So you have heaps of operational data and lots of unanswered questions about your business. What are the next steps for creating a process to analyze your data? On top of the technical challenges (ie: What technology stack to use? Should I use the cloud? How to cleanse, organize, and load data?), there are a few important non-technical considerations for management. Today, I’ve listed out my top 5 non-technical suggestions for starting an analytics initiative at your organization:

1. Make business intelligence a strategic goal

Treating BI as a one-off IT project is a big mistake. The idea is to move the company from making “gut-driven” business decisions to “data-driven“ decisions. In other words, the goal is not only to create nice dashboards for executives, but also create an ecosystem where information is easily shared and analyzed. By making the data project a strategic initiative, business users will hopefully see the importance of the new approach and start to tie their personal job success with understanding the data.

2. Have a change management plan

It is important to encourage the adoption and use of the new BI infrastructure. Show end-users how data will simplify their primary tasks specifically to encourage adoption. For example, if I’m a sales agent, give me a BI artifact that actually helps me sell (eg: an ordered list of prospects by likelihood to buy).

Training will also encourage change in your organization. However, coaching on BI should not only focus on how to build reports (that’s what YouTube is for), but also on why BI is important to effectively doing business. The staff that see the value of data for doing their job will wonder how they lived without it!

3. Scope projects based on business questions

Next, identify specific use cases for your business. The most effective BI environment will be in immediate use for specific members of your company. Consider your analytics based on a subject area or line-of-business (eg: Sales, Supply Chain, Operations), and prioritize questions based on the worker feedback:

· Who are my warmest prospects?

· When do I have the highest sales volume?

· How should I stock my products?

· Etc…

BI is meant to dynamically model the challenges of your company, and by focusing on the business rather than IT, it will keep focus on the true purpose of the project.

4. Work agile

There is no need to implement a large, challenging data project at your company. The best approach to starting an analytics program is to pilot small projects and iteratively grow on previous success. Identify a high value / low effort project to start, and develop a plan to integrate it into your normal business operations (shameless plug: Aptitive has expertise in data-centric projects like these, so please reach out if you would like some help getting started!)

5. Crawl before you walk

Although an initial driver of a BI initiative might be the cutting edge stuff (ie buzzwords like Deep Learning or Artificial Intelligence), it is important to integrate a solid foundation for working with your data. For example, consider mastering your structured, proprietary data before trying to analyze any external “Big Data”. Build to more difficult projects as your company grows in commitment to BI: