In this briefing, William underscored the importance of data access through connecting (vs collecting) data — i.e., creating connections between data sources and applications — as a key strategic focus for enterprises: “As no matter what layer of the stack that you’re in, there are innumerable connections needed in the enterprise today.”
In evaluating the plethora of data management, business intelligence (BI) and data integration options, a fundamental question to ask is: how are you going to scale your organization’s data connections to account for ongoing dramatic growth in enterprise data? Companies need to connect to an ever-expanding volume of valuable data from more and more sources, and this is proving to be challenging with the disintermediation of IT in many organizations. Scaling the connections for the enterprise of today — one that operates in a heterogeneous data environment — is a top priority.
Key takeaways from this Briefing Room episode:
Every enterprise is in the business of data: Business is real-time and all the time. Information is a central business asset and the quality of information impacts value creation, alongside usage, and serves as a competitive differentiator. For all intents and purposes, this makes every enterprise a technology company as they face the same challenges that a software company faces when it comes to accessing and integrating data.
Data maturity is highly correlated to business success: Given the direct correlation between data maturity and success, businesses are being measured differently today. Data and analytics leaders are expected to be business leaders. To do so requires more than focusing on user satisfaction; it also means driving data maturity and business performance metrics.
Data’s new highest use will be training AI algorithms: We’re on the cusp of a new generation of BI that incorporates natural language processing (NLP) and artificial intelligence (AI). Data is instrumental to determining the depth of AI that a company can achieve, such as statistical learning, machine learning, and deep learning … as well as its accuracy. The use of data for training AI algorithms will be higher than that of reporting or analytics. This represents a profound change that requires a new level of strategic focus and urgency — one that will disrupt current thinking in today’s organizations.
AI use cases are becoming mainstream across industries: AI use cases are becoming prevalent across every industry and its usage is growing. Some notable examples include: improving financial fraud detection and reducing costly false positives; improving call center experiences with chatbots; enhancing in-car navigation using computer vision; and predicting flight delays based on maintenance records and past flights.
The data-driven economy is reshaping business and data connectivity has become a powerful and strategic enabler for enterprises to unleash their greatest asset — its data. Access to data when and where it’s needed cannot be underestimated.
Access the entire episode of The Briefing Room here for more insights.