Self-service or Disservice? What is self-service BI?

I’ll start by saying that I’m quite adamant about what self-service business intelligence is, and isn’t.

There was a post on LinkedIn a week ago, that turned my thoughts to this, and made this the topic for today.

I find it baffling that the conventional idea of self-service analytics is for the “business user” to have the ability to create their own dashboards/views.

Why baffling? Well, 2 reasons.

1. It didn’t really work with Excel.
2. Data Literacy – It’s now becoming a well-recognised fact that data literacy is low amongst people that aren’t analysts. When it comes down to it (in the nicest possible way), most people wouldn’t know a dimension or measure if it introduced itself nicely in the street. To expect these people to easily create something, from nothing would, be pure fantasy, would probably lead to an increase in failed BI projects, and would be a dis-service to (not only) the IT/BI teams that work so hard to implement a project, but also to the users how would be left with something they largely wouldn’t use.

Excel Hell

Excel is (for me) the original self-service software, and a client once stated that they are “in excel hell” to describe the effect of Excel on the business.

Everyone can freely create with excel, and it quickly comes unmanageable. Not only from the amount of spreadsheets that are created, it also causes chaos because everyone creates their own view, from the same data, at different times, often with wildly different calculations and the business then becomes mired in a swamp of differing results and opinions.

I’ve also had exactly the same conversation with a Tableau user, who had to create a visualisation of all their Tableau dashboards because they gave everyone the ability to create, and found that it very quickly became untenable, with around 1500 tableau dashboards.

Don’t even get me started on errors in spreadheets! That’s a post for another day.

If the thought is that self-service is actually just for analysts, then this should be something that people are upfront about when discussing this, not saying that self-service is for everyone, when in fact it’s not.

This approach gives the IT and BI teams serious palpitations, not to mention the humble user, who even if they believe they have the right skills, generally don’t.

Where’s the governance? How do you ensure that everyone is using the same measures? My post last week mentioned a conversation I had with an organisation that had 15 different definitions of FTE. The BI Team seek to make sure that issues like this don’t arise, and are understandably reluctant to let everyone create their own views but the widely accepted view of self-service would only go and exacerbate these problems.

What’s my opinion on this?

Is self-service for everyone?

Absolutely!

I firmly side with the BI/IT teams about giving the general user the ability to create from scratch, sure there will be people that have that ability, but they’re in the minority.

As I said previously, the low standard of data literacy would mean this would create more problems than it would solve, and would move an organisation away from being more agile. Isn’t the desire for agility the point of self-service? So why would you do something that makes you less so?

I think Qlik says it best:

The premise of self-service business intelligence is to give all employees access to insights that will help them make better decisions, regardless of analytics skills.

Source: Self-Service BI: What it means, why it matters, and best practices https://bit.ly/32jhbE8

I’ve come to see self-service analytics as split into 2 distinct groups:

Guided and Self-Service Analytics. 

Guided Analytics (Think of this as “traditional” analytics) is where the BI team build an application (Qlik call them applications, not dashboards) with a certain need in mind, be that Sales Marketing, operations, project management to name a small number of uses.

The users then interact with the application, and if there are any other questions, then they have to raise a change request, and wait until the BI team can implement that for them.

Great for day to day, standard, requirements, but organisations are now needing to be more agile.

It’s a fact, the decision window has shrunk massively and people are now looking to make decisions on that day.

Any longer can seriously impact the business.  

All BI software does this very well, most “new age” BI software is just “Excel on steroids.”

Self-service Analytics 

I believe that self-service covers 2 areas:

1. Knowledge workers that need to be more flexible and build their own analyses.
2. Business users that need to make decisions quickly.

Yes the knowledge workers would be given the ability to create from the ground up, they would have the skills to be able to do this.

Business users, though, would have an application, built for them by the BI Team, as they would for Guided Analytics, but then the user is given the ability empowered (empowered is an overused word, but incredibly relevant here) to then make changes to charts, by use of an easy to understand, drag and drop governed library, called Master Items in Qlik Sense.

These users can change the chart type or replace/add dimensions and measures.

Qlik Sense will also suggest the most appropriate visualisation, and the user will then be prompted for a dimension/measure, which is contained in the governed library, under 3 headings (dimensions/measures and visualisations)

Governance

All this is all well and good, but how do you then gain the governance to ensure that these people are using the same dimensions and measures?

This comes down to the governed library, the users see this as the Master Items library, which is the cornerstone of self-service. The BI Team creates and curates this governed library, so the dimensions/measures are all guaranteed to be the same, and infinitely reusable. Not only for the business user, but for the knowledge workers/analysts.

This was something that had limited availability in QlikView, but has come to the fore in Qlik Sense.

Implementing self-service in this way, largely removes the change requests, allows the BI team to focus on what they do best, and empowers (again appropriate) the analysts and general users to do what they do best.

AI (Artificial Intelligence, or Augmented?)

AI has become a buzz word recently, and is often a way for vendors to make orders larger by adding more software to the requirement. There are lots of companies that have sprung up in this space.

What if you’re a small to medium business, without the budget to invest in this?

That’s where Augmented Intelligence (Qlik’s AI) comes in. Qlik’s AI is baked into Qlik Sense.

Simply stated – instead of just solely relying on pure machine automation, as you may find with typical Artificial Intelligence applications, Augmented Intelligence works with human interaction and perspective to solve complex business problems.

Mike Tarallo – Qlik

By combining the Associative experience and Qlik’s cognitive engine, it creates what is in effect an intelligent assistant right at your fingertips.

Why is this good?

Qlik’s AI allows you to conversationally search the data, giving a faster easier way to ask questions and get insights.

You can auto-generate visual analyses to help see the data in new ways, and uncover hidden insights.

For the developers, it speeds up the porcess of building an application with visualisation suggestions, association recommendations to name a couple of areas it helps.

Supplemental to Qlik’s AI is the Insight Advisor.

The Insight Advisor not only learns from the data, but the user’s behaviour AS WELL. Bringing important insights to the fore. Which lowers the skills required to analyse the data and to keep asking and answering the stream of questions that are always the result.

Qlik’s Mike Tarallo made an excellent video on this: https://bit.ly/3imJRBB

I think it’s time that the answer to what is self-service BI, is standardised, and everyone starts talking about the same thing. Much like the company with all those versions of FTE, how can we truly know what something is if the words used aren’t consistent.

Thanks again for reading this, I’d love to hear your thoughts on this topic.

Until next time, stay safe!

Qlik Sense, PowerBi, Tableau – Apples for Apples?

 

I read a lot of posts where people look to compare these 3 pieces of analytics software and overwhelmingly I see people try and position them as one is better than the other, mostly the through the age-old sales tactic of FUD (fear, uncertainty and doubt).

Having been in the field of analytics sales for coming up to 8 years (admittedly all Qlik) , I do get this, though there isn’t an objective view of which one to decide on. What I’d like to do here, is speak about the other things that don’t come to the fore in these comparisons are made by others that don’t know what makes Qlik diferent.

For me it comes down to this:

Do you want to see what has happened with your business/organisation? In other words, are you looking for a quick/easy way to visualise your data that would otherwise be represented in excel? Or, are you looking to find out why things have happened?

In my opinion, Qlik Sense, PowerBi and Tableau do the first thing very well, though it’s only Qlik that allows you to take that next step and answer the why questions.

How does Qlik enable you to do this?

Qlik’s Associative experience

The unique thing about Qlik Sense, is the “Associative Experience” or the green (your selection), white (everything associated to your selection) and grey (everything not associated to your selection) which is highlighting that appears when you make selections. This not only allows you to see what is related to your selections, it also allows you to see what isn’t.

Why is this important? Well, there are incredible examples of data that should have been associated but wasn’t. For example, during a proof of concept a utilities company was looking to visualise their managed debt. The associative experience showed that there was a huge amount of the debt that was unmanaged (it was grey). The total amount came to 10’s of millions. There are mountains of examples like this.

The way the associative experience works means that all of your data, in that particular application, is linked.

Nothing is left behind when you make selections.

This also means that if you have other questions, you can carry on asking and answering them.

PowerBi and Tableau are very like excel, in that you have to pretty much understand what questions the user will have, when you create a dashboard. If excel has been frowned on for this very limitation, then why would you want to just do the same but with some very nice graphics?

Qlik has come up with an excellent demo of the Associative Experience in action, I did try to embed it right here, but the restrictions of WordPress mean that it doesn’t work properly. I’ll leave the link at the bottom so you can jump to the page and try it out.

Colour blindness (Yeah, this one surprised me too)

Worldwide, there are approximately 300 million people with colour blindness, almost the same number of people as the entire population of the USA!

In the UK there are approximately 3 million colour blind people (about 4.5% of the entire population).

The Green, White and Grey Associative Experience in Qlik has been consciously developed with colour blindness in mind. 

Green, White, and Grey are some of the few colours that people with colour blindness can differentiate between, when they are on the same page.

BBC Sport in fact posted an excellent article about the impact of colourblindness on people watching sports. Colour blindness in football: Kit clashes and fan struggles – what is being done?

Build once, deploy anywhere

Qlik Sense is mobile enable “right out of the box”, and Qlik Sense was developed using the ethos of “build once, deploy anywhere.” Which means you don’t have to consider the different screen sizes when you are developing. This is responsive design.

Additionally, Qlik Sense is delivered entirely in your HTML 5 browser, so you don’t need separate clients for the different operating systems you have. Qlik Sense will work in Chrome, Safari, Firefox, etc. Be that on your desktop, laptop, tablet or mobile phone.

You can also install a specific Qlik Sense app on your Apple or Android phone, which would allow you to connect to the server, download the data/app and then work offline.

How much does the app cost?

Nothing.

Storytelling

There’s been a lot said about the value of storytelling with your data. Within Qlik Sense, you can create stories from your data, by taking snapshots, adding commentaries and highlighting salient data points.This is unique, though it does go even further. I use Qlik Sense (a lot) in Sales meetings, and when data is presented in any meeting, there’s always the chance that someone will question it. That then usually goes one of two ways. The meeting gets sidetracked into a discussion on if the data is right, or, it gets moved to, the well used (but rarely followed up), “We’ll take that offline” list of things that may or may not be addressed after the meeting finishes. When this happens, I simply right Qlik click (apologies it’s ingrained in me) on the snapshot, and go back into the QLik Sense application with all the selections, that were made when the snapshot was taken. We can then come to an agreement quickly, and move on. It’s saved me an enormous amount of time, and kept meetings on track.

Moving away from the business benefits:

Governance

There are a couple of layers of governance in Qlik Sense:

i) Security down to the row/cell to make sure that only those you want can see the data. This also applies geographically. ie. If you create a Qlik Sense application for a global sales team, then you can ensure that each salesperson only sees the data for them, or their team. Sales Managers can see their team, and the figures for other teams, Sales Directors get a higher level and so it goes on.

ii) Governed libraries. The central tenet around self-service, for me. I’ll go into what I believe self-service is in another post. Governed libraries enables the BI team to develop and curate a library of dimensions and measures, that are reusable. Not only does this enable self-service, at all levels, it also ensures that everyone is using the same calculations for their measures. 

I once spoke with a company that had 15 different definitions of FTE (Full Time Employee/Equivalent), and they could not report accurately on this vital measure. A governed library gave them a single definition, and trust in what was being reported.Ultimately a governed library creates agility and trust in your data. The much sought after Single Version of the Truth.

In memory (Qlik) vs query-based (Tableau and PowerBI)

Another difference is that Qlik also utilises an in-memory architecture.

Why does this matter?  That comes down to something that’s missed from most project scopes, and that’s acceptable performance.

In the words of Michael Distler in his blog post last year:

Even when moving BI processing to the data lake, a SQL-based query tool must cache/aggregate SQL-based views to have any chance of achieving acceptable performance. And since they are only caching some of the data, one needs to guess what questions a user may ask. Go beyond these pre-defined boundaries and the user is faced with waiting while the BI tool undertakes the slow process of a new query against a massive repository. And even more time-consuming would be using SQL-based queries to uncover the unexpected non-related or missing data. With the democratization of data and more business users being called on to use analytics in their daily tasks – making people wait even longer is plainly unacceptable.

The Whole Big Story – Michael Distler 

In memory enables the performance a user is seeking for when they are looking for answers, from the data. In memory brings the speed that’s associated with agility, not the waiting while the query is executed. This is one of the areas that others try and subject the FUD (Fear, Uncertainty, and Doubt) I mentioned earlier. The fact remains that if this was an issue, then some very large companies wouldn’t have rolled Qlik out to 1000’s of users.

You can also run the Qlik Associative Engine at source by using the Qlik Associative Big Data Index which gives the same performance against data sources of immense size.

Lastly (though I could go one, and on about what makes Qlik Sense unique), 

Speed and scale

I’ve spoken about the speed for the user, Qlik is also incredibly quick to implement. The majority of any time in a Qlik Sense project is (and should be) devoted to building the data model. Once this is done, you are set.

Though it’s impossible to predict exactly how long any implementation will take (be it Qlik Sense, Tableau or PowerBi), most organisations start with their developer team of 1 to 3 people, and then roll out when they’ve developed the applications or dashboards. Typically the first Qlik Sense app is developed in the first couple of days.

I worked on a tender for a public sector organisation last year, which was for 1000’s of users, and we estimated that bringing all the data together, creating the data models and first Qlik Sense applications would take 3 months. Sadly we didn’t win the bid, due to the buying authority having some bizarre objections about what was in our cloud, which is by the by. The project went to another organisation using PowerBI. Last I heard, 4 months into the project, they were having massive issues just scaling PowerBi to the number of users. Qlik Sense scales linearly. So each server you add, will support exactly the same number of users (let’s say server 1 = 350 users, Server 2 = another 350 users) whereas others don’t scale in this linear way.

A customer once made a comparison between Qlik and Tableau, which I think I pretty telling

What I can do in a day in Tableau, I can’t do in Qlik.

What I can do in 2 days in Qlik, I can’t do in Tableau.

Ultimately it’s down to what you want. Something that puts pictures on what would normally be a spreadsheet, or a solution that enables to ask (and answer) a stream of questions, and also lets you get to the why answers.

If you’d like to have a look at the Associative Experience demo I mentioned, here’s the link: Qlik Associative difference demo page

Thanks for sticking with this to the end, and I do hope this has been informative. I’d love to hear your comments below.