Business analytics is evolving. Generative AI in ERP is becoming commonplace, and decision intelligence is morphing from nice-to-have to necessity.
While these advancements are helping many organizations remain competitive, you must address foundational challenges before jumping on the train yourself.
Today, we’re exploring common challenges of implementing business analytics and sharing strategies to overcome these problems. Learn how to overcome challenges in business analytics with these solutions.
Challenges of Implementing Business Analytics
1. Encouraging User Adoption
Implementing business analytics software or ERP software requires employees to abandon their familiar workflows to adapt to the new system. Not everyone will be ready or willing to embrace that change.
A comprehensive organizational change management (OCM) plan that prioritizes user needs and incorporates ongoing communication is key to successful adoption. This involves understanding user pain points, collecting feedback, and continuously refining solutions to ensure they deliver value.
To make sure resistance doesn’t hinder your efforts, involve key stakeholders from the beginning of the ERP implementation. Then, once you’re in the design phase, show them how you’ve implemented their ideas. This will encourage them to support the project.
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2. Selecting the Right Business Analytics Solution
When you ask your employees what they want in a business analytics solution, expect to receive a slew of different answers. Everyone has their own pain points and their own ideas on how analytics can solve them.
We recommend isolating each user group and asking them to identify their key business analytics issues. When you approach this step intentionally, you can make sure the final solution is one that all teams can use.
3. Addressing Data Quality Issues
Finding a solution that can manage large data volumes is essential, but it’s not enough. Robust data governance is equally critical. According to one survey, poor data quality can cost companies up to 31% of their revenue.
Data cleansing, standardization, and governance are crucial not only for traditional analytics but also for the effective training of generative AI models.
If quality issues start to affect your big data system, it can lead to inaccurate insights and forecasts. The problems can become even more serious as your teams start to integrate more and different types of data.
To avoid this roadblock, make it a point to monitor and improve your data quality on a regular basis.
Duplicate entries and typos are common, especially when you’re working with data from multiple sources. To keep your data as clean as possible, create a system that can match duplicates with data variances and report on typos. While it might take a while to develop, this tool can save your team time and help you catch problems before they snowball.
4. Ensuring Executive Buy-In
When business intelligence (BI) first debuted, companies deployed systems that were based on large IT infrastructures. While these were advanced for their time, they ultimately delivered information in an inefficient manner.
As you make the case for more advanced BI, you may encounter some pushback from your C-suite. They may wonder why they need to change the current setup or add to existing BI investments.
Aligning your data strategy with business goals is essential for securing executive buy-in. Demonstrating the potential impact of data on specific business objectives can help garner support and resources for analytics initiatives.
The key is to quantify the expected ROI and explain how it outweighs any associated risks or expenses. Discuss how the new solutions will deliver real-time workflow improvements and generate time savings. These data and visualizations should speak for themselves, especially if your previous BI implementation has failed to meet expectations.
5. Contending With Ethical Issues and Bias
As the algorithms behind BI tools become increasingly sophisticated, ethical concerns around data privacy, fairness, and potential bias arise.
Companies must ensure:
- Transparency and explainability: Being transparent about how data is collected, used, and analyzed.
- Algorithmic fairness: Identifying and mitigating potential biases in data and algorithms to avoid discriminatory outcomes.
- Data privacy: Adhering to data privacy regulations like GDPR and CCPA to protect user information.
Avoid These Business Analytics Issues
Analytics software can transform your business, helping you make better use of the data that you interact with on a daily basis. However, implementing these business analytics solutions isn’t always as easy as it sounds.
By planning ahead and putting the right infrastructure in place, you can avoid most of the challenges of implementing business analytics. Contact our ERP implementation consultants to learn more.
