Moving from an on-prem infrastructure to a frictionless cloud model has become a key goal for many companies.  From scalability to improved security, as well as cost-saving benefits, moving to the cloud bring a lot of benefits. But what challenges are enterprises facing in their migration, and how can this process be made easier? In this episode of the EM360 Podcast, Analyst Susan Walsh speaks to Alex Merced, Developer Advocate at Dremio, to discuss: On-prem data lakes  Moving to a frictionless cloud How to streamline the migration process ..read more

Data pipeline automation is revolutionizing the way organisations handle data.  This new approach to data management is challenging the traditional modern data stack, which relied on a series of disparate tools to handle different aspects of data processing. With data pipeline automation, organizations can simplify their data stack, relying on a single tool to manage the entire process.  This shift will allow businesses to focus on insights and decision-making, rather than spending time wrangling data. As a result, data pipeline automation will become the key component of the po ..read more

Frictionless commerce is the ability to seamlessly find customers, establish trust, and transact and deliver products without any hiccups. However, enterprises face three significant problems in achieving frictionless commerce: getting the right data, managing tech debt, and ensuring data interoperability. And much like the old children's book "I Know an Old Lady Who Swallowed a Fly" - each problem seems to lead to another. That's where Data-as-a-Service (DaaS) comes in. A cloud-based service that provides users with on-demand access to high-quality data, it can help organisations with these c ..read more

Data management is essential for retailers to manage their data effectively to gain valuable insights into customer behavior, preferences, and purchase patterns. By doing so, retailers can make data-driven decisions and improve their operations, sales, and marketing strategies. However, managing data can be challenging for retailers due to the volume, variety, and velocity of data generated by multiple sources, such as social media, mobile devices, and online platforms.  Retailers must also comply with data privacy regulations and ensure the security of their customers' personal informati ..read more

In traditional data architecture, data was always centralised in a data warehouse or a data lake and governed by a centralised team.  However, with the rise of distributed systems, microservices, and agile methodologies, this approach has proven to be inadequate for managing data at scale. Data Mesh proposes a different approach, where data is treated as a product and owned by the teams that produce it.  In other words, it's a way of thinking about data management that focuses on decentralisation and autonomy. But how can companies use this to their advantage? In this episode of the ..read more

Location intelligence. Location intelligence is crucial for a company's data-driven decision-making process, providing insights into the relationship between location and business operations. It helps identify new market opportunities, optimize operations, improve customer experiences, mitigate risks, and provide a competitive advantage. By analyzing location-specific information, companies can make informed decisions that drive growth, reduce costs, and increase profitability. Ultimately, location intelligence is a valuable tool for companies seeking to make strategic decisions that opti ..read more

Data observability refers to the ability to collect, measure, and analyse data from various sources in order to understand the current state and behaviour of a system. This includes monitoring the system's performance, availability, and errors, as well as identifying patterns and anomalies in the data.  By implementing data observability, organisations can gain insights into their systems and make data-driven decisions to improve performance, optimize resources, and reduce costs. Common tools used for data observability include logging, metrics, tracing, and alerting.  In this episod ..read more

Predictive data intelligence is the use of data, AI-driven algorithms, and machine learning techniques to quickly connect data consumers with the most relevant data they need to drive their business initiatives and deliver greater value. The goal of predictive analytics is to go beyond simply describing data to enable data consumers to accelerate the next-best actions to take for more effective decision making. For example, a retailer can understand their most relevant and high-quality data sets to better forecast consumer demand for a product, or a financial institution may identify the most ..read more

Data management is essential for the enterprise to get right, especially now as firms across all sectors are collecting and using vast amounts of data.  But what exactly is good data management? Why do we need it? And what are some of the unacknowledged ramifications that have serious impacts on our day-to-day lives? In this episode of the EM360 Podcast, Analyst Doug Laney speaks to Misha Kapushesky, CEO at Genestack and Vivek Iyer, Team Lead, Human Genetics Informatics, from the Wellcome Institute, to discuss: What makes good data management How data i ..read more

Raw data is data that has not yet been processed for practical use.  It, of course, has the potential to become real and actionable information - but the transformation requires selective extraction, organisation and formatting. Businesses today are really beginning to recognise the value of raw data. With consumer data being one of the hottest commodities of the 21st century, the ability to make more informed decisions is driving the enterprise to make this unprocessed data work for them. In this episode of the EM360 Podcast, Analyst John Santaferraro speaks to Nick Jewell ..read more