Business intelligence and analytics may quite possibly be the most complex segment of a company’s IT environment. Your enterprise data is meant to be kept in a living, breathing information store that can meaningfully interface with business users from day to day.
Unfortunately, in the real world it doesn’t always work out this way. In many cases, insights are constrained to a flat, static report that fails to capture the true richness and variation of your data. Although useful, these reports can only tell you at best what did happen, so that you better prepare for the “next time” in any given context.
The good news is that the very best BI architectures can now easily get to the point of near real-time, where reporting is based on data points that are instantaneously updated from source to report. Moving past this benchmark and into the world of real-time insights requires a host of cutting-edge technologies: machine learning, artificial intelligence, and predictive analytical tools based on age-old statistical knowledge put into new algorithms and processing engines. This type of reporting can tell one what to do right now, as opposed to only what happened before or even just a second ago.
For example, the price that an individual shopper sees on the Amazon website is not the company’s set price for a specific product. Rather, the listed price is the result of a nearly real-time analysis of thousands of price checks over the web. This analysis yields an average price and the optimal price to charge an individual shopper, which depends on several factors including your physical location.
As a result, Amazon locks in a price based on the constantly shifting market value for specific items. (Note that you can watch the price change by leaving an item in your shopping cart unpurchased for a long period of time). Here, the data is driving the business decision (how much Amazon will charge right now) based on a history of real contractual business transactions (purchases).
That’s all very well and good for Amazon – but how can other companies have their data tell them the best course of action? What steps can be taken to make that journey? For the vast majority of organizations, achieving real-time BI and analytics will be much easier with a cloud-based architecture.
Some companies are intimidated by the sheer complexity of moving their entire operations to the cloud, yet the very core logic underpinning cloud technologies means that they shouldn’t be. Migrating to the cloud means that you can leave behind the world of on-premises dependencies. In fact, some BI tools already come with automatic cloud integrations, such as Microsoft’s Power BI.
Cloud BI/analytics solutions have become highly mature and feature-rich in recent years. Nearly half of enterprises now report using cloud BI, and 25 percent are considering moving to cloud BI in the next five years. In addition, the barriers to entry are much lower–it’s never been easier to build a modern, real-time predictive analytics platform that will start adding value to your business.
Of course, there’s no need to move everything to the cloud in the first place if that’s not what you want to do. “Hybrid” models that combine applications and data both in the cloud and on-premises have many benefits of their own.
All the standard advantages of cloud software apply to cloud analytics:
- Cloud platforms are highly flexible. They can be reconfigured, add to, and downsized to match your footprint in the market.
- “Scaling up” doesn’t exist when it comes to the cloud. Instead, businesses “scale out” by distributing the workload to more machines, only paying for the resources they need.
- Finding talent to build, maintain, and improve cloud-based systems is typically easier, because you don’t have to restrict your search to people who are local geographically.
- Implementing cloud infrastructure, with its recurring expense model, is often cheaper than the existing costs of on-premises resources and the staff required to maintain them.
During and after a cloud migration, your existing processes on-premises don’t have to be disrupted. Cloud analytics infrastructure and services can be built in parallel to your current system, contained in their own virtual private cloud. You can feed them data and test them on your own schedule and roll out the deployment when they’re production-ready. With cloud technologies, we don’t have to commit to an architecture for the next ten years, we only need to agree on an architecture that is best serves our business today.
Note that you may need more than one BI/analytics tool to satisfy the needs of all your different business groups: sales, marketing, operations, finance, etc. In addition to their business function, report consumers can be separated into a few common user types: data miners, data scientists, visual analysts, etc. The benefit of cloud analytics is that you can test and use more than one reporting tool at the same time. With BI reporting tools there is often a common question: which tool is the best for my architecture? Because the cloud offers such a flexible digital landscape, one must also consider which BI reporting tools can support several evolving architectures over time.
The perks of moving to the cloud go well beyond the most visible, immediate benefits. For example, the insights that you gain from your new cloud analytics solution can help you gain support and funding from key stakeholders for new ideas and projects.
Want to find out what the cloud can do for your BI/analytics workflow? Get in touch with our experienced cloud BI support team for some valuable help and advice.
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