Companies are currently leveraging advanced analytics to interpret data because it has features such as machine learning, predictive modeling, forecasting, and artificial intelligence. If you want to get accurate predictions of what may happen to your company in the future, you can’t avoid advanced analytics. However, when implementing advanced analytics, you may likely face specific challenges. This article highlights some of them.

  1. Finding The Right Professionals

Transitioning from conventional to advanced analytics needs you to have experienced staff with advanced machine learning skills, algorithms, and artificial intelligence. Fortunately, you can overcome this challenge by simply offering professional development programs for your current staff to enable them to update their skills.

However, if you have an amid-sized or small company, finding dedicated analytic professionals may be difficult. In that case, you can leverage an analytics professional’s services to help you plan and implement strategies that suit your business’s goals and need. Most advanced analytics staff have a vast wealth of knowledge and experience transitioning advanced analytics for large or small businesses.

  1. Defining Use Cases

Defining use cases for abstract things like advanced analytics may be complicated for some businesses. That’s because it’s quite tricky to figure the individuals who’ll benefit the most from advanced analytics, the appropriate types for your business’s objectives, and how your organization can make good use of it. It needs experienced staff to do internal brainstorming, send out questionnaires, and finding external consultations for possible use cases.

  1. Relating A Business Strategy With Advanced Analytics

An advanced analytics strategy involves understanding use cases, knowing analytics audiences, selecting appropriate algorithms and methodologies, and sourcing for the right data to find insights related to your business’s goals. Unfortunately, most companies can’t figure out how to use artificial intelligence, predictive algorithms, and forecasting to analyze data.

Working with internal audit teams can help you prioritize use cases, audit pain points, develop relevant business analytics, and identify high-quality data. It’s also imperative to know how to integrate frontline tools that your team understands to achieve the highest impact.

  1. Sourcing For Transitioning Funds

Convincing a company’s management on the importance of advanced analytics can be challenging. However, you must do this if you want proper funding to be allocated to the process. You can do this by demonstrating real use cases, telling the management what the ROI of using advanced analytics will be, and using insights that your staff expects.

The solution you develop to convince an organization to use advanced analytics may also help you get proper funding. For example, if you’re using a more modular analytics technique, the only costs you may incur besides the staff are adding new modules related to your business strategy. It may be challenging to get funding through custom in-house solutions that don’t need significant investment and development time.

Handing this work to a third party who has delivered solutions to other organizations of your size with similar aims is advisable.


Transitioning from traditional to advanced analytics can be quite challenging, especially if you don’t have the right use cases, professional staff, and business strategy. However, with the right set of plans and funding, this process can be a lot easier.