As enterprises seek to accelerate the process of getting insights from their data, they face numerous sources of friction. Data sprawl across silos, diverse formats, the explosion of data volumes, and the fact that data is spread across many data centers and clouds and processed by many disparate tools, all act to slow the progress.
The arrival of more and more data in all segments of the enterprise started out as an embarrassment of riches, but quickly transformed into something close to a nightmare of dark data. However, a raft of new technologies and the processes embodied in DataOps are charting a path forward in which a much higher percentage of data becomes useful. The challenge most companies face is how to manage and get access to all the data flooding in from all directions.
Amid the rapid pace of change felt by organizations this year, it’s no surprise that digital transformation projects have been high on the agenda for CIOs across the globe. To achieve both their organizational and transformation goals and become digitally agile as a result, CIOs are often tasked with creating the conditions needed to enable an intelligent and flexible digital core.
“40% of all enterprise workloads will be deployed in CIPS [cloud infrastructure and platform services] by 2023, up from only 20% in 2020.”.As the cloud permeates every aspect of business, decision-makers must make critical choices regarding infrastructure at every turn. Their answers will ultimately determine if every part of an organization is empowered to move forward in a cohesive way to reach business outcomes.
In traditional data warehouses, specific types of data are stored using a predefined database structure. Due to this “schema on write” approach, prior to all data sources being consolidated into one warehouse, there needs to be a significant transformation effort. From there, data lakes emerge!
“More than 15 billion IoT devices will connect to the enterprise infrastructure by 2029.” Finding data is not going to be a challenge, clearly, but taking advantage of it all to drive business outcomes will be. Combining AI and machine learning (ML) with data collection and processing capabilities of the edge and the cloud may hold the answer.
For all of the buzz surrounding both artificial intelligence and data-driven management, many companies have seen mixed results in their quest to harness the value of enterprise data. To avoid those pitfalls, we mixed best-of-breed and proprietary solutions to develop our enterprise data platform (EDP), focusing much of our attention on a combination of smart changes in technology, culture and process for data lakes.
Data paves the way for every strategic move made by banks and insurance companies. Whether looking to create a new service, complying with regulations, or overhauling and re-engineering legacy operations, a massive data project is always central to the effort. For financial services businesses, the pace at which they can reshape and repurpose data has become a key determinant of their ability to predict market trends and meet client expectations.
Co-authored by Samta Bansal More than two million British people use Open Banking-based products today, a number that, despite the disruption of COVID-19, is double that seen at the start of 2020. No wonder International Banker dubbed 2021 “The Year of Open Banking.” As open banking spreads worldwide, its growth comes with the pressing need to drive standardization that bridges geographic boundaries and regulatory frameworks.
The application landscape is swiftly moving from monolithic architectures towards next-gen applications running on microservices. It’s all about fast delivery of applications and services to stay competitive in the market. Making this change gives businesses the agility and flexibility to quickly launch innovative new digital services.
