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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

01/23
2013

Top 5 Overlooked Best Practices for BI Success

Everyone’s buzzing about big data and the vast opportunities available for companies to make use of data analysis and data discovery tools and techniques.

However, even though many business leaders are eager to exploit big data for their businesses, many also believe that they face sizeable obstacles to achieving success with business intelligence (BI).

Business Intelligence BI Solutions to improve business processes Top 5 Overlooked Best Practices for BI SuccessA whopping 85% of respondents cite significant impediments to managing and analyzing data, according to a recent survey of 569 C-level executives, business unit leaders, and IT executives. The challenges range from being overwhelmed by the sheer volume of data to not having enough dedicated staff to analyze it.

While these represent some of the more straightforward issues that business leaders face in achieving their goals with BI, hovering just below the surface are best practices that should be applied that are often less evident.

These best practices include:

1. Identifying the business problem to be addressed. Data discovery tools and techniques can help executives identify emerging customer or market trends that can be acted on (e.g., a sudden uptick in customer churn in a particular region or a drop in customers’ Net Promoter Scores or willingness to recommend).

That’s why it’s imperative for business leaders to establish their organizational priorities and have these drive the use of business intelligence, recommends Eric King in an article for TechTarget. Understanding the business objectives and challenges being faced provides business leaders and data scientists grounding for applying BI.

2. Providing the training needed by both data scientists and end users. For companies to successfully apply business intelligence, they need data scientists who not only have the scientific and mathematical training to manage and analyze the data but also a certain level of business acumen to identify the types of information that’s most important to business leaders.

It’s also useful for at least a few data scientists in the organization to have some background in a process-driven approach to using and applying analytics, King notes. And companies need data scientists who can both manage data and find insights in it, according to Harvard Business Review.

Additionally, as a growing number of companies are providing executives with self-service BI tools, these decision makers also need to be trained to be able to use the tools and harness insights effectively.

3. Executive commitment to BI and technology use. In order to inculcate the use of BI throughout an organization’s culture, these efforts need to be driven from the top and communicated throughout all corners of the organization.

But executives also must walk the walk and use the BI dashboards and tools that are provided to them in order to demonstrate to the rank-and-file their commitments to BI and to help foster faster and more effective decision making.

4. The value of building accessible analytical models. Business intelligence involves the capture and reporting of information based on events that have already occurred. Analytical modeling is what allows data scientists and business leaders to analyze the data and derive insight from it. As such, it’s important for analytical models to be accessible across a range of platforms (e.g., web browser, Excel) by anyone who needs them.

5. Conducting post-mortems to determine whether objectives are met and to identify areas for improvement. The use of BI and data analysis tools is an exercise in continual improvement. Even companies that have become mature, best-in-class leaders in data analysis are persistently looking for ways to improve how they go about gathering, storing, analyzing, and acting on data.

Developing a process-driven approach to conducting post-mortems on BI efforts can enable companies to continue to find better ways to approach data analysis.

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