We often hear from clients they aren’t ready for business intelligence (BI) because they don’t have clean data. Consider this in the reverse, however, and the same is also true:
Is it possible to produce clean data if you lack the tools to efficiently monitor and identify issues with your data?
This “chicken or egg” scenario presents a conundrum for businesses that strive to be more data-driven yet struggle with where to get started. In this post, we explore a few of the common pitfalls organizations face when starting a BI initiative along with practical tips for getting your organization “unstuck.”
Develop a Plan
You need to start somewhere, and the longer you wait, the further behind you will fall. Before diving in, some initial planning is essential to create the roadmap to take you from your current state to your desired future state with some realistic milestones established along the way.
This type of planning is vital to ensure you head in the right direction and can continuously follow a path that allows your organization to scale and mature your BI efforts. The plan does not need to be perfect—it just needs to chart a rough course.
This roadmap will likely include many facets such as technology infrastructure, business process changes, data quality initiatives, BI development, end-user training, and deployment. Fortunately, this task list has been fairly-well established by others that have already started on this journey.
But you will need to adapt this to your organization to find the right tools, pace and resources that work for your organization. Be sure to include some quick-win projects in your plan to establish momentum behind your transformation efforts.
The Data Won’t Clean Itself
While starting with clean data may be the ideal, this is also very rare in practice and can often lead to unnecessary delays. As companies mature their analytics capabilities, their data quality often improves in parallel.
Generating clean data is often a function of many things. These include well-defined processes, system controls, establishing a strong data culture, and implementing tools to allow for efficient identification and monitoring of outliers.
The deployment of BI solutions accelerates the path to clean data by providing the ability to efficiently monitor and produce alerts of exceptions to standardized processes. Additionally, it is very difficult to truly establish a data culture without providing people with the analytic tools needed to evaluate business performance.
If improving data quality is a key need in your organization, then you need to measure the quality and monitor its improvement. As Peter Drucker said, “You can’t improve what you cannot measure.”
Don’t Let Perfect Get In the Way of Progress
When identifying how clean or precise your data needs to be, accept that there are degrees of accuracy and consider the context when developing your reports. Most organizations have better data than they think if the alternative is reliance on a recent event or a gut feel based on a limited sample size.
Here are a few car analogies to help you determine what your BI maturity might currently be:
Avoid Focusing On the Tool
The success of your BI initiative is not dependent on the tool you select for delivering BI to end-users. Whether you have chosen to utilize Tableau, Microsoft Power BI, or any other modern data visualization tool, you most likely have an appropriate platform capable of deploying actionable intelligence. If you put too much focus on the tool, you have turned your effort into a “technical project” and may be setting yourself up for failure.
From a cost, effort, and skill set perspective, the tool only represents a small piece of the puzzle. Focusing on the tool is like picking the vehicle to complete your journey based on the quality of the dashboard display. Instead, focus the bulk of your efforts on the prioritization of key business objectives, establishing appropriate performance measures, implementing sound practices, and establishing a culture that values data.
Assemble the Right Team
It’s also important to realize achieving business intelligence success is a business challenge, not a technical one. Companies that embrace analytics and establish a data culture outperform their peers.
To build the right culture, form a team to support your BI project with a mix of skill sets:
In addition to bringing the right team members together, if you want to succeed, you need to prioritize. Analytics is an essential business skill set that you will need to build. There is a lot of value to utilizing experts such as ATX to accelerate your BI initiatives, but don’t expect to completely outsource the effort.
Additionally, find a partner that has a blend of business acumen along with the required technical skills. All too often, we have seen businesses fail in their efforts by hiring a BI development shop before they realize someone needs to direct them, define the requirements, identify key measures, and implement process changes.
Success with many of these BI concepts and components requires a deep understanding of your business processes and your industry. The challenges of filling all of these needs can be particularly acute for mid-market and small enterprises as they may lack the dedicated resources or experience to be successful in the deployment. To solve this challenge, hire a coach for your project who has proven success guiding companies on their BI journey.
And should you need any help or have any questions about implementing a business intelligence solution for your company, feel free to reach out to ATX.
Author: Mark DiGiovanni