What is
datademocratization ? It is the opposite of a conservative approach. It's a
liberal approach. It's about allowing everybody to use the data of your
organization, to make the right choice, the right decision, to build better
tools (with artificial intelligence for example). Everybody is to say all the collaborators from the CEO to field
people, all our partners, ouf subcontractors.
No more gategeepers
or bottlenecks.
Datademocratization
is not only a subject of data, we need to teach how to understand the meaning
of the data, use the methods and the tools and mainly spread knowledge.
To undestrand the
meaning you need a dictionnary with a three viewpoint approach : for technical
experts, for bi expert, and for newbies. What is this data, where it comes
from, what are the operational usages, what is is lifecycle from it birth
to is death, the transformations made by all the systems, … You also have to
document how to retrieve it (data owner, platform) and how to use it (including
the risks, the quality issues, some algorithms, …)
Speaking bout tools,
datademocratization have to be low tech. At first, you don't need a datalake or
bigdata cloud infrastructure. You can start with an excel sheet ! You can use
it to store, retrieve, visualize, document, … And then you can add gradualy
some tools to improve your works (a lot are open source), then some cloud to be
scalable, then some automatization tools (discovery, quality, security, …).
You can spread
kwoledge inside and outside your organization. Inside your company, open all
your BI, data, analytics to all in intern by default, except for risky data. I
will explain later what i call risky data and what we can do to reduce the
risks. Then teach collaborators on tools, on methods, on datas available, by
organizing workshops dashboard in a day, IA Day, data day. In my #rockthedata
team, we call them "Come as your data are" sessions. Colleagues comes
with their use case and their data and we help them during a day to fix their
issues. We also organize data tours, learning expeditions, …
Out of the company,
you have to try to share data with partners in each use case you have. I've
seen subcontractors comin to us with prototypes made with our open datas
and win contracts ! You can build easily
an open data platform, organize open innovation days, hackathon, … The good
idea is everywhere. But it only comes to you if you share who you are : your
data.
It's an angelic
approach isn't it ? Obviously there are some risks and you have to manage them
before sharing your data. For each data
risk, use a systemic approach, the ebios framework for example. It's an easy
understanding set of guides (and a freeware) dedicated to information system
risk managers.
You can add all this
informations to your governance documentation to be more efficient. DMBOK is an
other usefull framework dedicated to data management.
Note also that there
is always a way to desensibilize sensitive data : mask or group data, use a
symetric algorithm on keys, ... Keep your data still usefull for all but
unrisky.
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| Wim Delvoye -Concrete mixer |
With
datademocratization, everyone can improve his own expertize with data, with
facts, and everyone can contribute effectively in decision-making. More, your
organization is able to identify new data use case out of the box.
But be carefull,
datademocratization changes the place of the manager in the organization : is not any more the only owner of the
information, now he has to help the team to evolve by itself. It's viral. At
end, sharing data will break all the
silos in the company.
Data specialists can
be afraid by this approach. They can be if they think that they can live alone.
They have to improve their communication and sharing skills. A datalab
nerd is not valuable any more.
There's no cons,
only risks to manage as explained below.
Democratization is
an affordable lowtech game changer. It's about making our collaborators, our
partners smarter. The conservative data policy is not an option. Except if you
want to die alone with your data.
But deploy data is
not enough. Making a decision without data is a big risk. It's a point. But
making a decision with a bad uderstanding of data or with the wrong data is a
big risk too.
Cross reference data
and experience with the right methods and the right tools makes good decisions,
makes the thing happen.
Let's democratize !
