In part 1, I wrote about why is it important to enable business users to create their own BI reports. In this post, part 2, I am going to share the pre-training preparations before you start training. I’ve classified into three categories: 1) Data 2) Tools 3) Culture. Let’s go through them:

1. Data

Data assets needs to be published before you start training. It should be a business friendly analytic layer on top of your data sources. It could something as simple as a Power Pivot Model to a SQL server analysis cube. As long as you have an analytics layer – you’re good! Do NOT grant access to transactional systems. I’ve seen a business analyst who was considered the go-to-business-expert of a system having issues trying to create reports using the system’s relational data source – He had challenge trying to get his head around multiple tables, keys, unfriendly field names. He got something up & running but it was hard for him! What’s the lesson here? Try to make it as easy as possible for business users to use data – create an analytic layer over your data sources.

Apart from this, Data Integrity is very important! If the users don’t trust data, they are not going to use it. Invite selected set of business users to test the integrity of the data before you publish the data assets.

Also, the analytic layer that you developed should perform well. if it takes a minute to return fairly simple result, then you will have challenge driving adoption.

2. Tools

What tools would you use to teach business users reporting? Of course, Excel is a top choice since many of the users are already familiar with using excel. Also, Show them a Power View using YOUR data – that may get them excited enough to learn Power View.

How about SSRS report builder & performance point dashboard designer? This is mainly targeted for IT developers so it won’t be great idea to train business users using this tools.

What about Power Pivot/ Power Query? from #1, business users face challenge trying to analyze relational data sources, so test your audience to see Power Pivot/ Power Query is a fit or not. It might work well if they want to combine from couple of spreadsheets with IT’s data assets, that would work! But don’t expect business users to spend time trying to learn Power Pivot & Power Query to analyze data. Again, test it with your audience, see if they pick it up, some of your users may pick it up, great! But usually, you’ll have to create data assets (cubes/power pivot models/tabular models) to reach the masses!

3. Culture

Image Source: Economist & Tableau

The more time that you spend understanding the culture, the more successful you are going to get in training users. It’s because the things you’ll learn while trying to understanding an organization’s culture will be useful in content creation, delivery mechanisms, target audience selection & communicating business value of data driven decisions.

let’s step back. what is culture? It is a characteristics of a group of people. What characteristics are you trying to find before you start training? Try answering following questions:

a. Where are the “analysts”?

– Are they part of IT teams? Who requests reports from “IT analysts”. (they are your target audience!)

– Are they part of business units? (Great! Make them efficient by removing manual data copy-pasting from their to-do list)

– Do they report to CxO’s/presidents/VP’s? (Great! request examples of data driven decisions)

b. Are there examples of value generated using data driven decisions?

– understanding how business uses data to generate value is very important. you will create content using these examples!

c. How comfortable are users learning new technology?

– Have they shown resistance in learning new systems?

– Are they used to receiving ready-to-consume reports! (don’t expect them to change their behavior. But figure out the person creating reports for them. Train them! Make them better)

In summary, understand the culture of the organization, it would help you prepare before you start the training.

Conclusion:

In this post, we saw three pre training preparations (1. Data 2. Tool Selection 3. Culture) before you start training users.