machine learning
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Analytics as a service: Why more enterprises should consider outsourcing
With an increasing number of enterprise systems, growing teams, a rising proliferation of the web and multiple digital initiatives, companies of all sizes are creating loads of data every day. This data contains excellent business insights and immense opportunities, but it has become impossible for companies to derive actionable insights from this data consistently due to its sheer volume.
According to Verified Market Research, the analytics-as-a-service (AaaS) market is expected to grow to $101.29 billion by 2026. Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights. Through AaaS, managed services providers (MSPs) can help organizations get started on their analytics journey immediately without extravagant capital investment.
MSPs can take ownership of the company’s immediate data analytics needs, resolve ongoing challenges and integrate new data sources to manage dashboard visualizations, reporting and predictive modeling — enabling companies to make data-driven decisions every day.
AaaS could come bundled with multiple business-intelligence-related services. Primarily, the service includes (1) services for data warehouses; (2) services for visualizations and reports; and (3) services for predictive analytics, artificial intelligence (AI) and machine learning (ML). When a company partners with an MSP for analytics as a service, organizations are able to tap into business intelligence easily, instantly and at a lower cost of ownership than doing it in-house. This empowers the enterprise to focus on delivering better customer experiences, be unencumbered with decision-making and build data-driven strategies.
Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights.
In today’s world, where customers value experiences over transactions, AaaS helps businesses dig deeper into their psyche and tap insights to build long-term winning strategies. It also enables enterprises to forecast and predict business trends by looking at their data and allows employees at every level to make informed decisions.
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Heirlume raises $1.38M to remove the barriers of trademark registration for small businesses
Platforms like Shopify, Stripe and WordPress have done a lot to make essential business-building tools — like running storefronts, accepting payments and building websites — accessible to businesses with even the most modest budgets. But some very key aspects of setting up a company remain expensive, time-consuming affairs that can be cost-prohibitive for small businesses — but that, if ignored, can result in the failure of a business before it even really gets started.
Trademark registration is one such concern, and Toronto-based startup Heirlume just raised $1.7 million CAD (~$1.38 million) to address the problem with a machine-powered trademark registration platform that turns the process into a self-serve affair that won’t break the budget. Its AI-based trademark search will flag if terms might run afoul of existing trademarks in the U.S. and Canada, even when official government trademark search tools, and even top-tier legal firms, might not.
Heirlume’s core focus is on leveling the playing field for small business owners, who have typically been significantly out-matched when it comes to any trademark conflicts.
“I’m a senior-level IP lawyer focused in trademarks, and had practiced in a traditional model, boutique firm of my own for over a decade serving big clients, and small clients,” explained Heirlume co-founder Julie MacDonell in an interview. “So providing big multinationals with a lot of brand strategy, and in-house legal, and then mainly serving small business clients when they were dealing with a cease-and-desist, or an infringement issue. It’s really those clients that have my heart: It’s incredibly difficult to have a small business owner literally crying tears on the phone with you, because they just lost their brand or their business overnight. And there was nothing I could do to help because the law just simply wasn’t on their side, because they had neglected to register their trademarks to own them.”
In part, there’s a lack of awareness around what it takes to actually register and own a trademark, MacDonell says. Many entrepreneurs just starting out seek out a domain name as a first step, for instance, and some will fork over significant sums to register these domains. What they don’t realize, however, is that this is essentially a rental, and if you don’t have the trademark to protect that domain, the actual trademark owner can potentially take it away down the road. But even if business owners do realize that a trademark should be their first stop, the barriers to actually securing one are steep.
“There was an an enormous, insurmountable barrier, when it came to brand protection for those business owners,” she said. “And it just isn’t fair. Every other business service, generally a small business owner can access. Incorporating a company or even insurance, for example, owning and buying insurance for your business is somewhat affordable and accessible. But brand ownership is not.”
Heirlume brings the cost of trademark registration down from many thousands of dollars to just under $600 for the first, and only $200 for each additional after that. The startup is also offering a very small business-friendly “buy now, pay later” option supported by Clearbanc, which means that even businesses starting on a shoestring can take the step of protecting their brand at the outset.
In its early days, Heirlume is also offering its core trademark search feature for free. That provides a trademark search engine that works across both U.S. and Canadian government databases, which can not only tell you if your desired trademark is available or already held, but also reveal whether it’s likely to be able to be successfully obtained, given other conflicts that might arise that are totally ignored by native trademark database search portals.
Image Credits: Heirlume
Heirlume uses machine learning to identify these potential conflicts, which not only helps users searching for their trademarks, but also greatly decreases the workload behind the scenes, helping them lower costs and pass on the benefits of those improved margins to its clients. That’s how it can achieve better results than even hand-tailored applications from traditional firms, while doing so at scale and at reduced costs.
Another advantage of using machine-powered data processing and filing is that on the government trademark office side, the systems are looking for highly organized, curated data sets that are difficult for even trained people to get consistently right. Human error in just data entry can cause massive backlogs, MacDonell notes, even resulting in entire applications having to be tossed and started over from scratch.
“There are all sorts of data sets for those [trademark requirement] parameters,” she said. “Essentially, we synthesize all of that, and the goal through machine learning is to make sure that applications are utterly compliant with government rules. We actually have a senior-level trademark examiner that came to work for us, very excited that we were solving the problems causing backlogs within the government. She said that if Heirlume can get to a point where the applications submitted are perfect, there will be no backlog with the government.”
Improving efficiency within the trademark registration bodies means one less point of friction for small business owners when they set out to establish their company, which means more economic activity and upside overall. MacDonell ultimately hopes that Heirlume can help reduce friction to the point where trademark ownership is at the forefront of the business process, even before domain registration. Heirlume has a partnership with Google Domains to that end, which will eventually see indication of whether a domain name is likely to be trademarkable included in Google Domain search results.
This initial seed funding includes participation from Backbone Angels, as well as the Future Capital collective, Angels of Many and MaRS IAF, along with angel investors including Daniel Debow, Sid Lee’s Bertrand Cesvet and more. MacDonell notes that just as their goal was to bring more access and equity to small business owners when it comes to trademark protection, the startup was also very intentional in building its team and its cap table. MacDonell, along with co-founders CTO Sarah Ruest and Dave McDonell, aim to build the largest tech company with a majority female-identifying technology team. Its investor make-up includes 65% female-identifying or underrepresented investors, and MacDonnell says that was a very intentional choice that extended the time of the raise, and even led to turning down interest from some leading Silicon Valley firms.
“We want underrepresented founders to be to be funded, and the best way to ensure that change is to empower underrepresented investors,” she said. “I think that we all have a responsibility to actually do something. We’re all using hashtags right now, and hashtags are not enough […] Our CTO is female, and she’s often been the only female person in the room. We’ve committed to ensuring that women in tech are no longer the only woman in the room.”
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The health data transparency movement is birthing a new generation of startups
In the early 2000s, Jeff Bezos gave a seminal TED Talk titled “The Electricity Metaphor for the Web’s Future.” In it, he argued that the internet will enable innovation on the same scale that electricity did.
We are at a similar inflection point in healthcare, with the recent movement toward data transparency birthing a new generation of innovation and startups.
Those who follow the space closely may have noticed that there are twin struggles taking place: a push for more transparency on provider and payer data, including anonymous patient data, and another for strict privacy protection for personal patient data. What’s the main difference?
This sector is still somewhat nascent — we are in the first wave of innovation, with much more to come.
Anonymized data is much more freely available, while personal data is being locked even tighter (as it should be) due to regulations like GDPR, CCPA and their equivalents around the world.
The former trend is enabling a host of new vendors and services that will ultimately make healthcare better and more transparent for all of us.
These new companies could not have existed five years ago. The Affordable Care Act was the first step toward making anonymized data more available. It required healthcare institutions (such as hospitals and healthcare systems) to publish data on costs and outcomes. This included the release of detailed data on providers.
Later legislation required biotech and pharma companies to disclose monies paid to research partners. And every physician in the U.S. is now required to be in the National Practitioner Identifier (NPI), a comprehensive public database of providers.
All of this allowed the creation of new types of companies that give both patients and providers more control over their data. Here are some key examples of how.
Allowing patients to access all their own health data in one place
This is a key capability of patients’ newly found access to health data. Think of how often, as a patient, providers aren’t aware of treatment or a test you’ve had elsewhere. Often you end up repeating a test because a provider doesn’t have a record of a test conducted elsewhere.
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As concerns rise over forest carbon offsets, Pachama’s verified offset marketplace gets $15 million
Restoring and preserving the world’s forests has long been considered one of the easiest, lowest-cost and simplest ways to reduce the amount of greenhouse gases in the atmosphere.
It’s by far the most popular method for corporations looking to take an easy first step on the long road to decarbonizing or offsetting their industrial operations. But in recent months the efficacy, validity and reliability of a number of forest offsets have been called into question thanks to some blockbuster reporting from Bloomberg.
It’s against this uncertain backdrop that investors are coming in to shore up financing for Pachama, a company building a marketplace for forest carbon credits that it says is more transparent and verifiable thanks to its use of satellite imagery and machine learning technologies.
That pitch has brought in $15 million in new financing for the company, which co-founder and chief executive Diego Saez Gil said would be used for product development and the continued expansion of the company’s marketplace.
Launched only one year ago, Pachama has managed to land some impressive customers and backers. No less an authority on things environmental than Jeff Bezos (given how much of a negative impact Amazon operations have on the planet), gave the company a shoutout in his last letter to shareholders as Amazon’s outgoing chief executive. And the largest e-commerce company in Latin America, Mercado Libre, tapped the company to manage an $8 million offset project that’s part of a broader commitment to sustainability by the retailing giant.
Amazon’s Climate Pledge Fund is an investor in the latest round, which was led by Bill Gates’ investment firm Breakthrough Energy Ventures. Other investors included Lowercarbon Capital (the climate-focused fund from über-successful angel investor, Chris Sacca), former Uber executive Ryan Graves’ Saltwater, the MCJ Collective, and new backers like Tim O’Reilly’s OATV, Ram Fhiram, Joe Gebbia, Marcos Galperin, NBA All-star Manu Ginobili, James Beshara, Fabrice Grinda, Sahil Lavignia and Tomi Pierucci.
That’s not even the full list of the company’s backers. What’s made Pachama so successful, and given the company the ability to attract top talent from companies like Google, Facebook, SpaceX, Tesla, OpenAI, Microsoft, Impossible Foods and Orbital Insights, is the combination of its climate mission applied to the well-understood forest offset market, said Saez Gil.
“Restoring nature is one of the most important solutions to climate change. Forests, oceans and other ecosystems not only sequester enormous amounts of CO2 from the atmosphere, but they also provide critical habitat for biodiversity and are sources of livelihood for communities worldwide. We are building the technology stack required to be able to drive funding to the restoration and conservation of these ecosystems with integrity, transparency and efficiency” said Saez Gil. “We feel honored and excited to have the support of such an incredible group of investors who believe in our mission and are demonstrating their willingness to support our growth for the long term.”
Customers outside of Latin America are also clamoring for access to Pachama’s offset marketplace. Microsoft, Shopify and SoftBank are also among the company’s paying buyers.
It’s another reason that investors like Y Combinator, Social Capital, Tobi Lutke, Serena Williams, Aglaé Ventures (LVMH’s tech investment arm), Paul Graham, AirAngels, Global Founders, ThirdKind Ventures, Sweet Capital, Xplorer Capital, Scott Belsky, Tim Schumacher, Gustaf Alstromer, Facundo Garreton and Terrence Rohan were able to commit to backing the company’s nearly $24 million haul since its 2020 launch.
“Pachama is working on unlocking the full potential of nature to remove CO2 from the atmosphere,” said Carmichael Roberts from BEV, in a statement. “Their technology-based approach will have an enormous multiplier effect by using machine learning models for forest analysis to validate, monitor and measure impactful carbon neutrality initiatives. We are impressed by the progress that the team has made in a short period of time and look forward to working with them to scale their unique solution globally.”
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Arm launches its latest chip design for HPC, data centers and the edge
Arm today announced the launch of two new platforms, Arm Neoverse V1 and Neoverse N2, as well as a new mesh interconnect for them. As you can tell from the name, V1 is a completely new product and maybe the best example yet of Arm’s ambitions in the data center, high-performance computing and machine learning space. N2 is Arm’s next-generation general compute platform that is meant to span use cases from hyperscale clouds to SmartNICs and running edge workloads. It’s also the first design based on the company’s new Armv9 architecture.
Not too long ago, high-performance computing was dominated by a small number of players, but the Arm ecosystem has scored its fair share of wins here recently, with supercomputers in South Korea, India and France betting on it. The promise of V1 is that it will vastly outperform the older N1 platform, with a 2x gain in floating-point performance, for example, and a 4x gain in machine learning performance.
Image Credits: Arm
“The V1 is about how much performance can we bring — and that was the goal,” Chris Bergey, SVP and GM of Arm’s Infrastructure Line of Business, told me. He also noted that the V1 is Arm’s widest architecture yet. He noted that while V1 wasn’t specifically built for the HPC market, it was definitely a target market. And while the current Neoverse V1 platform isn’t based on the new Armv9 architecture yet, the next generation will be.
N2, on the other hand, is all about getting the most performance per watt, Bergey stressed. “This is really about staying in that same performance-per-watt-type envelope that we have within N1 but bringing more performance,” he said. In Arm’s testing, NGINX saw a 1.3x performance increase versus the previous generation, for example.
Image Credits: Arm
In many ways, today’s release is also a chance for Arm to highlight its recent customer wins. AWS Graviton2 is obviously doing quite well, but Oracle is also betting on Ampere’s Arm-based Altra CPUs for its cloud infrastructure.
“We believe Arm is going to be everywhere — from edge to the cloud. We are seeing N1-based processors deliver consistent performance, scalability and security that customers want from Cloud infrastructure,” said Bev Crair, senior VP, Oracle Cloud Infrastructure Compute. “Partnering with Ampere Computing and leading ISVs, Oracle is making Arm server-side development a first-class, easy and cost-effective solution.”
Meanwhile, Alibaba Cloud and Tencent are both investing in Arm-based hardware for their cloud services as well, while Marvell will use the Neoverse V2 architecture for its OCTEON networking solutions.
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Tellius announces $8M Series A to build ML-fueled business data query tool
Getting actionable business information into the hands of users who need it has always been a challenge. If you have to wait for experts to help you find the answers, chances are you’re going to be too late. Enter Tellius, an early-stage startup building a solution to help business users find the information they need when they need it.
Today the company announced an $8 million Series A led by Sands Capital Ventures, with participation from Grotech. Today’s investment brings the total raised to $17 million, according to the company.
CEO and founder Ajay Khanna says the company is attempting to marry two technologies that have traditionally lived in silos: business intelligence and artificial intelligence. He believes that bringing them together can lead to greater wisdom and help close the insight gap.
“Tellius is an AI-driven decision intelligence platform, and what we do is we combine machine learning — AI-driven automation — with a Google-like natural language interface, so combining the left brain and the right brain to enable business teams to get insights on the data,” Khanna told me.
The idea is to let the machine learning teams and the business analysts continue to do their thing, but provide an application where business users can put all of that to work. “We believe that to go from data to decisions, you need to know not only what happened, but why things change and how you can improve your company,” he said.
The product takes aim at three employee groups. The first is the business user, who can simply query the data with a natural language question to get results. The second is a data analyst, who can get more granular by choosing a specific model to base the query on, and finally a data scientist who can enhance the query with Python or Spark code.
It connects to various data sources, including Salesforce and Google Analytics, data lakes like Snowflake, csv files to take advantage of Excel data or cloud storage tools like Amazon S3. It comes in two versions: one that the customer can connect to the cloud infrastructure provider of choice, and one which they run as a service and manage for the customers.
Khanna says that as companies struggled to change the way they do business during the pandemic, they needed the kind of insights his company provides, and business grew 300% last year as a result.
The startup launched in 2016 after Khanna sold a previous company, which allowed him to bootstrap while in stealth. They spent a couple of years building the product and brought the first version of Tellius to market in Q3 2018. That’s when they took a $7.5 million seed round.
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Fraud prevention platform Sift raises $50M at over $1B valuation, eyes acquisitions
With the increase of digital transacting over the past year, cybercriminals have been having a field day.
In 2020, complaints of suspected internet crime surged by 61%, to 791,790, according to the FBI’s 2020 Internet Crime Report. Those crimes — ranging from personal and corporate data breaches to credit card fraud, phishing and identity theft — cost victims more than $4.2 billion.
For companies like Sift — which aims to predict and prevent fraud online even more quickly than cybercriminals adopt new tactics — that increase in crime also led to an increase in business.
Last year, the San Francisco-based company assessed risk on more than $250 billion in transactions, double from what it did in 2019. The company has over several hundred customers, including Twitter, Airbnb, Twilio, DoorDash, Wayfair and McDonald’s, as well a global data network of 70 billion events per month.
To meet the surge in demand, Sift said today it has raised $50 million in a funding round that values the company at over $1 billion. Insight Partners led the financing, which included participation from Union Square Ventures and Stripes.
While the company would not reveal hard revenue figures, President and CEO Marc Olesen said that business has tripled since he joined the company in June 2018. Sift was founded out of Y Combinator in 2011, and has raised a total of $157 million over its lifetime.
The company’s “Digital Trust & Safety” platform aims to help merchants not only fight all types of internet fraud and abuse, but to also “reduce friction” for legitimate customers. There’s a fine line apparently between looking out for a merchant and upsetting a customer who is legitimately trying to conduct a transaction.
Sift uses machine learning and artificial intelligence to automatically surmise whether an attempted transaction or interaction with a business online is authentic or potentially problematic.
Image Credits: Sift
One of the things the company has discovered is that fraudsters are often not working alone.
“Fraud vectors are no longer siloed. They are highly innovative and often working in concert,” Olesen said. “We’ve uncovered a number of fraud rings.”
Olesen shared a couple of examples of how the company thwarted fraud incidents last year. One recently involved money laundering through donation sites where fraudsters tested stolen debit and credit cards through fake donation sites at guest checkout.
“By making small donations to themselves, they laundered that money and at the same tested the validity of the stolen cards so they could use it on another site with significantly higher purchases,” he said.
In another case, the company uncovered fraudsters using Telegram, a social media site, to make services available, such as food delivery, with stolen credentials.
The data that Sift has accumulated since its inception helps the company “act as the central nervous system for fraud teams.” Sift says that its models become more intelligent with every customer that it integrates.
Insight Partners Managing Director Jeff Lieberman, who is a Sift board member, said his firm initially invested in Sift in 2016 because even at that time, it was clear that online fraud was “rapidly growing.” It was growing not just in dollar amounts, he said, but in the number of methods cybercriminals used to steal from consumers and businesses.
“Sift has a novel approach to fighting fraud that combines massive data sets with machine learning, and it has a track record of proving its value for hundreds of online businesses,” he wrote via email.
When Olesen and the Sift team started the recent process of fundraising, Insight actually approached them before they started talking to outside investors “because both the product and business fundamentals are so strong, and the growth opportunity is massive,” Lieberman added.
“With more businesses heavily investing in online channels, nearly every one of them needs a solution that can intelligently weed out fraud while ensuring a seamless experience for the 99% of transactions or actions that are legitimate,” he wrote.
The company plans to use its new capital primarily to expand its product portfolio and to scale its product, engineering and sales teams.
Sift also recently tapped Eu-Gene Sung — who has worked in financial leadership roles at Integral Ad Science, BSE Global and McCann — to serve as its CFO.
As to whether or not that meant an IPO is in Sift’s future, Olesen said that Sung’s experience of taking companies through a growth phase such as what Sift is experiencing would be valuable. The company is also for the first time looking to potentially do some M&A.
“When we think about expanding our portfolio, it’s really a buy/build partner approach,” Olesen said.
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Announcing our TC Sessions: SaaS virtual event happening October 27
Software-as-a-service (SaaS) is now the default business model for most B2B and B2C software startups. And while it’s been around for a while now, its momentum keeps accelerating and the ecosystem continues to expand as technologists and marketers are getting more sophisticated about how to build and sell SaaS products. For all of them, we’re pleased to announced TechCrunch Sessions: SaaS 2021, a one-day virtual event that will examine the state of SaaS to help startup founders, developers and investors understand the state of play and what’s next.
The single-day event will take place 100% virtually on October 27 and will feature actionable advice, Q&A with some of SaaS’s biggest names and plenty of networking opportunities. Importantly, $75 Early Bird passes are now on sale. Book your passes today to save $100 before prices go up.
We’re not quite ready to disclose our agenda yet, but you can expect a mix of superstars from across the industry, ranging from some of the largest tech companies to up-and-coming startups that are pushing the limits of SaaS.
The plan is to look at a broad spectrum of what’s happening with B2B startups and give you actionable insights into how to build and/or improve your own product. If you’re just getting started, we want you to come away with new ideas for how to start your company, and if you’re already on your way, then our sessions on scaling both your technology and marketing organization will help you to get to that $100 million annual run rate faster.
In addition to other founders, you’ll also hear from enterprise leaders who decide what to buy — and the mistakes they see startups make when they try to sell to them.
But SaaS isn’t only about managing growth — though ideally, that’s a problem founders will face sooner or later. Some of the other specific topics we will look at are how to keep your services safe in an ever-growing threat environment, how to use open source to your advantage and how to smartly raise funding for your company.
We will also highlight how B2B and B2C companies can handle the glut of data they now produce and use it to build machine learning models in the process. We’ll talk about how SaaS startups can both do so themselves and help others in the process. There’s nary a startup that doesn’t want to use some form of AI these days, after all.
And because this is 2021, chances are we’ll also talk about building remote companies and the lessons SaaS startups can learn from the last year of working through the pandemic.
Don’t miss out. Book your $75 Early Bird pass today and save $100.
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Cape Privacy announces $20M Series A to help companies securely share data
Cape Privacy, the early-stage startup that wants to make it easier for companies to share sensitive data in a secure and encrypted way, announced a $20 million Series A today.
Evolution Equity Partners led the round with participation from new investors Tiger Global Management, Ridgeline Partners and Downing Lane. Existing investors Boldstart Ventures, Version One Ventures, Haystack, Radical Ventures and a slew of individual investors also participated. The company has now raised approximately $25 million, including a $5 million seed investment we covered last June.
Cape Privacy CEO Ché Wijesinghe says that the product has evolved quite a bit since we last spoke. “We have really focused our efforts on encrypted learning, which is really the core technology, which was fundamental to allowing the multi-party compute capabilities between two organizations or two departments to work and build machine learning models on encrypted data,” Wijesinghe told me.
Wijesinghe says that a key business case involves a retail company owned by a private equity firm sharing data with a large financial services company, which is using the data to feed its machine learning models. In this case, sharing customer data, it’s essential to do it in a secure way and that is what Cape Privacy claims is its primary value prop.
He said that while the data sharing piece is the main focus of the company, it has data governance and compliance components to be sure that entities sharing data are doing so in a way that complies with internal and external rules and regulations related to the type of data.
While the company is concentrating on financial services for now, because Wijesinghe has been working with these companies for years, he sees uses cases far beyond a single vertical, including pharmaceuticals, government, healthcare telco and manufacturing.
“Every single industry needs this and so we look at the value of what Cape’s encrypted learning can provide as really being something that can be as transformative and be as impactful as what SSL was for the adoption of the web browser,” he said.
Richard Seewald, founding and managing partner at lead investor Evolution Equity Partners likes that ability to expand the product’s markets. “The application in Financial Services is only the beginning. Cape has big plans in life sciences and government where machine learning will help make incredible advances in clinical trials and counter-terrorism for example. We anticipate wide adoption of Cape’s technology across many use cases and industries,” he said.
The company has recently expanded to 20 people and Wijesinghe, who is half Asian, takes DEI seriously. “We’ve been very, very deliberate about our DEI efforts, and I think one of the things that we pride ourselves in is that we do foster a culture of acceptance, that it’s not just about diversity in terms of color, race, gender, but we just hired our first nonbinary employee,” he said,
Part of making people feel comfortable and included involves training so that fellow employees have a deeper understanding of the cultural differences. The company certainly has diversity across geographies with employees in 10 different time zones.
The company is obviously remote with a spread like that, but once the pandemic is over, Wijesinghe sees bringing people together on occasion with New York City as the hub for the company, where people from all over the world can fly in and get together.
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Data scientists: Bring the narrative to the forefront
By 2025, 463 exabytes of data will be created each day, according to some estimates. (For perspective, one exabyte of storage could hold 50,000 years of DVD-quality video.) It’s now easier than ever to translate physical and digital actions into data, and businesses of all types have raced to amass as much data as possible in order to gain a competitive edge.
However, in our collective infatuation with data (and obtaining more of it), what’s often overlooked is the role that storytelling plays in extracting real value from data.
The reality is that data by itself is insufficient to really influence human behavior. Whether the goal is to improve a business’ bottom line or convince people to stay home amid a pandemic, it’s the narrative that compels action, rather than the numbers alone. As more data is collected and analyzed, communication and storytelling will become even more integral in the data science discipline because of their role in separating the signal from the noise.
Data alone doesn’t spur innovation — rather, it’s data-driven storytelling that helps uncover hidden trends, powers personalization, and streamlines processes.
Yet this can be an area where data scientists struggle. In Anaconda’s 2020 State of Data Science survey of more than 2,300 data scientists, nearly a quarter of respondents said that their data science or machine learning (ML) teams lacked communication skills. This may be one reason why roughly 40% of respondents said they were able to effectively demonstrate business impact “only sometimes” or “almost never.”
The best data practitioners must be as skilled in storytelling as they are in coding and deploying models — and yes, this extends beyond creating visualizations to accompany reports. Here are some recommendations for how data scientists can situate their results within larger contextual narratives.
Make the abstract more tangible
Ever-growing datasets help machine learning models better understand the scope of a problem space, but more data does not necessarily help with human comprehension. Even for the most left-brain of thinkers, it’s not in our nature to understand large abstract numbers or things like marginal improvements in accuracy. This is why it’s important to include points of reference in your storytelling that make data tangible.
For example, throughout the pandemic, we’ve been bombarded with countless statistics around case counts, death rates, positivity rates, and more. While all of this data is important, tools like interactive maps and conversations around reproduction numbers are more effective than massive data dumps in terms of providing context, conveying risk, and, consequently, helping change behaviors as needed. In working with numbers, data practitioners have a responsibility to provide the necessary structure so that the data can be understood by the intended audience.
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