Most organizations are some way along a digital transformation journey that is changing how they run their operations as well as the way they interact with customers and partners. As more transactions depend on optimized infrastructure that can deliver the consistency and performance a modern business demands, the way digital assets are managed becomes business critical.
Organizations realize that the game has shifted on what’s important and they require new strategies, assets, processes, and KPIs to effectively assess, manage, and grow their digital business. Demographic targeting has become much more narrow and precise. Measuring delivery cycles in months and years is no longer acceptable. Cyber security is a core business discipline. Customer interaction must be immediate, continuous, and on demand or customers change providers. Employees expect more and customers demand more.
Technology infrastructure, once the substantial portion of the iceberg below the surface, not to be seen or understood by the business, is highly relevant to business leaders. Managing a digital business requires a new kind of intelligence – digital intelligence – that allows business leaders to correlate aspects of the commercial and business activities with customer experience and outcomes with underlying technology performance and security.
As with most things nowadays, the answers lie in data – or at least in the analysis of data. The good news is that the process of digital transformation generates huge amounts of data. Organizations know that this data is gold for the multiple stakeholders tasked with optimizing strategy, operations, performance, customer management and maintaining security. The bad news is that they are struggling to manage it let alone extract insight from it.
There is a litany of challenges that we call the 12 Stages of Big Data Grief:
None of these stages are easy, but there’s no doubt that heavy lifting occurs at the start when data is initially sourced. When it comes to analytics, the quality, precision and normalization of the data is a foundational building block (garbage in, really expensive garbage out).
Sometimes, the best sources of data are unexpected ones. At Corvil, we passively capture and extract information from data in motion over the network – at scale and in real-time. Granular detail reveals not just that a transaction occurred but the precise time, how long it took and, often, even the customer or user.
We precision timestamp and sequence that information, normalize it, enrich and analyze it, making it available to stream in real-time to business, security and IT solutions as well as big data analytics platforms. And, because the data is voluminous, contextual, and normalized, it provides a strong foundation for applying machine learning techniques.
With Corvil Intelligence Hub, the grief is gone.
A rapidly deployable, self-service solution replaces the effort of securing the triumvirate skills of data analytics technology, data science, and domain knowledge to construct a bespoke solution over months or years. Instead of wrestling with scope creep, diverse teams are empowered to explore data how they each want to see it with rich comprehensive visualizations. Users can triangulate meaningful anomalies by applying machine learning algorithms to their key metrics, without requiring them to have a data science expertise.
In short, there is a simpler path to the digital intelligence needed improve performance, agility, and
digital experience of today's business.
Learn more about Corvil Intelligence Hub
Explore the Solution
Download the Solution Brochure
Learn about Self-Service Intelligence for Electronic Trading