data management
Auto Added by WPeMatico
Startups must curb bureaucracy to ensure agile data governance
By now, all companies are fundamentally data driven. This is true regardless of whether they operate in the tech space. Therefore, it makes sense to examine the role data management plays in bolstering — and, for that matter, hampering — productivity and collaboration within organizations.
While the term “data management” inevitably conjures up mental images of vast server farms, the basic tenets predate the computer age. From censuses and elections to the dawn of banking, individuals and organizations have long grappled with the acquisition and analysis of data.
By understanding the needs of all stakeholders, organizations can start to figure out how to remove blockages.
One oft-quoted example is Florence Nightingale, a British nurse who, during the Crimean war, recorded and visualized patient records to highlight the dismal conditions in frontline hospitals. Over a century later, Nightingale is regarded not just as a humanitarian, but also as one of the world’s first data scientists.
As technology began to play a greater role, and the size of data sets began to swell, data management ultimately became codified in a number of formal roles, with names like “database analyst” and “chief data officer.” New challenges followed that formalization, particularly from the regulatory side of things, as legislators introduced tough new data protection rules — most notably the EU’s GDPR legislation.
This inevitably led many organizations to perceive data management as being akin to data governance, where responsibilities are centered around establishing controls and audit procedures, and things are viewed from a defensive lens.
That defensiveness is admittedly justified, particularly given the potential financial and reputational damages caused by data mismanagement and leakage. Nonetheless, there’s an element of myopia here, and being excessively cautious can prevent organizations from realizing the benefits of data-driven collaboration, particularly when it comes to software and product development.
Taking the offense
Data defensiveness manifests itself in bureaucracy. You start creating roles like “data steward” and “data custodian” to handle internal requests. A “governance council” sits above them, whose members issue diktats and establish operating procedures — while not actually working in the trenches. Before long, blockages emerge.
Blockages are never good for business. The first sign of trouble comes in the form of “data breadlines.” Employees seeking crucial data find themselves having to make their case to whoever is responsible. Time gets wasted.
By itself, this is catastrophic. But the cultural impact is much worse. People are natural problem-solvers. That’s doubly true for software engineers. So, they start figuring out how to circumvent established procedures, hoarding data in their own “silos.” Collaboration falters. Inconsistencies creep in as teams inevitably find themselves working from different versions of the same data set.
Powered by WPeMatico
No-code business intelligence service y42 raises $2.9M seed round
Berlin-based y42 (formerly known as Datos Intelligence), a data warehouse-centric business intelligence service that promises to give businesses access to an enterprise-level data stack that’s as simple to use as a spreadsheet, today announced that it has raised a $2.9 million seed funding round led by La Famiglia VC. Additional investors include the co-founders of Foodspring, Personio and Petlab.
The service, which was founded in 2020, integrates with more than 100 data sources, covering all the standard B2B SaaS tools, from Airtable to Shopify and Zendesk, as well as database services like Google’s BigQuery. Users can then transform and visualize this data, orchestrate their data pipelines and trigger automated workflows based on this data (think sending Slack notifications when revenue drops or emailing customers based on your own custom criteria).
Like similar startups, y42 extends the idea data warehouse, which was traditionally used for analytics, and helps businesses operationalize this data. At the core of the service is a lot of open source and the company, for example, contributes to GitLabs’ Meltano platform for building data pipelines.
y42 founder and CEO Hung Dang. Image Credits: y42
“We’re taking the best of breed open-source software. What we really want to accomplish is to create a tool that is so easy to understand and that enables everyone to work with their data effectively,” Y42 founder and CEO Hung Dang told me. “We’re extremely UX obsessed and I would describe us as a no-code/low-code BI tool — but with the power of an enterprise-level data stack and the simplicity of Google Sheets.”
Before y42, Vietnam-born Dang co-founded a major events company that operated in more than 10 countries and made millions in revenue (but with very thin margins), all while finishing up his studies with a focus on business analytics. And that in turn led him to also found a second company that focused on B2B data analytics.
Image Credits: y42
Even while building his events company, he noted, he was always very product- and data-driven. “I was implementing data pipelines to collect customer feedback and merge it with operational data — and it was really a big pain at that time,” he said. “I was using tools like Tableau and Alteryx, and it was really hard to glue them together — and they were quite expensive. So out of that frustration, I decided to develop an internal tool that was actually quite usable and in 2016, I decided to turn it into an actual company. ”
He then sold this company to a major publicly listed German company. An NDA prevents him from talking about the details of this transaction, but maybe you can draw some conclusions from the fact that he spent time at Eventim before founding y42.
Given his background, it’s maybe no surprise that y42’s focus is on making life easier for data engineers and, at the same time, putting the power of these platforms in the hands of business analysts. Dang noted that y42 typically provides some consulting work when it onboards new clients, but that’s mostly to give them a head start. Given the no-code/low-code nature of the product, most analysts are able to get started pretty quickly — and for more complex queries, customers can opt to drop down from the graphical interface to y42’s low-code level and write queries in the service’s SQL dialect.
The service itself runs on Google Cloud and the 25-people team manages about 50,000 jobs per day for its clients. The company’s customers include the likes of LifeMD, Petlab and Everdrop.
Until raising this round, Dang self-funded the company and had also raised some money from angel investors. But La Famiglia felt like the right fit for y42, especially due to its focus on connecting startups with more traditional enterprise companies.
“When we first saw the product demo, it struck us how on top of analytical excellence, a lot of product development has gone into the y42 platform,” said Judith Dada, general partner at LaFamiglia VC. “More and more work with data today means that data silos within organizations multiply, resulting in chaos or incorrect data. y42 is a powerful single source of truth for data experts and non-data experts alike. As former data scientists and analysts, we wish that we had y42 capabilities back then.”
Dang tells me he could have raised more but decided that he didn’t want to dilute the team’s stake too much at this point. “It’s a small round, but this round forces us to set up the right structure. For the Series A, which we plan to be towards the end of this year, we’re talking about a dimension which is 10x,” he told me.
Powered by WPeMatico
A crypto company’s journey to Data 3.0
Data is a gold mine for a company.
If managed well, it provides the clarity and insights that lead to better decision-making at scale, in addition to an important tool to hold everyone accountable.
However, most companies are stuck in Data 1.0, which means they are leveraging data as a manual and reactive service. Some have started moving to Data 2.0, which employs simple automation to improve team productivity. The complexity of crypto data has opened up new opportunities in data, namely to move to the new frontier of Data 3.0, where you can scale value creation through systematic intelligence and automation. This is our journey to Data 3.0.
The complexity of crypto data has opened up new opportunities in data, namely to move to the new frontier of Data 3.0, where you can scale value creation through systematic intelligence and automation.
Coinbase is neither a finance company nor a tech company — it’s a crypto company. This distinction has big implications for how we work with data. As a crypto company, we work with three major types of data (instead of the usual one or two types of data), each of which is complex and varied:
- Blockchain: decentralized and publicly available.
- Product: large and real-time.
- Financial: high-precision and subject to many financial/legal/compliance regulations.
Image Credits: Michael Li/Coinbase
Our focus has been on how we can scale value creation by making this varied data work together, eliminating data silos, solving issues before they start and creating opportunities for Coinbase that wouldn’t exist otherwise.
Having worked at tech companies like LinkedIn and eBay, and also those in the finance sector, including Capital One, I’ve observed firsthand the evolution from Data 1.0 to Data 3.0. In Data 1.0, data is seen as a reactive function providing ad-hoc manual services or firefighting in urgent situations.
Powered by WPeMatico
Microsoft Azure expands its NoSQL portfolio with Managed Instances for Apache Cassandra
At its Ignite conference today, Microsoft announced the launch of Azure Managed Instance for Apache Cassandra, its latest NoSQL database offering and a competitor to Cassandra-centric companies like Datastax. Microsoft describes the new service as a “semi-managed offering that will help companies bring more of their Cassandra-based workloads into its cloud.”
“Customers can easily take on-prem Cassandra workloads and add limitless cloud scale while maintaining full compatibility with the latest version of Apache Cassandra,” Microsoft explains in its press materials. “Their deployments gain improved performance and availability, while benefiting from Azure’s security and compliance capabilities.”
Like its counterpart, Azure SQL Manages Instance, the idea here is to give users access to a scalable, cloud-based database service. To use Cassandra in Azure before, businesses had to either move to Cosmos DB, its highly scalable database service that supports the Cassandra, MongoDB, SQL and Gremlin APIs, or manage their own fleet of virtual machines or on-premises infrastructure.
Cassandra was originally developed at Facebook and then open-sourced in 2008. A year later, it joined the Apache Foundation and today it’s used widely across the industry, with companies like Apple and Netflix betting on it for some of their core services, for example. AWS launched a managed Cassandra-compatible service at its re:Invent conference in 2019 (it’s called Amazon Keyspaces today), Microsoft launched the Cassandra API for Cosmos DB in September 2018. With today’s announcement, though, the company can now offer a full range of Cassandra-based servicer for enterprises that want to move these workloads to its cloud.
Early Stage is the premiere “how-to” event for startup entrepreneurs and investors. You’ll hear firsthand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company-building: Fundraising, recruiting, sales, legal, PR, marketing and brand building. Each session also has audience participation built-in — there’s ample time included in each for audience questions and discussion.
Powered by WPeMatico
After 80% ARR growth in 2020, Saltmine snags $20M to help employees return to a ‘new normal’ office
What is working in the office going to look like in a post-COVID-19 world?
That’s something one startup hopes to help companies figure out.
Saltmine, which has developed a web-based workplace design platform, has raised $20 million in a Series A funding round.
Existing backers Jungle Ventures and Xplorer Capital led the financing, which also included participation from JLL Spark, the strategic investment arm of commercial real estate brokerage JLL.
Notably, JLL is not only investing in Saltmine, but is also partnering with the San Francisco-based startup to sell its service directly to its clients — opening up a whole new revenue stream for the four-year-old company.
Saltmine claims its cloud-based technology does for corporate real estate heads what Salesforce did for CROs in digitizing and streamlining the office design process. It saw an 80% spike in ARR (annual recurring revenue) last year while doubling the number of companies it works with, according to CEO and founder Shagufta Anurag. Its more than 35 customers include PG&E, Snowflake, Fidelity and Workday, among others. Its mission, put simply, is to help companies “create the best possible workplaces for their employees.”
Saltmine claims to have a 95% customer retention rate and in 2020 saw 350% year over year growth in monthly active users of its SaaS platform. So far, the square footage of all the office real estate properties designed and analyzed by customers on Saltmine totals 50 million square feet across 1,500 projects.
Saltmine says it offers companies tools to do things like establish social distancing measures in the office. Its platform, the company says, houses all workplace data — including strategy, design, pricing and portfolio analytics — in one place. It combines and analyzes floor plans with project requirements with real-time behavioral data (aggregated through a combination of utilization sensors and employee feedback) to identify companies’ design needs. Besides aiming to improve the workplace design process, Saltmine claims to be able to help companies “optimize their real estate portfolios.”
The pandemic has dramatically increased the need for a digital transformation of how workplaces are designed and reimagined, according to Anurag.
“Given the need for social distancing capabilities and a greater emphasis on work-life balance in many office settings, few workers expect a complete ‘return to normal,’ ” she said. “There is now enormous pressure on corporate heads of real estate to adapt and modify their workplaces.”
Once companies identify their new needs, Saltmine uses “immersive” digital 3D renderings to help them visualize the necessary changes to their real estate properties.
Singapore-based Anurag has previous experience in the design world, having founded Space Matrix, a large interior design firm in Asia, as well as Livspace, a digital home interior design company.
“I saw the same pain points and unmet needs in office real estate that I did in the residential market,” she said. “Real estate is the second-largest cost for companies and has a direct impact on their largest cost — their people.”
Looking ahead, Saltmine plans to use its new capital to (naturally) do some hiring and continue to acquire customers — in particular, seeking to expand its portfolio of Global 2000 companies.
Saltmine has about 125 employees in five offices across Asia, Europe and North America. It expects to have 170 employees by year’s end and to be profitable by the end of fiscal year 2021.
The company’s initial focus has been in North America, but it is now beginning to expand into APAC and Australia.
JLL Technologies’ co-CEO Yishai Lerner said JLL Spark was drawn to Saltmine’s approach of making data and analytics accessible in one place.
“Having a single source of truth for data also facilitates collaboration across teams, which is important, for example, in workspace planning,” he told TechCrunch. “This reduces inefficiencies and improves workflows in today’s fragmented design, build and fit-out market.”
JLL Spark invests in companies that it believes can benefit from its distribution and network — hence the firm’s agreement to sell Saltmine’s software directly to its customers.
“As JLL tenants and clients continue to embrace the future of work, they are seeking technology solutions that keep their buildings running efficiently and effectively,” Lerner said. “Saltmine’s platform checks all of the boxes by streamlining stakeholder collaboration, increasing transparency and simplifying data management.”
Powered by WPeMatico
Census raises $16M Series A to help companies put their data warehouses to work
Census, a startup that helps businesses sync their customer data from their data warehouses to their various business tools like Salesforce and Marketo, today announced that it has raised a $16 million Series A round led by Sequoia Capital. Other participants in this round include Andreessen Horowitz, which led the company’s $4.3 million seed round last year, as well as several notable angles, including Figma CEO Dylan Field, GitHub CTO Jason Warner, Notion COO Akshay Kothari and Rippling CEO Parker Conrad.
The company is part of a new crop of startups that are building on top of data warehouses. The general idea behind Census is to help businesses operationalize the data in their data warehouses, which was traditionally only used for analytics and reporting use cases. But as businesses realized that all the data they needed was already available in their data warehouses and that they could use that as a single source of truth without having to build additional integrations, an ecosystem of companies that operationalize this data started to form.
The company argues that the modern data stack, with data warehouses like Amazon Redshift, Google BigQuery and Snowflake at its core, offers all of the tools a business needs to extract and transform data (like Fivetran, dbt) and then visualize it (think Looker).
Tools like Census then essentially function as a new layer that sits between the data warehouse and the business tools that can help companies extract value from this data. With that, users can easily sync their product data into a marketing tool like Marketo or a CRM service like Salesforce, for example.
Image Credits: Census
“Three years ago, we were the first to ask, ‘Why are we relying on a clumsy tangle of wires connecting every app when everything we need is already in the warehouse? What if you could leverage your data team to drive operations?’ When the data warehouse is connected to the rest of the business, the possibilities are limitless,” Census explains in today’s announcement. “When we launched, our focus was enabling product-led companies like Figma, Canva, and Notion to drive better marketing, sales, and customer success. Along the way, our customers have pulled Census into more and more scenarios, like auto-prioritizing support tickets in Zendesk, automating invoices in Netsuite, or even integrating with HR systems.“
Census already integrates with dozens of different services and data tools and its customers include the likes of Clearbit, Figma, Fivetran, LogDNA, Loom and Notion.
Looking ahead, Census plans to use the new funding to launch new features like deeper data validation and a visual query experience. In addition, it also plans to launch code-based orchestration to make Census workflows versionable and make it easier to integrate them into an enterprise orchestration system.
Powered by WPeMatico
Datastax acquires Kesque as it gets into data streaming
Datastax, the company best known for commercializing the open-source Apache Cassandra database, is moving beyond databases. As the company announced today, it has acquired Kesque, a cloud messaging service.
The Kesque team built its service on top of the Apache Pulsar messaging and streaming project. Datastax has now taken that team’s knowledge in this area and, combined with its own expertise, is launching its own Pulsar-based streaming platform by the name of Datastax Luna Streaming, which is now generally available.
This move comes right as Datastax is also now, for the first time, announcing that it is cash-flow positive and profitable, as the company’s chief product officer, Ed Anuff, told me. “We are at over $150 million in [annual recurring revenue]. We are cash-flow positive and we are profitable,” he told me. This marks the first time the company is publically announcing this data. In addition, the company also today revealed that about 20 percent of its annual contract value is now for DataStax Astra, its managed multi-cloud Cassandra service and that the number of self-service Asta subscribers has more than doubled from Q3 to Q4.
The launch of Luna Streaming now gives the 10-year-old company a new area to expand into — and one that has some obvious adjacencies with its existing product portfolio.
“We looked at how a lot of developers are building on top of Cassandra,” Anuff, who joined Datastax after leaving Google Cloud last year, said. “What they’re doing is, they’re addressing what people call ‘data-in-motion’ use cases. They have huge amounts of data that are coming in, huge amounts of data that are going out — and they’re typically looking at doing something with streaming in conjunction with that. As we’ve gone in and asked, “What’s next for Datastax?,’ streaming is going to be a big part of that.”
Given Datastax’s open-source roots, it’s no surprise the team decided to build its service on another open-source project and acquire an open-source company to help it do so. Anuff noted that while there has been a lot of hype around streaming and Apache Kafka, a cloud-native solution like Pulsar seemed like the better solution for the company. Pulsar was originally developed at Yahoo! (which, full disclosure, belongs to the same Verizon Media Group family as TechCrunch) and even before acquiring Kesque, Datastax already used Pulsar to build its Astra platform. Other Pulsar users include Yahoo, Tencent, Nutanix and Splunk.
“What we saw was that when you go and look at doing streaming in a scale-out way, that Kafka isn’t the only approach. We looked at it, and we liked the Pulsar architecture, we like what’s going on, we like the community — and remember, we’re a company that grew up in the Apache open-source community — we said, ‘okay, we think that it’s got all the right underpinnings, let’s go and get involved in that,” Anuff said. And in the process of doing so, the team came across Kesque founder Chris Bartholomew and eventually decided to acquire his company.
The new Luna Streaming offering will be what Datastax calls a “subscription to success with Apache Pulsar.’ It will include a free, production-ready distribution of Pulsar and an optional, SLA-backed subscription tier with enterprise support.
Unsurprisingly, Datastax also plans to remain active in the Pulsar community. The team is already making code contributions, but Anuff also stressed that Datastax is helping out with scalability testing. “This is one of the things that we learned in our participation in the Apache Cassandra project,” Anuff said. “A lot of what these projects need is folks coming in doing testing, helping with deployments, supporting users. Our goal is to be a great participant in the community.”
Powered by WPeMatico
Data virtualization service Varada raises $12M
Varada, a Tel Aviv-based startup that focuses on making it easier for businesses to query data across services, today announced that it has raised a $12 million Series A round led by Israeli early-stage fund MizMaa Ventures, with participation by Gefen Capital.
“If you look at the storage aspect for big data, there’s always innovation, but we can put a lot of data in one place,” Varada CEO and co-founder Eran Vanounou told me. “But translating data into insight? It’s so hard. It’s costly. It’s slow. It’s complicated.”
That’s a lesson he learned during his time as CTO of LivePerson, which he described as a classic big data company. And just like at LivePerson, where the team had to reinvent the wheel to solve its data problems, again and again, every company — and not just the large enterprises — now struggles with managing their data and getting insights out of it, Vanounou argued.
Image Credits: Varada
The rest of the founding team, David Krakov, Roman Vainbrand and Tal Ben-Moshe, already had a lot of experience in dealing with these problems, too, with Ben-Moshe having served at the chief software architect of Dell EMC’s XtremIO flash array unit, for example. They built the system for indexing big data that’s at the core of Varada’s platform (with the open-source Presto SQL query engine being one of the other cornerstones).
Image Credits: Varada
Essentially, Varada embraces the idea of data lakes and enriches that with its indexing capabilities. And those indexing capabilities is where Varada’s smarts can be found. As Vanounou explained, the company is using a machine learning system to understand when users tend to run certain workloads, and then caches the data ahead of time, making the system far faster than its competitors.
“If you think about big organizations and think about the workloads and the queries, what happens during the morning time is different from evening time. What happened yesterday is not what happened today. What happened on a rainy day is not what happened on a shiny day. […] We listen to what’s going on and we optimize. We leverage the indexing technology. We index what is needed when it is needed.”
That helps speed up queries, but it also means less data has to be replicated, which also brings down the cost. As MizMaa’s Aaron Applbaum noted, since Varada is not a SaaS solution, the buyers still get all of the discounts from their cloud providers, too.
In addition, the system can allocate resources intelligently so that different users can tap into different amounts of bandwidth. You can tell it to give customers more bandwidth than your financial analysts, for example.
READ MORE
“Data is growing like crazy: in volume, in scale, in complexity, in who requires it and what the business intelligence uses are, what the API uses are,” Applbaum said when I asked him why he decided to invest. “And compute is getting slightly cheaper, but not really, and storage is getting cheaper. So if you can make the trade-off to store more stuff, and access things more intelligently, more quickly, more agile — that was the basis of our thesis, as long as you can do it without compromising performance.”
Varada, with its team of experienced executives, architects and engineers, ticked a lot of the company’s boxes in this regard, but he also noted that unlike some other Israeli startups, the team understood that it had to listen to customers and understand their needs, too.
“In Israel, you have a history — and it’s become less and less the case — but historically, there’s a joke that it’s ‘ready, fire, aim.’ You build a technology, you’ve got this beautiful thing and you’re like, ‘alright, we did it,’ but without listening to the needs of the customer,” he explained.
The Varada team is not afraid to compare itself to Snowflake, which at least at first glance seems to make similar promises. Vananou praised the company for opening up the data warehousing market and proving that people are willing to pay for good analytics. But he argues that Varada’s approach is fundamentally different.
“We embrace the data lake. So if you are Mr. Customer, your data is your data. We’re not going to take it, move it, copy it. This is your single source of truth,” he said. And in addition, the data can stay in the company’s virtual private cloud. He also argues that Varada isn’t so much focused on the business users but the technologists inside a company.
Powered by WPeMatico
Microsoft’s Project Natick underwater data center experiment confirms viability of seafloor data storage
Microsoft has concluded a years-long experiment involving use of a shipping container-sized underwater data center, placed on the sea floor off the cost of Scotland’s Orkney Islands. The company pulled its “Project Natick” underwater data warehouse up out of the water earlier this year (at the beginning of the summer) and spent the last few months studying the data center, and the air it contained, to determine the model’s viability.
The results not only showed that using these offshore submerged data centers seems to work well in terms of performance, but also revealed that the servers contained within the data center proved to be up to eight times more reliable than their dry-land counterparts. Researchers will be looking into exactly what was responsible for this greater reliability rate in the hopes of also translating those advantages to land-based server farms for increased performance and efficiency across the board.
Other advantages included being able to operate with greater power efficiency, especially in regions where the grid on land is not considered reliable enough for sustained operation. That’s due in part to the decreased need for artificial cooling for the servers located within the data farm because of the conditions at the sea floor. The Orkney Island area is covered by a 100% renewable grid supplied by both wind and solar, and while variances in the availability of both power sources would’ve proven a challenge for the infrastructure power requirements of a traditional, overland data center in the same region, the grid was more than sufficient for the same size operation underwater.
Microsoft’s Natick experiment was meant to show that portable, flexible data center deployments in coastal areas around the world could prove a modular way to scale up data center needs while keeping energy and operation costs low, all while providing smaller data centers closer to where customers need them, instead of routing everything to centralized hubs. So far, the project seems to have done spectacularly well at showing that. Next, the company will look into seeing how it can scale up the size and performance of these data centers by linking more than one together to combine their capabilities.
Powered by WPeMatico
With technology to perfect product pitches in digital marketplaces, Pattern raises $52 million
Pattern, a Lehi, Utah-based reseller that offers large and small brands a way to optimize their sales on marketplaces like Amazon, eBay, Walmart and Google Shopping, has raised $52 million in growth funding, the company said.
The money, from Ainge Advisory and KSV Global, will be used to expand the company’s business worldwide.
Founded in 2013, the e-commerce reseller uses analytics to lock down market-specific keywords in advertising and has managed to reach a run-rate that should see it hit $500 million in annual revenue by the end of 2020, according to Pattern co-founder and chief investment officer, Melanie Alder.
Brands like Nestlé, Pandora, Panasonic, Zebra and Skechers sell their goods to Pattern in an effort to juice sales on digital marketplaces.
“Pattern represents our brands in the US, across Europe, and in select markets in Asia, selling for us on global marketplaces such as Amazon, Walmart, Tmall, and JD as well as building and managing three of our direct-to-consumer sites,” said Kyle Bliffert, CEO and president of Atrium Innovations, a Nestlé Health Science company, in a statement. “The global e-commerce growth we have experienced by leveraging Pattern’s expertise is extraordinary.”
Pattern places bets on where a product is likely to receive the most attention using specific keywords, according to the company’s chief executive, Dave Wright. The company buys products from its brand partners and then sells them widely across marketplaces in the U.S., Europe and Asia. These markets represent $2.7 trillion in total sales and Wright expects it to reach $7 trillion by 2024.
As Wright noted, a majority of searches for sales begin on Amazon . The company just opened its eighteenth location in Germany. Pattern has grown sales for brands from $3 million to $26 million and the company makes money off of the margin on the sales of products. With the new funding, the company intends to expand into other geographies like Japan and India.
Wright says his company addresses one of the fundamental problems with advertising technology — the proliferation of tools hasn’t meant better optimization for most brands, because they’re teams aren’t equipped to specialize.
While there may be hundreds of different advertising and marketing folks working at a company, each company may have hundreds of brands that it sells and the dedicated teams to specific brands may only have one or two people on staff.
“Data makes all the difference,” said co-founder and CEO Dave Wright. “I’ve spent the bulk of my career in data science and data management, and our ability to detect and act on ‘patterns’ on e-commerce platforms has allowed the brands we represent to be incredibly successful.”
Powered by WPeMatico