Integrate’s LinkedIn Native Connector automates list scrubbing process for leads

Integrate’s latest solution offers a new level of automation for adding cleaned-up LinkedIn leads to a CRM database.

What does it do? Integrate has joined LinkedIn Marketing Solutions certified marketing partners program as the first platform to offer a list-scrubbing tool that validates, de-duplicates and automatically adds LinkedIn leads directly to a CRM database.

Who is the target customer? The LinkedIn Native Connector is aimed at B2B mid-market and enterprise organizations looking for an automated process to deliver clean lists from the their LinkedIn campaigns directly into their marketing automation system.

How does it work? Instead of downloading LinkedIn Lead Gen forms to a spreadsheet to process or uploading them to a CRM without first being validated or de-duplicated, marketers can automate each of those steps — validating and de-duplicating leads from LinkedIn before directly uploading them to a CRM — with Integrate’s tool.

It can connect to LinkedIn Lead Gen forms, LinkedIn InMail campaigns and LinkedIn sponsored content. Also, as part of the Integrate platform, users will have access to Integrate’s reporting tools to track their LinkedIn lead nurturing efforts.

“With the LinkedIn Marketing Solutions lead gen capability exploding in usage, Integrate’s Demand Orchestration software can add this capability to other channels and sources B2B marketing teams are using to generate clean, intelligent leads,” said Integrate CMO Scott Vaughan.

Why it matters. AJ Wilcox, founder of the LinkedIn certified partner agency B2Linked, believes Integrate’s LinkedIn Native Connector remedies a challenge that has long been a problem for marketers using LinkedIn’s lead gen forms.

“Integrate is solving a problem that enterprise users face on LinkedIn Ads when using the LinkedIn Lead Gen Form ad format, which is that it’s pretty straightforward to push form fills into a CRM or marketing automation tool, but then it’s difficult to do actions like de-duplicating or scoring those as they come in,” said Wilcox, “De-duping and lead scoring are activities that are regularly done on landing pages, but since LinkedIn’s Lead Gen Ads is a new way of collecting personal data, there hasn’t be a good solution for this.”

Now with Integrate’s tool, marketers will be able to streamline the process of capturing LinkedIn leads, scrubbing their LinkedIn lead lists, and adding them to their CRMs — significantly shortening the amount of time it takes to being moving leads through the sales funnel.

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What Does ParkMyCloud User Data Tell Us?

These strategies can also result in wasted time, money, and computing capacity.

The latest statistics on cloud computing all point to multi-cloud and hybrid cloud as the reality for most companies. This is confirmed by what we see in our customers’ environments, as well as by what industry experts and analysts report. At last week’s CloudHealth Connect18 in Boston we heard from Dave Bartoletti, VP and Principal Analyst at Forrester Research, who broke down multi-cloud and hybrid cloud by the numbers:

  • 62% of public cloud adopters are using 2+ unique cloud environments/platforms
  • 74% of enterprises describe their strategy as hybrid/multi-cloud today
  • But only:
    • 42% regularly optimize cloud spending
    • 41% maintain an approved service catalog
    • 37% enforce capacity limits or expirations

More often than not, public cloud users and enterprises have adopted a multi-cloud or hybrid cloud strategy to meet their cloud computing needs. Taking advantage of features and capabilities from different cloud providers can be a great way to get the most out of the benefits that cloud services can offer, but if not used optimally, these strategies can also result in wasted time, money, and computing capacity.

Big-Data-Analytics

 

The data is telling – but we won’t stop there. For more insight on the rise of multi-cloud and hybrid cloud strategies, and to demonstrate the impact on cloud spend (and waste) – we have compiled a few more statistics on cloud computing.

Multi-Cloud and Hybrid Cloud Adoption Statistics

The statistics on cloud computing show that companies not only use multiple clouds today, but they have plans to expand multi- and hybrid cloud use in the future:

Avoiding-dark-clouds-picture

  • According to a 451 Research survey, 69% of organizations plan to run a multi-cloud environment by 2019. As they said, “the future of IT is multi-cloud and hybrid” – but with this rise, cloud spending optimization also becomes more of a challenge.
  • In a survey of nearly 1,000 tech executives and cloud practitioners, over 80% of companies were utilizing a multi-cloud strategy, commonly including a hybrid cloud model consisting of both public and private clouds.
  • And by multi-cloud, we don’t mean just two. On average, the number of private and public clouds used by companies to run applications and test out new services is 4.8.
  • On hybrid cloud strategy:
    • 83% of workloads are virtualized today (IDC)
    • 60% of large enterprises run VMs in the public cloud (IDC)
    • 65% of organizations have a hybrid cloud strategy today (IDC)
You know this what these statistics on Cloud Computing Mean http://bit.ly/2xoAcVZ

 

Life science industry with Digital Technology (Rebooting)

Digital technology is connecting genetic information with real world data and companies are already combining drugs, advanced application devices, and apps to be more patient-centric.

Digital technology has been driving change throughout the life science industry for years, however the sector is currently standing on the precipice of revolutionary development – some organizations have already taken the jump towards a more digital future.

Data collection and visualization for decision making to improve the overall performance of themanufacturing supply chain is a huge opportunity for the life science industry, however it’s not about being new – it’s about using proven solutions andapproaches to decision making to improve quality, reliability and reducing waste.

Businesses across the life science industry have been collecting data using large historian systems for years. Many currently have so much data arising from different sources it can be hard to focus on what is important. Right now, almost every device in a GMP manufacturing facility collects data and our clients have been completing projects to physically connect all these devices and systems for many years. The drive to physically connect the systems has come from many strategic objectives, including serialization.

All this excellent work has put the industry in a great position to use the data it is currently collecting in the best possible way.Although the robotics and automotive industries may be in a better position to use Artificial Intelligence (AI) and self-learning systems to improve manufacturing in efforts linked to Industry 4.0 – the life science industry has been using data and evidence to improve its manufacturing for nearly forty years.

deep_learning_life_sciences_by_clusterone01-dcf027i

New tools and processes are emerging that can enable smart, decentralized production, with intelligent factories, integrated IT systems, the Internet of Things (IoT), and flexible, highly integrated manufacturing systems. In addition, future developments may mean that machine learning algorithms will be able to quickly adjust manufacturing lines and production scheduling. New developments will also pave the way for predictive maintenance and the opportunity to identify and correct issues before they happen.

Integrating with single use systems
The adoption of single-use technologies, such as single-use bioreactors and other unit operations is on the rise. Fueled by the growing pipeline of high potency and biological drugs and coinciding with the growth in personalized medicine and its inherent need for smaller batches, single-use technology will play an increasingly important role in the coming years.

Both upstream and downstream manufacturing processes benefit from single-use systems. During this manufacturing method the biopharma process system is disposed of after use as opposed to being cleaned, enabling quick set up while reducing cleaning and validation need.

Currently,the integration of manufacturing execution system (MES) solutions with start-to-finish technologies and single-use manufacturing platforms is helping the industry to deploy biopharmaceutical manufacturing with increased productivity and efficiency.The upshot is that manufacturers can significantly reduce the time-to-market for new products.

Single-use components are also an enabling technology for smaller scale production of biopharmaceuticals, including antibodies, proteins, vaccines and cell therapies, which would otherwise be much more difficult to produce. Increased productivity and efficiency are also a necessity when it comes to manufacturing smaller batches and a wider range of product. In this environment, single-use technology will naturally flourish as a simple, cost-effective solution.

Digital manufacturing
The first steps towards fully connected, self-optimizing production processes have been taken – the advent of digital manufacturing is on the horizon. Enterprise Manufacturing Intelligence (MI) involves accessing more meaningful data to give a better, more holistic view of operations and allowing for improved analytics and real-time responsive decision-making to drive continuous improvement.

Access to this data, or Big data, also allows for the creation of digital twins. A digital twin can be made up of data captured from the entireend-to-end manufacturing process of a product – this twin can then be used to find invaluable insights. Extension of the traditional ‘golden batch’, where data was very much process control-based, will be supplemented and surrounded with environmental data, raw material data, training data and any other digital data available that goes towards influencing the golden batch.

With this digital information available across multiple
Sites, batches and suppliers, sophisticated analytics can provide a digital twin that best represents the golden batch and alerts controllers to any problems based on these specific data sets.

Patient centricity
Digital technology is connecting genetic information with real world data and companies are already combining drugs, advanced application devices, and apps to be more patient-centric. This push towards customized medicines solutions, driven by technological advances and pressure from patients who want to be more involved in their own care, will take the market in new directions.

The impact on the market, and more notably manufacturers, is that there will be a growing demand for smaller batches which will highlight any inflexibility in a manufacturer’s supply chain.

The propagation of product variants and smaller batch sizes will mean that launch approaches, process technologies and validation concepts will need to be overhauled.

Read more at http://bit.ly/2NildlV

SQL database aimed at real-time processing of Internet of Things

The upgrade also targets SQL developers who previously relied on NoSQL approaches to handle machine data applications.

Startup Crate.io’s strategy of advancing an open source scale-out SQL database as an alternative to complex NoSQL versions for handling fast-moving machine data appears to be paying dividends with the close of an early funding round and the release of an upgraded version of its platform.

Crate.io this week announced the close of a Series A funding round that garnered $11 million. The round was led by Zetta Venture Partners and Deutsche Invest Equity. Among the other investors is Solomon Hykes, founder of application container pioneer Docker. The funds will be used to accelerate development and adoption of commercial and open source versions of the CrateDB machine data platform, the company said Tuesday (June 19).

The San Francisco-based startup also released the third version of it open source database emphasizing time-series storage and analytics for industrial and other users dealing with large volumes of machine-generated data. The upgrade also targets SQL developers who previously relied on NoSQL approaches to handle machine data applications.

The upgrade also targets users seeking to harness data generated by connected factory equipment along with smart buildings and vehicles. “The capability for real-time processing of machine data [was] a key constraint in many Industry 4.0 endeavors,” noted Torsten Kreindl, managing partner at Deutsche Invest Venture Capital.Industry 4.0 refers to factory automation efforts that incorporate data analytics into manufacturing technologies.

5 Myths About Artificial Intelligence (AI) You Must Stop Believing

Crate.io said it is addressing the requirements with an upgraded platform that includes faster data ingestion and real-time analytics as well as data visualizations. Along with SQL, it loads JSON and other data points in a variety of structures, including nested objects and arrays.

Meanwhile, data platform administration is based on a cloud-native micro-services approach managed around the Kubernetes cluster orchestrator.

http://bit.ly/2tbdYV8