Today we discuss the 4 tiers that drive a data driven company via the "Data Pyramid". Included in this pyramid are key performance metrics, data warehousing , analytics and business intelligence and data science. Tune in to see how each of these tiers can play a role in helping you measure the success of your business.
8. These are the hard, quantitative numbers that are
attached to your business goals
9. For example, an e-commerce site might measure
revenue per day
While an online social gaming company might
prefer to measure daily active users or monthly
active users
10. Figure out what makes your company tick
and what drives its success.
After defining your quantitative goals, you
can align your entire company’s culture and
team around it.
11. A lot of companies will hire data scientists,
data analysts, and product managers
without necessarily figuring out the metrics
that define your business’ success.
13. Data Warehousing is taking all of your
production data (from web pages, queries, user
behavior, demographics, etc.) and storing them
into one centralized source of truth.
14. Data Warehousing is taking all of your
production data (from web pages, queries, user
behavior, demographics, etc.) and storing them
into one centralized source of truth.
The schema (organization) of your data
warehouse will be centered around your KPMs.
i.e. If you’re tracking daily active users, it might
make sense to have a user login table.
15. (Yes it sounds a bit like database 101, but
you’d be surprised to see how many
companies skip that step)
17. Now that you have a data warehouse, you or
your analysts can use the information to
determine answers to key questions.
How many users logged on in
the last 7 days?
How many people logged on
from Idaho on Tuesday night?
How many women above 40
bought a virtual good from our
online game?
18. The next goal is to extract
numbers and generate
hypotheses, reports, and
dashboards to figure out
where we want to go next.
19. We did it!!
Many companies can stop here and
pretty much be a reasonably data
driven company.
20. However, in the last couple of years we’ve
rediscovered machine learning and have
made it part of our everyday arsenal.
22. Roughly, data science means taking features and
variables from the hypotheses we defined earlier
in analytics and BI, and using these to craft
machine learning models to either explain our
past or predict the future.
23. Machine learning models inform
product decisions, strategic
decisions, and form the basis for a
curiosity based company that is
now on the offensive.
How can we use our data to monetize the company
further?
How can we use our data as a key asset?
How can we use our data to figure out what our users
are doing?
24. (BTW, Framed Data provides painless data science services that any company can use to
increase their user retention. Give our free trial a shot and click the logo below!)
28. At the bottom is a section labeled
“Customers Who Bought This Item Also
Bought”
29. At the bottom is a section labeled
“Customers Who Bought This Item Also Bought”
Given what I’ve bought in the past, they use machine
learning to try to predict what I might be interested in
buying in the future.
30. At the bottom is a section labeled
“Customers Who Bought This Item Also Bought”
The main KPM they are trying to optimize for is revenue.