In the words of J.R.R. Tolkien, "shortcuts make long delays." I get it, we live in an age of instant gratification, with Doordash and Grubhub meals on-demand, fast-paced social media and same-day Amazon Prime deliveries. But I've learned that in some cases, shortcuts are just not possible. Such is the case with comprehensive AI implementations; you cannot shortcut success. Operationalizing AI at scale mandates that your full suite of data–structured, unstructured and semi-structured get organized and architected in a way that makes it useable, readily accessible and secure.

Regeneron isn't the only company eager to derive more value from its data. Despite the enormous amounts of data they collect and the amount of capital they invest in data management solutions, business leaders are still not benefitting from their data. According to IDC research, 83% of CEOs want their organizations to be more data driven, but they struggle with the cultural and technological changes needed to execute an effective data strategy. In response, many organizations, including Regeneron, are turning to a new form of data architecture as a modern approach to data management. In fact, by 2024, more than three-quarters of current data lake users will be investing in this type of hybrid "data lakehouse" architecture to enhance the value generated from their accumulated data, according to Matt Aslett, a research director with Ventana Research.

Why do so many organizations find it difficult to leverage the power of data analytics and AI? According to Matei Zaharia, the cofounder and chief technologist at Databricks, the reason is not that data-related problems are intrinsically hard, but that the technology infrastructure that businesses build to manage their data is often more complicated than it needs to be. For the uninitiated, Zaharia started the Apache Spark project during his PhD at UC Berkeley in 2009 before founding Databricks, and today is also an assistant professor of Computer Science at Stanford. He was in town at the STACK conference in November to share his insights about the future of data and the role of the data lakehouse. To illustrate the benefits of data, Zaharia started with a diagram to illustrate data and AI maturity against the competitive advantages that businesses can expect to gain.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Entropy is a scientific concept associated with a state of disorder, randomness, or uncertainty. It is widely used in diverse fields, from classical thermodynamics to statistical physics and information theory.

Bernd Greifeneder is the CTO and founder of Dynatrace, a software intelligence company that helps to simplify enterprise cloud complexity. Continuous digital transformation now defines modern, competitive organizations. Yet, the infrastructure that supports this transformation--powering everything from mobile banking to personalized, omnichannel retail experiences and "smart" healthcare--is built on complex multicloud architectures. The scale and complexity of these data and application environments are increasing relentlessly, and many companies already use five different cloud service platforms on average, according to research conducted by Coleman Parkes and commissioned by Dynatrace. This complexity exceeds humans' ability to manage.

… Intelligence and Machine Learning or Deep Learning. Databricks Lakehouse stores your data in your comprehensively scalable cloud object store, …

Organizations create more data than ever before. Newer, faster technologies and storage systems have risen to the challenge, but the storage elements of artificial intelligence can be complex. AI data often requires high-performance, scalable storage and long retention periods. Organizations must find cost-effective storage systems to protect, manage and analyze large amounts of data; to ensure short and long-term success, it's crucial for organizations to assess their storage and data management needs throughout an AI project. Chinmay Arankalle, author and data engineer, has spent years working on big data systems.

The pandemic has not only highlighted the importance of speed for medical discoveries, but also how data science and artificial intelligence (AI) can aid this acceleration. For example, machine learning in medicine has taken significant strides in recent years, with drug molecules discovered through AI used in human trials. Despite this, a recent report from the Alan Turing Institute revealed that difficulties with data collection, use, storage, processing and integration with different systems, namely the lack of a robust data architecture, hindered efforts to build helpful AI tools in response to the pandemic. To tap into the full potential of AI, organisations, especially in healthcare and pharmaceuticals, need to get their data in order. While great efforts have been placed into the likes of drug and medical discovery, particularly in light of recent events, it can be a lengthy, complex and costly process. Not to mention, its low success rates – only a couple of years ago, the overall failure rate of drug development was reported to sit at 96%.

Databricks, the Data and AI company and pioneer of the data lakehouse architecture, today announced Databricks Partner Connect, a one-stop portal for customers to quickly discover a broad set of validated data, analytics, and AI tools and easily integrate them with their Databricks lakehouse across multiple cloud providers. Integrations with Databricks partners Fivetran, Labelbox, Microsoft Power BI, Prophecy, Rivery, and Tableau are initially available to customers, with Airbyte, Blitzz, dbt Labs, and many more to come in the months ahead. Enterprises want to drive complexity out of their data infrastructure and adopt more open technologies to take better advantage of analytics and AI. The data lakehouse enabled by Databricks has put thousands of customers on this path, collectively processing multiple exabytes of data a day on a single platform for analytics and AI workloads. But, the data ecosystem is vast, and no one vendor can accomplish everything.