In a world where computer processing power doubles every 18 months and disk storage density doubles every year, no matter what industry we’re in, we’ve all got a lot of valuable data sitting idle. It’s just sitting there inside that computer-shell, waiting to be discovered.
Even less-than-perfect data situations (a crummy database, or records that haven’t been updated regularly, for example) have important stories to tell us about what we’ve done well (or badly) and where opportunity lies. You don’t need Big Data to get big answers.
Even though the professionalized use of analytics has been around for about 10 years in fundraising, I think many shops are still reluctant to give it a try because the language is so off-putting. Many of the terms aren’t at all self-explanatory and it makes analytics sound overly complicated.
And in our world, complicated usually translates to expensive.
Some of it is expensive. But much of it is very straight-forward. There are simple ways of gathering insight from your database that will translate immediately into not missing opportunities that are right in front of you.
But since the terms uses in analytics are definitely off-putting, I thought I’d share a short glossary of 4 business analytics terms to help illustrate what it’s all about. Follow each of the two examples as we go through each term.
Descriptive analytics: Here’s what happened.
- Example 1: Donations dropped sharply two years ago.
- Example 2: We gained 52 more donors/investors than last year for this project.
Diagnostic analytics: Here’s why it happened.
- Donations went down because there was a huge scandal on campus.
- We gained 52 more donors/investors because we hired a new fundraiser/manager to oversee that project.
Predictive analytics: Here’s what will probably happen
- Donations will likely continue to go down for the next 15 years unless we do something.
- We will likely gain another 104 donors/investors next year due to the stronger relationships the new fundraiser/manager will be building.
Prescriptive analytics: When it will happen, why it will happen and how it will happen
- If we factor in Plan A to avoid scandals in the future; hire a new president; and undergo an entire house-cleaning; donations will start to climb again in Year 3.
- If we hire 5 additional staff to support the project, and if we involve this group of 15 stakeholders, we will be able to build phase 2 of the project in Year 5.
As you can guess, things get more complicated as you go up the ladder of terms, but don’t let that stop you from starting out.
Want more? Check this out:
Information Week: “Big Data Analytics: Descriptive Vs. Predictive Vs. Prescriptive
What distinguishes these three key types of analytics? A data scientist explains the differences.”