Capability Maturity Models, can be used, to measure, your company’s data capabilities.
I suggest, you use similar maturity model, from this presentation, and, assess,
where you are standing today,
and where you want to be, in terms of your data capabilities.
In order to enable your data capabilities, you need to understand,
what data solutions you want to offer to YOUR customers.
Once you assess your own position,
you will know what foundational architecture
and resources are required, to enable these data capabilities.
Let’s start by looking at how Dell explored it’s own data maturity model.
At Dell, they created a data maturity model to chart their own data maturity.
and help their customers track theirs.
The customers are grouped by their main data goals.
What is that a customer wants to do with their data?
Such question will determine how you move up the maturity curve.
Stage 1: In the Data Aware phase,
a customer usually compiles non-standardised reports
from different system with the goal of standardising these reports.
There are multiple BI systems, data sources,
and there is a lack of data and app integration.
The business is doing ad-hoc reporting
and doesn’t always trust its own reports.
If this sounds like your organisation, you’ll want to focus,
on building capabilities to move up the maturity curve,
such as:
data modelling, database design, data normalisation
or feeding data to one BI or reporting system.
Stage 2: In the Data Proficient phase,
data quality is questioned.
IT continues to have multiple databases
or data marts and an incomplete data warehouse,
and there is no app integration.
The business has begun to track organisational KPIs
and is now ready to pilot a data initiative,
but it lacks executive sponsorship
and the know-how to manipulate or use unstructured data.
It’s common at this stage for the business to demand innovation
beyond IT’s capabilities or capacity.
Companies focus here on building proficiency in data quality
and app integration, provisioning data for speedy access by users,
implementing new technologies like Modern Data Warehouse
that address unstructured data, optimising the data warehouse
(including ETL offload), and implementing a Master Data Management strategy.
These actions can enable the organisation
to achieve standardised reporting on a platform available across the company.
At this phase, the organisation is now using its resources more efficiently
and is ready to realise the promise of big data
and analytics: transforming the way it operates.
Stage 3: In the Data Savvy stage,
organisations are using data to make critical business decisions for key initiatives.
This is the stage where the all-important business
and IT partnership goes to the next level
and executive sponsorship
and executive sponsorship is put in place to quickly break down both organisational
and data silos.
As several business functions begin to use data
as a competitive differentiator,
IT must keep up by implementing new technologies
that integrate all data sources
and applications to provision and store data effectively
and serve up data on demand.
The IT-business partnership should focus on building advanced capabilities
such as a data lake
and data integration with external data sources.
Stage 4: The Data-Driven phase is the final stage of data maturity:
becoming data-driven.
No data, means no decision.
The objective is to scale the data strategy
while continuing to take out cost.
IT and the business are functioning as a tight, cohesive unit.
IT, has integrated all data sources and apps
and has implemented, an advanced analytics platform.
The business has identified where
and how to embed analytics in its processes.
The challenges to remaining data-driven
and realising the competitive advantages inherent in this maturity level
are embedding analytics seamlessly into business processes,
scaling beyond databases and data lakes,
and moving up the analytics maturity curve
from descriptive
and predictive analytics into prescriptive analytics.
The Dell Maturity Model is an excellent example
on how to move up the data maturity curve.
But Executives in charge, will not accept predictions
and more sophisticated analytics without a trust of what has happened
and what is happening in the business first
and they will be seeking in first instance to measure basic business analytics.
Dell Maturity Model has demonstrated that each company’s maturity sits
within the “Low Data Capability” and “High Data Capability” bands.
It is a good practice to make a self-assessment of your own data capabilities.
This will allow you to offer data solutions to customers
that are under your capability band.
As a supplier you need to demonstrate to your customer
that you can help them mature in their own data capabilities.
In this example we have three customers.
This bar chart basically represents the degree of maturity across five bands.
Customer A is in the lower capability band
while customer B and C are moving towards the higher capability bands.
We can therefore say, that Customer A has low data capabilities
while Customer C has a managed and optimised control of their data.
Let’s learn what each of these maturity levels mean.
In order for you to progress in the maturity levels, you need to assess where you are today
and implement the required transformation projects to evolve in your data capabilities.
So, let’s just summarise what these five maturity bands actually mean:
At the ad-hoc level, processes are disorganised, even chaotic.
Success is likely to depend on individual efforts,
and is not considered to be repeatable,
because processes would not be sufficiently defined and documented
to allow them to be repeated.
For instance, Customer A has the majority of data projects in the ad-hoc band.
At the repeatable level, basic project management techniques are established,
and successes could be repeated, because the requisite processes
would have been made established and documented.
For instance, Customer A
has some of it repeatable data projects in the “repeatable” band.
Costs and resources are better managed on these few initiatives.
But most data projects follow a disorganised approach
and remain in the “Ad-hoc” band.
The "goal", is to help this customer, evolve to the next maturity stage.
Now, at the defined level, an organisation
has developed its standard IM&T process
through greater attention to documentation, standardisation, and data integration.
For instance, we are spotting that Customer B is doing better than A,
because they have evolved to a higher maturity band
and as a result they are saving time
and resources on most of their data projects.
At the managed level, an organisation monitors
and controls its own IM&T processes
through data collection and analysis.
For instance, Customer C has evolved from discrete analytics to more advanced enterprise analytics
and they are properly governing and monitoring data quality of most of their data projects.
At the optimising level,
the data capabilities are constantly being improved through monitoring feedback
from current processes and introducing innovative analytics
to better serve the organisation's particular needs.
For instance, Customer B and C,
are leading continuous innovation projects by introducing predictive to prescriptive data solutions
throughout their business processes.
So here is the question:
Where, do you want to be, with your customers?
On the "longer term", “Ad-hoc type" of organisations,
won’t survive in the digital transformation economy
and won’t develop the capabilities
or culture to retain knowledge and talent.
It all sounds great. But how fast, can you start your digital transformation journey?
Basically, once you completed your one to two weeks capability assessment,
you will know, where you are at, in terms of your data maturity level,
we can draft a data strategy that is in line with the overall business plan
side-by-side with your company IT and sourcing strategy.
A data strategy development can span from 1 to 3 months,
it really depends on the size and scope of your organisation.
While we develop the data strategy, a small talented team
can already start a number of transformative projects.
You can take an existing product and potentially develop
what we call a “shippable product increment”
that bring business value to your business process, staff or customers.
Or you can take and plan the design of a new APP
that would offer huge productivity levels to your operations business units.
This really depends whether your organisation
is on the low band or high band of the maturity model.
So, we believe very much in balancing out the overall “Corporate Goals” and
allow talented resources
at an early stage to deliver agile transformation projects.
This takes us to a business framework of activities
that will help you evolve in a coordinated fashion.
A data capability framework spans across your Corporate Drivers.
our Corporate Drivers are your main business focus
and also sum up a number of Challenges.
Your organisation has a business strategy,
an IT & Sourcing Strategy
and we need to develop the data strategy.
In order to evolve in your strategic and operational developments
we have a baseline of governance and policies.
Your operational developments will evolve by adopting methods and standards
and acquiring and developing the required people,
processes and technologies.
In relation to developing your data capabilities,
it is necessary to build your foundational data architecture.
We have adopted a simple combination of data management methodologies
that allow you to evolve in your transformation product developments.
Let’s look, at how you can tackle your Corporate challenges.
We discovered earlier,
We discovered earlier, that any organisation requires a top down development of their data capabilities
and your data strategy should be aligned to your business-, IT- and sourcing strategies.
And these high-level developments will take you a few months the gain consensus.
But that won’t stop you from evolving with a number of bottom-up product developments
that you can tackle with a small team.
The pieces in the centre of our data capability framework
is the engine that will enable your digital transformation.
This is called the body of knowledge in data management.
Data Security and Privacy will be as relevant
as having the correct levels of Master Data or Data Quality.
On the other hand, your data sources will remain in a dispersed location without
proper data integration, metadata and classification models.
It will be challenging to think “big data or advanced analytics” unless
we start classifying, securing, integrating
and sharing your valuable structured and unstructured data.
All this transformative developments will build the confidence of all stakeholders
and operations and help you evolve in your data maturity levels.
But why are data capabilities so important?
What goes beyond maturing your data capabilities?
Trusting your data will be the fundamental component to mature
in your analytics capabilities.
It will be extremely difficult for an investor
or stakeholder to move into advanced, predictive or prescriptive solutions
when the fundamental executive insights around data are not trusted.
When you look at this analytics graph
your organisation naturally will evolve from foundational data capabilities
to augmented intelligence with the support of AI.
This will lead you into the analytics roadmap.
The speed at which you mature in your data capabilities,
will also determine the speed in which you mature in your analytics capabilities.
The more you know about your data, market and customers,
the more your organisation is factual, data-driven
and will develop a competitive advantage.
We take a simple example of a transformation project.
This organisation has a challenging time
in getting on-time accurate data from dispersed business units.
The aim is to build a Modern Data Warehouse
that integrates structured and unstructured data from dispersed data sources.
All these data sources have similar Master Data such as Customers,
Vendors, Suppliers, Parts, Locations etc.
And a data team will look into modelling the data
and building a data integration pipeline
that connects the clean data into your warehouse.
But in order to do that,
you will require to review Data security and Privacy requirements
and ensure that the consumers only see the authorised data in the final report.
Nowadays, any data solution needs to be designed with “Privacy in mind”
and the Foundational Data architecture components
must offer the required knowledge in data management,
to deal with master data, data modelling, data integration, and data privacy.
In this example we would adopt agile developments
where several small teams works towards one goal...
and that is delivering in 30 weeks a Modern Data Warehouse.
By now,
you should get a good sense as to the importance of enabling data capabilities.
They prepare you for your analytics roadmap.
As a wider spectrum of enterprise data becomes available
and data quality improves,
your company will improve its analytics capabilities
and move towards prescriptive analytics.
The full data literacy becomes culture
and converts into an asset in your organisation.
To reach such degree of data literacy
you increase your data maturity
and analytics insights
with a year-on-year analytics programme.
As a company
that is interested in developing the data capabilities,
you need to align your data strategy to your corporate business plan,
IT strategy as well as your talent strategy.
It is of premium importance that you cover the technology
and skill gap required to enable these data capabilities
and you may also need to hire, train and retain talent
and partners that will help you develop your competitive advantage.
We started this session with exploring Dell Maturity Model,
we further evolved to defining a data maturity model for your company and customers.
We then explored the data capability framework
where you can implement your high-level data strategy
and also start a number of bottom-up digital transformation projects.
It became very clear half way through this workshop,
that in order to excel in your analytics capabilities
you need to build your foundational architecture
and develop a body of knowledge areas in data management.
Once your company is spearheaded in their analytics roadmap
it will do more with data
and gain a competitive advantage in their market.
I hope you enjoyed today’s session
and I’ll see you in our next workshop.