Cloud analytics. Two buzzwords crashed together, that, unlike most other buzzwords, can have serious benefits and implications for your business. If you’re not yet familiar with analytics, it’s a subset of business intelligence, which is anything that can be considered knowledge valuable to operating a business. You might be familiar with Google Analytics, a popular web analytics suite, which is a part of a broader offering of business intelligence and business analytics tools.
Business analytics is any quantifiable data that has been collected on just about any facet of the business world. Combine that with the cloud, which if you don’t know by now, is a computer, service or application hosted off-site, then you’ve got cloud analytics. Plenty of companies are rolling out cloud analytics software solutions these days, including Google and Microsoft, so you might feel like you’re getting left behind by a fancy new piece of technology. Don’t fret, because cloud data analytics really isn’t that much different from business analytics. In fact, if you own a piece of cloud-based software that has embedded analytics, then you’ve already got cloud analytics.
They (cloud analytics) provide the same services as compared to on-premise solutions (roughly speaking) but just go about it in different ways. And still, that doesn’t paint a clear picture of what exactly cloud analytics is.
Put simply, cloud analytics is a service model where elements of the analytics process are provided via the cloud — be it public or private, standalone or embedded — and enables your business to be more data-driven in ways that an on-premise solution (read: something that’s entirely local) won’t allow you to do.
According to SearchBusinessAnalytics, cloud data analytics has six distinct components, all of which can be delivered via the cloud. We’re going to break down each one of them and see how they relate to the cloud, and how they might differ from an on-premise deployment.
The 6 Components of Cloud Analytics
1. Data Sources
Without data, there is no analytics to speak of. To equip your business with the intel and insights it needs to gain a competitive edge, you need to have a robust, useful pool of data to draw from. When it comes to business analytics, any business operation is fair game, regardless of whether you’re working with a cloud analytics software service or an on-premise service.
The critical difference in data sources for cloud analytics is that it’s all stored and delivered via the cloud. A cloud solution offers you the ability to have access to your real-time data at any time, just hosted, warehoused and stored off-premise. An on-premise solution means that your data is slow and subject to being out-of-date when your intel updates. Imagine your sales team losing a sale because they didn’t have proper customer intel when they needed it, or marketing’s campaign fails because they were also out-of-date. With the cloud, data is accessible and current so long as there is an internet connection.
Cloud models also allow you to pull in multiple different plots of data — be it web or business analytics — and thread them all together in real time. This could lead to tremendous connections and game-changing insights. Or, it could lead you astray by showing false relationships between different platforms or areas of your business. Cloud data analytics delivers data as soon as it’s available from a litany of different sources. But to the untrained eye, this might be too much at once, and it could be polluting your other data with false correlations.
2. Data Models
Data models refer to how clusters of data elements are organized and how they relate to their real-world counterparts. For example, a car could be a data model, as it has hundreds, if not thousands of potential data points that comprise the car as an entity.
Looking for how data models relate to the cloud? Cloud data models are capable of learning based on constantly updating data pools. Better yet, a cloud solution comes equipped with progressive models, applications, uploads and more. You won’t have to spend any time building out your own for your own uses, and if you do, you can add the models yourself.
Cloud models are often more user-friendly than their on-premise solution, which, by all means, have their place in the world of analytics software. A cloud solution is meant to be easily interpreted by the end-user, and ease of use is everything when it comes to software buying.
3. Processing Applications
When you’ve got all the data you could ask for, what do you do with it? When you catch a fish for dinner, you need to clean and “prepare” the fish first. The same is true for data — it needs to be properly prepared before being used. In terms of a cloud solution, your processing application is up in the cloud, often managed and maintained by your third-party vendor, which means you can process data faster than you could with a self-installed and on-premise system. It’s more readily available too.
Your processing application is always on and always ready to crunch data for you. An on-site solution might not be accessible in a consistent way, meaning that you’re as slow as you can retrieve processed data from it. Which brings us to our next point: computing power.
4. Cloud-Based Computing Power
When it comes to your cloud solution, computing power is a big deal. In order to process a ton of data in a reasonable amount of time, a decent hardware stack is needed. A local solution could end up becoming your biggest bottleneck, as well as price-sink. If you don’t have the right hardware, you might find yourself waiting for data to be processed and parsed, tempted to upgrade your hardware and or waste your IT team’s time having them babysit your custom analytics build.
A cloud solution means all of your data processing happens off-site on highly specialized cluster computers owned by your vendor. Your data is ready and processed within minutes and is available to you wherever there is a network. This takes a significant burden off of your agency as having adequate computing power is no longer a priority for you, nor a budgetary concern.
If you double up by getting a cloud-based software with analytics embedded, then you’re getting both cloud-based computational power and cloud-based analytics. Any software can really have embedded BI, but when the software it’s attached to is hosted in the cloud, then you’ve got embedded, cloud-based analytics. This can present a significant advantage in that you benefit from the raw computational power given by stand-alone BI, but attached to another piece of software.
5. Analytics Models
Analytics models, according to Searchbusinessanalytics, are mathematical and statistical equations that help describe trends and interesting points of data. They are the backbone to business analytics, as they help pull abstract plots of data into more cohesive and understandable terms, even if those terms still have to be mathematically translated.
A cloud analytics program is “always on” and thus always has access to the latest and greatest models that the vendor can create. This is a distinct advantage for cloud data analytics — the models are always being updated and managed by someone else. Most cloud analytics software will have models that should cover 95 percent of your business needs, taking a significant burden off of your agency. In an ideal setting, you won’t ever have to come up with your own complex data models to parse data — at least, that’s what cloud analytics seeks to do.
6. Warehousing and Storage
All that data has to end up somewhere, doesn’t it? And if you plan on keeping data around for long-term analysis, then you might need to think carefully about where and how it’s going to be stored. Sure, storage can be cheap, but retrieval isn’t always cheap or efficient, and long term storage might become yet another headache for your IT department.
A cloud solution solves all of those problems for you. It’s managed by a third party (and not by your already over-booked IT department), allows you to store petabytes worth of information, and costs less than a penny per gigabyte to retrieve. Cloud data analytics takes the burden off your company for storing and maintaining its own pools of data. They also make it more readily accessible and provide that data on multiple platforms in combination with their own powerful data models.
One con is that your data is in the hands of someone else. If that service disappears or suffers a service interruption, your data will be lost until the service is brought back online or it can be retrieved. In a sense, you have to pick your poison; store data in the cloud and leave it up to someone else to manage, or stash it yourself but incur a sizeable cost depending on how much you plan on storing. However, inquiring about your vendor’s uptime may make this an easier decision.
Cloud Offers an Edge in Social Media
Cloud analytics is a popular choice for any business that operates in the social media sphere or utilizes social media analytics. Cloud analytics offer immediacy to the data and responses generated. Because cloud analytics are so instantaneous, managers can watch how their campaigns unfold in real time and make any tweaks as needed.
On-Premise Offers Superior Customization
The thing about cloud analytics is that you don’t really have access to the software itself. You might have access to a client control panel or some kind of interface, but true customization of that specific software is going to be out of your hands. Have a specific analytics goal that can be accomplished with some tweaking and original models? You more than likely won’t be able to follow through on it and will be stuck with requesting features from the devs or using the configuration settings built in. With cloud data analytics, you usually have access to the service, not the software.
Availability of Cloud Analytics Software Solutions
Earlier we highlighted Google and Microsoft as two players in the game that are either releasing their initial cloud offerings or are already adding additional features and extensions to their existing cloud platform. Many vendors are already offering their own unique cloud services as either full-fledged suites or additional modules to be added on to their on-premise offerings. TIBCO Spotfire, for example, is a popular on-premise analytics solution that also offers the Spotfire Cloud: An installation-free cloud service. The same is true of Board, Dundas BI, Sisense and SAS Business Analytics.
Embedded Analytics Can Be Cloud-based
Embedded analytics is a type of analytics that is embedded in non-business intelligence software. It could be a CRM like Salesforce, that has analytics panels and metrics built into its software.
It empowers users with dashboards, data, and visual KPIs as they relate to CRM (and other types of software) goals. And the best part is since the software embedded analytics is built into is cloud-based, the analytics themselves are also cloud-based. This gives users powerful analytics solutions that are both portable and managed. It’s all the benefits of cloud analytics without having to have dedicated business analytics or business intelligence software that stands alone (and costs plenty).
Final Thoughts on Cloud Analytics … More Than Just Buzzwords
Cloud analytics may sound like two buzzwords cobbled together, but there are some substantial differences between on-premise and cloud-analytics solutions that might give you or your agency some pause. Utilizing the power of the cloud, you can have access to your data quickly and with little maintenance on your end to maintain that data. Here are the top ten highest rated software from our curated list of business analytics software vendors. Take a look, and see if there’s any vendor that catches your eye. What other unexpected benefits do you think the cloud could bring to business analytics?