Businesses across all sectors are looking to reap the benefits of becoming more data-driven. But CIOs and CDOs know that meeting this increasing demand for analytics is not easy.
IT capacity and funding for projects can't keep up with all the requests coming from the business. Rapidly growing volumes of data mean usage caps are regularly exceeded, creating havoc with operating budgets.
Often, the data the business wants to use is scattered across multiple platforms and inadequate security for data stores and analytics solutions, increases the risk to both business operations and business reputation.
Traditional data warehouses won't address these challenges.
- They can't scale fast enough to keep up with data growth
- They can't serve the business with data that's fresh or current enough
- They can't provide the right level of access to let teams work effectively while still keeping the business and its data safe
- They don't provide a good foundation for resource-intensive applications such as machine learning and AI
- They come with pricing plans that can easily result in cost overruns, as well as licence renewal processes that are a hassle
What does a modern, smart data analytics solution look like?
The advent of cloud computing has changed the data analytics landscape, with smart data analytics platforms that:
- are fully managed, so the IT team doesn't have to spend time managing servers and storage
- take advantage of serverless computing to allow data analytics applications to seamlessly and automatically scale
- can protect systems, data and users at any scale
- provide users with actionable insights at the same speed as business operations, including analysis of real-time data streams
- are part of a wider ecosystem of open-source analytics solutions that let businesses benefit from innovative tools
- provide a robust foundation for business innovation based around ML and AI-driven apps
- offer flexible, predictable pricing, so there are no nasty budget surprises — for a lower TCO than a traditional data warehouse handling the same loads
Supporting cost-effective and streamlined migration
Migrating to this kind of modern, smart data analytics solution will typically halve your TCO over three years compared with building and operating a traditional on-prem legacy data warehouse. And the savings don't just come from moving to the cloud.
Moving to Google Cloud's smart analytics platform — based around Google BigQuery — will typically reduce your TCO over three years by about 40% compared with simply shifting to a traditional enterprise data warehouse running on a cloud platform like AWS. On top of that, you'll be able to eliminate the need for hefty up-front capital investment in infrastructure and deployment before you start to see any value from your new solution.
Even better, that holds true for every subsequent data analytics project you launch. You don't have to spend time and money planning, procuring and deploying new servers and storage before you can begin. You can start building your data pipelines, application logic and visualisations straight away, knowing that the compute, storage and network resources you need will be immediately available.
You also don't have the challenge of going it alone and figuring it out on your own when you migrate. Vendors like Google have developed proven data warehouse modernisation paths that, with assistance from partners like Ancoris, will guide and support you as you:
- Prepare for your migration with workload and use case discovery
- Assess your proposed smart data analytics platform through a proof of concept project that will also provide useful insights about areas such as data governance and security to feed into your full migration plan
- Execute your migration for each use case, moving your data, applications, and ingestion pipelines into the new platform, and verifying and validating your new solution, before you move on to tackling your next use case.
The uses cases for a smart data analytics platform are as varied as the businesses that take advantage of them, but there are some common themes in how a smart data analytics platform changes how businesses are able to exploit their data. With a modern solution in place, they can:
- Jump-start new projects. Digital marketing agency QiH Group, for example, uses a data analytics solution based on BigQuery to allow "business owners" for each of the brands it works with to evaluate the effectiveness of their marketing campaigns. It can now add data from new marketing channels or spin up reporting for an entirely new client in a matter of hours.
- Get supply chain, customer or pricing insights in real time. Causeway, which provides cloud solutions to the construction industry, is using operational data to understand how customers are using its products. Product managers can use those insights to drive product development, while customer-facing teams can identify how they can help customers get more value from their existing investment in Causeway's solutions.
- Forecast demand and detect and mitigate risks. Film studio 20th Century Fox is using Google's Cloud-based Machine Learning tools to analyse the content of film trailers and use that knowledge to predict a film's likely audience and financial performance based on the trailer alone. This then helps it to identify the likely audience and performance when considering which new movies to back.
- Run mission-critical data-driven initiatives with confidence. Play Sports Network (PSN), which runs the Global Cycling Network (GCN) social media app, is using Google Cloud machine learning layered over BigQuery to deliver personalised content feeds to users, strengthening the community around its brands. That is a critical piece of the infrastructure needed to support PSN's plans over the next two years.