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Is poor data quality undermining your marketing AI?

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primary data quality issues

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Marketing’s potential to deliver results relies on data quality, but data accuracy, consistency, and validity continue to be a challenge for many organizations. Inconsistent data quality is holding marketing teams back from converting leads into sales, accurately tracking campaign performance, and taking on the larger challenges of optimizing product mix and product/service revenue forecasts.

The latest analytics, Account-Based Marketing (ABM), CRM, marketing automation, and lead scoring tools all provide real-time data capture and analysis. How the tools ensure consistent data quality directly impacts the quality of the AI and machine learning models the tools use.

Inconsistent data drives opportunities away

Marketing teams can’t deliver on their goals with bad data quality. For example, inaccurate prospect data clogs sales pipelines by slowing down efforts to turn marketing qualified leads (MQLs) into sales qualified leads (SQLs).

Problems with data quality increase the odds of failure for AI initiatives such as predictive audience offers and promotions, personalization, AI-enabled chatbots for advanced service, and automated service recovery. A quarter of organizations attempting to adopt AI report an up to a 50% failure rate, IDC said recently. The leading causes of inconsistent data quality in marketing include problems with taxonomy and meta-tagging, lack of data governance, and loss of productivity.

No data consistency

The most common reason AI and ML fail in the marketing sector is that there’s little consistency to the data across all campaigns and strategies. Every campaign, initiative, and program has its unique meta-tags, taxonomies, and data structures. It’s common to find marketing departments with 26 or more systems supporting 18 or more taxonomies, each created at one point in a marketing department’s history to support specific campaigns. O’Reilly’s The State of Data Quality In 2020 survey found that over 60% of enterprises see their AI and machine learning projects fail due to too many data sources and inconsistent data. While the survey was on the organization level, it would not be a stretch to assume the failure rate would be higher within marketing departments, as it’s common to create unique taxonomies, databases, and metatags for each campaign in each region.

Above: Marketing departments face a variety of data quality issues. (O’Reilly, State of Data Quality in 2020)

The larger, more globally based, and more fragmented a marketing department is, the harder it is to achieve data governance. The O’Reilly State of Data Quality Survey found that just 20% of enterprises publish information about data provenance or data lineage, which are essential tools for diagnosing and resolving data quality issues. Creating greater consistency across taxonomies, data structures, data field definitions, and meta-tags would give marketing data scientists a higher probability of succeeding with their ML models at scale.

Up to a third of a typical marketing team’s time is spent dealing with data quality issues, which has a direct impact on productivity, according to Forrester’s Why Marketers Can’t Ignore Data Quality study. Inaccurate data makes tactical decisions harder to get right, which could impact revenues. Forrester found that 21 cents of every media dollar have been wasted over the last 12 months (as of 2019) due to poor data quality. Taking the time to improve data quality and consistency in marketing would convert the lost productivity to revenue.

Start with change management and data governance

Too often, marketers and the IT teams supporting them rely on data scientists to improve inconsistent data. It’s time-consuming, tedious work and can consume up to 80% or more of the data scientist’s time. It is no surprise that data scientists rate cleaning up data as their least-liked activity.

Instead of asking data scientists to solve the marketing department’s data quality challenges, it would be far better to have the marketing department focus on creating a single, unified content data model. The department should consolidate diverse data requirement needs into a single, unified model with a taxonomy rigid enough to ensure consistency, yet adaptive enough to meet unique campaign needs. Change management makes the marketer’s job easier and more productive because there is a single, common enterprise taxonomy. Data governance is key to solving this problem, and marketing leaders have to be able to explain how improving metadata consistency and content data models fits within the context of each team member’s role. After that, the marketing organization should focus on standardizing across all taxonomies and the systems supporting them.

The bottom line is that inconsistent data quality in marketing impacts the team by jeopardizing new sales cycles and creating confusion in customer relationships. The ability to get AI and ML pilots into production and provide insights valuable enough to change a company’s strategic direction depends on reliable data. Companies will find their marketing campaigns’ future contributions to growth are defined by how the team improves data quality today.

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Colonial Pipeline resumes operations after ransomware prompted closure

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A paper sign reading no gas in both English and Spanish has been taped to a gasoline pump.

Colonial Pipeline said it restarted operations on Wednesday afternoon after a five-day outage brought on by a ransomware attack caused gasoline shortages and panic buying in East Coast states.

colonial pipeline

“Following this restart, it will take several days for the product delivery supply chain to return to normal,” the operator of the 5,500-mile pipeline said on its website. “Some markets served by Colonial Pipeline may experience, or continue to experience, intermittent service interruptions during the start-up period. Colonial will move as much gasoline, diesel, and jet fuel as is safely possible and will continue to do so until markets return to normal.”

Colonial temporarily halted operations on Saturday, after determining that it was the victim of a ransomware attack. The pipeline runs through 11 states, from New Jersey to Texas.

The closure of a major fuel artery sent businesses and consumers scrambling. American Airlines added temporary refueling stops to two long-haul flights out of Charlotte, North Carolina, and Southwest Airlines flew planes with extra fuel into airports including Nashville International Airport.

Filling stations in some states, meanwhile, were selling up to three times their normal amount of gasoline, leading to price hikes of 8 to 10 cents a gallon. Some stations have run out of fuel, and others have limited purchases to 10 gallons or less.

While all indications are that the attack hit the IT portion of the company’s network and didn’t extend to the operational technology portion that controls pipeline operations, Colonial said on Saturday that it initiated the shutdown as a precautionary measure.

Colonial Pipeline has said it’s working with third-party cybersecurity experts, law enforcement, and other federal agencies, including the Department of Energy and FBI. Company representatives didn’t immediately respond to a request for comment.

An outside audit of Colonial Pipeline in 2018 found “atrocious” information management practices and “a patchwork of poorly connected and secured systems,” The Associated Press reported, citing an author of the report. Meanwhile, Reuters, citing unnamed sources, said that Colonial Pipeline had no plans to pay the ransom.

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Former Blizzard and Epic veterans raise $5M for Lightforge Games

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Former Blizzard and Epic veterans raise $5M for Lightforge Games

Join Transform 2021 this July 12-16. Register for the AI event of the year.


When game companies become successful, they tend to breed offspring. That’s the case with veterans of Blizzard Entertainment and Epic Games, who have raised $5 million to open a new studio called Lightforge Games.

The new studio is based near Epic Games in Raleigh, North Carolina, and its quest is to change how role-playing games are made. The team is developing a new cross-platform, social video game where players have the power to create worlds and tell stories with freedom.

CEO Matt Schembari said in an interview with GamesBeat that the company is hiring people for remote jobs.

“Our blended DNA from both Blizzard and Epic extends to the entire studio at this point,” Schembari said. “About a year ago, a bunch of us got together and have been operating quietly, building up the company and our early game prototypes. We’re testing and validating crazy game ideas that we have been coming up with.”

While he isn’t talking about the game yet, Schembari said the game will be highly social and creative and it will run across multiple platforms.

“We love experiences where players can come together and build worlds together, create stories together, tell stories together, where they’re able to have this kind of emergent gameplay. Telling stories together is really the part that we’re most focused on,” he said. “We really believe that there’s no barrier between creation and play. It’s not user-generated content in the classic sense of you create something and then you publish it and people can download it. It’s a different kind of model than just UGC.”

The funding came from Galaxy Interactive, NetEase Games, Dreamhaven, Maveron, 1UP Ventures, and angel investors from the gaming and tech industries.

One of the surprises is that Dreamhaven is another game startup itself, started by former Blizzard president Mike Morhaime and Amy Morhaime. In a statement, Mike Morhaime said that Lightforge is creating a game in a space with a lot of potential and he is excited about the team’s vision.

Above: Lightforge’s team

Image Credit: Lightforge

Schembari has 20 years of experience and he shipped games played by millions as former lead engineer at Blizzard and director of user interface at Epic Games, where he led the Fortnite platform team.

Other founders include Dan Hertzka, Nathan Fairbanks, Glenn Rane, and Marc Hutcheson. Hertzka is engineering director and he led a team at Fortnite that added the client social layer to the battle royale game. Fairbanks has been games for 13 years and has worked on titles such as Fortnite, Star Wars: The Old Republic, and Elder Scrolls Online. He is serving as studio director. Rane is art director and he has worked on World of Warcraft, Hearthstone, and Diablo Immortal. Hutcheson is product director and he has 18 years of experience in marketing and publishing games such as World of Warcraft, StarCraft II, Overwatch, Diablo III, Fortnite, and Hearthstone.

Lightforge has a total of 11 people and is on the verge of hiring three more. The team brings decades of experience from Epic, Blizzard, Riot, Bioware, and Zenimax Online and have shipped top games such as Fortnite, World of Warcraft, Diablo 3, Star Wars: The Old Republic, Hearthstone, the StarCraft 2 trilogy, Overwatch, Elder Scrolls Online, and more.

Lightforge is an all-remote studio where employees can work and live nearly anywhere. Schembari said that his startup received multiple offers and went with Galaxy Interactive as the lead investor because of their understanding about games and online communities.

“We are all remote and have been since the very beginning and this is something that was really important to us,” Schembari said. “One of our values is to really think about embracing empathy with everything we do. And, in particular, in the case of being all remote. We’ve all lived the experience that one of the most disruptive things you can do to someone’s life is to ask them to relocate for a job. And that was something that we really just strongly didn’t want to do. We are now at a point both technologically and culturally where you can totally work remotely.”

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Amazon’s SaaS Boost tool addresses dev challenges

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AWS SaaS Boost

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Amazon today open-sourced Amazon Web Services (AWS) SaaS Boost, an open source tool that helps software developers migrate their existing solutions to software-as-a-service (SaaS) delivery models. Amazon says that SaaS Boost — which launched in preview at AWS Re:Invent 2020 — has the potential to offload development efforts by supporting app transformations to SaaS, freeing up developers to focus on other aspects.

SaaS apps are constantly evolving. Many of them use industry-standard protocols and interface with other products, but they all need certain foundational capabilities to onboard users, provision infrastructure, and surface key metrics. These functions are critical for enabling SaaS providers to scale. However, if every company invested in building these capabilities, it’d take resources — slowing down the time to market.

To address this challenge, AWS SaaS Boost provides functionality including tenant isolation, data partitioning, monitoring, metering, and billing. According to Amazon, the focus is on creating an environment that brings together all the elements of a ready-to-use SaaS architecture, removing much of the heavy lifting commonly associated with migrating a solution to a SaaS model.

Unifying data across disparate sources is one key feature in AWS SaaS Boost. Between 60% and 73% of all data within corporations is never analyzed for insights or larger trends, a Forrester survey found. The opportunity cost of this unused data is substantial, with a Veritas report pegging it at $3.3 trillion by 2020. That’s perhaps why organizations have taken an interest in technologies like AWS SaaS Boost that help to ingest, understand, organize, share, and act on data from multiple environments.

Data challenges

According to Gartner, creating a‌n architecture‌ ‌that helps‌ ‌operationalize data‌ ‌pipelines‌ ‌is one‌ ‌of‌ ‌the‌ ‌major‌ ‌trends‌ ‌for‌ ‌2021. Organizations want to make better use of their data, but most lack a mature strategy. Indeed, surveys show that data’s business impact is limited by challenges in lifecycle management.

Recognizing this, Amazon designed AWS SaaS Boost to be adaptable to the needs of individual projects and organizations. The management and core services of SaaS Boost were built using a serverless application model, with a dashboard where users can configure the ports, domains, compute settings, databases, file systems, and billing options unique to their apps.

New tenants are introduced to the AWS SaaS Boost environment through an onboarding process that collects a tenant’s configuration options and launches an automation. From there, AWS SaaS Boost provisions tenants with separate subdomains that are used to route them to their architectures. The specific resources that apps will need are set up automatically, so that when new versions of the apps are uploaded, SaaS Boost can deploy the updates to all tenants.

Above: A portion of the SaaS Boost onboarding process.

Image Credit: Amazon

On the analytics side, SaaS Boost includes a collection of tenant-focused graphs that can be used to analyze trends. Beyond this, the tool enables integration with preprovisioned infrastructure that can aggregate and surface custom metrics views.

In a blog post, AWS worldwide partner solution architecture Adrian De Luca said that the goal is to “build a vibrant community of developers using AWS SaaS Boost” for production workloads. “We’d like to [encourage] contributors [to donate] code to enhance and optimize … features. As the project matures, we plan to invite other maintainers to take active roles in determining the project’s direction,” he wrote. “Throughout the preview period with developers all over the world, we received interest from large industry-leading software companies who want to offer their traditional products in an easier way, startups who want to build new products with it, and systems integrators modernizing enterprise software on behalf of customers.”

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  • up-to-date information on the subjects of interest to you
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