This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review's editorial staff. Modern integration platforms are helping enterprises streamline fragmented IT environments and prepare their data pipelines for AI-driven transformation. Enterprise IT ecosystems are often akin to sprawling metropolises--multi-layered environments where aging infrastructure intersects with sleek new technologies against a backdrop of constantly ballooning traffic. Similarly to how driving through a centuries-old city that's been retrofitted for automobiles and skyscrapers can cause gridlock, enterprise IT systems frequently experience data bottlenecks.

Public data ecosystems (PDEs) represent complex socio-technical systems crucial for optimizing data use in the public sector and outside it. Recognizing their multifaceted nature, previous research pro-posed a six-generation Evolutionary Model of Public Data Ecosystems (EMPDE). Designed as a result of a systematic literature review on the topic spanning three decade, this model, while theoretically robust, necessitates empirical validation to enhance its practical applicability. This study addresses this gap by validating the theoretical model through a real-life examination in five European countries - Latvia, Serbia, Czech Republic, Spain, and Poland. This empirical validation provides insights into PDEs dynamics and variations of implementations across contexts, particularly focusing on the 6th generation of forward-looking PDE generation named "Intelligent Public Data Generation" that represents a paradigm shift driven by emerging technologies such as cloud computing, Artificial Intelligence, Natural Language Processing tools, Generative AI, and Large Language Models (LLM) with potential to contribute to both automation and augmentation of business processes within these ecosystems. By transcending their traditional status as a mere component, evolving into both an actor and a stakeholder simultaneously, these technologies catalyze innovation and progress, enhancing PDE management strategies to align with societal, regulatory, and technical imperatives in the digital era.

Model-induced distribution shifts (MIDS) occur as previous model outputs pollute new model training sets over generations of models. This is known as model collapse in the case of generative models, and performative prediction or unfairness feedback loops for supervised models. When a model induces a distribution shift, it also encodes its mistakes, biases, and unfairnesses into the ground truth of its data ecosystem. We introduce a framework that allows us to track multiple MIDS over many generations, finding that they can lead to loss in performance, fairness, and minoritized group representation, even in initially unbiased datasets. Despite these negative consequences, we identify how models might be used for positive, intentional, interventions in their data ecosystems, providing redress for historical discrimination through a framework called algorithmic reparation (AR). We simulate AR interventions by curating representative training batches for stochastic gradient descent to demonstrate how AR can improve upon the unfairnesses of models and data ecosystems subject to other MIDS. Our work takes an important step towards identifying, mitigating, and taking accountability for the unfair feedback loops enabled by the idea that ML systems are inherently neutral and objective.

I recently had a conversation with a senior executive who had just landed at a new organization. He had been trying to gather new data insights but was frustrated at how long it was taking. After walking his executive team through the data hops, flows, integrations, and processing across different ingestion software, databases, and analytical platforms, they were shocked by the complexity of their current data architecture and technology stack. It was obvious that things had to change for the organization to be able to execute at speed in real time. Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence.

This is the third part of the blog series. The 1st blog focused on the maturity model and explained how the large monolith data warehouses were created. The 2nd blog focused on metadata driven development or "smart manufacturing" of data ecosystems. This 3rd blog will talk about reverse engineering or how existing data assets can be discovered to accelerate the development of new data products. Companies have increasing pressure to start addressing the data silos to reduce cost, improve agility & accelerate innovation, but they struggle to deliver value from their data assets. Many companies have hundreds of systems, containing thousands of databases hundreds of thousands of tables, millions of columns, and millions of lines of code across many different technologies. The starting point is a "data spaghetti" that nobody knows well.

In all cases, data and AI are enabling the public sector to achieve its missions with more pace, efficiency, and security. The implementation of large-scale automation fosters engagement, as liberated citizens can interact with public servants and processes around the clock. Moreover, the level of security and service is markedly improved, with automation powering real-time threat, incident, and anomaly detection. When taken together, data and AI generates insight that can be leveraged to feed a better decision-making process – from understanding a situation to suggesting next-best actions. It is important to remember that data and AI are tools, just like a carpenter's electric saw. They make the work easier, add precision, and improve efficiency.

Cofounder & CEO at Obviously AI, a no-code AI tool that empowers businesses to build industry-leading predictive analytics models. Not too long ago, professional web designers wouldn't dream of using a no-code website builder--if you didn't personally write each line of HTML and CSS, could you really call yourself a real designer? Today, many professional web designers have enthusiastically embraced no-code solutions, using them to get more done in less time without sacrificing quality. Similarly, we're now seeing advanced artificial intelligence (AI) tools combined with the ease of no-code platforms. These new solutions are changing the way we use data and opening up exciting possibilities for all sorts of businesses.

Today, organizations that are using data to uncover opportunities and are applying that knowledge to differentiate themselves are the ones leading into the future. Whether looking for patterns in financial transactions to detect fraud, using recommendation engines to drive conversion, mining, social media posts for customer voice or brands personalizing their offers based on customer behavior analysis, business leaders realized that data holds the key to competitive advantage. To get value from data, you need a vast number of skill sets and people playing different roles. In this article, we're going to look at the role, BI analysts play in helping organizations tap into vast amounts of data and turn them into actionable insights. It all starts with a data engineer.

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.

Artificial intelligence (AI) has evolved from hype to reality over the past few years. Algorithmic advances in machine learning and deep learning, significant increases in computing power and storage, and huge amounts of data generated by digital transformation efforts make AI a game-changer across all industries.8 AI has the potential to radically improve business processes with, for instance, real-time quality prediction in manufacturing, and to enable new business models, such as connected car services and self-optimizing machines. Traditional industries, such as manufacturing, machine building, and automotive, are facing a fundamental change: from the production of physical goods to the delivery of AI-enhanced processes and services as part of Industry 4.0.25 This paper focuses on AI for industrial enterprises with a special emphasis on machine learning and data mining. Despite the great potential of AI and the large investments in AI technologies undertaken by industrial enterprises, AI has not yet delivered on the promises in industry practice. The core business of industrial enterprises is not yet AI-enhanced. AI solutions instead constitute islands for isolated cases--such as the optimization of selected machines in the factory--with varying success. According to current industry surveys, data issues constitute the main reasons for the insufficient adoption of AI in industrial enterprises.27,35 In general, it is nothing new that data preparation and data quality are key for AI and data analytics, as there is no AI without data. This has been an issue since the early days of business intelligence (BI) and data warehousing.3 However, the manifold data challenges of AI in industrial enterprises go far beyond detecting and repairing dirty data. This article profoundly investigates these challenges and rests on our practical real-world experiences with the AI enablement of a large industrial enterprise--a globally active manufacturer.