
The Myth of Self-Service Analytics - vincentbarr
http://www.perceptualedge.com/blog/?p=2467
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calinet6
For advanced analytics and true sensemaking, much of this might be true. You
need a human to really deeply understand data and find what it really means.

However, let's apply the pareto principle. 80% of the needs are served by 20%
of the effort. I think for most (let's say 80%) of what most (about 80%) of
companies really need, a computer can get at least most of the way there, with
much less time and effort than a human.

For example, sure, you need a human to really understand your web analytics.
However, to understand it at a basic level, pull out clusters of interesting
patterns, and discover anomalies and outliers that you wouldn't have gone
looking for otherwise, the computer can get the average intelligent person
(not an analyst) most of the way there, and certainly can provide a great deal
of value for generally much less cost than a human analyst on salary.

Plus, this frees analysts up for work that truly needs their expertise. As
many data folks will tell you, it'd be great if everything they did was using
their full skill set to the fullest; but often they spend half their day
pulling reports that a computer truly could have done for the requester
without any intervention, if designed correctly (not easy, mind).

So, it's not about computers replacing humans entirely. It's about reducing
waste, finding ways to cover common cases and repeated work easily, and
freeing up human minds for what they're really good at and needed for.

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locusm
Web analytics is a poor example, most web analytics solutions are single
source and a well understood domain. I think the article is referring more to
a typical BI scenario where you might be consolidating data from multiple
sources into a data warehouse and have (had) typical BI processes in place
like ETL etc. We've been through self service BI before except it was more
like a "self service covert BI" thing where silo upon silo of Excel
spreadsheets existed in secret locations to those in the "know".

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bmh100
I work in business intelligence, and my understanding of "self-service BI" has
never been "no developer needed at any point" or even "unskilled knowledge
work". I have always approached it as a particular environment. Specifically,
BI team must produce the appropriate data lakes and interfaces to support
user-driven design and simple aggregations. The BI team would model the data,
optimize for analytical speed, apply business logic, improve data quality,
etc. They would then create an interface with a "tool box" of dimensions,
facts, and aggregations. At that point, the user takes over with the freedom
to choose which dimensions and aggregations go into a chart, what type of
visualization to use, and how to drill-down or slice-and-dice the data. That
is true "self-service BI", and I have to be honest that I haven't encountered
sales pitches with level of deceit claimed by the author.

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locusm
> I haven't encountered sales pitches with level of deceit claimed by the
> author.

Get along to a Microsoft sales presso on Power BI for that.

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mistermann
What would be an example of this deceit?

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blahi
I am interested in this as well. Power BI is head and shoulders above anyone
else in BI at the moment and I have never seen them make any deceitful claims.
Every time it's "easy, but for advanced functionality you need DAX".

They are underselling it if anything, since it just punches so far beyond it's
price point that most people who need entry-level BI would not understand how
much of a good deal it is.

Self service means that you don't need to go through corporate IT, not that
you need grand total of 0 dev skills. And even then, those tools abstract a
lot of dev work. In the PowerBI case, DAX feels intuitive to anyone with Excel
formulas experience. Other vendors have similar tooling.

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tuna-piano
The opinion piece doesn't give much concrete for me to go on, but I'm not sure
I agree. Of course terms like "self-service" have many meanings, but I've seen
it used successfully by people with a range of data skills. Some examples:

1\. Previously, static reports delivered weekly are replaced with daily-
updated interactive tools in Tableau (or similar), which allow business users
to drill down and filter things.

2\. Previous reports that would be custom SQL reports are replaced with
reports built by a business user (or a power Tableau user) - with much quicker
turnaround and better results.

And of course, Excel, the most widely used self-service BI tool is used
successfully every day by almost every business in the world. It's use has
it's problems, but overall it's extremely valuable... and it's definitely
self-service and definitely BI.

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dworin
In my experience "expertise in making sense of data" is only one piece of the
puzzle, and often not even the most important one.

Domain expertise is hugely important at making sense of data. Self-service
allows domain experts to quickly look at data themselves. They may have to
learn skills in data-sensemaking, but the expert in data will have to learn
about the specific domain (often much harder).

I'm noticing that more and more people in a variety of fields have at least a
passable understanding of how to make sense of data. For quick questions,
self-service access to data makes the process much faster with little risk.

I've been in organizations that tried to put data behind gatekeepers who would
protect users from making mistakes. In those cases, we made a lot more
mistakes because not enough analysis was done, or people didn't have access to
data.

I've been in other organizations where we let everyone look at the data. Sure,
some people made mistakes, but we used that as an opportunity to teach.

If I had to bet on which type of firm would win, I'd bet on the latter. I'm
deeply skeptical of the promises made by BI vendors, but self-service
analytics isn't one of them.

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nwenzel
When companies want self-service BI, what they really want is, zero-code-
required tools that a BI developer/analyst can use to build on-demand standard
reports for business users with predefined click-through paths.

And yes, software makers exaggerate in their advertising.

As compared to the author's other posts, this one seems hurried and maybe a
little grumpy. But, still makes an important point.

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fiatjaf
Since in Brazil we have 0 self-service gas stations, should I understand that
Brazilians do not have the skill necessary to pump gas?

~~~
fiatjaf
If I find a country in which most car owners repair their own cars, instead of
delegating it to specialised mechanics, will you take back everything you
said?

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sgt101
An article about myth that contains no evidence ? I keep reading opinions
about data science, where are the findings?

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kfk
The most used BI tool out there, Excel, is self service and it has been so for
at least 30 years now. SSBI is not about taking people out, it is about
empowering them with the right tools. Tableau, for instance, is nothing more
than an Excel 2.0 and nobody I know, not even Tableau salesguys, says we will
get all our analytics automagically, very far from that. Every department in
Corporations is struggling to find people that are good with data, every
department. Also, data analytics comes at the very end, again, nobody is
saying your data strategy (blending, complex calcs, etc.) will be
autogenerated, this would be crazy talk. You should consider SSBI more like
what happened to mainframes and PCs, where the key to success was empowering
more and more humans.

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gp05zmzpjl
If A is the set of people who can write SQL and B is the set of people who
know how to make inferences from data, self-service BI tools can expand the
universe of people who can analyze data at your organization from A∩B to B.
That's a big deal!

Yes, there are pitfalls if you give people in ~B access to the BI tool. They
could make bad decisions based on the data. On the other hand, they are
already making bad decisions without the data. In a world where |B| is
limited, it can be hard to make the right tradeoffs. You might choose to let
people in ~B use the tool but have a policy against sharing the results
without a once-over from someone in B.

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akerfonta
I work for a company making a "self-service analytics" tool, but we don't sell
it as a "magical analytics" tool that does everything for you. Rather, we see
it as a "you don't have to involve the IT department" tool for those quick
jobs that analysts or other knowledgeable people would ideally like to do
themselves, but can't due to lack of tools.

To me, what is wrongly called self-service analytics is often really self-
service reporting. You are consuming the end result, not the analysis itself.

~~~
robzyb
Off topic, but whats the advantage of Transdata over Alteryx?

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akerfonta
We support "ragged" data (ie non-rectangular) and are a lot cheaper.

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dgudkov
Many good points. Nevertheless self-service analytics is the only way to give
business users what they need in a reasonable time frame. We've already seen
what happens when every little change request has to be implemented by IT
people -- time to production becomes weeks and months, instead of hours.
Perhaps, there should be some kind of framework with reasonable restrictions
and simplifications. But that's a very open question. I've yet to see a good
example of a reasonably restricted self-service.

~~~
sgt101
There are other ways - for example if IT is embedded in a team and works hand
in hand with them for a sprint rather than via change requests and so on.

~~~
dgudkov
That's usually the best way to organize BI teams. The problem with this
approach is that embedded teams don't have strategic scope. For instance, they
do not build or manage enterprise data warehouses. Therefore, embedded teams
tend to have rather limited influence.

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skybrian
As usual it depends. Some data is easy to analyze, and we don't have enough
experts to rely on them for everything.

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ommunist
Well, most self-service analysts I met, mixed statistical correllations with
causation quite blindly. One needs to have a plenty of common sense before
making judgements about the data. Otherwise dynamics of floods of Danuba will
be considered cause of change in quantity of students in Budapest, or vice
versa.

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buckbova
I would agree that most business managers who sponsor the purchase of such
tools grossly overestimate their utility and underestimate the cost to get a
self service solution in place.

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newjersey
Also served over https thanks to the RapidSSL cert

[https://www.perceptualedge.com/blog/?p=2467](https://www.perceptualedge.com/blog/?p=2467)

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baconner
As much as I like Few's books and have relied on them heavily in my time
building SSBI tools he has a tendency to take something I basically agree with
and blow it way out of proportion into an all-or-nothing type declaration.
Seems he's true to form today again with a basically correct point that many
SSBI tool vendors over-promise and mislead with their marketing and turned it
into "self service bi is a lie" which it absolutely is not.

Self Service BI tools are not intended to take the place of analytic skill any
more than they take the place of domain expertise and that has never been the
meaning of the term. The promise of SSBI is to reduce the incredible friction
domain experts traditionally had to deal with to get their key business
questions answered. Yes, your users need to develop other analytic skills to
go along with their domain expertise! Turns out most of us have stronger and
weaker points and have to learn and evolve our skills to get our jobs done
well. Taking on SSBI means exactly that for your users who most likely have at
least one of the key skills (domain expertise) already and maybe more.

Using an exploratory type ssbi tool is a conversation with your data via an
interactive tool. One question leads to another leads to another and if the
alternative is having to stop and ask another department to put each follow up
question on their backlog the conversation is basically broken and often
business users just stop asking and revert to pure gut feel decision making. I
think most of the progress made over the past 20 years in BI has been about
making this kind of process more agile in the same sense as iterative
development. SSBI is part of that. The inversion of analytic process in big
data systems is part of that as well. ML can also play a role in that with the
right circumstances.

What we can do, as BI vendors, is build tools and documentation that guide
users who start with only part of the skills they need into learning the rest
while using the tools we provide. We can present defaults that guide the user
towards visualizations and views that are easier to interpret. We can embed
analytic skill building into our applications in tutorials and hints. We can
build metadata up as users inform us about the data as they use it rather than
requiring them to do it up front in a big-bang DW modeling session. We can
inspect the data with simple heuristics to try and hint the user how to use it
with the tool or apply better defaults. We can build better cleansing munging
and data consolidation tools. We can build tools to let data analyst teams
also turn things around more quickly. And yes, perhaps we can try using
machine learning to suggest possible avenues for the user to explore which _of
course_ must be interpreted by a user with the right skills because ML is
going to be wrong a lot of the time and users need to understand false
positives. It's all part of the process.

Bailing out on SSBI because of marketers being marketers just isn't pragmatic.
Better that we just keep evolving our products from both the data science team
end and the business user ssbi end.

