Businesses are accumulating data faster than ever before. The challenge is to provide meaningful insights rapidly, while ingesting and processing data at scale.
With most organisations hampered by immature business intelligence and analytics capabilities, businesses now have a compelling opportunity to invest in data-driven decision-making to secure a competitive edge. Deployed and used effectively, business intelligence and analytics tools can help predict the outcomes of projects and initiatives, reduce go-to-market times and improve customer experience.
However, analysing exploding volumes of data for insights can initially be intimidating to businesses of all sizes. Industry analyst IDC predicts the volume of data created over the next three years will exceed data created over the past 30 years, and people and systems will create over three times the data over the next five years than they did in the previous five.
The type of data used to inform decisions is also vastly different today than in previous decades. Traditional structured data sources comprise only a small part of today’s data landscape, with valuable information accessible in unstructured data such as images, videos, audio files, emails and handwritten documents.
Businesses need to accommodate the reality that data is pouring in from all directions and often streaming in real-time from mobile devices, third party applications, social media, field-installed surveillance cameras, drones, handheld product scanners, IoT sensors and even live phone conversations with customers. With the right tools, businesses can mine a wide range of interactions with their people and systems for insights.
According to analysts Gartner, nearly 97% of data is unused by organisations. This makes improving data access and usage a ‘no-brainer’ in the transition to data-driven decision-making.
Cloud-based data warehousing and smart analytics services can ingest, process and analyse large volumes of data without requiring up-front investment in hardware or ongoing manual administration. A scalable data management platform featuring highly functional, easy to use analysis and visualisation tools should be central to any cloud architecture.
Platforms that rely on open rather than proprietary technologies minimise the risk of vendor lock-in or additional training requirements, while seamless integration with spreadsheets and other productivity tools can help non-technical users access and use data without seeking the assistance of IT.
Features such as robust geographic information capabilities built in to analytics tools can help overcome the obstacles presented by some particularly challenging data analysis tasks, such as tracking objects in the field or even enabling proactive responses to business impacts from extreme weather conditions or other external events.
Google Cloud’s powerful machine learning and AI can complement a data management platform to unlock new opportunities and efficiencies from data. For example, machine learning-powered speech-to-text enables businesses to index the content of audio files and optical character recognition engines can capture text from images. In addition, many contact centres use AI to provide relevant knowledge to agents to help them assist callers faster and more effectively.
Leading businesses are already embracing data analytics to adapt to fast-changing markets and regulatory environments. For example, in Australia and New Zealand, ANZ Bank is relying on a data platform to execute its open banking vision.
Emma Gray, Group Executive, Data and Automation, explains: “Google Cloud improves how we serve our customers and how we process and use data internally, providing the technology and expertise to help us execute on our open banking strategy. There’s a genuine sense of collaboration in solving our unique challenges and positioning ANZ Bank and our customers for success today and in the future.”
In addition, Australia Post Executive General Manager, Transformation & Enablement, John Cox, says the Google Cloud AI has allowed the 210-year-old organisation to get “incredibly fast insights around data activities that were taking hours, but are now taking seconds.”
To learn more about data platforms and data warehousing, review The Future of Data Warehousing with Google Cloud.
About Nandini Bhardwaj:
Nandini has over 16 years of IT experience architecting complex enterprise solutions and helping businesses shape data & AI strategies to achieve their strategic and business transformation outcomes. She is passionate about solving business problems with Google Cloud and building for scale and resilience, especially edge computing and IoT architectures.