As the business intelligence market continues to grow and evolve, more companies, from huge enterprises to SMBs, are purchasing state-of-the-art business intelligence tools to make use of the data they collect.

However, though more business intelligence software is being acquired and implemented, adoption rates remain stagnant. That is particularly vexing in regards to digital marketing departments, which have already grown accustomed to working with a wide variety of data tools.

Why is this happening?

Over-reliance on IT

One issue that often hinders adoption of new analytical tools is that many platforms being implemented are heavily dependent on IT for day-to-day operations.

If marketing managers need to bother overworked IT teams to get answers to specific business questions, the process becomes cumbersome and the marketers (who should be the foremost consumers of these BI systems) quickly lose interest.

Unsurprisingly, this process has a negative effect on adoption.

This problem exists in companies that use legacy systems, which typically require a team of specialists (developers or external consultants) to create new reports or facilitate the addition of new data sources.

However, even some of the more modern "data discovery" tools, which are often touted as self-service, can also turn out to be extremely IT-centric in complex data scenarios. Moreover, since data discovery tools are focused on providing a quick fix, they are generally not built to handle Big Data coming from multiple sources—which many marketers deal with today, considering the abundance of digital platforms (Adwords, social media, etc.).

An attempt to solve the complex data problem often leads to a new issue being created.

Too Many Systems in Place

When modern data discovery tools begin to stutter under the pressures of complex data, companies are channeled into purchasing additional, specialized tools to provide stronger back-end capabilities. However, each of these software stacks will, in turn, require increasingly large technical resources to maintain—both on its own, and as a working part of the complete "assembly line" the company now employs to turn raw data into useable information.

The emergence of such an assembly line tends to lead to one of two unfavorable results:

  1. Either the burden is back on the IT department, which is now in charge of operating this hodgepodge of BI systems
  2. The non-technical marketing managers will, once again, find themselves perplexed by the increasingly difficult nature of driving insights from data, and once again adoption rates will falter.

Making Adoption Easier for Your Organization

The key to increasing BI adoption is to simplify. That means removing the three aforementioned hurdles and giving everyday users the ability to crunch data independently—without having to go back to IT and without having to spend months familiarizing themselves with four or five different software tools.

To simplify, the first thing to be done is to get rid of unnecessary tools. In some cases, that might not be possible. For example, a company that works with massive, petabyte-scale data will probably need a Hadoop-based storage system, along with analytical and visualization layers. However, in the more common complex data scenarios, one should aim for a single-stack solution that incorporates all the components needed to prepare, analyze, and visualize data.

That need doesn't mean a return to the cumbersome, IT-centric legacy systems. Instead, new business intelligence technologies are now capable of achieving the same results in terms of joining, querying, and aggregating complex data, without the tremendous amounts of data engineering work required by legacy tools.

Getting Everyone on Board

Modern software can harness the stronger computational capabilities of existing hardware to process increasingly large loads of data while employing a relatively simple data model and streamlining the analytical process. All that enables true self-service and ad-hoc analysis for business users.

By carefully investigating and selecting the right BI platform, companies can have a complete, agile analytical solution with a single tool.

After finding a BI tool that can facilitate simplification, a good idea is to immediately give as many users hands-on experience and familiarity with the new system. A good example of how to achieve that adoption comes from Skullcandy, a consumer electronics manufacturer and distributor.

After implementing new business intelligence software, Skullcandy's goal was that every department uses it as its primary reporting system. So, the company initiated a "dashboard contest" between the various departments; it challenged non-technical teams to create, independently and without IT assistance, the best BI dashboard. Engagement was incredibly high with over 40 entrances, and the contest was won by the HR department.

That type of exercise immediately drives wider use of the company's newly acquired business intelligence tools, serves as proof for business users that they now have the ability to analyze and visualize data independently, and can help drive long-term engagement and adoption.

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Why Marketers Are Lagging in Their Adoption of New Analytical Tools

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image of Saar Bitner

Saar Bitner is vice-president of marketing at Sisense Business Intelligence Software, which simplifies business analytics for complex data.

LinkedIn: Saar Bitner