This has also put tremendous pressure on funding for startups. During the second quarter, venture capitalists (VCs) struck 24% fewer deals on a quarter-over-quarter basis, according to PitchBook. And the IPO market is having its worst year in a decade, further hurting startup funding.

“VCs are definitely getting more selective,” said Muddu Sudhakar, the CEO and founder of Aisera. “The bar is much higher now.”

As for his own firm, Sudhakar was able to raise $90 million in a Series D round. The lead was Goldman Sachs and other investors included True Ventures, Zoom, and Khosla Ventures.

It helped that Aisera has a unique platform that leverages predictive AI for managing customer service, IT and sales. The technology has shown to be effective in lowering operating costs.

Also read: 5 Top VCs For Data Startups

Getting Funded in a Down Market

So what are some other enterprise software startups that have been able to buck today’s tough environment? What are the factors for success in current markets?

Let’s take a look at a few success stories.

CleverTap: AI-based User Engagement

“The best way to attract investors is to build a growing and sustainable business,” said Sunil Thomas, co-founder and executive chairman of CleverTap. “Focus on unit economics, growth, cash efficiency, and profitability.”

The strategy has worked out quite well for him. In August, CleverTap announced a Series D funding for $105 million. The lead on the deal was CDPQ, which wrote a check for $75 million. Other investors were Tiger Global and Sequoia India.

CleverTap software leverages artificial intelligence (AI) and machine learning (ML) to engage and retain users. Since the launch six years ago, the company has amassed a customer base of 1,200 brands.

“The overall funding environment has gone back to basics,” said Thomas. “Funding is definitely available for great ideas — at the early stages — and sustainable businesses at the growth stage.”

See the Top Artificial Intelligence (AI) Software for 2022

airSlate: Document Automation

airSlate raised $51.5 million in June. The lead investors were G Squared and UiPath. The valuation of the round came to $1.25 billion.

Founded in 2008, airSlate has created an automation platform that allows for e-signatures, PDF editing, document management and workflow solutions. There are over 100 million users.

“So what attracts investors?” said Borya Shakhnovich, CEO of airSlate. “Put simply, financials that speak for themselves. This means breaking even early on in the company’s journey, procuring impressive revenue figures, and demonstrating growth of the customer base.

“Touting solid financials for venture capital interest might sound painstakingly intuitive, but it’s not always that simple,” Shakhnovich added. “I often liken investors to shoes — there’s a lot of them to choose from, and some will fit better than others. A lot of founders feel like their purpose is to win every investor, but that’s not always possible. Many investors demand brand recognition and a firm customer base over financial stability. The best approach is to stand by your organization’s strength and identify like-minded investors.”

Also read: Top RPA Tools 2022: Robotic Process Automation Software

Tropic: Procurement Analytics

Earlier in the year, Tropic raised $40 million in a Series A round that Insight Partners led. The company’s software allows for better procurement. Keep in mind that the average company overpays by 30% for software.

Some of the customers are Vimeo, Zapier and Qualtrics. The company manages over $300 million in spend.

“At Tropic, we have a unique vantage point in that we can see how businesses are truly performing based on the purchasing behaviors of hundreds of companies,” said Dave Campbell, CEO and co-founder of Tropic. “We power these purchases, which gives us line of sight into who is performing well, who is churning, and who is struggling to get traction.”

Campbell points out the following learnings for those companies getting funding:

• They offer something that thrives in a downturn like cost-cutting and efficiency-improving approaches.
• They emphasize retention over growth. Companies raising now are in the 120% NRR (Net Revenue Retention) range, even if they are only growing 50% year-over-year. 300% growth with 50% NRR won’t attract investors.
• They have strong efficiency. Sales efficiency of over 1 and CAC (Customer Acquisition Cost) payback of less than 12 months.
• They power a mission-critical service. Nice-to-haves are out.
• They are willing to discount their valuation.

Lightning AI

In June, Lightning AI announced a Series B funding of $40 million. The lead was Coatue and other investors included Index, Bain, First Minute Capital, and the Chainsmokers’ Mantis VC.

The company has an open source platform to build AI models. It has been downloaded more than 22 million times since 2019 and used by 10,000 organizations across the globe.

“These latest changes in the funding environment have made it more important than ever for businesses to make it explicitly clear how they create value for their users and customers,” said William Falcon, CEO and co-founder of Lightning AI. “We expect to see an increasing amount of focus placed on the ability to synthesize what a business does into clear and well-articulated value propositions and a larger focus on efficient growth backed by strong unit economics.”

Falcon stresses that founders need to find investors that align with the vision of the company. True, in a rough funding environment, it can be difficult to say “no” to an offer of millions of dollars. But for the long-term prospects, this may be the right choice.

“While there’s no shortage of MLOps products today, it was important to us from the beginning that we found investors who understood that Lightning AI is not building simply another machine learning platform, we are building the foundational platform that will unite the machine learning space,” said Falcon.

Read next:

• 10 Top Data Companies
• 8 Top Data Startups

The post Enterprise Software Startups: What It Takes To Get VC Funding appeared first on IT Business Edge.

Unfortunately, Match had challenges in making the strategy work. In the latest shareholder letter, Match CEO Bernard Kim noted: “I’ve instructed the Hyperconnect team to iterate but not invest heavily in metaverse at this time. We’ll continue to evaluate this space carefully, and we will consider moving forward at the appropriate time when we have more clarity on the overall opportunity and feel we have a service that is well-positioned to succeed.”

This is not a one-off. Even the giant Meta has had its own problems. This is despite the company’s enormous resources and global user base. During the past year, the stock price has plunged from $378 to $178.

The metaverse clearly faces some challenges.

See also: How Revolutionary Are Meta’s AI Efforts?

First-mover Opportunities in the Enterprise?

The metaverse’s early challenges should come as no surprise. It’s never easy to launch new technologies.

But for the metaverse, the first-mover opportunities may actually not be in the consumer space.  Surprise: They may emerge in the enterprise.

“Businesses today are already leveraging the metaverse to drive new interactions,” said Matt Barrington, Principal of Digital & Emerging Technologies, EY. “These experiences are driven by both existing technology stacks and Web 3.0 technology stacks, bringing in new business models and ways to create, store, and exchange value. We are seeing mass experimentation across the market as companies explore business-relevant use cases and assess the impact of the metaverse on their business and customers.”

So let’s take a deeper look at the enterprise opportunities in the new virtual world of the metaverse.

Metaverse Applications

When it comes to the consumer metaverse, the types of use cases are limited. It’s really about gaming-type experiences. In terms of monetization, there is the purchase of digital items, subscriptions, and sponsorships. Interestingly enough, there are various brands that have purchased virtual real estate on the metaverse.

But as for the enterprise, there are seemingly endless applications. In fact, each industry can have its own set of metaverses.

“Consistent with the findings of our recent Metaverse surveys, using metaverse environments for purposes of delivering new experiences to the workforce for training, onboarding or recruiting are immediate use cases,” said Emmanuelle Rivet, Vice Chair, U.S. TMT and Global Technology Leader, PwC. “In addition, metaverse environments provide a place for connecting and engaging with a dispersed workforce including front line workers who may feel detached from the ‘center’ or ‘corporate.’ This is interesting but it also provides the opportunity for employees to be exposed to the metaverse, get familiar with it and effectively be up-skilled by experimentation, providing a platform for innovation and development of more use cases for companies.”

There are also interesting use cases with digital twins of physical environments that can be made hyper realistic and physically accurate.

“The physical environment to be replicated may be natural, or it may be something that was constructed, such as a building or other type of structure, an industrial operation, or a transportation network,” said Andrew Blau, Managing Director, U.S. Leader, Eminence & Insights, Deloitte Consulting. “Humans, robots, and AI agents can work together inside these digital twins to plan, design, and test—accelerating innovation and planning cycles for a variety of business needs.”

Also read: The Metaverse: Catching the Next Internet-Like Wave

Metaverse Strategies

The playbook for the metaverse is still in the early stages. Mistakes will be inevitable. But there are some guidelines that will help.

“Employees and customers are both looking for new experiences in the metaverse – and that means ensuring that virtual avatars, augmented reality and other forms of interaction are user-friendly enough to make collaboration and training simpler than it is in real life,” said Adrian McDermott, CTO of Zendesk.  “You need to prioritize immersion.”

And yes, there will need to be much due diligence of the tech stacks. They can be expensive and complicated.

“Firms need trusted technology partners that build, or vet and collaborate with, the best-in-class technology, as well as the means to plan, deploy and manage the technology so solutions that accelerate business today don’t become a roadblock tomorrow,” said Vishal Shah, General Manager of XR and Metaverse, Lenovo. “This also requires an open solution to always make the best hardware and software components for the use cases. … The fact is ‘Open’ always wins and will again in this new world.”

Another part of the strategy – which can easily be overlooked – is finance transformation.  Without this, the chances of success decline precipitously.

“Organizations will need to develop completely different approaches to finance, accounting, risk and compliance processes to sustain all of the major innovations coming with the metaverse, including monetization and metaverse economy innovations such as crypto currency and NFTs,” said Brajesh Jha, SVP & Global Head of Media, Publishing and Entertainment, Genpact.

Don’t Get Left Behind

The temptation for enterprises, though, is to take a wait-and-see approach with the metaverse.  But this could mean falling behind competitors. And it may be extremely tough to catch up.

“The metaverse presents a significant opportunity for business,” said Mike Storiale, VP, Innovation Development, Synchrony. “This is potentially a new dimension of commerce that we haven’t seen since the late 1990s with e-commerce.”

Read next: The Value of the Metaverse for Small Businesses

The post Metaverse’s Biggest Potential Is In Enterprises appeared first on IT Business Edge.

But for purposes of this article, the term will be narrower. The focus will be on those operators that build platforms and tools to leverage data – one of the most important technologies in enterprises these days.

Yet even this category still has many companies. For example, if you do a search for data analytics on G2, you will see results for over 2,200 products.

So when coming up with a list of top data companies, it will be, well, imperfect. Regardless, there are companies that are really in a league of their own, from established names to fast-growing startups, publicly traded and privately held. Let’s take a look at 10 of them.

Also see out picks for Top Data Startups.

Databricks

In 2012, a group of computer scientists at the University of California, Berkeley, created the open source project, Apache Spark. The goal was to develop a distributed system for data over a cluster of machines.

From the start, the project saw lots of traction, as there was a huge demand for sophisticated applications like deep learning. The project’s founders would then go on to create a company called Databricks.

The platform combines a data warehouse and data lakes, which are natively in the cloud. This allows for much more powerful analytics and artificial intelligence applications. There are more than 7,000 paying customers, such as H&M Group, Regeneron and Shell. Last summer, the ARR (annual recurring revenue) hit $600 million.

About the same time, Databricks raised $1.6 billion in a Series H funding and the valuation was set at a stunning $38 billion. Some of the investors included Andreessen Horowitz, Franklin Templeton and T. Rowe Price Associates. An IPO is expected at some point, but even before the current tech stock downturn, the company seemed in no hurry to test the public markets.

We’ve included Databricks on our lists of the Top Data Lake Solutions, Top DataOps Tools and the Top Big Data Storage Products.

SAS

SAS (Statistical Analysis System), long a private company, is one of the pioneers of data analytics. The origins of the company actually go back to 1966 at North Carolina State University. Professors created a program that performed statistical functions using the IBM System/360 mainframe. But when government funding dried up, SAS would become a company.

It was certainly a good move. SAS would go on to become the gold standard for data analytics. Its platform allows for AI, machine learning, predictive analytics, risk management, data quality and fraud management.

Currently, there are 80,800 customers, which includes 88 of the Top 100 on the Fortune 500.  There are 11,764 employees and revenues hit $3.2 billion last year.

SAS is one of the world’s largest privately-held software companies. Last summer, SAS was in talks to sell to Broadcom for $15 billion to $20 billion. But the co-founders decided to stay independent and despite having remained private since the company’s 1976 founding, are planning an IPO by 2024.

It should surprise absolutely no one that SAS made our list of the top data analytics products.

Snowflake

Snowflake, which operates a cloud-based data platform, pulled off the largest IPO for a software company in late 2020. It raised a whopping $3.4 billion. The offering price was $120 and it surged to $254 on the first day of trading, bringing the market value to over $70 billion. Not bad for a company that was about eight years old.

Snowflake stock would eventually go above $350. But of course, with the plunge in tech stocks, the company’s stock price would also come under extreme pressure. It would hit a low of $110 a few weeks ago.

Despite all this, Snowflake continues to grow at a blistering pace. In the latest quarter, the company reported an 85% spike in revenues to $422.4 million and the net retention rate was an impressive 174%. The customer base, which was over 6,300, had 206 companies with capacity arrangements that led to more than $1 million in product revenue in the past 12 months.

Snowflake started as a data warehouse. But the company has since expanded on its offerings to include data lakes, cybersecurity, collaboration, and data science applications. Snowflake has also been moving into on-premises storage, such as querying S3-compatible systems without moving data.

Snowflake is actually in the early stages of the opportunity. According to its latest investor presentation, the total addressable market is about $248 billion.

Like Databricks, Snowflake made our lists of the best Data Lake, DataOps and Big Data Storage tools.

Splunk

Founded in 2003, Splunk is the pioneer in collecting and analyzing large amounts of machine-generated data. This makes it possible to create highly useful reports and dashboards.

A key to the success of Splunk is its vibrant ecosystem, which includes more than 2,400 partners. There is also a marketplace that has over 2,400 apps.

A good part of the focus for Splunk has been on cybersecurity. By using real-time log analysis, a company can detect outliers or unusual activities.

Yet the Splunk platform has shown success in many other categories. For example, the technology helps with cloud migration, application modernization, and IT modernization.

In March, Splunk announced a new CEO, Gary Steele. Prior to this, he was CEO of Proofpoint, a fast-growing cloud-based security company.

On Steele’s first earnings report, he said: “Splunk is a system of record that’s deeply embedded within customers’ businesses and provides the foundation for security and resilience so that they can innovate with speed and agility. All of this translated to a massive, untapped, unique opportunity, from which I believe we can drive long-term durable growth while progressively increasing operating margins and cash flow.”

Cloudera

While there is a secular change towards the cloud, the reality is that many large enterprises still have significant on-premises footprints. A key reason for this is compliance. There is a need to have much more control over data because of privacy requirements.

But there are other areas where data fragmentation is inevitable. This is the case for edge devices and streaming from third parties and partners.

For Cloudera – another one of our top data lake solutions – the company has built a platform that is for the hybrid data strategy. This means that customers can take full advantage of their data everywhere.

Holger Mueller at Constellation Research praises Cloudera’s reliance on the open source Apache Iceberg technology for the Cloudera Data Platform.

“Open source is key when it comes to most infrastructure-as-a-service and platform-as-a-service offerings, which is why Cloudera has decided to embrace Apache Iceberg,” Mueller said. “Cloudera could have gone down a proprietary path, but adopting Iceberg is a triple win. First and foremost, it’s a win for customers, who can store their very large analytical tables in a standards-based, open-source format, while being able to access them with a standard language. It’s also a win for Cloudera, as it provides a key feature on an accelerated timeline while supporting an open-source standard. Last, it’s a win for Apache, as it gets another vendor uptake.”

Last year, Cloudera reported revenues over $1 billion. Among its thousands of customers, they include over 400 governments, the top ten global telcos and nine of the top ten healthcare companies.

Also read: Top Artificial Intelligence (AI) Software for 2022

MongoDB

The founders of MongoDB were not from the database industry. Instead, they were pioneers of Internet ad networks. The team – which included Dwight Merriman, Eliot Horowitz and Kevin Ryan – created DoubleClick, which launched in 1996. As the company quickly grew, they had to create their own custom data stores and realized that traditional relational databases were not up to the job.  

There needed to be a new type of approach, which would scale and allow for quick innovation.  So when they left DoubleClick after selling the company to Google for $3.1 billion, they went on to develop their own database system. It was  based on an open source model and this allowed for quick distribution.

The underlying technology relied on a document model and was called NoSQL. It provided for a more flexible way for developers to code their applications. It was also optimized for enormous transactional workloads.

The MongoDB database has since been downloaded more than 265 million times. The company has also added the types of features required by enterprises, such as high performance and security.  

During the latest quarter, revenues hit $285.4 million, up 57% on a year-over-year basis. There are over 33,000 customers.

To keep up the growth, MongoDB is focused on taking market share away from the traditional players like Oracle, IBM and Microsoft. To this end, the company has built the Relational Migrator. It visually analyzes relational schemas and transforms them into NoSQL databases.

Confluent

When engineers Jay Kreps, Jun Rao and Neha Narkhede worked at LinkedIn, they had difficulties creating infrastructure that could handle data in real time. They evaluated off-the-shelf solutions but nothing was up to the job.

So the LinkedIn engineers created their own software platform. It was called Apache Kafka and it was open sourced. The software allowed for high-throughput, low latency data feeds.

From the start, Apache Kafka was popular. And the LinkedIn engineers saw an opportunity to build a company around this technology in 2014. They called it Confluent.

The open source strategy was certainly spot on. Over 70% of the Fortune 500 use Apache Kafka.

But Confluent has also been smart in building a thriving developer ecosystem. There are over 60,000 meet-up members across the globe. The result is that developers outside Confluent have continued to build connectors, new functions and patches.

In the most recent quarter, Confluent reported a 64% increase in revenues to $126 million.  There were also 791 customers with $100,000 or more in ARR (Annual Recurring revenue), up 41% on a year-over-year basis.

Datadog

Founded in 2010, Datadog started as an operator of a real-time unified data platform. But this certainly was not the last of its new applications.

The company has been an innovator – and has also been quite successful getting adoption for its technologies. The other categories Datadog has entered include infrastructure monitoring, application performance monitoring, log analysis, user experience monitoring, and security. The result is that the company is one of the top players in the fast-growing market for observability. 

Datadog’s software is not just for large enterprises. In fact, it is available for companies of any size.

Thus, it should be no surprise that Datadog has been a super-fast grower. In the latest quarter, revenues soared by 83% to $363 million. There were also about 2,250 customers with more than $100,000 in ARR, up from 1,406 a year ago.

A key success factor for Datadog has been its focus on breaking down data silos. This has meant much more visibility across organizations.  It has also allowed for better AI.

The opportunity for Datadog is still in the early stages. According to analysis from Gartner, spending on observability is expected to go from $38 billion in 2021 to $53 billion by 2025.

See the Top Observability Tools & Platforms

Fivetran

Traditional data integration tools rely on Extract, Transform and Load (ETL) tools. But this approach really does not handle modern challenges, such as the sprawl of cloud applications and storage.

What to do? Well, entrepreneurs George Fraser and Taylor Brown sought out to create a better way. In 2013, they cofounded Fivetran and got the backing of the famed Y Combinator program.

Interestingly enough, they originally built a tool for Business Intelligence (BI). But they quickly realized that the ETL market was ripe for disruption. 

In terms of the product development, the founders wanted to greatly simplify the configuration. The goal was to accelerate the time to value for analytics projects. Actually, they came up with the concept of zero configuration and maintenance. The vision for Fivetran is to make “business data as accessible as electricity.”

Last September, Fivetran announced a stunning round of $565 million in venture capital. The valuation was set at $5.6 billion and the investors included Andreessen Horowitz, General Catalyst, CEAS Investments, and Matrix Partners.

Tecton

Kevin Stumpf and Mike Del Balso met at Uber in 2016 and worked on the company’s AI platform, which was called Michelangelo ML. The technology allowed the company to scale thousands of models in production. Just some of the use cases included fraud detection, arrival predictions and real-time pricing.

This was based on the first feature store. It allowed for quickly spinning up ML features that were based on complex data structures.

However, this technology still relied on a large staff of data engineers and scientists. In other words, a feature store was mostly for the mega tech operators.

But Stumpf and Del Balso thought there was an opportunity to democratize the technology. This became the focus of their startup, Tecton, which they launched in 2019.

The platform has gone through various iterations. Currently, it is essentially a platform to manage the complete lifecycle of ML features. The system handles storing, sharing and reusing feature store capabilities. This allows for the automation of pipelines for batch, streaming and real-time data.

In July, Tecton announced a Series C funding round for $100 million. The lead investor was Kleiner Perkins. There was also participation from Snowflake and Databricks.

Read next: 5 Top VCs For Data Startups

The post 10 Top Data Companies appeared first on IT Business Edge.

“Observability goes beyond traditional monitoring and is especially relevant as infrastructure and application landscapes become more complex,” said Joseph George, Vice President of Product Management, BMC.  “Increased visibility gives stakeholders greater insight into issues and user experience, reducing time spent firefighting, and creating time for more strategic initiatives.”

Observability is an enormous category. It encompasses log analytics, application performance monitoring (APM), and cybersecurity, and the term has been applied in other IT areas like networking. For example, in terms of APM, spending on the technology is expected to hit $6.8 billion by 2024, according to Gartner.

So then, what makes observability unique? And why is it becoming a critical part of the enterprise tech stack? Well, let’s take a look.

Also read: Top Observability Tools & Platforms

How Observability Works

The ultimate goal of observability is to go well beyond traditional monitoring capabilities by giving IT teams the ability to understand the health of a system at a glance.

An observability platform has several important functions. One is to find the root causes of a problem, which could be a security breach or a bug in an application. In some cases, the system will offer a fix. Sometimes an observability platform will make the corrections on its own.

“Observability isn’t a feature you can install or a service you can subscribe to,” said Frank Reno, Senior Product Manager, Humio. “Observability is something you either have, or you don’t. It is only achieved when you have all the data to answer any question about the health of your system, whether predictable or not.”

The traditional approach is to crunch huge amounts of raw telemetry data and analyze it in a central repository. However, this could be difficult to do at the edge, where there is a need for real-time solutions.

“An emerging alternative approach to observability is a ‘small data’ approach, focused on performing real-time analysis on data streams directly at the source and collecting only the valuable information,” said Shannon Weyrick, vice president of research, NS1. “This can provide immediate business insight, tighten the feedback loop while debugging problems, and help identify security weaknesses. It provides consistent analysis regardless of the amount of raw data being analyzed, allowing it to scale with data production.”

Also read: Observability’s Growth to Evolve into Automation Solutions in 2022

The Levers for Observability

The biggest growth factor for observability is the strategic importance of software. It’s become a must-have for most businesses.

“Software has become the foundation for how organizations interact with their customers, manage their supply chain, and are measured against their competition,” said Patrick Lin, VP of Product Management for Observability, Splunk. “Particularly as teams modernize, there are a lot more things they have to monitor and react to — hybrid environments, more frequent software changes, more telemetry data emitted across fragmented tools, and more alerts. Troubleshooting these software systems has never been harder, and the way monitoring has traditionally been done just doesn’t cut it anymore.”

The typical enterprise has dozens of traditional tools for monitoring infrastructure, applications and digital experiences. The result is that there are data silos, which can lessen the effectiveness of those tools. In some cases, it can mean catastrophic failures or outages.

But with observability, the data is centralized. This allows for more visibility across the enterprise.

“You get to root causes quickly,” said Lin. “You understand not just when an issue occurs but what caused it and why. You improve mean time to detection (MTTD) and mean time to resolution (MTTR) by proactively detecting emerging issues before customers are impacted.”

Also read: Dynatrace vs Splunk: Monitoring Tool Comparison

Observability Challenges

Of course, observability is not a silver bullet. The technology certainly has downsides and risks.  

In fact, one of the nagging issues is the hype factor. This could ultimately harm the category.  “There is a significant amount of observability washing from legacy vendors, driving confusion for end users trying to figure out what observability is and how it can benefit them,” said Nick Heudecker, Senior Director of Market Strategy & Competitive Intelligence, Cribl.

True, this is a problem with any successful technology. But customers definitely need to do the due diligence.

Observability also is not a plug-and-play technology.There is a need for change management. And yes, you must have a highly skilled team to get the max from the technology.

“The biggest downside of observability is that someone – such as an engineer or a person from DevOps or the site reliability engineering (SRE) organization — needs to do the actual observing,” said Gavin Cohen, VP of Product, Zebrium.  “For example, when there is a problem, observability tools are great at providing access and drill-down capabilities to a huge amount of useful information. But it’s up to the engineer to sift through and interpret that information and then decide where to go next in the hunt to determine the root cause. This takes skill, time, patience and experience.”

Although, with the growth in artificial intelligence (AI) and machine learning (ML), this can be addressed. In other words, the next-generation tools can help automate the observer role. “This requires deep intelligence about the systems under observation, such as with sophisticated modeling, granular details and comprehensive AI,” said Kunal Agarwal, founder and CEO, Unravel Data.

Read next: AI and Observability Platforms to Alter DevOps Economics

The post Observability: Why It’s a Red Hot Tech Term appeared first on IT Business Edge.

Just look at Sequoia. On May 16, the top VC firm made a presentation to its portfolio companies entitled “Adapting to Endure.” It noted that the economy was at a “crucible moment” and founders need to be careful with cash burn rates.

Despite all this, top venture capitalists understand that some of the best opportunities come during hard times. Besides, there remain plenty of secular trends that will continue to drive growth.

One is data. There’s little argument from CEOs that this is a strategic asset. However, there needs to be effective tools to get value from data, and that will continue to drive investment in data startups for some time to come.

Here we’ll look at five of the top venture capital firms for data – along with some insight into where they see current investment opportunities.

Also read:

• 10 Top Data Companies
• 8 Top Data Startups

Accel

Founded in 1983, Accel has invested in many categories over the years, like consumer, media, security, ecommerce and so on. But the firm has also shown strong data chops.

Its most iconic investment occurred in the summer of 2005. Accel agreed to invest $12.7 million in Facebook – which is now called Meta – for a 10.7% stake.

In terms of its enterprise data deals, they include companies like UiPath, Cloudera, Atlassian and Slack. As for recent investments, there is the $60 million funding of Cyera. The company has built a cloud-native data security platform that evaluates whether data – on AWS, Azure and GCP — is sensitive and vulnerable to risk. This is all done in real time.

Accel just raised a mega $4 billion fund that is focused on late-stage deals, an impressive display of confidence by the firm’s limited partners (LPs). This is certainly a contrarian bet as this category of investments has softened during the past year. But with valuations much more attractive, the timing could actually be good for Accel.

Greylock

Another name with some staying power, 24-year-old Greylock Partners focuses on enterprise and consumer software companies. The investments span early seed levels to later stages. In fact, the firm will incubate some of its deals at its offices. This was the case with companies like Palo Alto Networks, Workday and Sumo Logic.

One of Greylock’s best deals was for LinkedIn. The firm invested in the startup – when it had fewer than one million members – a year after its founding in 2004.

Then in 2016, Microsoft agreed to acquire LinkedIn for $26.5 billion. Reid Hoffman, who is the cofounder of LinkedIn, is currently a partner at Greylock.

An interesting recent funding for a data startup is for Baseten. The company’s system allows for fast and easy migration of machine learning to production applications. It automates the complex backend and MLOps processes. Greylock participated in the seed and Series A financings.

Sequoia

Sequoia is one of the pioneers of the venture capital industry. Don Valentine founded the firm in 1972 and he raised his first fund a couple years later. It wasn’t easy, as he had to convince investors about the potential benefits of investing in startups. At the time, it was a fairly radical concept for institutions.

But Valentine had a knack for finding the next big thing. For example, he was an early investor in Atari and Apple.

This was just the beginning. Sequoia would go on to have one of the best track records in venture capital. Just some of its huge winners include Snowflake, Stripe, WhatsApp, ServiceNow, Cisco, Yahoo! and Google.

No doubt, a big part of the investment thesis for Sequoia is on data. For example, in early June the firm led a $4.5 million seed round for CloseFactor. The startup leverages sophisticated machine learning to customize sales pitches and target the right prospects. The system has shown 2-to-4 times improvements in the quality of pipelines.

Also read: Top 7 Data Management Trends to Watch in 2022

Andreessen Horowitz

It usually takes at least a decade to become an elite venture firm. The reason is that early-stage investments generally need lots of time to generate breakout returns.

But for Andreessen Horowitz, it was able to become an elite firm within a few years. Then again, it certainly helped that its founders are visionary entrepreneurs Marc Andreessen and Ben Horowitz.

Yet they also set out to disrupt the traditional model for venture capital. For example, it set out to operate like a Hollywood talent agency. Andressen Horowitz hired specialists to help entrepreneurs with many parts of their business, such as PR, sales, marketing, and design.

The formula has been a winner. Some of Andressen Horowitz’s notable investments include: Stripe, Databricks, Plaid, Figma, Tanium and GitHub. And yes, many other venture capital firms have replicated the model.

As for a recent data deal from Andreessen Horowitz, there is the $100 million Series D funding for Imply Data (the valuation came to $1.1 billion). The founders of the company are the creators of Apache Druid, which is an open source database for analytics applications. With Imply, it has focused on the large market for developers building analytics applications.

Andreessen Horowitz certainly has lots of fire power for many more deals. In January, the company announced $9 billion in new capital for venture opportunities, growth stage and biotech.

Lightspeed

Lightspeed got its start at the depths of the dotcom bust – October 2000. But the timing would be propitious. The firm had fresh capital and the valuations were much more attractive.

In the early days, Lightspeed was focused on consumer startups. For example, it was an early investor in Snapchat. Lightspeed contributed $485,000 in the seed round.

However, during the past decade, Lightspeed has upped its game with enterprise software and infrastructure opportunities. Some of its standout deals include AppDynamics, MuleSoft, and Nutanix.

Among recent data deals for Lightspeed, Redpanda Data is one that stands out. The venture capital firm led a $50 million Series B round. Redpanda has built a streaming platform for developers. Think of it as a system of record for real-time and historical data.

In 2020, Lightspeed raised three funds for a total of $4.2 billion. The firm is now seeking about $4.5 billion for its next set of financing vehicles.

Read next: Top Artificial Intelligence (AI) Software 2022

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But his motto could also apply to data. If anything, the opportunity could be much larger. Data is becoming a competitive advantage for many companies.

Yet that data can be difficult to process. The fact is that it’s common for AI and analytics projects to fail or underperform.  

But there is good news. There are startups that are developing tools to help companies with their data journeys.

Here’s a look at eight of them to put on your radar. No Databricks in here, which has become so big that the next step is likely an IPO, but there are some billion-dollar “unicorn” valuations even in a slowing market.

Also read: Data Startups: Why the Eye-Popping Funding Rounds?

People Data Labs

People Data Labs (PDL) is focused on B2B and professional data. By processing resumes, the company has been able to provide valuable insights for recruiting, market research, sales and marketing.

“We see every company in the world building data solutions,” said PDL CEO Sean Thorne. “This is a rapidly growing market.’

The company does not focus on selling flat files of leads or contracts, which is the traditional approach. Instead, it uses a data-as-a-service model and is part of the AWS Data Exchange platform. This makes it easier to provide data to customers in an easy-to-use format.

In 2021, PDL raised $45 million in a Series B round of funding.

Airbyte

Airbyte is focused on rethinking the data integration market. The company’s technology is based on an open source platform, which has supercharged adoption and innovation. There are more than 20,000 companies on the system and the community includes about 7,000 data practitioners.

A key to Airbyte is that it can handle virtually any data pipeline, such as with database replication and long-tail and custom connectors. There is no need for in-house data engineers to maintain the systems.

Last year, the company raised more than $181 million.

Imply

The founders of Imply are the creators of Apache Druid, which is an open source database system for high-performance, real-time analytics. This experience has been critical in evolving the technology and tailoring it to the needs of enterprise customers.

The target end-user is software developers. With Imply, they can create sophisticated analytics applications.

“While adoption of Druid started with digital natives like Netflix, AirBnB and Pinterest, increasingly enterprises in the Fortune 1000 are recognizing the value of analytics applications as a way of differentiating their businesses,” said Fangjin Yang, CEO and cofounder, Imply. “And that’s what’s fueling the tremendous market opportunity for our category of real-time analytics databases.”

This year, the company raised $100 million at a $1.1 billion valuation.

Also read: Best Database Management Software 2022

MinIO

A majority of data is unstructured, which can be difficult to store and manage.

This is where MinIO comes in. Consider that its system gets over 1 million Docker pulls per day and more than half the Fortune 500 use the technology.

“The market for MinIO’s object storage product can be described simply: everywhere AWS S3 isn’t,” said Garima Kapoor, COO and cofounder, MinIO. “Even accounting for AWS’s size, this is a massive market. MinIO delivers AWS S3-like infrastructure across any cloud, virtual or bare-metal deployment scenario.”

To date, the company has raised $126 million.

Cribl

A major challenge for enterprises is dealing with diverse sources of data. But for Cribl, this has been a great opportunity. The company has built an open and interoperable platform to manage data better and get more value from it.

“What we hear from our IT and security customers is that they have an array of important tools they use across the enterprise but none of those tools talk to one another,” said Nick Heudecker, Senior Director, Market Strategy & Competitive Intelligence, Cribl. “Cribl’s solutions are open by design, seek to connect the disparate parts of the data ecosystem – such as complementing tools like Datadog, Exabeam, and Elastic — and give customers choice and control over all the event data that flows through their corporate IT systems.”

For fiscal year 2021, the company more than tripled its customer count. Ten of the 50 Fortune companies have signed on.

Cribl has raised a total of $254 million since inception.

Observable

Observable operates a SaaS platform for real-time data collaboration, visualization and analysis. The founders created the company because of their frustration of constant “tool hopping” with existing data products. This made the process error-prone, tedious and slow.

Observable is JavaScript-native, which helps to lower the learning curve. The company also has the benefit of a large community of 5 million users. This has resulted in the largest public library of data visualizations.

In all, the company has raised $46.1 million.

Reltio

Reltio is a cloud-native platform that focuses on the master data management category. There are many legacy players in the market, such as Informatica, Tibco, IBM, SAP and Oracle. As for Reltio, it sees an opportunity for disruption.

“We have various integration options, including a low-code/no-code solution, that allow for rapid deployment and time to value,” said Manish Sood, founder and CTO, Reltio. “Our system also uses machine learning to discover deeper data insights and improve data quality. Then there is built-in workflow management, which helps simplify compliance requirements and improve information stewardship productivity.”

The company counts 14 of the Fortune 100 as customers. To date, it has raised $237 million, with a valuation at over $1.7 billion.

TigerGraph

TigerGraph is a system that allows for advanced analytics and AI with connected data. The technology has diverse applications, such as for anti-money laundering, fraud detection, IoT (Internet of Things) and network analysis.

Traditional analytics systems are built on relational databases. But this can be expensive and rigid. It can also be more difficult to leverage next-generation analytics like deep learning.

This is why graph databases are becoming more popular. “Customers want to model their data from the viewpoint of the customer, supplier, or whatever entity they want to analyze and how they interact with the company across systems like CRMs, procurement, logistics and so on,” said Todd Blaschka, COO, TigerGraph.

Last year, the company raised $105 million in a Series C funding.  

A tougher market in 2022?

2022 may not give us as many eye-popping funding rounds, but if any area stays strong, it’s likely to be the startups fueling the data analytics craze.

Read next: Top Artificial Intelligence (AI) Software 2022

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dbt Labs’s evolving strategy paid off in a big way. In February, the company announced a $222 million Series D round (the total raised since inception is $413.4 million). Some of the investors included Altimeter, Andreeson Horowitz and Sequoia. There were also strategic investors like Databricks and Snowflake – two of the hottest Big Data companies out there.

The interesting thing is dbt Labs wasn’t even actively trying to raise money when investors came calling.

“The process happened fairly quickly,” said Tristan Handy, co-founder and CEO of dbt Labs.  “Our existing investors had expressed strong interest to invest and new investors also reached out along the way. We weren’t actively looking but wanted to plan ahead for the next few years to give us room to aggressively invest in our business as needed. It’s always better to raise when we don’t need to.”

Of course, the funding success of dbt Labs is not an outlier. Data startups have seen a surge in interest. According to PitchBook, the venture funding in the space tripled from $2.5 billion in 2020 to $7.5 billion in 2021. As for this year, the investments have come to about $2.4 billion so far.

So what are the industry drivers? And might the good times continue? Let’s take a look.

Also read: Top 7 Data Management Trends to Watch in 2022

The Data Explosion

Data growth continues at a rapid clip. IDC projects that the rate will be 23% per year, reaching a staggering 175 zettabytes by 2025 (a zettabyte is a trillion gigabytes). Yet there is something interesting about this analysis. Only about 2% is saved or retained. In other words, much of the available data goes wasted. But therein lies the opportunity for data startups.

“We need new and innovative ways to store large data sets as efficiently as possible,” said Michael O’Malley, SVP of Strategy, SenecaGlobal.  “We need ways to quickly analyze, compare and retrieve data records in these datasets so that AI and machine learning can offer more real-time or near real-time insights.”

The Breakthrough Data Company

History shows that data can be an incredibly valuable business. Just look at Oracle. The company was able to leverage a technology it did not invent – that is, the relational database – into a massive business. Despite all the innovation since the company was founded in the mid-1970s, Oracle remains a dominant player.

“When considering both the strategic value of data and its broad appeal, growth in data companies tend to grow exponentially when they find true product-market fit,” said Will Lin, Managing Director, Forgepoint Capital. “As data becomes a competitive advantage for organizations wanting to better understand everything, VCs tend to look for 10x improvements in this space, either individually or collectively between ease of use, better results, and cheaper to store. These are the hallmarks of standout data startups and what drives larger funding rounds.”

But of course, there are some elements that are not necessarily about the technology. There needs to be a rock-solid business sales and marketing organization. Again, Oracle showed the importance of this.

“VCs invest in people,” said O’Malley.  “Visionary leaders have the ability to form and grow teams and drive focus to get things done. Good leaders also understand what their people excel at and find outside experts to complement their team’s strong suits.”

Also read: AI Suffers from Bias—But It Doesn’t Have To

The Bear Market

After a 10+ year bull market for tech stocks, the category has come under tremendous pressure lately. The Federal Reserve’s tightening of monetary policy is having an impact. Since late December, the NASDAQ-100 Technology Sector Index has plunged from 9764 to 7064 or 27%.

Some of the high-flier data companies have suffered significant losses. For example, Snowflake’s shares have gone from a high of $405 to $166.

This does not necessarily mean that funding will dry up. The fact is that there is still large amounts of venture capital sloshing around. In one hopeful sign, Pyramid Analytics today announced a $120 million Series E funding round – an oversubscribed round exceeding the company’s target by $20 million. For startups offering genuine solutions to data challenges, investor demand will continue to be there.

But valuations will inevitably get more tempered and it could get tougher for some companies to attract interest from investors.

“The most important component to consider has less to do with valuation and more to do with budgets,” said Lin. “How do customers store, transform and analyze more data and with more techniques if their budgets slow or reduce?”

Read next: Top Artificial Intelligence (AI) Software 2022

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But as the cloud becomes more pervasive, it may be tougher to keep up the growth.

“More and more of those workloads will live in the cloud, but that will take time, and inevitably, some of them will probably never move over,” said AWS CEO Adam Selipsky at last year’s VMworld conference.

The reality of increasingly demanding regulations, especially in financial services in healthcare, is proving to be an obstacle for cloud growth.

According to Gartner, about 65% of the world’s population will have their data covered by privacy regulations. This is up from 10% in 2020. In light of this, it should be no surprise that enterprises still see on-premises environments as a good option.

“The last great barrier to migrating IT to the cloud is addressing the understandable fears of the chief information security officer (CISO) regarding data security,” said Ayal Yogev, CEO and co-founder of Anjuna Security. “There’s good reason to be concerned; workloads and data executed and stored in the public cloud expose data to too many people—good and bad.”

Yet, there are emerging innovations in security to make it more palatable to migrate sensitive workloads to the cloud. One of them is confidential computing, which provides security at the chip level. It has been showing lots of traction during the past few years.

Also read: 5 Emerging Cloud Computing Trends for 2022

What is Confidential Computing?

Simply put, data has three states. It can be at rest, in use, or in transit.

It’s true that data can be encrypted while it is in the first two states, yet there are still vulnerabilities when it is in use or being processed. The reason is that before the application can be executed, the data is temporarily unencrypted, allowing just enough time for an intrusion.

To secure this data, there needs to be another layer of protection, which is embedded in the central processing unit (CPU). This is called a trusted execution environment (TEE), or enclave, which uses embedded encryption keys. The result is that the data remains protected while in memory.

“Memory and other resources will be protected by cryptographic keys that prevent unauthorized access to the data,” said Ben Richardson, senior software engineer at SecureW2. “Encrypting data in memory means it can’t be read by software that isn’t authorized to do so … even if the unauthorized software is running on the same physical server as the data.”

In 2016, Intel launched the first confidential computing system with its SGX platform. Since then, many other large vendors have adopted the technology, including IBM, Amazon, Advanced Micro Devices, Alibaba, Google, and Nvidia.

Also read: The ABCs of Smart Cloud Migration

Why Confidential Computing is Important

While working with third-party cloud providers can result in lower costs, there is the issue of trust and knowing whether the company is doing enough with security.

But with confidential computing, there is a zero-trust security. That is, the cloud provider does not have access to the data because there is no time when it lacks encryption. This certainly provides for confidence in moving workloads to the cloud.

“Confidential computing is ushering in a new era to enable analytics of sensitive data without violating privacy and confidentiality requirements,” said Baffle CEO Ameesh Divatia. “It will address a significant roadblock for enterprises wishing to migrate to the cloud because it prevents cloud administrators from being able to view their customers’ data.”

A report from Gartner predicts that by 2025 about 65% of large organizations will use one or more privacy-enhancing computations approaches. The report highlights that confidential computing will be one of the most important.

The Challenges and Future of Confidential Computing

Even though confidential computing has much promise, there are still some lingering issues.  For example, it can require substantial processing compute to enable it.

“Cloud providers are creating a dedicated infrastructure that supports confidential computing, but this approach creates a hardware dependency that may restrict customers’ ability to run across disparate cloud providers or pursue a multicloud strategy,” said Divatia.  “Existing application and database environments will need to be redesigned to work with confidential computing; although, there is scope for innovation to minimize the operational burden.”

Another problem is that it can be complicated to set up and manage. The fact is you need a team with deep technological skills.

Yet, startups are already beginning to address this issue. And given the importance of cloud migration, there will likely be more investment in confidential computing. It definitely helps that there has already been adoption from large chip manufacturers.

“With the rise of data consolidation in the cloud and the accompanying need for secure data sharing, organizations will need to protect their sensitive data in the cloud with new computation models that incorporate privacy-preserving analytics and innovative architectural models,” said Divatia.

Read next: Cloud Security Best Practices for 2022

The post The Emergence of Confidential Computing appeared first on IT Business Edge.

Qlik’s Data Literacy: The Upskilling Evolution report, which surveyed more than 1,200 executives and 6,000 employees, also revealed that approximately 21 percent of employees think their employers are preparing them for a data-oriented workplace and 40 percent are looking for a new role with better reskilling. Note that 78 percent are spending close to seven hours a month on learning about data.  

“Our research shows that only one in ten employees in finance, marketing and HR teams are offered data literacy training, despite the fact that approximately 70% of these staff need these skills on an everyday basis,” said Paul Barth, Global Head of Data Literacy, Qlik.  

Yet the survey also shows that 85% of business leaders believe that data literacy will become critical for their future. Roughly 89% say they expect their team to make data-driven decisions.

There is certainly a disconnect between goals and results, which can make it more difficult for enterprises to remain competitive and achieve meaningful digital transformation.

What can be done?  

Why is Data Literacy Important for Enterprises?

While artificial intelligence (AI) has made significant strides, there still needs to be a data-savvy workforce that understands how to use data to gain meaningful insights for effective decision making. 

“There will never be enough data scientists to solve all of a company’s data-related problems,” said Roman Stanek, CEO, GoodData. “This is exactly why ‘data citizens’ are needed in the enterprise. The fact is that many organizations do not provide any or enough training to all employees.”  

This is potentially an existential problem for companies. Data literacy should not just be the exclusive domain of tech experts.  

“Business leaders need to fully embrace data and analytics by putting the right people, processes, and technologies in place to improve current manual processes and, as a result, customer experience,” said Libby Duane Adams, co-founder and chief advocacy officer, Alteryx.  “Companies that aren’t democratizing data analytics, maximizing its value, and upskilling their people to perform transformative analysis will struggle to anticipate changing customer needs, strengthen supplier networks, and be on top of logistics in order to respond quickly.”

Also read: Bringing Data Democratization to Your Business

How to Reskill for Digital Literacy

Some companies will create their own courses for digital literacy. This can provide more customization for the organization’s needs. However, course development can be expensive, time consuming, and impractical for smaller enterprises.  

The good news is that there are many affordable and free courses available. Just a quick search on YouTube will show some of the quality content available.

But before procuring the curriculum, you need to have assessments.  

“Don’t rely on preconceptions or assumptions about team members’ current comfort level with data literacy,” said Barth.  “There are a variety of free assessment tools in the market, such as the [Data Literacy Project], to jump start this process.”

Hybrid learning is something else to consider. Different jobs have their own levels of data literacy. Thus, the curriculum needs to be tailored to them.  

“An effective way to encourage widespread data literacy is to promote role-based training programs where every employee is required to have some level of data knowledge based on their role,” said Stanek. “This can be complemented by an emphasis and training on data visualization tools where employees can simplify complex information.”

Data literacy should also become part of the culture. “Make data a part of meetings and communications and regularly ask for it from your team,” said Barth.  “But change takes time—don’t expect it to happen overnight.”

If you have data scientists, you should enlist them in the data literacy efforts. “Look to transition existing data scientists from ‘workers’ into ‘coaches’ to bring everyone on the journey and share best practices for data,” said Duane Adams.

Also read: Top Data Mining Tools for Enterprise 2022

Teaching Data Literacy

Data literacy is a huge topic that is constantly evolving. Enterprise leaders need to narrow it down and focus on those areas that have practical applications to their business. To get started there should be coverage of basic statistical concepts, including probabilities, distributions, means, variances, outliers, and bias.  

“Data usually represents complex underlying phenomena, and it doesn’t always speak clearly,” said Chris Nicholson, data science team lead, Clipboard Health.  “And sometimes it’s useful to go over high-school math you might have forgotten.”

Enterprises should also embrace “soft” skills. The goal is to provide the tools to help employees to transform data into actionable insights. 

“Data storytelling hones their ability to communicate effectively to a non-technical audience,” said Barth. “And collaboration skills enable teams to share their insights and stay aligned to deliver continuous improvements.”

Read next: How to Turn Your Business Data into Stories that Sell

The post Data Literacy is Key for Successful Digital Transformation appeared first on IT Business Edge.

“Most of the time, the work of a citizen developer is not directly requested by the manager or department but comes from a frustration with a process,” said Frédéric Harper, director of developer relations at Mindee.

Moreover, research from IDC claims that citizen developers will become increasingly important, with estimates that they will create over 500 million apps by 2023.

“Citizen developers are often in a better position than anyone else in their company to understand the gaps in processes and the business problems that need to be solved,” said Micah Smith, senior director of developer evangelism at Automation Anywhere.

Yet there are challenges. Let’s take a look at how you can best leverage citizen developers.

What Do Citizen Developers Do?

The citizen developer is a role that dates back to the 1980s. When Lotus 123 was the popular spreadsheet, there was the capability to create macros, which allowed business users to make sophisticated automations. This trend would continue as Microsoft Excel became the dominant platform.

While macros were powerful, they also could lead to major problems, including a lack of documentation. When a citizen developer leaves, it could be extremely difficult to adapt the macros. And the sprawl of macros may also conflict with other IT systems and make it difficult to manage.

Fortunately, citizen developers currently have access to LC/NC tools that are built for them. These platforms are not only easy to use but allow for integration with existing applications as well as systems for governance and compliance.

“This is where we see citizen developers shining today, working on marketing and customer interactions, putting together user experiences like surveys, forms, and other marketing-related activities,” said Christian Kelly, managing director at Accenture. “Internally, this translates into users configuring business applications to better serve their own needs.”

RPA (robotic process automation) has also been essential to citizen developers. This technology makes it easier to automate tedious and repetitive processes, which can allow for strong ROI (return on investment).

“The most commonly used RPA interface is the studio, allowing users to create and configure automation wizards in minutes,” said Harel Tayeb, CEO at Kryon. “Users without coding experience should be able to interact with a wizard design tool and easily create a bot.”

Also read: Effectively Using Low-Code/No-Code in the Developer Cycle

The Advantages

No doubt, finding and retaining tech talent is an enormous challenge. You need to compete against many companies, and the impact of COVID-19 affected much of the workforce, as many people reevaluated their career goals and paths.

Aside from that, experienced developers can also require a much higher compensation package that some smaller or mid-sized businesses can’t compete with. Using citizen developers can help with the talent shortage as well as alleviate the burden on IT teams.

Some other advantages may also include increased innovation and the development of custom apps.

Innovation

By employing citizen developers, you broaden the number of employees who can make contributions.

“Citizen developers often identify automation opportunities that go unnoticed by technical leaders,” said Palak Kadakia, VP of product management at UiPath. “They are an organization’s eyes and ears at the grassroots-level for inspiration.”

Custom Apps

Because of the domain expertise of citizen developers, they can create apps that are tailored to an organization’s particular needs.

“The most obvious benefit of the rise of citizen developers is the democratization of value-creation within an organization,” said Kelly. “Thanks to citizen developers, businesses have the ability to move faster and create more value for both internal and external stakeholders.”

Getting Real Value

To be successful with citizen developers, there needs to be a balance between independence and maintaining the requirements for security and quality. While LC/NC and RPA tools often have guardrails built in, there still needs to be a clear understanding of the policies. This can take time, and there will certainly still be mistakes along the way.

“Citizen developers’ main role is to solve a specific problem for themselves and their teammates, not rectify all technological inefficiencies across the business,” said Rachel Brennan, vice president at Bizagi.

But the benefits of citizen developers should outweigh the potential issues. A key is having a culture of experimentation and innovation.

“The process of creating digital products using these tools is a great way of increasing collaboration, bringing together IT and business leaders as never before,” said Kinjan Shah, senior architect at Infostretch.

In other words, it is not meant as a replacement for IT staff. They will continue to build mission-critical applications and systems.

“As citizen developers address their own technical hurdles themselves, IT organizations see their backlogs thin out, freeing up time and resources to focus on more-strategic projects such as digital transformation,” said Rich Waldron, CEO and co-founder at Tray.io.

Read next: Democratizing Software Development with Low-Code

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