Business Intelligence, Big Data
Business Wire | August 24, 2023
Global retail specialists Smollan have formalised an international reseller and implementation partnership agreement with next-generation decision and business intelligence (BI) software provider Pyramid Analytics. The agreement will see Smollan, via its data and technology business, DataOrbis, expanding its current service offering to include the ability to both sell Pyramid Analytics licenses and implement Pyramid Analytics software.
“Our clients operate in incredibly competitive industries, where the ability to make the best decision, quickly is the key to strong performance. Our hunt for a BI solution that delivers superior functionality, excellent value, and next-generation capability led us to Pyramid Analytics. We migrated the majority of our DataOrbis solutions onto their software last year, and already our clients are noticing a step-change in how we can help them capitalise on the explosive amount of data they have at their fingertips,” said James Collett, Smollan’s Chief Executive: Data & Technology.
The Pyramid Analytics partnership also provides DataOrbis with a growth opportunity geared at expanding DataOrbis’ services and footprint into verticals outside of the fast-moving consumer goods (FMCG) space where they have traditionally played. This is further supported by DataOrbis and Smollan’s global footprint, which includes operations in all major gateways.
“DataOrbis has an excellent understanding of what it takes to implement a successful data strategy across an organisation, regardless of industry. We are incredibly excited to be partnering with them internationally. By leveraging their global presence throughout their client and partner networks, I believe DataOrbis, together with Pyramid Analytics, can streamline and optimise their data analytics ecosystems to prepare them for what’s next. We are thrilled to have them on board,” said Omri Kohl, Pyramid Analytics CEO.
The Pyramid Platform consistently receives top ratings among leading analyst firms that evaluate business intelligence and analytics technologies. Most recently, Pyramid earned top rankings from Gartner in the 2023 Critical Capabilities for Analytics and Business Intelligence Platforms report, including #1 for Business Analyst Use Case, #1 for Augmented Consumer Use Case, #2 for Data Scientist Use Case, and #4 for Analytics Developer Use Case.
The Pyramid Platform consolidates data preparation, business analytics, and data science into a single, integrated, self-service platform that can be accessed by all levels of data users.
Implementation is key to success
As the need to unlock the power of data has accelerated, businesses are realising the benefit of having one enterprise BI platform across the organisation, ensuring a single source of truth that can be safely, securely, and ethically managed. As an early adopter, DataOrbis has invested in creating and upskilling several Pyramid Analytics’ implementation teams working out of South Africa, India, and Slovenia to assist clients in creating and implementing their own unique global views. These teams, with over 50 certified Pyramid Analytics specialists, include senior resources with expertise across industries and geographies.
“The choice of BI platform is one part of the insights puzzle. The skill and time needed to implement the software is the next piece. In our experience, most companies don’t have these two vital resources readily available. By partnering with DataOrbis we can ensure the Pyramid Analytics software is expertly implemented and used to its fullest potential—all based on global best-practises. I am excited by the opportunities the Pyramid Analytics partnership will unlock for us and our clients,” said Collett.
About Pyramid Analytics
Pyramid Analytics is the next generation of business analytics. The award-winning Pyramid Decision Intelligence Platform empowers people with augmented, automated, and collaborative insights that simplify and guide the use of data in decision-making. Critically, the Pyramid Analytics Platform operates directly on any data, enabling governed self-service for any person, and meeting analytical needs in a no-code environment without data extraction. It combines data prep, business analytics, and data science into one frictionless platform to empower anyone with intelligent decision-making. This enables a strategic, enterprise-wide approach to business intelligence and analytics, from the simple to the sophisticated. Schedule a demo. To find out more about Pyramid Analytics contact firstname.lastname@example.org.
Founded in 1931, Smollan is a global commerce business, delivering growth for retailers and brand owners across five continents. Influencing what, when, where and shoppers browse and buy across multiple touchpoints, through Sales & Merchandising, Activation & Experience, Data & Technology and Digital Commerce. Internationally recognised for our exceptional human platform of over 75 000 people and our sophisticated systems, we drive sales and create brilliant shopper experiences for some of the world’s most loved brands. We are a global business that helps retailers and brands win at the point of purchase. Contact us at www.smollan.com.
A leading data and technology company, DataOrbis uses its SMART ecosystem of services, solutions and technology to expertly help companies transform into businesses driven by data. With over 50 blue-chip clients, DataOrbis currently processes over 300 sources of data across 60+ countries in multiple languages. In 2020, DataOrbis joined forces with global retail specialist Smollan, increasing its ability to scale, expand and network globally.
Business Intelligence, Big Data Management, Data Science
Business Wire | August 09, 2023
Airbyte, creators of the fastest-growing open-source data movement platform, today made available connectors for the Pinecone and Chroma vector databases as the destination for moving data from hundreds of data sources, which then can be accessed by artificial intelligence (AI) models.
“We are the first general-purpose data movement platform to add support for vector databases – the first to build a bridge between data movement platforms and AI,” said Michel Tricot, CEO, Airbyte. “Now, Pinecone and Chroma users don’t have to struggle with creating custom code to bring in data; they can use the new Airbyte connector to select the data sources they want.”
Because vector databases have the ability to interpret data to create relationships, their usage is increasingly popular as users seek to gain more meaning from data. Vector databases are ideal for applications like recommendation systems, anomaly detection and natural language processing, and as sources for AI applications – specifically Large Language Models (LLM).
The vector database destination in Airbyte now enables users to configure the full ELT pipeline, starting from extracting records from a wide variety of sources to separating unstructured and structured data, preparing and embedding text contents of records, and finally loading them into vector databases – all through a single, user-friendly interface. These vector databases can then be accessed by LLMs. All existing advantages of the Airbyte platform are now extended to vector databases, including:
The largest catalog of data sources that can be connected within minutes, and optimized for performance.
Availability of the no-code connector builder that makes it possible to easily and quickly create new connectors for data integrations that addresses the “long-tail” of data sources.
Ability to do incremental syncs to only extract changes in the data from a previous sync.
Built-in resiliency in the event of a disrupted session moving data, so the connection will resume from the point of the disruption.
Secure authentication for data access.
Ability to schedule and monitor status of all syncs.
Airbyte continues to innovate and support cutting-edge technologies to empower organizations in their data integration journey. The addition of vector database support marks another significant milestone in Airbyte's commitment to providing powerful and efficient solutions for data integration and analysis.
The vector database destination is currently in alpha status and available supporting: Pinecone on both Airbyte Cloud and the Open Source Software (OSS) version; Chroma and the embedded DocArray database on Airbyte OSS; plus more options in the future.
Airbyte makes moving data easy and affordable across almost any source and destination, helping enterprises provide their users with access to the right data for analysis and decision-making. Airbyte has the largest data engineering contributor community – with more than 800 contributors – and the best tooling to build and maintain connectors.
Airbyte is the open-source data movement leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Enterprise, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.
Big Data Management
Microsoft | September 22, 2023
AI models rely heavily on vast data volumes for their functionality, thus increasing risks associated with mishandling data in AI projects.
Microsoft's AI research team accidentally exposed 38 terabytes of private data on GitHub.
Many companies feel compelled to adopt generative AI but lack the expertise to do so effectively.
Artificial intelligence (AI) models are renowned for their enormous appetite for data, making them among the most data-intensive computing platforms in existence. While AI holds the potential to revolutionize the world, it is utterly dependent on the availability and ingestion of vast volumes of data.
An alarming incident involving Microsoft's AI research team recently highlighted the immense data exposure risks inherent in this technology. The team inadvertently exposed a staggering 38 terabytes of private data when publishing open-source AI training data on the cloud-based code hosting platform GitHub. This exposed data included a complete backup of two Microsoft employees' workstations, containing highly sensitive personal information such as private keys, passwords to internal Microsoft services, and over 30,000 messages from 359 Microsoft employees. The exposure was a result of an accidental configuration, which granted "full control" access instead of "read-only" permissions. This oversight meant that potential attackers could not only view the exposed files but also manipulate, overwrite, or delete them.
Although a crisis was narrowly averted in this instance, it serves as a glaring example of the new risks organizations face as they integrate AI more extensively into their operations. With staff engineers increasingly handling vast amounts of specialized and sensitive data to train AI models, it is imperative for companies to establish robust governance policies and educational safeguards to mitigate security risks.
Training specialized AI models necessitates specialized data. As organizations of all sizes embrace the advantages AI offers in their day-to-day workflows, IT, data, and security teams must grasp the inherent exposure risks associated with each stage of the AI development process. Open data sharing plays a critical role in AI training, with researchers gathering and disseminating extensive amounts of both external and internal data to build the necessary training datasets for their AI models. However, the more data that is shared, the greater the risk if it is not handled correctly, as evidenced by the Microsoft incident. AI, in many ways, challenges an organization's internal corporate policies like no other technology has done before. To harness AI tools effectively and securely, businesses must first establish a robust data infrastructure to avoid the fundamental pitfalls of AI.
Securing the future of AI requires a nuanced approach. Despite concerns about AI's potential risks, organizations should be more concerned about the quality of AI software than the technology turning rogue.
PYMNTS Intelligence's research indicates that many companies are uncertain about their readiness for generative AI but still feel compelled to adopt it. A substantial 62% of surveyed executives believe their companies lack the expertise to harness the technology effectively, according to 'Understanding the Future of Generative AI,' a collaboration between PYMNTS and AI-ID.
The rapid advancement of computing power and cloud storage infrastructure has reshaped the business landscape, setting the stage for data-driven innovations like AI to revolutionize business processes. While tech giants or well-funded startups primarily produce today's AI models, computing power costs are continually decreasing. In a few years, AI models may become so advanced that everyday consumers can run them on personal devices at home, akin to today's cutting-edge platforms. This juncture signifies a tipping point, where the ever-increasing zettabytes of proprietary data produced each year must be addressed promptly. If not, the risks associated with future innovations will scale up in sync with their capabilities.