The rise of Big Data and the Internet of Things has changed the way Business Intelligence (BI) and analytics systems are built. BI analysis is relying more on software and automated systems than ever before, and the implementation of those systems falls on the shoulders of CIOs.

So it should be no surprise that in Gartner’s annual survey of almost 3,000 CIOs, BI and analytics were the number one priority for CIOs in 2016. To help you in your strategy and implementation process, we’ve put together a list of some of the common hurdles CIOs have to face with a BI or analytics application.

Hurdle #1: Complicated and Hard to Use Software

Software is only one part of any BI/analytics strategy. The other part is making sure that the people using it can get the information they need. This happens less frequently than one might think.

  • 64% of decision-makers have trouble getting answers from their dashboards.
  •  50% of decision-makers struggle to ask questions that they don’t know how to formulate.

Software issues like this lead to low usage rates, drastically reducing the effectiveness of your BI/analytics investment. Complex and confusing systems can lead to even bigger problems. When a user doesn’t know how to use the system, there’s a good chance they’ll end up with incorrect data. Basing a strategy on that data could have disastrous consequences.

Solve the problem by constructing the software from the ground up with your business and its data in mind. Large scale analytics tools have to include an enormous amount of variety of tools to hit as many markets as possible, and the variety of tools can give your system unnecessary complication. Custom-designed and custom-built analysis tools keep things much simpler and much more functional.

Hurdle #2: Managing the Data

2.5 exabytes of data are produced every single day.

While your business isn’t going to be gathering nearly that much data, the core point is still valid. You’re going to have a lot of data going through your BI/analytics system. That means:

  •  Storage – Whether it’s an on-site server farm, managed data centers, or cloud storage, you’ll need to have a secure place for the data.
  •  Bandwidth – Your system has to have the capability to handle enormous amounts of data running from data storage to the software, no matter where that software is located.
  • Analysis capabilities – The software itself is going have to do a lot of number-crunching. Depending on how the software is set up, that crunching may have to be done for multiple users at the same time.

The solution is to  build a multi-pronged data handling strategy.

On one hand, the software should be kept lean and mean. Don’t add excess capabilities or data handling that your business doesn’t need. Just because you can gather information like the last drink your customers ordered at Starbucks doesn’t mean that it will help your business.

On the other hand, your data capability needs to be flexible and adaptable. Multiple data solutions may be the best option. For example, secondary or tertiary data that doesn’t need to be accessed regularly might work better on a server farm or data center only accessible on site, while primary data can be stored on more accessible and scalable cloud-based storage

Hurdle #3: Finding the Best Model in a Changing Marketplace

For years the BI model that reigned supreme used a team of analysts and data scientists, but modern systems have completely changed the way BI and analytics are approached. Self-service analysis tools are widely available, and cloud-based tools have expanded and can be accessed anywhere there’s a network connection.

The risk is that all of that accessibility comes at the expense of accuracy. Self-service systems can often produce multiple answers to the same question depending on who’s asking it, and cloud-based systems may only perform analysis on partial data sets within a limit data silo when the user needs a complete data analysis.

The answer is to find a balance. Embracing the latest and greatest technology doesn’t mean that you have to implement it throughout the entire system. Try to find a balance of approaches that works with your system. Perhaps only some data is available through self-service access, while queries of all the data will only be available through the IT department or for executive level users. Or perhaps cloud access is limited to analysis of reports that are pre-generated elsewhere.

Most importantly, if you do limit data to certain access points, make sure that all of the users know about it. That way they’ll never be expecting to operate off more data than they have.

Hurdle #4: Keeping the System Secure and Adaptable at the Same Time

Poor data security simply isn’t an option. Even high-profile companies like Home Depot, Target, and Sony have suffered severe data breaches in recent years. More importantly, organized crime rings have realized the value of data, and they now carry out 80% of cyberattacks.

At the same time, your system has to be accessible in order to be useful, both by users on the front end and by data-gathering applications on the back end. The system also needs to be able to adapt to any new developments in data mining technology, and be flexible enough to scale with the organization.

Sadly, there’s no one-size-fits-all solution to this quandary. Every system and every business will face its own unique hurdles and issues when implementing security, not to mention their own requirements for scalability and accessibility.

The best practice for this situation is to make sure security, scalability, and accessibility are native to the software and built-in from day one. Come up with solutions to these risks and find that proper balance in the planning stage. This will avoid tacking on security, extensions, or access points later on that could open crucial vulnerabilities.

Outside Expertise is Often the Best Solution

No matter what business you’re in, hurdles are best navigated and overcome through a combination of experience, ability, and practice. Many CIOs have only implemented a handful of complete BI and analytics systems that fit the modern approach.

Outside partners who specialize in enterprise solutions, like AAJ Technologies, have customized, or even built, numerous BI platforms. AAJ’s team of engineers and analysts bring application development best practices and business acumen to the table, making it easy to navigate common hurdles.

Ready to see what an IT partner can do for your BI/analytics? Schedule a free 2016 Strategy Session!