MSPs: Here's How to Get Started with Data Analytics as a Service

Data storage is so last decade. We’re in the throes of a data revolution and businesses are no longer satisfied with simply storing data. They need new tools and tactics that help them turn raw information into insights that let them out-fox competition, anticipate up-and-coming trends, and move confidently into a profitable future. The key to making it happen is data analytics. Businesses need them, and if you’re an IT firm looking for new ways to add value to your clients, look no further than the big data bonanza. Here are some things that can help you get started providing data analytics as a service.

Understand What Data Analytics Can Do

Data analytics can provide an array of benefits, but the biggest ones are often related to empowering leaders to make quick decisions. By compiling various types of Business Intelligence (BI) – from market trends to forecasts, sales, social media information, and more – data analytics tools can help turn masses of information into helpful insights. Instead of hours building reports, BI tools can provide always-up-to-date information with minimal manual effort. What these analytics tools need to do will depend on the business using them. Understanding their unique needs is key. An eCommerce business may care about what items are selling, when they sell, where site visitors are coming from, and which of their Google ad words are succeeding. Meanwhile, an ad agency might want to see how web traffic looks, how display ads are performing, and which of their efforts are helping their clients sell more goods or services. A business intelligence dashboard can pull all this information into one place, so users can identify correlations and use that to inform their decisions about new products, campaigns, and so forth – the potential is boundless.

Decide How You’ll Offer Data Analytics

There are a few different approaches to data analytics. You could lean on pre-built tools like Domo or Dundas BI (there are dozens of these kinds of tools) to bring up-to-date info to your clients. There are even analytics tools like Keen IO which allow you to white-label their apps as your own. Tools like these have the advantage of being fleshed out, but in some ways, you’re just reselling someone else’s tool and acting as a middle man. However, the alternative is building apps yourself.

Depending on your current capabilities, developing analytics apps for clients can pay great dividends in the long run. Luckily, you don’t have to build everything from the ground up. You can build data analytics platforms leveraging open-source tools such as those in the Hadoop ecosystem, which are designed to be a foundation for various applications. Apache Spark and Apache Kudu are few geared toward real-time analytics. Depending on your current capabilities, and how you plan to offer analytics as a service, you may have to invest a significant amount into hardware and staff.

Add Staff or Get Training

Are you hoping to use existing staff to add these services? What skills and data analytics certifications will aid their efforts? To get everything right the first time, is it more practical to hire a data analytics expert to manage data and built platforms? If you’re using an off-the-shelf platform, it’s likely training and certifications are adequate, but if you’re going all in, you may want to bring someone aboard. Can you support new hires while you get services off the ground? Note also, that skills are just one side of the coin. You may need to augment your staff to manage the hardware that makes data analytics as a service possible.

Evaluate Your Hardware

If you're building data analytics platforms, your infrastructure needs serious power to manage vast data sets across customers. Do you have the infrastructure (either through your own data center or commercial data center) to support big data efforts? If not, is it practical to invest in a data center or collocation facility? Remember, data centers can offer plenty of other profitable services ranging from data storage to data backup and recovery – analytics are just a part of it. In any case, capable hardware is essential.

Conclusion

Offering data analytics as a service is a big deal. Developing apps, providing dashboards, and insights sounds awesome, but it takes careful planning and time to get it right. If your customers have the need for analytics, or if you identify lots of opportunities in your local market to provide these services, your efforts will pay off. Be meticulous in your planning, and soon enough you’ll start to see profits rise along with customer loyalty and satisfaction.