Sunday, February 1, 2015

Why deep linking isn’t enough for mobile app integration


Imagine your big night out on the town being completely planned and paid for with no more than five taps on your mobile phone.

The seconds and minutes saved may seem trivial, but aggregated across thousands of users and instances, “deep linking” has the potential to save an immense amount of time. The investment in deep-linking player Button last week shows the economic value of more intuitively streamlining how consumer mobile apps work together.  Suggesting and even executing actions on behalf of users saves time, increases app engagement, and is a step in the right direction.

This approach of pre-built app integration is not unlike the approach enterprises have taken to make their business apps work together seamlessly. Triggers in one app drive actions in another, and data from one is mapped and transferred to another in a predetermined manner.

However, these pre-built integrations have fallen short of solving the problem for business customers. For example, Intuit introduced an integration product to connect Salesforce and QuickBooks. Three years later, Intuit’s explanation for shutting it down crystallized the shortcoming of pre-built integrations:

“Our original intention for the Salesforce for QuickBooks and Salesforce for QuickBooks Integration business was to serve the needs of our Desktop (Pro/Premier) base with an out of the box solution…Instead, we have seen a high volume of use by customers who have needed a higher level of customization than our solution can provide – and our solution was unable to meet their needs.”

This had nothing to do with Intuit’s ability to program, code or collaborate with Salesforce. Instead, the company could not support the massive number of variations different customers had for how they needed Salesforce and QuickBooks to work with each other. The challenge in connecting consumer mobile apps is not that different. The model Button is using, while an exciting step in the right direction, doesn’t fully address the issue at-hand: People use combinations of apps in unique, infinite ways. It is impossible to predict their usage in terms of features, sequencing, and preferences.

Currently, deep linking requires serious engineering on a custom, one-off basis. App vendors must create mini-products for each app-to-app linkage. They must be prepared to handle the unique variations for how customers want to make their apps work with other apps. This engineering-intensive model inherently limits how much deep linking can occur.

Out of necessity, cloud business apps have evolved to make integration more adaptable, providing clues for how mobile app integrations might do the same. First, APIs have made it easier and faster to connect cloud-based apps, but APIs for mobile are not as prevalent or functional. Creating a more systematic, secure API approach would rapidly accelerate flexible deep linking. From here, app vendors must empower their users to link their apps as they want. Intuit showed us what can happen in the enterprise space when trying to meet the idiosyncratic needs of many with a single, standardized solution.

In the case of Button, smarter integration beyond simple automation is a step in the right direction. But such technologies must enable the myriad variations for how people want their apps to work together. Moreover, to make this work at scale, app users should be empowered to link apps themselves. Otherwise the promise of interoperable mobile apps will remain an elusive one for most users.

Vijay Tella is the founder and CEO of Workato. He created some of the first and market-leading integration technologies at TIBCO and Oracle. Vijay was also the CEO of Qik, a popular consumer video mobile app acquired by Skype in 2011.

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Saturday, January 31, 2015

4 Super Bowl tech ads to see ahead of the big day


The days leading up to the Super Bowl are always fraught with nervous excitement, not only over the game, but the advertisements that accompany it.

Super Bowl ads are a particularly special occasion for tech companies. After all, Apple’s “1984” commercial is credited with instigating the event of creating epic advertisements around the big day. And website builder GoDaddy has continued the tradition by creating controversial advertisements, like this year’s “Journey Home,” which caused such a stir that it’s already been pulled.

In anticipation of tomorrow’s festivities (and fiascos), we put together a list of our favorite 2015 tech company Super Bowl ads — at least so far. We’re excited to see what else gets served up tomorrow. In the mean time, enjoy.


We dug the apocalyptic vibe of on-the-go mobile phone charger Mophie’s 2015 Super Bowl commercial.


Website builder Squarespace, will feature Jeff Bridges soothing voice for its upcoming ad. In addition to the Jeff Bridges ad, Squarespace also made an ad called “A Better Web” that’s worth checking out. Here’s a teaser of Jeff Bridges., also a website builder, tapped football player Brett Favre for its Super Bowl 2015 pitch. The resulting video, though weird, is charmingly funny.


Mobile carrier T-Mobile booked the girl who’s butt broke the Internet to be the face of their “lost data” campaign. Take a look.

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How to build a world-saving startup

save the world

Here’s a depressing fact — most startups fail. That’s not just for web startups, but all startup businesses of any kind.

If you’re a do-gooder, it’s even harder.

Social good startups have the odds stacked against them. Most companies focus specifically on the bottom line, but social good startups are dealing with two measures of success: financial and social impact.

Why does doing good and doing well have to be so hard?

It may be challenging, but there have been many social good startups that have succeeded including prominent companies like Tom’s and Warby Parker. I started CauseVox, a nonprofit crowdfunding platform, with nothing more than a dream and now we have grown it to serving thousands of charities all over the world, including the American Red Cross.

Here’s what we learned that can help you launch your own social good startup.

Remember, you are creating a business

As a social good startup, you’re looking to make the world a better place. Helping people is fun and intrinsically rewarding, but you also need to find a scalable business model.

The first step in creating the right model is to ask:

  • Who specifically am I helping?
  • How big is that market?
  • How much will it cost for them to buy my product or service?

The best way to answer these questions is to talk to and observe your potential customers every day. When we started, we booked dozens of calls and meetings with nonprofits every week. These insights will help you refine your product/service and become more useful.

Keep your day job (for now)

Most social good startups fail because they run out of time and money before they can find a scalable business model. To increase our runway, we kept our day jobs for as long as we could and worked on our startup after work.

In some cases, your employer can help grow your social good startup. Chances are there’s a program at your current employer that you can take advantage of. Look out for these common ones:

  • Sabbaticals — A sabbatical is an extended leave of absence that allows you to pursue projects and passions. Consulting firms like McKinsey and Deloitte are well know for paid sabbaticals.
  • Corporate Social Responsibility (CSR) Programs — These are formal programs at corporations that focus on doing good. It’s a great way to hone your idea and build a network while on the job.
  • Volunteer Programs — More than 20 percent of employers pay their employees to volunteer. Why not use this as a way to meet potential customers for your social good startup?

If your current employer doesn’t have these programs, there’s nothing that you can’t do by hustling during nights, weekends, or your lunch break. Once you’re ready to take the leap, look into structured startup programs.

Apply to an accelerator program

Startup accelerator programs help speed along the early stage development of your social venture. Typically, you get mentorship, office space, and/or access to funding.

Since we’re a tech-based startup, we went through Founder Institute to prepare us for the startup world. You can also apply to the usual suspects like Y Combinator (funds startup nonprofits as well) and Techstars. The following accelerators are specifically focused on social good:

Get out of your cave

There’s a saying that if you want to go fast, you go it alone, but if you want to go far, you go with friends. Finding people with a like-minded passion for social change is crucial for you to build a team and refine your idea.

We did that through StartingBloc, an institute for social ventures, but you can also immerse yourself in a social good coworking space. There are dozens out there; here are just a few:

Find your funding

Whether it’s building a minimum viable product, testing your marketing strategy, or just surviving until your social good startup takes off, it takes capital. You can get funding in a few ways.

  • Potential customers — Get your potential customers to fund you by charging upfront or by doing pre-sales. This can also help you test your pricing model and your true value-add. Paid consulting projects and contracts can also help fund your startup.
  • Self-fund — This is how we did it at CauseVox. We slaved away on the day job and saved up our own money to start the venture. It takes longer to accumulate enough capital, but you get to keep 100 percent of your company.
  • Friends and family — Rich uncle? Supportive parents? People that you’re connected to on Linkedin? These are the people who respect you and want to see you succeed. Organize a friends-and-family seed round or use a crowdfunding platform.
  • Angels or VC – Angels are wealthy people that want to see new ideas flourish. Organizations like Investor Circle, Omidyar Network, and Kapor Capital are also funding opportunities once you have a concept ready to scale.
  • Grants — Look for government or foundation grants (international as well) that are aligned with your mission. Typically this is better for startups with traction.

For more ideas on funding, check out this good (if a bit old) list.

Rob Wu is a founder at CauseVox, a crowdfunding platform for nonprofits and social good projects. Rob’s work has been recognized by the Mayor of Austin and featured in the NYTimes, CNN, Forbes, and WSJ.

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Drones need software, too

Drone Maurizio Pesce Flickr

Venture capitalists aren’t seers. But at Bessemer Venture Partners, we try to dive deep into a particular sector to find great investments. The thinking is that by thoroughly getting to know the pain points — and players — in a particular sector, we can identify trends and predict opportunities for investment. And, in turn, we will be better equipped to identify and support the next generation of transformative companies.

With that in mind, here are a few of our best guesses about industry-specific areas ripe for innovation — some of which are already starting to be explored, and some that are still untapped:

Mobile-centric industry applications

Enterprise software historically catered only to people at their desks. But today’s mobile apps can make a field worker’s day far more productive, freeing up hours that would otherwise be spent physically filling out forms and pushing paperwork. By addressing industry-specific workflows, mobile solutions create better collaboration and communication. This seems obvious — until you come across an industry untouched by technology.

Commercial real estate was one such category. Brokers worked in the field, but returned to their desks to print tenant reports, enter deal updates, and share materials. Now, those common tasks are easily completed from the field using a mobile app from Hightower that enables landlords and brokers to quickly input and share transaction data.

Data entry challenges don’t just affect the real estate world, though. These sorts of slowdowns impact everything from hotels (housekeeping) to transportation (repair logs) to restaurants (food inspections) and local governments (work orders to fix potholes). And each of these industries is a prospective growth field.

Enterprise software for emerging hardware platforms

The emergence of a new hardware platform always prompts an era of profound opportunity for software makers building applications for it. Some of the emerging platforms we are excited about include wearables (which will be common in both our personal and professional lives), robots, drones, and sensors.

Even satellites are an area of possibility now that they’re no longer prohibitively expensive. Skybox, a company recently acquired by Google, sells low-cost access to satellite imagery, making it possible for farmers to monitor crop health, mining companies to explore new sites and transportation companies to optimize logistics. As hardware costs continue to drop and access becomes pervasive, we expect to see industry-specific applications built upon space imagery and other data.

Software tackling neglected industries

Dozens of industries are still held captive by terrible software. Among them:

  • Banking, which continues to be controlled by core vendors like FIS, Fiserv, and Jack Henry. These companies use their footprint in core transactional systems to cross-sell mediocre point solutions. We believe next-generation solutions like nCino and Q2 can take market share from the cores.
  • Asset-heavy industries like oil and gas, mining, agriculture, manufacturing and industrials have historically lacked innovative software startups altogether. This will change. Many of the trends we have discussed — including mobility and hardware platforms — are particularly well suited for these markets.
  • Government is responsible for more IT spending than any other industry but is dominated by dinosaurs like Tyler Technologies, Constellation, Infor, and SunGard — all of whom are focused on cash flow and not innovation. We expect companies like Socrata and OpenGov will shake things up.
  • Local services are all about small and medium-sized businesses (SMBs), which have historically lacked access to software. With the cloud making it easy to sell software to SMBs, we’re seeing a wave of solutions emerge for local restaurants, yoga studios, hotels and retailers, including companies like Square, Mindbody, Revel and Booker.
  • Health care is a trillion-dollar-plus industry that continues to be largely operated with paper, fax and the phone. Large electronic medical record companies like Epic and McKesson built out their systems in the 1970s and 1980s, and we see opportunities for newer software companies such as athenahealth, DocuTAP, and Modernizing Medicine to gain share.

Occasionally, an application is so revolutionary that it can transform the way an entire industry works. This is perhaps the most compelling opportunity.

Uber is perhaps the best example of this. Uber didn’t build a software application to connect livery companies with passengers. Instead, it used technology to reshape the industry and fundamentally change how we book cars. Amazon uses technology to reimagine retail. Airbnb challenged the hospitality sector. And Lending Club reinvented how borrowers access capital.

Reimagining an industry isn’t easy. In fact, for most entrepreneurs, it’s virtually impossible. But if you can find a field that needs disruption (and may not even realize it), that’s a good start. The few who manage to capture big parts of those value chains can see tremendous upsides.

Brian Feinstein is a partner in Bessemer Venture Partners‘ Larchmont office. Brian focuses on consumer Internet and enterprise software investments and leads the firm’s efforts in Brazil and Russia.

Trevor Oelschig is a partner in Bessemer’s Menlo Park office, focused primarily on cloud software and infrastructure.

This is the final installment in a three-part series on industry clouds. Check out the first and second parts if you haven’t already.

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Building data science teams: The power of the technology stack

technology stack

A factor that is frequently overlooked when setting up a data team is the selection of the technology stack. Often, this decision is delegated to the first hire in data science. Due to a lack of information about the right technologies, those in charge avoid making a decision. There is a case to be made for building a multilingual team. Nevertheless, I would like to highlight the advantages of choosing a technology stack during the conceptualization of a data team.


More often than not, Internet companies looking for a data scientist phrase their current job openings like this: “Expert knowledge of an analysis tool such as R, Matlab, or SAS and ability to write efficient code in at least one language (preferably Java, C++, Python, or Perl).” The problem here is that these are seven different skills for very different use cases. As a consequence, the company receives a huge variety of profiles and this does not help to ease the selection process at all.

It is important to distinguish between using exotic and sexy technologies to attract top talent and the tools that will be actually used for the day-to-day job. Therefore, it is possible to search for a data scientist who is proficient in Java and Scala but who will have the opportunity to work with Clojure. All three languages are part of the Java Virtual Machine, they are used extensively in the data science world, and they complement each other. The team might actually only use Scala, but Clojure is used as bait to lure top candidates. Other popular choices here are the languages Julia and Haskell. However, be careful not to overuse the strategy of picking popular choices just to get good candidates. The company should ask itself which technologies and programming languages it can and wants to support. For example, other teams might already be working with certain languages for other tasks and it may be possible to do knowledge sharing.

Additionally, the company should analyze the realities of the job market. Some of the languages listed above are in great demand, but only small communities are able to use them. At the moment, trying to hire a good data engineer proficient in Python and based in Europe is a very difficult task. Despite salary tags, the market is dry. Companies have to look overseas for ideal candidates and deal with the added overhead. My experience has been that hiring a non-EU national and bringing him or her to continental Europe can take up to six months due to legal paperwork and relocation. Therefore, building a quality team can take at least one year of active searching and even longer with the wrong decisions regarding technology.


Similarly, as the team grows and time passes, they will accumulate expertise and a code base. People come and go, but your technical debt stays. I have seen cases where technology choices were an afterthought and changes were painful. Data teams are similar to any other software team where migrations and major refactoring are significant undertakings that always come at a cost.

For example, one team decided to use R as their main programming language but months later realized that it did not fit in their pipeline; they migrated to Python and were set back by six months. Similarly, one team let their first data science hire freely choose his technology stack. The person decided to use Haskell, a relatively obscure programming language, as their main tool. One year later, the person left the company, and now they have a codebase that cannot be maintained because they cannot find appropriate talent.

Your team should not be dependent on specific contributors. Many people imagine that technologies are interchangeable and that once you know one programming language or algorithm, you know all of them. Reality is very different. Everyone can learn a technology (programming language, storing, algorithm, API, etc.) in one weekend, but it takes much longer to produce results that can go to production code. Therefore, strategically select technologies together with other stakeholders, and base the decision on which type of know-how you want to foster in the company.

Team Culture

Every technology and machine learning technique has its own community and idiosyncrasies. This should be considered during the selection process, as you might be wooing individuals that might not be the right fit. Furthermore, using bleeding edge technology attracts a completely different type of profile than selecting tried and tested choices. As previously mentioned, hiring the right talent for data science is hard and takes time; you do not want to bring in somebody who fits on paper but does not adapt and later leaves. The choice of technology plays a big role here.

Additionally, do not underestimate the risks of working with the bleeding edge. It tends to attract top candidates willing to accept less competitive packages. However, the cutting edge tends to be unstable, sometimes poorly documented, and often it is not fully understood how to scale it best. Similarly, not everyone in the team might be able to embrace it with the speed that you require. This can be very frustrating and toxic for your team culture if the team hits a wall and cannot go into production due to poor technology choices. Hence, if you are on a tight deadline, adopting a new technology can be detrimental for team performance.

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The type of projects and the scope of the team will have a significant influence on the choice of technology. Some stacks are better suited for some use cases than others. For example, a data science team with a focus on analytics and ad-hoc reporting works perfectly under an R-centric or Python stack. On the other hand, a team requiring robust recommender systems or fraud detection might be better served with the JVM or even with C++.

In the early days of the team, the scope might not be clear. Nonetheless, it is important to discuss the type of potential projects that can fall into the area of responsibility of the team during the planning stage. If, after these discussions, the mission of the team is not clarified, then it is better to make use of general technologies where the pool of candidates is larger.

The question therefore arises: Which technologies should I choose for my stack? The answer is not simple and this article only touches on some of the factors to consider. But for now you can use this rule of thumb: If your data qualifies as big data, then go for JVM-related technologies. If it does not, go for the Python or R ecosystem. These technology choices have robust libraries for the whole value chain (ETL, middleware, analytics, visualization, etc.), most of them are well documented, there is talent available, and the ecosystems are solid enough to offer peace of mind to your CTO yet modern enough to attract top talent.

How did you decide which technology stack is the best for your data science team? Let me know in the comments.

Rodrigo Rivera is a Mexican German data entrepreneur and founder of Emplido, an analytics recruiting company acquired by Experteer Inc. In Asia and Europe, he has built and led data science teams for Rocket Internet in the areas of product management, advertisement technology, CRM, data insights and sales.

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