Sep
13
2021
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Trade promotion management startup Cresicor raises $5.6M to keep tabs on customer spend

Cresicor, a consumer packaged goods trade management platform startup, raised $5.6 million in seed funding to further develop its tools for more accurate data and analytics.

The company, based remotely, focuses on small to midsize CPG companies, providing them with an automated way to manage their trade promotion, a process co-founder and CEO Alexander Whatley said is done primarily manually using spreadsheets.

Here’s what happens in a trade promotion: When a company wants to run a discount on one of their slower-selling items, the company has to spend money to do this — to have displays set up in a store or have that item on a certain shelf. If it works, more people will buy the item at the lower price point. Essentially, a trade promotion is the process of spending money to get more money in the future, Whatley told TechCrunch.

Figuring out all of the trade promotions is a complicated process, Whatley explained. Companies receive data feeds on the promotions from several different places, revenue data from retailers, accounting source data to show how many units were shipped and then maybe data directly from retailers. All of that has to be matched against the promotion.

“No API is bringing this data back to brands, so our software helps to automate and track these manual processes so companies can do analytics to see how the promotions are doing,” he added. “It also helps the finance team understand expenses, including which are valid and those that are not.”

What certain companies spend on trade promotions can represent their second-largest cost behind manufacturing, and companies often end up reinvesting between 20% and 30% of their revenue into trade promotions, Whatley said. This is a big market, representing untapped growth, especially with U.S. CPG sales topping $720 billion in 2020.

“You can see how messy the whole industry is, which is why we have a bright future and huge TAM,” he added. “With this new funding, we can target other parts of the P&L like supply chain and salaries. We also provide analytics for their strategy and where they should be spending it — which store, on which supply. By allocating resources the right way, companies typically see a 10% boost in sales as a result.”

Whatley started the company in 2017 with his brother, Daniel, Stuart Kennedy and Nikki McNeil while a Harvard undergrad. Since raising the funding back in February, the company has grown 2.5x in revenue, while employee headcount grew 4x over the past 12 months to 20.

Costanoa Ventures led the investment and was joined by Torch Capital and a group of angel investors including Fivestars CTO Matt Doka and Hu’s Kitchen CEO Mark Ramadan.

John Cowgill, partner at Costanoa, said though Cresicor raised a seed round, the company was already acquiring brands and capital before releasing a product and grew to almost a Series A company without any outside capital, saying it “blew me away.”

Cresicor is the “perfect example” of a company that Costanoa would get excited about — a vertical software company using data or machine learning to augment a pain point, Cowgill added.

“The CPG industry is in the middle of a rapid change where we see all of these emerging, digital native and mission-driven brands rapidly eating share from incumbents,” he added. “For the next generation of brands to compete, they have to win in trade promotion management. Cresicor’s opportunity to go beyond trade is significant. It is just a starting point to build a company that is the core enabler of great brands.”

The new funding will be used mainly to hire more talent in the areas of engineering and customer success so the company can hit its next benchmarks, Alexander Whatley said. He also intends to use the funding to acquire new brands and on software development. Cresicor boasts a list of customers including Perfect Snacks, Oatly and Hint Water.

The retail industry is valued at $5.5 trillion, and one-fifth of it is CPG, Whatley said. As a result, he has his eye on going after other verticals within CPG, like electronics and pet food, and then expanding into other areas.

“We are also going to work with enterprise companies — we see an opportunity to work with companies like P&G and General Mills, and we also want to build an ecosystem around trade promotion and launch into other profit and loss areas,” Whatley said.

Sep
10
2021
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DataRobot CEO Dan Wright coming to TC Sessions: SaaS to discuss role of data in machine learning

Just about every company is sitting on vast amounts of data, which they can use to their advantage if they can just learn how to harness it. Data is actually the fuel for machine learning models, and with the proper tools, businesses can learn to process this data and build models to help them compete in a rapidly changing marketplace, to react more quickly to shifting customer requirements and to find insights faster than any human ever possibly could.

Boston-based DataRobot, a late-stage startup that has built a platform to help companies navigate the machine learning model lifecycle, has been raising money by the bushel over the last several years, including $206 million in September 2019 and another $300 million in July. DataRobot CEO Dan Wright will be joining us on a panel to discuss the role of data in business at TC Sessions: SaaS on October 27th.

The company covers the gamut of the machine learning lifecycle, including preparing data, operationalizing it and finally building APIs to make it useful for the organization as it attempts to build a soup-to-nuts platform. DataRobot’s broad platform approach has appealed to investors.

As we wrote at the time of the $206 million round:

The company has been catching the attention of these investors by offering a machine learning platform aimed at analysts, developers and data scientists to help build predictive models much more quickly than it typically takes using traditional methodologies. Once built, the company provides a way to deliver the model in the form of an API, simplifying deployment.

DataRobot has raised a total of $1 billion on $6.3 billion post valuation, according to PitchBook data, and it’s been putting that money to work to add to its platform of services. Most recently the company acquired Algorithmia, which helps manage machine learning models.

As the pandemic has pushed more business online, companies are always looking for an edge, and one way to achieve that is by taking advantage of AI and machine learning. Wright will be joined on the data panel by Monte Carlo co-founder and CEO Barr Moses and AgentSync co-founder and CTO Jenn Knight to discuss the growing role of data in business operations

In addition to our discussion with Wright, the conference will also include Microsoft’s Jared Spataro, Amplitude’s Olivia Rose, as well as investors Kobie Fuller and Laela Sturdy, among others. We hope you’ll join us. It’s going to be a thought-provoking lineup.

Buy your pass now to save up to $100. We can’t wait to see you in October!


Sep
09
2021
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Affinity, a relationship intelligence company, raises $80M to help close deals

Relationships ultimately close deals, but long-term relationships come with a lot of baggage, i.e. email interactions, documents and meetings.

Affinity wants to take what Ray Zhou, co-founder and CEO, refers to as “data exhaust,” all of those daily interactions and communications, and apply machine learning analysis and provide insights on who in the organization has the best chance of getting that initial meeting and closing the deal.

Today, the company announced $80 million in Series C funding, led by Menlo Ventures, which was joined by Advance Venture Partners, Sprints Capital, Pear Ventures, Sway Ventures, MassMutual Ventures, Teamworthy and ECT Capital Partners’ Brian N. Sheth. The new funding gives the company $120 million in total funding since it was founded in 2014.

Affinity, based in San Francisco, is focused on industries like investment banking, private equity, venture capital, consulting and real estate, where Zhou told TechCrunch there aren’t customer relationship management systems or networking platforms that cater to the specific needs of the long-term relationship.

Stanford grads Zhou and co-founder Shubham Goel started the company after recognizing that while there was software for transactional relationships, there wasn’t a good option for the relationship journeys.

He cites data that show up to 90% of company profiles and contact information living in traditional CRM systems are incomplete or out of date. This comes as market researcher Gartner reported the global CRM software market grew 12.6% to $69 billion in 2020.

“It is almost bigger than sales,” Zhou said. “Our worldview is that relationships are the biggest industries in the world. Some would disagree, but relationships are an asset class, they are a currency that separates the winners from the losers.”

Instead, Affinity created “a new breed of CRM,”  Zhou said, that automates the inputting of that data constantly and adds information, like revenue, staff size and funding from proprietary data sources, to assign a score to a potential opportunity and increase the chances of closing a deal.

Affinity people profile. Image Credits: Affinity

He intends to use the new funding to expand sales, marketing and engineering to support new products and customers. The company has 125 employees currently; Zhou expects to be over 200 by next year.

To date, the company’s platform has analyzed over 18 trillion emails and 213 million calendar events and currently drives over 500,000 new introductions and tracks 450,000 deals per month. It also has more than 1,700 customers in 70 countries, boasting a list that includes Bain Capital Ventures, Kleiner Perkins, SoftBank Group, Nike, Qualcomm and Twilio.

Tyler Sosin, partner at Menlo Ventures, said he met Zhou and Goel at a time when the firm was looking into CRM companies, but it wasn’t until years later that Affinity came up again when Menlo itself wanted to work with a more modern platform.

As a user of Affinity himself, Sosin said the platform gives him the data he cares about and “removes the manual drudgery of entry and friction in the process.” Affinity also built a product that was intuitive to navigate.

“We have always had an interest in getting CRMs to the next generation, and Affinity is defining itself in a new category of relationship intelligence and just crushing it in the private capital markets,” he said. “They are scaling at an impressive growth rate and solving a hard problem that we don’t see many other companies in the space doing.”

 

Sep
09
2021
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Fin names former Twilio exec Evan Cummack as CEO, raises $20M

Work insights platform Fin raised $20 million in Series A funding and brought in Evan Cummack, a former Twilio executive, as its new chief executive officer.

The San Francisco-based company captures employee workflow data from across applications and turns it into productivity insights to improve the way enterprise teams work and remain engaged.

Fin was founded in 2015 by Andrew Kortina, co-founder of Venmo, and Facebook’s former VP of product and Slow Ventures partner Sam Lessin. Initially, the company was doing voice assistant technology — think Alexa but powered by humans and machine learning — and then workplace analytics software in 2020. You can read more about Fin’s origins at the link below.

The new round was led by Coatue, with participation from First Round Capital, Accel and Kleiner Perkins. The original team was talented, but small, so the new funding will build out sales, marketing and engineering teams, Cummack said.

“At that point, the right thing was to raise money, so at the end of last year, the company raised a $20 million Series A, and it was also decided to find a leadership team that knows how to build an enterprise,” Cummack told TechCrunch. “The company had completely pivoted and removed ‘Analytics’ from our name because it was not encompassing what we do.”

Fin’s software measures productivity and provides insights on ways managers can optimize processes, coach their employees and see how teams are actually using technology to get their work done. At the same time, employees are able to manage their workflow and highlight areas where there may be bottlenecks. All combined, it leads to better operations and customer experiences, Cummack said.

Graphic showing how work is really done. Image Credits: Fin

Fin’s view is that as more automation occurs, the company is looking at a “renaissance of human work.” There will be more jobs and more types of jobs, but people will be able to do them more effectively and the work will be more fulfilling, he added.

Particularly with the use of technology, he notes that in the era before cloud computing, there was a small number of software vendors. Now with the average tech company using over 130 SaaS apps, it allows for a lot of entrepreneurs and adoption of best-in-breed apps so that a viable company can start with a handful of people and leverage those apps to gain big customers.

“It’s different for enterprise customers, though, to understand that investment and what they are spending their money on as they use tools to get their jobs done,” Cummack added. “There is massive pressure to improve the customer experience and move quickly. Now with many people working from home, Fin enables you to look at all 130 apps as if they are one and how they are being used.”

As a result, Fin’s customers are seeing metrics like 16% increase in team utilization and engagement, a 25% decrease in support ticket handle time and a 71% increase in policy compliance. Meanwhile, the company itself is doubling and tripling its customers and revenue each year.

Now with leadership and people in place, Cummack said the company is positioned to scale, though it already had a huge head start in terms of a meaningful business.

Arielle Zuckerberg, partner at Coatue, said via email that she was part of a previous firm that invested in Fin’s seed round to build a virtual assistant. She was also a customer of Fin Assistant until it was discontinued.

When she heard the company was pivoting to enterprise, she “was excited because I thought it was a natural outgrowth of the previous business, had a lot of potential and I was already familiar with management and thought highly of them.”

She believed the “brains” of the company always revolved around understanding and measuring what assistants were doing to complete a task as a way to create opportunities for improvement or automation. The pivot to agent-facing tools made sense to Zuckerberg, but it wasn’t until the global pandemic that it clicked.

“Service teams were forced to go remote overnight, and companies had little to no visibility into what people were doing working from home,” she added. “In this remote environment, we thought that Fin’s product was incredibly well-suited to address the challenges of managing a growing remote support team, and that over time, their unique data set of how people use various apps and tools to complete tasks can help business leaders improve the future of work for their team members. We believe that contact center agents going remote was inevitable even before COVID, but COVID was a huge accelerant and created a compelling ‘why now’ moment for Fin’s solution.”

Going forward, Coatue sees Fin as “a process mining company that is focused on service teams.” By initially focusing on customer support and contact center use case — a business large enough to support a scaled, standalone business — rather than joining competitors in going after Fortune 500 companies where implementation cycles are long and there is slow time-to-value, Zuckerberg said Fin is better able to “address the unique challenges of managing a growing remote support team with a near-immediate time-to-value.”

 

Sep
07
2021
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Seqera Labs grabs $5.5M to help sequence COVID-19 variants and other complex data problems

Bringing order and understanding to unstructured information located across disparate silos has been one of the more significant breakthroughs of the big data era, and today a European startup that has built a platform to help with this challenge specifically in the area of life sciences — and has, notably, been used by labs to sequence and so far identify two major COVID-19 variants — is announcing some funding to continue building out its tools to a wider set of use cases, and to expand into North America.

Seqera Labs, a Barcelona-based data orchestration and workflow platform tailored to help scientists and engineers order and gain insights from cloud-based genomic data troves, as well as to tackle other life science applications that involve harnessing complex data from multiple locations, has raised $5.5 million in seed funding.

Talis Capital and Speedinvest co-led this round, with participation also from previous backer BoxOne Ventures and a grant from the Chan Zuckerberg Initiative, Mark Zuckerberg and Dr. Priscilla Chan’s effort to back open source software projects for science applications.

Seqera — a portmanteau of “sequence” and “era”, the age of sequencing data, basically — had previously raised less than $1 million, and quietly, it is already generating revenues, with five of the world’s biggest pharmaceutical companies part of its customer base, alongside biotech and other life sciences customers.

Seqera was spun out of the Centre for Genomic Regulation, a biomedical research center based out of Barcelona, where it was built as the commercial application of Nextflow, open source workflow and data orchestration software originally created by the founders of Seqera, Evan Floden and Paolo Di Tommaso, at the CGR.

Floden, Seqera’s CEO, told TechCrunch that he and Di Tommaso were motivated to create Seqera in 2018 after seeing Nextflow gain a lot of traction in the life science community, and subsequently getting a lot of repeat requests for further customization and features. Both Nextflow and Seqera have seen a lot of usage: the Nextflow runtime has been downloaded more than 2 million times, the company said, while Seqera’s commercial cloud offering has now processed more than 5 billion tasks.

The COVID-19 pandemic is a classic example of the acute challenge that Seqera (and by association Nextflow) aims to address in the scientific community. With COVID-19 outbreaks happening globally, each time a test for COVID-19 is processed in a lab, live genetic samples of the virus get collected. Taken together, these millions of tests represent a goldmine of information about the coronavirus and how it is mutating, and when and where it is doing so. For a new virus about which so little is understood and that is still persisting, that’s invaluable data.

So the problem is not if the data exists for better insights (it does); it is that it’s nearly impossible to use more legacy tools to view that data as a holistic body. It’s in too many places, and there is just too much of it, and it’s growing every day (and changing every day), which means that traditional approaches of porting data to a centralized location to run analytics on it just wouldn’t be efficient, and would cost a fortune to execute.

That is where Segera comes in. The company’s technology treats each source of data across different clouds as a salient pipeline which can be merged and analyzed as a single body, without that data ever leaving the boundaries of the infrastructure where it already exists. Customised to focus on genomic troves, scientists can then query that information for more insights. Seqera was central to the discovery of both the Alpha and Delta variants of the virus, and work is still ongoing as COVID-19 continues to hammer the globe.

Seqera is being used in other kinds of medical applications, such as in the realm of so-called “precision medicine.” This is emerging as a very big opportunity in complex fields like oncology: cancer mutates and behaves differently depending on many factors, including genetic differences of the patients themselves, which means that treatments are less effective if they are “one size fits all.”

Increasingly, we are seeing approaches that leverage machine learning and big data analytics to better understand individual cancers and how they develop for different populations, to subsequently create more personalized treatments, and Seqera comes into play as a way to sequence that kind of data.

This also highlights something else notable about the Seqera platform: it is used directly by the people who are analyzing the data — that is, the researchers and scientists themselves, without data specialists necessarily needing to get involved. This was a practical priority for the company, Floden told me, but nonetheless, it’s an interesting detail of how the platform is inadvertently part of that bigger trend of “no-code/low-code” software, designed to make highly technical processes usable by non-technical people.

It’s both the existing opportunity and how Seqera might be applied in the future across other kinds of data that lives in the cloud that makes it an interesting company, and it seems an interesting investment, too.

“Advancements in machine learning, and the proliferation of volumes and types of data, are leading to increasingly more applications of computer science in life sciences and biology,” said Kirill Tasilov, principal at Talis Capital, in a statement. “While this is incredibly exciting from a humanity perspective, it’s also skyrocketing the cost of experiments to sometimes millions of dollars per project as they become computer-heavy and complex to run. Nextflow is already a ubiquitous solution in this space and Seqera is driving those capabilities at an enterprise level – and in doing so, is bringing the entire life sciences industry into the modern age. We’re thrilled to be a part of Seqera’s journey.”

“With the explosion of biological data from cheap, commercial DNA sequencing, there is a pressing need to analyse increasingly growing and complex quantities of data,” added Arnaud Bakker, principal at Speedinvest. “Seqera’s open and cloud-first framework provides an advanced tooling kit allowing organisations to scale complex deployments of data analysis and enable data-driven life sciences solutions.”

Although medicine and life sciences are perhaps Seqera’s most obvious and timely applications today, the framework originally designed for genetics and biology can be applied to any a number of other areas: AI training, image analysis and astronomy are three early use cases, Floden said. Astronomy is perhaps very apt, since it seems that the sky is the limit.

“We think we are in the century of biology,” Floden said. “It’s the center of activity and it’s becoming data-centric, and we are here to build services around that.”

Seqera is not disclosing its valuation with this round.

Sep
02
2021
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Explosion snags $6M on $120M valuation to expand machine learning platform

Explosion, a company that has combined an open source machine learning library with a set of commercial developer tools, announced a $6 million Series A today on a $120 million valuation. The round was led by SignalFire, and the company reported that today’s investment represents 5% of its value.

Oana Olteanu from SignalFire will be joining the board under the terms of the deal, which includes warrants of $12 million in additional investment at the same price.

“Fundamentally, Explosion is a software company and we build developer tools for AI and machine learning and natural language processing. So our goal is to make developers more productive and more focused on their natural language processing, so basically understanding large volumes of text, and training machine learning models to help with that and automate some processes,” company co-founder and CEO Ines Montani told me.

The company started in 2016 when Montani met her co-founder, Matthew Honnibal in Berlin where he was working on the spaCy open source machine learning library. Since then, that open source project has been downloaded over 40 million times.

In 2017, they added Prodigy, a commercial product for generating data for the machine learning model. “Machine learning is code plus data, so to really get the most out of the technologies you almost always want to train your models and build custom systems because what’s really most valuable are problems that are super specific to you and your business and what you’re trying to find out, and so we saw that the area of creating training data, training these machine learning models, was something that people didn’t pay very much attention to at all,” she said.

The next step is a product called Prodigy Teams, which is a big reason the company is taking on this investment. “Prodigy Teams is [a hosted service that] adds user management and collaboration features to Prodigy, and you can run it in the cloud without compromising on what people love most about Prodigy, which is the data privacy, so no data ever needs to get seen by our servers,” she said. They do this by letting the data sit on the customer’s private cluster in a private cloud, and then use Prodigy Team’s management features in the public cloud service.

Today, they have 500 companies using Prodigy including Microsoft and Bayer in addition to the huge community of millions of open source users. They’ve built all this with just six early employees, a number that has grown to 17 recently (they hope to reach 20 by year’s end).

She believes if you’re thinking too much about diversity in your hiring process, you probably have a problem already. “If you go into hiring and you’re thinking like, oh, how can I make sure that the way I’m hiring is diverse, I think that already shows that there’s maybe a problem,” she said.

“If you have a company, and it’s 50 dudes in their 20s, it’s not surprising that you might have problems attracting people who are not white dudes in their 20s. But in our case, our strategy is to hire good people and good people are often very diverse people, and again if you play by the [startup] playbook, you could be limited in a lot of other ways.”

She said that they have never seen themselves as a traditional startup following some conventional playbook. “We didn’t raise any investment money [until now]. We grew the team organically, and we focused on being profitable and independent [before we got outside investment],” she said.

But more than the money, Montani says that they needed to find an investor that would understand and support the open source side of the business, even while they got capital to expand all parts of the company. “Open source is a community of users, customers and employees. They are real people, and [they are not] pawns in [some] startup game, and it’s not a game. It’s real, and these are real people,” she said.

“They deserve more than just my eyeballs and grand promises. […] And so it’s very important that even if we’re selling a small stake in our company for some capital [to build our next] product [that open source remains at] the core of our company and that’s something we don’t want to compromise on,” Montani said.

Aug
26
2021
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Workera.ai, a precision upskilling platform, taps $16M to close enterprise skills gap

Finding the right learning platform can be difficult, especially as companies look to upskill and reskill their talent to meet demand for certain technological capabilities, like data science, machine learning and artificial intelligence roles.

Workera.ai’s approach is to personalize learning plans with targeted resources — both technical and nontechnical roles — based on the current level of a person’s proficiency, thereby closing the skills gap.

The Palo Alto-based company secured $16 million in Series A funding, led by New Enterprise Associates, and including existing investors Owl Ventures and AI Fund, as well as individual investors in the AI field like Richard Socher, Pieter Abbeel, Lake Dai and Mehran Sahami.

Kian Katanforoosh, Workera’s co-founder and CEO, says not every team is structured or feels supported in their learning journey, so the company comes at the solution from several angles with an assessment on technical skills, where the employee wants to go in their career and what skills they need for that, and then Workera will connect those dots from where the employee is in their skillset to where they want to go. Its library has more than 3,000 micro-skills and personalized learning plans.

“It is what we call precision upskilling,” he told TechCrunch. “The skills data then can go to the organization to determine who are the people that can work together best and have a complementary skill set.”

Workera was founded in 2020 by Katanforoosh and James Lee, COO, after working with Andrew Ng, Coursera co-founder and Workera’s chairman. When Lee first connected with Katanforoosh, he knew the company would be able to solve the problem around content and basic fundamentals of upskilling.

It raised a $5 million seed round last October to give the company a total of $21 million raised to date. This latest round was driven by the company’s go-to-market strategy and customer traction after having acquired over 30 customers in 12 countries.

Over the past few quarters, the company began working with Fortune 500 companies, including Siemens Energy, across industries like professional services, medical devices and energy, Lee said. As spending on AI skills is expected to exceed $79 billion by 2022, he says Workera will assist in closing the gap.

“We are seeing a need to measure skills,” he added. “The size of the engagements are a sign as is the interest for tech and non-tech teams to develop AI literacy, which is a more pressing need.”

As a result, it was time to increase the engineering and science teams, Katanforoosh said. He plans to use the new funding to invest in more talent in those areas and to build out new products. In addition, there are a lot of natural language processes going on behind the scenes, and he wants the company to better understand it at a granular level so that the company can assess people more precisely.

Carmen Chang, general partner and head of Asia at NEA, said she is a limited partner in Ng’s AI fund and in Coursera, and has looked at a lot of his companies.

She said she is “very excited” to lead the round and about Workera’s concept. The company has a good understanding of the employee skill set, and with the tailored learning program, will be able to grow with company needs, Chang added.

“You can go out and hire anyone, but investing in the people that you have, educating and training them, will give you a look at the totality of your employees,” Chang said. “Workera is able to go in and test with AI and machine learning and map out the skill sets within a company so they will be able to know what they have, and that is valuable, especially in this environment.”

 

Aug
25
2021
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Bodo.ai secures $14M, aims to make Python better at handling large-scale data

Bodo.ai, a parallel compute platform for data workloads, is developing a compiler to make Python portable and efficient across multiple hardware platforms. It announced Wednesday a $14 million Series A funding round led by Dell Technologies Capital.

Python is one of the top programming languages used among artificial intelligence and machine learning developers and data scientists, but as Behzad Nasre, co-founder and CEO of Bodo.ai, points out, it is challenging to use when handling large-scale data.

Bodo.ai, headquartered in San Francisco, was founded in 2019 by Nasre and Ehsan Totoni, CTO, to make Python higher performing and production ready. Nasre, who had a long career at Intel before starting Bodo, met Totoni and learned about the project that he was working on to democratize machine learning and enable parallel learning for everyone. Parallelization is the only way to extend Moore’s Law, Nasre told TechCrunch.

Bodo does this via a compiler technology that automates the parallelization so that data and ML developers don’t have to use new libraries, APIs or rewrite Python into other programming languages or graphics processing unit code to achieve scalability. Its technology is being used to make data analytics tools in real time and is being used across industries like financial, telecommunications, retail and manufacturing.

“For the AI revolution to happen, developers have to be able to write code in simple Python, and that high-performance capability will open new doors,” Totoni said. “Right now, they rely on specialists to rewrite them, and that is not efficient.”

Joining Dell in the round were Uncorrelated Ventures, Fusion Fund and Candou Ventures. Including the new funding, Bodo has raised $14 million in total. The company went after Series A dollars after its product had matured and there was good traction with customers, prompting Bodo to want to scale quicker, Nasre said.

Nasre feels Dell Technologies Capital was “uniquely positioned to help us in terms of reserves and the role they play in the enterprise at large, which is to have the most effective salesforce in enterprise.”

Though he was already familiar with Nasre, Daniel Docter, managing director at Dell Technologies, heard about Bodo from a data scientist friend who told Docter that Bodo’s preliminary results “were amazing.”

Much of Dell’s investments are in the early-stage and in deep tech founders that understand the problem. Docter puts Totoni and Nasre in that category.

“Ehsan fits this perfectly, he has super deep technology knowledge and went out specifically to solve the problem,” he added. “Behzad, being from Intel, saw and lived with the problem, especially seeing Hadoop fail and Spark take its place.”

Meanwhile, with the new funding, Nasre intends to triple the size of the team and invest in R&D to build and scale the company. It will also be developing a marketing and sales team.

The company is now shifting from financing to customer- and revenue-focused as it aims to drive up adoption by the Python community.

“Our technology can translate simple code into the fast code that the experts will try,” Totoni said. “I joined Intel Labs to work on the problem, and we think we have the first solution that will democratize machine learning for developers and data scientists. Now, they have to hand over Python code to specialists who rewrite it for tools. Bodo is a new type of compiler technology that democratizes AI.”

 

Aug
19
2021
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Companies betting on data must value people as much as AI

The Pareto principle, also known as the 80-20 rule, asserts that 80% of consequences come from 20% of causes, rendering the remainder way less impactful.

Those working with data may have heard a different rendition of the 80-20 rule: A data scientist spends 80% of their time at work cleaning up messy data as opposed to doing actual analysis or generating insights. Imagine a 30-minute drive expanded to two-and-a-half hours by traffic jams, and you’ll get the picture.

As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now.

While most data scientists spend more than 20% of their time at work on actual analysis, they still have to waste countless hours turning a trove of messy data into a tidy dataset ready for analysis. This process can include removing duplicate data, making sure all entries are formatted correctly and doing other preparatory work.

On average, this workflow stage takes up about 45% of the total time, a recent Anaconda survey found. An earlier poll by CrowdFlower put the estimate at 60%, and many other surveys cite figures in this range.

None of this is to say data preparation is not important. “Garbage in, garbage out” is a well-known rule in computer science circles, and it applies to data science, too. In the best-case scenario, the script will just return an error, warning that it cannot calculate the average spending per client, because the entry for customer #1527 is formatted as text, not as a numeral. In the worst case, the company will act on insights that have little to do with reality.

The real question to ask here is whether re-formatting the data for customer #1527 is really the best way to use the time of a well-paid expert. The average data scientist is paid between $95,000 and $120,000 per year, according to various estimates. Having the employee on such pay focus on mind-numbing, non-expert tasks is a waste both of their time and the company’s money. Besides, real-world data has a lifespan, and if a dataset for a time-sensitive project takes too long to collect and process, it can be outdated before any analysis is done.

What’s more, companies’ quests for data often include wasting the time of non-data-focused personnel, with employees asked to help fetch or produce data instead of working on their regular responsibilities. More than half of the data being collected by companies is often not used at all, suggesting that the time of everyone involved in the collection has been wasted to produce nothing but operational delay and the associated losses.

The data that has been collected, on the other hand, is often only used by a designated data science team that is too overworked to go through everything that is available.

All for data, and data for all

The issues outlined here all play into the fact that save for the data pioneers like Google and Facebook, companies are still wrapping their heads around how to re-imagine themselves for the data-driven era. Data is pulled into huge databases and data scientists are left with a lot of cleaning to do, while others, whose time was wasted on helping fetch the data, do not benefit from it too often.

The truth is, we are still early when it comes to data transformation. The success of tech giants that put data at the core of their business models set off a spark that is only starting to take off. And even though the results are mixed for now, this is a sign that companies have yet to master thinking with data.

Data holds much value, and businesses are very much aware of it, as showcased by the appetite for AI experts in non-tech companies. Companies just have to do it right, and one of the key tasks in this respect is to start focusing on people as much as we do on AIs.

Data can enhance the operations of virtually any component within the organizational structure of any business. As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now. The goal for any company looking to tap data today comes down to getting it from point A to point B. Point A is the part in the workflow where data is being collected, and point B is the person who needs this data for decision-making.

Importantly, point B does not have to be a data scientist. It could be a manager trying to figure out the optimal workflow design, an engineer looking for flaws in a manufacturing process or a UI designer doing A/B testing on a specific feature. All of these people must have the data they need at hand all the time, ready to be processed for insights.

People can thrive with data just as well as models, especially if the company invests in them and makes sure to equip them with basic analysis skills. In this approach, accessibility must be the name of the game.

Skeptics may claim that big data is nothing but an overused corporate buzzword, but advanced analytics capacities can enhance the bottom line for any company as long as it comes with a clear plan and appropriate expectations. The first step is to focus on making data accessible and easy to use and not on hauling in as much data as possible.

In other words, an all-around data culture is just as important for an enterprise as the data infrastructure.

Aug
19
2021
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UiPath CEO Daniel Dines is coming to TC Sessions: SaaS to talk RPA and automation

UiPath came seemingly out of nowhere in the last several years, going public last year in a successful IPO during which it raised more than $527 million. It raised $2 billion in private money prior to that with its final private valuation coming in at an amazing $35 billion. UiPath CEO Daniel Dines will be joining us on a panel to discuss automation at TC Sessions: SaaS on October 27th.

The company has been able to capture all this investor attention doing something called robotic process automation (RPA), which provides a way to automate a series of highly mundane tasks. It has become quite popular, especially to help bring a level of automation to legacy systems that might not be able to handle more modern approaches to automation involving artificial intelligence and machine learning. In 2019 Gartner found that RPA was the fastest growing category in enterprise software.

In point of fact, UiPath didn’t actually come out of nowhere. It was founded in 2005 as a consulting company and transitioned to software over the years. The company took its first VC funding, a modest $1.5 million seed round, in 2015, according to Crunchbase data.

As RPA found its market, the startup began to take off, raising gobs of money, including a $568 million round in April 2019 and $750 million in its final private raise in February 2021.

Dines will be appearing on a panel discussing the role of automation in the enterprise. Certainly, the pandemic drove home the need for increased automation as masses of office workers moved to work from home, a trend that is likely to continue even after the pandemic slows.

As the RPA market leader, he is uniquely positioned to discuss how this software and other similar types will evolve in the coming years and how it could combine with related trends like no-code and process mapping. Dines will be joined on the panel by investor Laela Sturdy from CapitalG and ServiceNow’s Dave Wright, where they will discuss the state of the automation market, why it’s so hot and where the next opportunities could be.

In addition to our discussion with Dines, the conference will also include Databricks’ Ali Ghodsi, Salesforce’s Kathy Baxter and Puppet’s Abby Kearns, as well as investors Casey Aylward and Sarah Guo, among others. We hope you’ll join us. It’s going to be a stimulating day.

Buy your pass now to save up to $100. We can’t wait to see you in October!

Is your company interested in sponsoring or exhibiting at TC Sessions: SaaS 2021? Contact our sponsorship sales team by filling out this form.


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