Many organizations try to approach BI projects the same as an IT project and don’t get the results they expect. The reality is that BI and IT projects are very different - here's how.

Many organizations still struggle with data and analytics basics. Getting weekly/monthly/quarterly reports to the appropriate staff in a timely manner with accurate data and data definitions that everyone understands is a challenge for many organizations; yet, they realize that they must do much more with data to outpace their competition and better serve their customers.

Looking for quick solutions to gaining value with data, a lot of companies will invest in the ‘latest and greatest’ BI tools and quickly implement the technology in the same manner they would approach a project to update their network hardware. While the implementation may go smoothly, we often see companies soon experience low user adoption because of untrustworthy data, overburdened data infrastructure, no data governance, lack of training, and more.

The reality is BI and IT projects are very different. When you approach BI and analytics projects, there are different steps you must take to ensure success.

Read our 8 Ways Business Intelligence Projects are Different Whitepaper

Here are 8 ways BI projects are different from IT projects:

  1.  BI projects are primarily business projects, not IT projects.

We recommend that subject matter experts (SMEs) be among the primary members of the delivery team and involved from day one. Having business users participate in prototyping and involved in each project iteration will ensure that there are no surprises or delays at the testing phases.

  1.  Intensive IT business analyst support is required throughout the project.

IT should understand that business analysts are crucial to a BI project, perhaps unlike a typical IT project. Because BI solutions are intended to be used by all departments and users with varying technical expertise, these projects should be completed with a close IT-business alignment.

  1.  Requirements for BI projects are impossible to define completely in advance.

For an IT project, you can often completely define your needs for a network upgrade project, and can almost completely define your needs for an accounting or CRM system up front. For a data and analytics projects, you don’t know what the data is going to tell you until you start uncovering insight and asking more questions about your business. Something you had a hunch would be important may be much less important than something you hadn’t previously considered.

Data discovery and other BI tools allow us to change requirements throughout a project, and continue to make it easier to do fast prototyping, data profiling, and put more power in business users’ hands.

  1.  An agile project management approach is needed.

We recommend an agile methodology for a successful BI or analytics project. This allows for fast time-to-value – which means short iterations and confidence that a feature left out now will be incorporated into the next iteration a few weeks (or less) later.

  1.  Building the BI solution is just the beginning – extensive testing is needed.

Testing should be pervasive throughout a project, during each iteration. The rapid prototyping we do allows a better understanding of the data at earlier stages of our projects which allows our clients to fix things before any dedicated testing phase.

  1.  Users are attached to their current toolset – change management is critical.

Self-service BI tools are more popular than ever because they are more intuitive and flexible to the business needs. But with powerful and decentralized analytic tools, comes a need for centralized data stewardship so the company doesn’t lose the “one-version of the truth” trust in the data.

Rather than teaching new tools and managing adoption, change management today should be focused on standardizing and managing processes, master data, and access to data.

  1.  Tight integration with existing systems and business processes.

Business processes may need to be re-engineered as a result of findings during an analytics project – even during discovery or prototyping activities. For example, if you encounter a data quality issue, address source systems to help keep data cleaner. One simple example: changing a field from a free-form text field to a drop-down with defined options.

  1.  BI is a program, not a project.

It gets our clients’ attention when they ask how long it takes to finish a BI effort and we say, “you will never finish.” Companies can finish a well-scoped project in as little time as a week, but for an organization to really use data as a strategic asset, they will need a Data Strategy and long-term roadmap that bridges the gap from current state to the desired future state.

See how this company tackled their short and long term data problems.

Analytics8 Analytics8 is a data and analytics consultancy. We help companies translate their data into meaningful and actionable information so they can stay ahead in a rapidly changing world.
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