Analytical Maturity Models - Machine Learning and Artificial Intelligence

According to a recent Gartner study (Dec ’18), “87% of organisations have low BI and analytics maturity” (Source- 'Low Bi and Analytical Maturity')

On first impressions, this sounds like ‘shots fired’ by Gartner. So, what can they mean by ‘low BI and analytics maturity’, and can this possibly be true?

Analytics Maturity Levels

There are well-documented ‘Analytics Maturity Models’ that typically range from basic reporting capability via predictive analytics to an operational/active analytic capability.

Maturity level chart

A typical Analytics Maturity Model consists of 5 levels:

level 1 - ‘reporting’

level 2 - ‘analysing’

level 3 - ‘predicting’

level 4 - ‘operationalising’

level 5 - ‘activating’

We’d also add the following extra analytic maturity levels to the above list:

level 0 - ‘operating’

level 6 - ‘learning’

Let’s take a closer look at the extended BI/analytics maturity levels, from 0 to 6.

Level 0 – Operating

Based on VLDB’s client experiences, we’ve added a ‘level 0’ (operating) below the normal analytic maturity entry point at ‘level 1’ (reporting).

The premise here is that some folks still rely on operational reports produced by the source system(s), and have no dedicated BI/analytics capability. Yes, these organisations do still exist!

Personal data extracts from operational systems, followed by desktop processing in Excel, is endemic at this level of capability, which Gartner generously describes as ‘Basic’.

Folks that rely on operational reports produced by source systems don’t really know what happened, and certainly, don’t know why. Operational reports are often either wrong, so inflexible to be of limited use, or interpreted incorrectly. Worst case, all three.

“But we’ve used these reports for years” doesn’t make operational reports correct, far from it. If you can’t prove operational reports are correct then you shouldn’t rely on them.

Level 1 - Reporting

At the ‘reporting’ level of analytic maturity, organisations are able to understand ‘what happened’.

Level 1 represents a basic KPI report capability, also known as ‘rear view reporting’.

The main challenge at level 1 is to deliver tested KPI reports in the right format, to the right consumers at the right time. Folks that consume KPI reports at level 1 are known a ‘farmers’.

If you can’t prove that your KPI reports are correct or if they don’t always arrive on time and SLAs are missed, then you can’t claim to be at level 1. Shame on you!

Level 2 - Analysing

At the ‘analysing’ level of analytic maturity, organisations are able to understand ‘why it happened’.

Level 2 represents a KPI report capability, with the added ability to ‘drill-down’ to the atomic data so that aggregate values of interest can be understood/defended/debated. Folks that slice/dice the data at level 2 are known a ‘explorers’.

The key point here is that all of the atomic data used for KPI reports must be retained and made available via the BI toolset so that drill-down and slice/dice by explorers is possible. This is a big departure from a ‘reporting only’ capability serving farmers using aggregate data.

Level 2 is the minimum analytic maturity level all organisations should attain.

Level 3 - Predicting

At the ‘predicting’ level of analytic maturity, organisations are able to understand ‘what will happen’.

Level 3 represents the world historically inhabited by analysts wielding SAS, SPSS, KXEN or similar predictive tools.

The development, refinement, and deployment of statistical models predicting things like customer churn, pricing change impact, and loan default rates are typical endeavours at this level of analytic maturity.

Your organisation is at level 3 if you have a small team of oh-so-clever analysts that have created an entire de-normalised copy of your data warehouse somewhere else that they manage themselves ;)

Level 4 – Operationalising

At the ‘operationalising’ level of analytic maturity, organisations are able to understand ‘what is happening’ in (near) real-time.

Over the last few decades, ETL latency has reduced from monthly to weekly, daily and hourly. As latency has reduced we have moved ever nearer to ‘real-time’ analytics. That said, few organisations achieve true real-time analytics.

Technical challenges aside, there is seldom a solid business case for true real-time ‘operationalised’ analytics.

Level 5 - Activating

At the ‘activating’ level of analytic maturity, organisations are able to ‘react to what is happening’ in (near) real-time.

It’s not easy to integrate analytics and operational systems, far from it. As a result, operationalising analytics is an aspiration for most organisations.

Activating analytics is quite scary for us analytics folks. We’re not used to being mission critical and dealing with customer-facing issues that folks actually care about!

Level 6 - Learning

The analytics world is currently ‘all about’ Machine Learning (ML) and Artificial Intelligence (AI). Activities such as ML and AI try and help us mere mortals understand ‘what could happen’?

According to Wikipedia:

“Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task.”

“In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.”

There are lots and lots of free open source ML and IA tools out there. The gotcha is that you’re going to need some smart folks to drive said tools. Even worse, they’re calling themselves ‘data scientists’ (ugh) these days, and they’re ‘reassuringly expensive’.

A key challenge at the ‘learning’ level of analytic maturity is data preparation. The choice most organisations face is whether to let data management folks wrestle with data science, or let data science folks wrestle with data preparation. Those with the skills to conquer both the data preparation and subsequent ‘data science’ are rarer than the proverbial…

By pursuing ML and AI, organisations are often leaping from level 2 (analysing) or 3 (predicting) and bypassing levels 4 (operationalising) and 5 (activating). This is quite common and is a great way for the oft-overlooked analytics folks to get back on the exec radar. Hurrah!!!