Analytics-as-a-Service is the combination of analytics software and cloud technology. Instead of hosting any analytics software on premises using your own servers, you use a ready-to-go solution that is easy to deploy and most of the time has a pay-as-you-go payment system. It is part of a larger ‘as-a-Service’ solutions such as ‘Software-as-a-Service’ or ‘Platform-as-a-Service’. Thanks to the advancements made by well-known hosting providers such as AWS and Microsoft Azure, Analytics-as-a-Service has really taken off in the past years and is here to stay.
There are a lot of advantages for organisations if they use an Analytics-as-a-Service solution. Of course, the elimination of manual IT tasks will benefit many organisations, removing the need to hire expensive DevOps and Engineers. But the most benefits for organisations are in the central use and access to all internal, and external, data. This enables business analysts and end-users to have easy access to all the data and to explore the data at hand interactively, and potentially collaboratively.
Getting Started with Analytics-as-a-Service
In order to benefit from an Analytics-as-a-Service, organisations should make all of their internal data available in the cloud. That does include legacy data, which quite often is very important for organisations, but is also hidden away in out-dated data warehouses that are difficult to access.
Many organisations don’t use this legacy data because, due to those out-dated systems and its complexity, it is difficult to process. While at the same time it costs organisations tons of money to maintain the old systems. Getting rid of the legacy systems and importing the legacy data into the Analytics-as-a-Service solution is the first step in truly benefiting from Big Data Analytics.
The next step would be to incorporate your other internal data sets such as your CRM data, your financial data and your sales data. Importing multiple data sources in different formats into, for example, your Hadoop cluster in the cloud, will offer you a complete picture of what is going on and will enable you to make the right decisions. Making your data searchable and easy to combine with each other will offer you significant cost-savings and improve your decision-making.
3 Applications of Analytics-as-a-Service
Cost savings and improved decision-making are not the only benefits of Analytics-as-a-Service. Using Analytics-as-a-Service within your business can drive multiple applications on a business level. Let’s have a look at some examples in different industries:
1. Small Businesses Benefiting from Big Data
Many small business owners believe that Big Data is not something they can use because of the required (big) investments and because the need for a lot of data. While both might be true for large multinationals, this is not the case for small companies. Using an Analytics-as-a-Service solution, small business owners can easily deploy a Hadoop cluster in the cloud, integrate their customer data, combine it with external, social, data and gain valuable insights. Insights such as customer purchase behaviour, customer sentiment and effectiveness of marketing campaigns. These insights can then be used to change marketing activities and increase revenue.
2. A Single View of Your Healthcare Organisation
Healthcare organisations tend to have a vast array of information stored in all kinds of siloed databases across the organisation. Ranging from patient data, medicine data, supplier data, financial data, staff data and many more. Due to its nature, healthcare organisations have to be very careful with their data and that’s why Analytics-as-Service can become useful.
Data stored in the cloud using a well-known organisation such as Amazon or Microsoft tends to be more secure than on-premises solutions. The added benefit is that bringing all data into the cloud, offers healthcare organisations the possibility to mix and match their data for additional insights. This would enable the healthcare organisation to better determine risks (financial risks, clinical risks or operational risks), predict operational performances and take action accordingly and create a single view of the healthcare organisation at any given moment in time.
3. Predictive Maintenance for the Transportation Industry
Transport organisations deal with a (large) fleet of vehicles that need to be on the road as much as possible. A truck that is not driving costs money and if that happens to often, it could seriously harm the business. Therefore, transportation companies are turning to predictive maintenance to monitor their fleet and to ensure that they don’t break down.
As it happens, Analytics-as-a-Service and predictive maintenance go hand-in-hand, because of the large variety of data-sources that need to be incorporated and the real-time insights that need to be provided. Fleets deal with large amounts of trip data from multiple trucking management and maintenance systems as well as data from on board sensors such as GPS or engine sensors. Add to that data on how the driver drives, where the trucks need to go to and the price of fuel at different location and you have a lot of data that needs to be combined.
Analytics can help transportation companies to synchronise their data from a wide range of sources and bring all that data together in the cloud and making it available, in real-time, to different users. All that without the need for large IT departments and high upfront investments.
The Future of Analytics-as-a-Service
Analytics-as-a-Service solutions offer significant benefits to organisations. Enabling organisations to integrate multiple data sources and create real-time insights that improve decision-making, without the need for large IT departments and upfront IT investments are especially useful for organisations where IT is not the core business. For these organisations, Analytics-as-a-Service will become the way to go and it is very likely that 5-10 years from now these organisations will no longer use on-premises solution, thereby creating a more agile and flexible organisation.