitive. Instead of just following if-then rules, the technologies help gather insights from the data. The decision-making capabilities enable bots to make decisions.

 

Simple automation versus hyperautomation

Here’s a story of evolving from simple automation to hyperautomation with an example: an order-to-cash process.

Jul
08
2021
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Achieving digital transformation through RPA and process mining

Understanding what you will change is most important to achieve a long-lasting and successful robotic process automation transformation. There are three pillars that will be most impacted by the change: people, process and digital workers (also referred to as robots). The interaction of these three pillars executes workflows and tasks, and if integrated cohesively, determines the success of an enterprisewide digital transformation.

Robots are not coming to replace us, they are coming to take over the repetitive, mundane and monotonous tasks that we’ve never been fond of. They are here to transform the work we do by allowing us to focus on innovation and impactful work. RPA ties decisions and actions together. It is the skeletal structure of a digital process that carries information from point A to point B. However, the decision-making capability to understand and decide what comes next will be fueled by RPA’s integration with AI.

From a strategic standpoint, success measures for automating, optimizing and redesigning work should not be solely centered around metrics like decreasing fully loaded costs or FTE reduction, but should put the people at the center.

We are seeing software vendors adopt vertical technology capabilities and offer a wide range of capabilities to address the three pillars mentioned above. These include powerhouses like UiPath, which recently went public, Microsoft’s Softomotive acquisition, and Celonis, which recently became a unicorn with a $1 billion Series D round. RPA firms call it “intelligent automation,” whereas Celonis targets the execution management system. Both are aiming to be a one-stop shop for all things related to process.

We have seen investments in various product categories for each stage in the intelligent automation journey. Process and task mining for process discovery, centralized business process repositories for CoEs, executives to manage the pipeline and measure cost versus benefit, and artificial intelligence solutions for intelligent document processing.

For your transformation journey to be successful, you need to develop a deep understanding of your goals, people and the process.

Define goals and measurements of success

From a strategic standpoint, success measures for automating, optimizing and redesigning work should not be solely centered around metrics like decreasing fully loaded costs or FTE reduction, but should put the people at the center. To measure improved customer and employee experiences, give special attention to metrics like decreases in throughput time or rework rate, identify vendors that deliver late, and find missed invoice payments or determine loan requests from individuals that are more likely to be paid back late. These provide more targeted success measures for specific business units.

The returns realized with an automation program are not limited to metrics like time or cost savings. The overall performance of an automation program can be more thoroughly measured with the sum of successes of the improved CX/EX metrics in different business units. For each business process you will be redesigning, optimizing or automating, set a definitive problem statement and try to find the right solution to solve it. Do not try to fit predetermined solutions into the problems. Start with the problem and goal first.

Understand the people first

To accomplish enterprise digital transformation via RPA, executives should put people at the heart of their program. Understanding the skill sets and talents of the workforce within the company can yield better knowledge of how well each employee can contribute to the automation economy within the organization. A workforce that is continuously retrained and upskilled learns how to automate and flexibly complete tasks together with robots and is better equipped to achieve transformation at scale.

Jul
01
2021
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To guard against data loss and misuse, the cybersecurity conversation must evolve

Data breaches have become a part of life. They impact hospitals, universities, government agencies, charitable organizations and commercial enterprises. In healthcare alone, 2020 saw 640 breaches, exposing 30 million personal records, a 25% increase over 2019 that equates to roughly two breaches per day, according to the U.S. Department of Health and Human Services. On a global basis, 2.3 billion records were breached in February 2021.

It’s painfully clear that existing data loss prevention (DLP) tools are struggling to deal with the data sprawl, ubiquitous cloud services, device diversity and human behaviors that constitute our virtual world.

Conventional DLP solutions are built on a castle-and-moat framework in which data centers and cloud platforms are the castles holding sensitive data. They’re surrounded by networks, endpoint devices and human beings that serve as moats, defining the defensive security perimeters of every organization. Conventional solutions assign sensitivity ratings to individual data assets and monitor these perimeters to detect the unauthorized movement of sensitive data.

It’s painfully clear that existing data loss prevention (DLP) tools are struggling to deal with the data sprawl, ubiquitous cloud services, device diversity and human behaviors that constitute our virtual world.

Unfortunately, these historical security boundaries are becoming increasingly ambiguous and somewhat irrelevant as bots, APIs and collaboration tools become the primary conduits for sharing and exchanging data.

In reality, data loss is only half the problem confronting a modern enterprise. Corporations are routinely exposed to financial, legal and ethical risks associated with the mishandling or misuse of sensitive information within the corporation itself. The risks associated with the misuse of personally identifiable information have been widely publicized.

However, risks of similar or greater severity can result from the mishandling of intellectual property, material nonpublic information, or any type of data that was obtained through a formal agreement that placed explicit restrictions on its use.

Conventional DLP frameworks are incapable of addressing these challenges. We believe they need to be replaced by a new data misuse protection (DMP) framework that safeguards data from unauthorized or inappropriate use within a corporate environment in addition to its outright theft or inadvertent loss. DMP solutions will provide data assets with more sophisticated self-defense mechanisms instead of relying on the surveillance of traditional security perimeters.

Jun
30
2021
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Dispense with the chasm? No way!

Jeff Bussgang, a co-founder and general partner at Flybridge Capital, recently wrote an Extra Crunch guest post that argued it is time for a refresh when it comes to the technology adoption life cycle and the chasm. His argument went as follows:

  1. VCs in recent years have drastically underestimated the size of SAMs (serviceable addressable markets) for their startup investments because they were “trained to think only a portion of the SAM is obtainable within any reasonable window of time because of the chasm.”
  2. The chasm is no longer the barrier it once was because businesses have finally understood that software is eating the world.
  3. As a result, the early majority has joined up with the innovators and early adopters to create an expanded early market. Effectively, they have defected from the mainstream market to cross the chasm in the other direction, leaving only the late majority and the laggards on the other side.
  4. That is why we now are seeing multiple instances of very large high-growth markets that appear to have no limit to their upside. There is no chasm to cross until much later in the life cycle, and it isn’t worth much effort to cross it then.

Now, I agree with Jeff that we are seeing remarkable growth in technology adoption at levels that would have astonished investors from prior decades. In particular, I agree with him when he says:

The pandemic helped accelerate a global appreciation that digital innovation was no longer a luxury but a necessity. As such, companies could no longer wait around for new innovations to cross the chasm. Instead, everyone had to embrace change or be exposed to an existential competitive disadvantage.

But this is crossing the chasm! Pragmatic customers are being forced to adopt because they are under duress. It is not that they buy into the vision of software eating the world. It is because their very own lunches are being eaten. The pandemic created a flotilla of chasm-crossings because it unleashed a very real set of existential threats.

The key here is to understand the difference between two buying decision processes, one governed by visionaries and technology enthusiasts (the early adopters and innovators), the other by pragmatists (the early majority).

The key here is to understand the difference between two buying decision processes, one governed by visionaries and technology enthusiasts (the early adopters and innovators), the other by pragmatists (the early majority). The early group makes their decisions based on their own analyses. They do not look to others for corroborative support. Pragmatists do. Indeed, word-of-mouth endorsements are by far the most impactful input not only about what to buy and when but also from whom.

Jun
30
2021
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How to cut through the promotional haze and select a digital building platform

Everyone from investors to casual LinkedIn observers has more reasons than ever to look at buildings and wonder what’s going on inside. The property industry is known for moving slowly when it comes to adopting new technologies, but novel concepts and products are now entering this market at a dizzying pace.

However, this ever-growing array of smart-building products has made it confusing for professionals who seek to implement digital building platform (DBP) technologies in their spaces, let alone across their entire enterprise. The waters get even murkier when it comes to cloud platforms and their impact on ROI with regard to energy usage and day-to-day operations.

Breaking down technology decisions into bite-sized pieces, starting with fundamental functions, is the most straightforward way to cut through the promotional haze.

Facility managers, energy professionals and building operators are increasingly hit with daily requests to review the latest platform for managing and operating their buildings. Here are a few tips to help decision-makers clear through the marketing fluff and put DBP platforms to the test.

The why, how and what

Breaking down technology decisions into bite-sized pieces, starting with fundamental functions, is the most straightforward way to cut through the promotional haze. Ask two simple questions: Who on your team will use this technology and what problem will it solve for them? Answers to these questions will help you maintain your key objectives, making it easier to narrow down the hundreds of options to a handful.

Another way to prioritize problems and solutions when sourcing smart-building technology is to identify your use cases. If you don’t know why you need a technology platform for your smart building, you’ll find it difficult to tell which option is better. Further, once you have chosen one, you’ll be hard put to determine if it has been successful. We find use cases draw the most direct line from why to how and what.

For example, let’s examine the why, how and what questions for a real estate developer planning to construct or modernize a commercial office building:

  • Why will people come? — Our building will be full of amenities and technological touches that will make discerning tenants feel comfortable, safe and part of a warm community of like-minded individuals.
  • How will we do it? — Implement the latest tenant-facing technology offering services and capabilities that are not readily available at home. We will create indoor and outdoor environments that make people feel comfortable and happy.
  • What tools, products and technology will we use?

This last question is often the hardest to answer and is usually left until the last possible moment. For building systems integrators, this is where the real work begins.

Focus on desired outcomes

When various stakeholder groups begin their investigations of the technology, it is crucial to define the outcomes everyone hopes to achieve for each use case. When evaluating specific products, it helps to categorize them at high levels.

Several high-level outcomes, such as digital twin enablement, data normalization and data storage are expected across multiple categories of systems. However, only an enterprise building management system includes the most expected outcomes. Integration platform as a service, bespoke reports and dashboarding, analytics as a service and energy-optimization platforms have various enabled and optional outcomes.

The following table breaks down a list of high-level outcomes and aligns them to a category of smart-building platforms available in the market. Expanded definitions of each item are included at the end of this article.

Jun
17
2021
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How to launch a successful RPA initiative

Robotic process automation (RPA) is rapidly moving beyond the early adoption phase across verticals. Automating just basic workflow processes has resulted in such tremendous efficiency improvements and cost savings that businesses are adapting automation at scale and across the enterprise.

While there is a technical component to robotic automation, RPA is not a traditional IT-driven solution. It is, however, still important to align the business and IT processes around RPA. Adapting business automation for the enterprise should be approached as a business solution that happens to require some technical support.

A strong working relationship between the CFO and CIO will go a long way in getting IT behind, and in support of, the initiative rather than in front of it.

A strong working relationship between the CFO and CIO will go a long way in getting IT behind, and in support of, the initiative rather than in front of it.

More important to the success of a large-scale RPA initiative is support from senior business executives across all lines of business and at every step of the project, with clear communications and an advocacy plan all the way down to LOB managers and employees.

As we’ve seen in real-world examples, successful campaigns for deploying automation at scale require a systematic approach to developing a vision, gathering stakeholder and employee buy-in, identifying use cases, building a center of excellence (CoE) and establishing a governance model.

Create an overarching vision

Your strategy should include defining measurable, strategic objectives. Identify strategic areas that benefit most from automation, such as the supply chain, call centers, AP or revenue cycle, and start with obvious areas where business sees delays due to manual workflow processes. Remember, the goal is not to replace employees; you’re aiming to speed up processes, reduce errors, increase efficiencies and let your employees focus on higher value tasks.

Jun
10
2021
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The fintech endgame: New supercompanies combine the best of software and financials

If money is the ultimate commodity, how can fintechs — which sell money, move money or sell insurance against monetary loss — build products that remain differentiated and create lasting value over time?

And why are so many software companies — which already boast highly differentiated offerings and serve huge markets— moving to offer financial services embedded within their products?

A new and attractive hybrid category of company is emerging at the intersection of software and financial services, creating buzz in the investment and entrepreneurial communities, as we discussed at our “Fintech: The Endgame” virtual conference and accompanying report this week.

These specialized companies — in some cases, software companies that also process payments and hold funds on behalf of their customers, and in others, financial-first companies that integrate workflow and features more reminiscent of software companies — combine some of the best attributes of both categories.

Image Credits: Battery Ventures

From software, they design for strong user engagement linked to helpful, intuitive products that drive retention over the long term. From financials, they draw on the ability to earn revenues indexed to the growth of a customer’s business.

Fintech is poised to revolutionize financial services, both through reinventing existing products and driving new business models as financial services become more pervasive within other sectors.

The powerful combination of these two models is rapidly driving both public and private market value as investors grant these “super” companies premium valuations — in the public sphere, nearly twice the median multiple of pure software companies, according to a Battery analysis.

The near-perfect example of this phenomenon is Shopify, the company that made its name selling software to help business owners launch and manage online stores. Despite achieving notable scale with this original SaaS product, Shopify today makes twice as much revenue from payments as it does from software by enabling those business owners to accept credit card payments and acting as its own payment processor.

The combination of a software solution indexed to e-commerce growth, combined with a profitable payments stream growing even faster than its software revenues, has investors granting Shopify a 31x multiple on its forward revenues, according to CapIQ data as of May 26.

How should we value these fintech companies, anyway?

Before even talking about how investors should value these hybrid companies, it’s worth making the point that in both private and public markets, fintechs have been notoriously hard to value, fomenting controversy and debate in the investment community.

Jun
01
2021
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4 proven approaches to CX strategy that make customers feel loved

Customers have been “experiencing” business since the ancient Romans browsed the Forum for produce, pottery and leather goods. But digitization has radically recalibrated the buyer-seller dynamic, fueling the rise of one of the most talked-about industry acronyms: CX (customer experience).

Part paradigm, part category and part multibillion-dollar market, CX is a broad term used across a myriad of contexts. But great CX boils down to delighting every customer on an emotional level, anytime and anywhere a business interaction takes place.

Great CX boils down to delighting every customer on an emotional level, anytime and anywhere a business interaction takes place.

Optimizing CX requires a sophisticated tool stack. Customer behavior should be tracked, their needs must be understood, and opportunities to engage proactively must be identified. Wall Street, for one, is taking note: Qualtrics, the creator of “XM” (experience management) as a category, was spun-out from SAP and IPO’d in January, and Sprinklr, a social media listening solution that has expanded into a “Digital CXM” platform, recently filed to go public.

Thinking critically about customer experience is hardly a new concept, but a few factors are spurring an inflection point in investment by enterprises and VCs.

Firstly, brands are now expected to create a consistent, cohesive experience across multiple channels, both online and offline, with an ever-increasing focus on the former. Customer experience and the digital customer experience are rapidly becoming synonymous.

The sheer volume of customer data has also reached new heights. As a McKinsey report put it, “Today, companies can regularly, lawfully, and seamlessly collect smartphone and interaction data from across their customer, financial, and operations systems, yielding deep insights about their customers … These companies can better understand their interactions with customers and even preempt problems in customer journeys. Their customers are reaping benefits: Think quick compensation for a flight delay, or outreach from an insurance company when a patient is having trouble resolving a problem.”

Moreover, the app economy continues to raise the bar on user experience, and end users have less patience than ever before. Each time Netflix displays just the right movie, Instagram recommends just the right shoes, or TikTok plays just the right dog video, people are being trained to demand just a bit more magic.

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