This document discusses using data-driven management approaches. It notes that humans have limitations like biases and limited rationality, while data is permanent, historical, and can be analyzed statistically. It advocates defining user cases, metrics, alerts, and using data for discussions over opinions. Regular reports in various timeframes are recommended, as is mapping data geographically. The goal is for a company to understand what its people know by structuring all information queries through a chatbot interface linked to data.

1. Management by data
Luca Foresti, 14 Ottobre 2017
TAG Family

2. People 1

3. People 2
• We get tired
• We make mistakes (logical)
• We are full of psychological biases
• Time devoted to work is limited (we work too much)
• We may decide to leave the institution
• We don’t use statistics in our understanding of facts
• We don’t follow the “evidence based” approach in our understanding of the
world. We use the “personal experience based” approach
• We have limited rationality (H. Simon)

4. People 3
• We are full of super powerful sensors (5 senses)
• We have the most complex and high performing neural net at present: our brain
• We are able to develop abilities like leadership that machines cannot (today?)
• We live by and search actively emotions, and others do the same
• Trust is the main asset for working well together with others
• We develop creative path you cannot describe in an algorithm
• We develop continuously new curiosities about the world
• We have: Compassion, Ethical Values, Socratic Discussion Skills
• We have rights and we are citizens in a democracy

5. People 4
• We have a managerial culture coming from Taylorism
• We have a culture coming from our Families, Schools and
Universities
• Power and money are important in what we do
• We look for meaningful lives
• Typically we were not taught things like: management,
leadership, commercial abilities, etc. etc. The best among
us learnt this stuff on the job

6. Software & Hardware
•Tireless (24/7)
•Accurate (No mistakes we didn’t create in the system)
•Powerful complex calculation
•Costs going down at several Moore’s law (CPU, RAM, Non-Volatile Memory,
Bandwidth, Sensors)
•IOT gaining data everywhere
•AI entering in our daily job much faster that you can imagine
•Human to Computer Interfaces getting better and better
•Computer to Computer interfaces (API) almost standard practice for software
worldwide
•Dimension of hardware going down rapidly (Mobile, Wearable, Microsensors)

7. Data
• Permanent
• Historical time series
• Needed for having a statistical approach
• Big data —> detailed and personalised description of actions and events
• Needed for creating alerts about events
• Via Data-Warehouses we can mix data from all sources
• With AI we can discover clusters
• With Pattern-Recognition we can automatise human simple activities
• Chatbots and Voice-bots allow to transform institution data in continuous learning of
people and increasing diffuse know-how

9. The only exercise you
should know about statistics
• “If you have a box into which there are millions and
millions of white and black balls and you get out of it 100
balls, 70 white and 30 Black. 2 Questions:
• If you try to get out another one which probability you
have that it is white? (Easy one)
• What is the statistical error on that probability and how
does it scale with the number of balls you got out of the
box at the beginning? (Difficult one)

10. results:
p=70%
sqrt(p(1-p))/sqrt(N)
for general distributions (sigma=standard deviation):
sigma/sqrt(N)

11. What do we believe in Santagostino
when we talk about management?
• If you don’t measure it doesn’t exist
• People should do human things, machine should do machine things
• First things you should define when you do a project: user cases and testing protocols
• Every problem you spot should be inserted as alerts into the system
• Every report should be translated in statistical alerts
• People should use data as discussion tools before inserting gut-feelings into the game
• Changes start always from “data prisons” you apply to the system
• Every ring of the value-chain should have service-parameters
• We don’t create technologies, we let the best technologies around work for us
• The main technology we have is dialogues among humans and we spend a lot of time and efforts
for having it getting better continuously (but this is a topic for another talk)

12. Reports structure
• Intra-daily: alerts and operational parameters (For example: % of answers in the
call-center, queues, etc. etc.)
• Daily: with the most important business data: bookings and income per business
unit
• Weekly: with a deeper look to the business relevant data (per specialities,
comparisons with previous year, etc. etc.)
• Monthly: coring and clustering (new Vs old patients, Insurances, costs, people
costs, budget checks)
• Quarterly: P&L & Balance Sheet
• Yearly: P&L, Balance sheet and all other official documents
• Ad-hoc: every time somebody wants to go deeper in something

13. Geo-marketing
• We are a retailer, therefore we put all our data on the map
• Competitors
• General Pratictioners
• Pharmacies
• Hospitals
• Our patients
• Istat data on the cell
• Our Centers

14. Alerts
• Events: every time we have a problem we didn’t spot
promptly we create an alert inside our system that send to
the responsible person an email spotting it
• Statistical: every event that is more than 3 Standard
Deviations from the average of the historical time series

15. Time Series
• With a single measures you try to get the value at time t of a certain parameter
• With multiple measures you try to get the value depending on the time V(t)
• You are trying to understand the hidden process behind your measures (that are
always only proxies of the reality)
• You don’t know if the process changes and how fast it changes
• As said before the statistical error about an average of N data is around 1/sqrt(N),
therefore more data you have more precise you are, but…
• If the process behind is changing fast, more data you get and less you understand
• For these reasons the amount of data you have to use is more an art than a
science

16. THE problem
• How can you have a company that knows what its people
know?

17. Chatbots
• We are restructuring all our internal information system
• The human interface will be only a chatbot
• The software behind will be linked to our data-warehouse (Google BigQuery)
• Everyone may ask questions to the chatbot
• If the system has a good answer it will provide it automatically
• If it doesn’t, will recognise the semantic structure of the question and it will understand who in the company
most likely has the answer, sending automatically the question to that person. His/her answer will be routed
back to whom did the question and the system will learn from that moment on what the right question is
• Questions can be parametric (“Give me the total income of yesterday”, “Yesterday” is a parameter that
changes every day). Parametric questions should have sql statements called into the Data-Warehouse
• Everyone will be able to create his own dashboard as a Lego structure of parametric chatbot questions
• From the moment this system is live we know perfectly all the questions for each person and therefore we
have incredibly powerful data in terms of ideas, HR, know-how, curiosity-driven analysis. We may spot
great people we didn’t know we have from their questions

18. Radical Transparency +
Meritocracy
• Watch Ray Dalio’s TED talk: “How to build a company
where the best ideas win”
• Watch Ricardo Semler’s TED talk:”How to run a company
with (almost) no rules”

19. Noi siamo quel che facciamo.
Le intenzioni, specialmente se buone, e i
rimorsi, specialmente se giusti, ognuno, dentro
di sé, può giocarseli come vuole, fino alla
disintegrazione, alla follia. Ma un fatto è un
fatto: non ha contraddizioni, non ha ambiguità,
non contiene il diverso e il contrario.
We are what we do.
Intentions, especially if good, and remorse,
especially if right, each one within themselves,
can play as he pleases, up to disintegration,
madness. But a fact is a fact: it does not
contradict, it has no ambiguity, it does not
contain the different and the opposite.
Leonardo Sciascia