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
15
2021
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The CockroachDB EC-1

Every application is a palimpsest of technologies, each layer forming a base that enables the next layer to function. Web front ends rely on JavaScript and browser DOM, which rely on back-end APIs, which themselves rely on databases.

As one goes deeper down the stack, engineering decisions become ever more conservative — changing the location of a button in a web app is an inconvenience; changing a database engine can radically upend an entire project.

It’s little surprise then that database technologies are among the longest-lasting engineering projects in the modern software developer toolkit. MySQL, which remains one of the most popular database engines in the world, was first released in the mid-1990s, and Oracle Database, launched more than four decades ago, is still widely used in high-performance corporate environments.

Database technology can change the world, but the world in these parts changes very, very slowly. That’s made building a startup in the sector a tough equation: Sales cycles can be painfully slow, even when new features can dramatically expand a developer’s capabilities. Competition is stiff and comes from some of the largest and most entrenched tech companies in the world. Exits have also been few and far between.

That challenge — and opportunity — is what makes studying Cockroach Labs so interesting. The company behind CockroachDB attempts to solve a long-standing problem in large-scale, distributed database architecture: How to make it so that data created in one place on the planet is always available for consumption by applications that are thousands of miles away, immediately and accurately. Making global data always available immediately and accurately might sound like a simple use case, but in reality it’s quite the herculean task. Cockroach Labs’ story is one of an uphill struggle, but one that saw it turn into a next-generation, $2-billion-valued database contender.

The lead writer of this EC-1 is Bob Reselman. Reselman has been writing about the enterprise software market for more than two decades, with a particular emphasis on teaching and educating engineers on technology. The lead editor for this package was Danny Crichton, the assistant editor was Ram Iyer, the copy editor was Richard Dal Porto, figures were designed by Bob Reselman and stylized by Bryce Durbin, and illustrations were drawn by Nigel Sussman.

CockroachDB had no say in the content of this analysis and did not get advance access to it. Reselman has no financial ties to CockroachDB or other conflicts of interest to disclose.

The CockroachDB EC-1 comprises four main articles numbering 9,100 words and a reading time of 37 minutes. Here’s what we’ll be crawling over:

We’re always iterating on the EC-1 format. If you have questions, comments or ideas, please send an email to TechCrunch Managing Editor Danny Crichton at danny@techcrunch.com.

Jul
15
2021
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CockroachDB, the database that just won’t die

There is an art to engineering, and sometimes engineering can transform art. For Spencer Kimball and Peter Mattis, those two worlds collided when they created the widely successful open-source graphics program, GIMP, as college students at Berkeley.

That project was so successful that when the two joined Google in 2002, Sergey Brin and Larry Page personally stopped by to tell the new hires how much they liked it and explained how they used the program to create the first Google logo.

Cockroach Labs was started by developers and stays true to its roots to this day.

In terms of good fortune in the corporate hierarchy, when you get this type of recognition in a company such as Google, there’s only one way you can go — up. They went from rising stars to stars at Google, becoming the go-to guys on the Infrastructure Team. They could easily have looked forward to a lifetime of lucrative employment.

But Kimball, Mattis and another Google employee, Ben Darnell, wanted more — a company of their own. To realize their ambitions, they created Cockroach Labs, the business entity behind their ambitious open-source database CockroachDB. Can some of the smartest former engineers in Google’s arsenal upend the world of databases in a market spotted with the gravesites of storage dreams past? That’s what we are here to find out.

Berkeley software distribution

Mattis and Kimball were roommates at Berkeley majoring in computer science in the early-to-mid-1990s. In addition to their usual studies, they also became involved with the eXperimental Computing Facility (XCF), an organization of undergraduates who have a keen, almost obsessive interest in CS.

Jul
15
2021
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How engineers fought the CAP theorem in the global war on latency

CockroachDB was intended to be a global database from the beginning. The founders of Cockroach Labs wanted to ensure that data written in one location would be viewable immediately in another location 10,000 miles away. The use case was simple, but the work needed to make it happen was herculean.

The company is betting the farm that it can solve one of the largest challenges for web-scale applications. The approach it’s taking is clever, but it’s a bit complicated, particularly for the non-technical reader. Given its history and engineering talent, the company is in the process of pulling it off and making a big impact on the database market, making it a technology well worth understanding. In short, there’s value in digging into the details.

Using CockroachDB’s multiregion feature to segment data according to geographic proximity fulfills Cockroach Labs’ primary directive: To get data as close to the user as possible.

In part 1 of this EC-1, I provided a general overview and a look at the origins of Cockroach Labs. In this installment, I’m going to cover the technical details of the technology with an eye to the non-technical reader. I’m going to describe the CockroachDB technology through three questions:

  1. What makes reading and writing data over a global geography so hard?
  2. How does CockroachDB address the problem?
  3. What does it all mean for those using CockroachDB?

What makes reading and writing data over a global geography so hard?

Spencer Kimball, CEO and co-founder of Cockroach Labs, describes the situation this way:

There’s lots of other stuff you need to consider when building global applications, particularly around data management. Take, for example, the question and answer website Quora. Let’s say you live in Australia. You have an account and you store the particulars of your Quora user identity on a database partition in Australia.

But when you post a question, you actually don’t want that data to just be posted in Australia. You want that data to be posted everywhere so that all the answers to all the questions are the same for everybody, anywhere. You don’t want to have a situation where you answer a question in Sydney and then you can see it in Hong Kong, but you can’t see it in the EU. When that’s the case, you end up getting different answers depending where you are. That’s a huge problem.

Reading and writing data over a global geography is challenging for pretty much the same reason that it’s faster to get a pizza delivered from across the street than from across the city. The essential constraints of time and space apply. Whether it’s digital data or a pepperoni pizza, the further away you are from the source, the longer stuff takes to get to you.

Jul
15
2021
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“Developers, as you know, do not like to pay for things”

In the previous part of this EC-1, we looked at the technical details of CockroachDB and how it provides accurate data instantaneously anywhere on the planet. In this installment, we’re going to take a look at the product side of Cockroach, with a particular focus on developer relations.

As a business, Cockroach Labs has many things going for it. The company’s approach to distributed database technology is novel. And, as more companies operate on a global level, CockroachDB has the potential to gain some significant market share internationally. The company is seven years into a typical 10-year maturity model for databases, has raised $355 million, and holds a $2 billion market value. It’s considered a double unicorn. Few database companies can say this.

The company is now aggressively expanding into the database-as-a-service space, offering its own technology in a fully managed package, expanding the spectrum of clients who can take immediate advantage of its products.

But its growth depends upon securing the love of developers while also making its product easier to use for new customers. To that end, I’m going to analyze the company’s pivot to the cloud as well as its extensive outreach to developers as it works to set itself up for long-term, sustainable success.

Cockroach Labs looks to the cloud

These days, just about any company of consequence provides services via the internet, and a growing number of these services are powered by products and services from native cloud providers. Gartner forecasted in 2019 that cloud services are growing at an annual rate of 17.5%, and there’s no sign that the growth has abated at all.

Its founders’ history with Google back in the mid-2000s has meant that Cockroach Labs has always been aware of the impact of cloud services on the commercial web. Unsurprisingly, CockroachDB could run cloud native right from its first release, given that its architecture presupposes the cloud in its operation — as we saw in part 2 of this EC-1.

Jul
15
2021
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Scaling CockroachDB in the red ocean of relational databases

Most database startups avoid building relational databases, since that market is dominated by a few goliaths. Oracle, MySQL and Microsoft SQL Server have embedded themselves into the technical fabric of large- and medium-size companies going back decades. These established companies have a lot of market share and a lot of money to quash the competition.

So rather than trying to compete in the relational database market, over the past decade, many database startups focused on alternative architectures such as document-centric databases (like MongoDB), key-value stores (like Redis) and graph databases (like Neo4J). But Cockroach Labs went against conventional wisdom with CockroachDB: It intentionally competed in the relational database market with its relational database product.

While it did face an uphill battle to penetrate the market, Cockroach Labs saw a surprising benefit: It didn’t have to invent a market. All it needed to do was grab a share of a market that also happened to be growing rapidly.

Cockroach Labs has a bright future, compelling technology, a lot of money in the bank and has an experienced, technically astute executive team.

In previous parts of this EC-1, I looked at the origins of CockroachDB, presented an in-depth technical description of its product as well as an analysis of the company’s developer relations and cloud service, CockroachCloud. In this final installment, we’ll look at the future of the company, the competitive landscape within the relational database market, its ability to retain talent as it looks toward a potential IPO or acquisition, and the risks it faces.

CockroachDB’s success is not guaranteed. It has to overcome significant hurdles to secure a profitable place for itself among a set of well-established database technologies that are owned by companies with very deep pockets.

It’s not impossible, though. We’ll first look at MongoDB as an example of how a company can break through the barriers for database startups competing with incumbents.

When life gives you Mongos, make MongoDB

Dev Ittycheria, MongoDB CEO, rings the Nasdaq Stock Market Opening Bell. Image Credits: Nasdaq, Inc

MongoDB is a good example of the risks that come with trying to invent a new database market. The company started out as a purely document-centric database at a time when that approach was the exception rather than the rule.

Web developers like document-centric databases because they address a number of common use cases in their work. For example, a document-centric database works well for storing comments to a blog post or a customer’s entire order history and profile.

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.

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