As an enterprise with a lot of different sectors and moving parts, having disparate, siloed data is hard to avoid. After all, the marketing department may deal with certain information while the IT team works with other data. The details the finance department leverages aren’t the same as what’s used by HR, and so on. However, when this information exists in separate silos and never fully comes together, it could be holding your organization back considerably, particularly when it comes to your big data initiatives.

Today, we’ll look at just a few of the ways disparate data sets could be a problem for today’s companies, and how your business can help address this prevalent problem.

1) A world of enterprise apps

One of the biggest sources of disparate data is the range of business applications employee users leverage. While these activities may take place under the watchful eye of the IT team, each application will contain information unique to that platform and if this data isn’t brought together at some point, it can create skewed analytics results.

According to Cyfe, the average small business utilizes just over 14 different applications. This number jumps to 500 when examining large enterprises.

“[T]he more apps your organization uses the harder it is to make data-driven decisions,” Cyfe noted in a blog post. “Why? Because keeping a pulse on your business’ sales, marketing, finances, web analytics, customer service, internal R&D, IT, and more as isolated sources of data never gives you a complete picture. In other words, big data doesn’t lead to big insights if you can’t bring it together.”

2) Stuck in the information-gathering phase

It’s not only the location of data that can cause an issue – the sheer volume of information can also create significant challenges, particularly when an organization is working to gather all of that information in a single place.

“It can take considerable time to bring this information together without the right resources.”

Informatica pointed out that that as much as 80 percent of an analytics initiative involves the actual collection of information in order to establish a bigger, better picture for analysis. However, when a large number of details are located in several different locations, it can take considerable time to bring this information together without the right resources. What’s more, as the company is working to pull data from different sources, new, more relevant information is being created that will further impact analysis.

In this type of environment, it’s easy to get stuck in the gathering phase, where data is constantly being collected, while the team doesn’t move on to the analysis part of the initiative as quickly as they should.

3) Fear of missing out: Reducing repetition

Silos

There are a few main challenges that many organizations face with their big data initiatives that could be holding them back from success.

This leads us to the next issue: fear of missing out. Because big data is constantly being created and changing so quickly, businesses may hesitate to analyze and leverage the insights due to a fear of missing out on the next piece of data that is just coming to light.

Furthermore, Informatica noted that when data isn’t organized and kept in several different locations, it can cause problems on not just one, but a number of analysis initiatives, as employees will have to repeatedly pull these details, wasting time and effort.

“The key to minimizing repetitive work is finding a way to easily reuse your logic on the next data set, rather than starting from square one each time,” Informatica pointed out.

This is only possible, however, with the right big data platform that can help gather information from all disparate sources in the shortest time possible. In this way, businesses can eliminate costly repetitive processes while still ensuring that nothing falls through the cracks as information is gathered for analysis.

4) Missing information: Is it possible to know what isn’t there?

Siloed data can also lead to gaps in knowledge, which can considerably impact analysis results. For instance, a company seeking to learn more about their client base may include a range of different data sources, but may overlook details in the customer relationship management solution, causing them to miss important insights about brand interactions. While this is an extreme example, it helps illustrate the pitfalls of incomplete data sets.

Addressing disparate data: Partnering for success

These are challenges that can affect businesses in every sector, but can be easily and expertly addressed when companies partner with a leading big data solution provider like Aunalytics. Aunalytics has everything your enterprise needs to fully support its big data initiatives. Our unique, best-of-breed technology, Aunsight, ensures that information is gathered from all disparate sources, and that analysis is always as complete as possible. We help you collect and integrate your data so that workflows and unique algorithms can be established, leading to the most high-quality, actionable insights.