DSA 3×3 Matrix Concept Intro

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(Last Updated On: 19 Oct 2019)

Reading time: 58 min 14 sec

Central Data Intelligence Unit with Cross-Function Support across 3×3 Matrix

Any organisation thrives on these core foundation principles. Any company, any business, any government – anywhere around the world.

From large global multinationals, to medium national enterprises, to small private family businesses. Even down to the local small corner store, street food vendor, or neighbourhood gang.

What you basically get is:

  • 3 Pillars: Marketing – Product – Customer
  • 3 Cornerstones: People – Platform – Profitability
  • All bounded by a data communication network.

DSA 3x3 Matrix Concept

Pillars and cornerstones are the key building blocks to a thriving organisation, but more on this later. Let’s first cover this part of the data science and analytics stream.


Data science and analytics, or DSA, with its data information process, systems, and network platforms – are all essentially the bond that binds all parts of the organisation into one functioning dynamic whole.

It is the glue or essence the facilitates the breathing into the life and soul of any organisation. It is also the more underrated, underappreciated, mistreated, misrepresented, misguided, and misunderstood factor – without which everything within falls apart.

This intro into the 3×3 Matrix Concept will simply touch on high level essentials, as each section will be explored in detail with future blog article posts.

Pillars and Cornerstone – Co-existing Relationship

The building blocks of any organisation essentially boils down to 3 key pillars and 3 supportive cornerstones.


The 3 vertical pillars that drive down the organisational strategies and implements actions, i.e. walking the talk, are made up of the follow core business functions:

  1. Marketing
  2. Product
  3. Customer


The 3 horizontal cornerstones that drive sideways in cross functional support of enabling pillars, are made of up the following core business functions:

  1. People
  2. Platform
  3. Profitability

Pillars are the frontline forces, whilst cornerstones are the backup crews.

Pillars are the showboaters, but cornerstones work hard in the background.

Both important and complimentary structures.


DSA 3×3 Matrix Pillars

The ultimate goal is ensuring the product is marketed to the right customer and this customer relationship is maximised.

3 DSA Pillars (Marketing - Product - Customers) : a co-existing relationship

Marketing covers everything that embodies the meaning of the business and conveys that meaning to the customer in the most effective and efficient way. It includes advertisements and promotions, market research, tradeshows and seminars, lead generation

Product covers everything around presenting the solution in meeting the needs and wants of the customer. The common departments are sales/operations, delivery/service, product engineering/development.

Customer covers everything to manage understand the customer and manage that relationship in maximising the lifetime value of that customer. Common departments include call centres, customer research, customer relationship management, lifecycle and consumer journey, churn and retention, rewards / referral programs.


These pillars co-exist in a symbiotic relationship with various cross overs and collaborations. For example:

  • Marketing/Product – working together to ensure product reflects the correct brand message
  • Marketing/Customer – working together to ensure customers perceive the correct brand message
  • Product/Customer – working together to ensure the product satisfies the needs / wants of the customer
  • Marketing/Product/Customer – working together to ensure a consistent brand message and customer satisfaction flow

However, it is also most commonly siloed in many organisations. Due to various reasons. Politics. Lack of resources. Self interests. Inefficient communication networks. The first step is initiating a conversation between the pillars and aligning efforts, strategies, and focus.


DSA 3×3 Matrix Cornerstones

The cornerstones facilitate and enable the pillars to drive the business forward.

The ultimate goal is to ensure pillars are provided the right resources, tools, and skillsets to implement organisation goals. Cornerstones work in a horizontal cross function support of pillars, across three key areas.

3 DSA Cornerstones (People - Platforms - Profitability) : a co-existing relationship

People covers everything around managing the workforce to carry out organisation activities. It includes recruiting, engagement, training, remuneration, safety, benefits – anything the motivates and drives the people to succeed. Typical departments include HR and training.

Platform covers everything around processes, systems, and workflows to support organisation architecture. It includes such areas as information technology, hardware / software, analytics, data management systems, policies / procedures, guidelines, documentations. Typical department includes IT.

Profitability covers everything to ensure financial viability and feasibility for shareholders and business operations. It typically consists of the Accounting / Finance / Payroll departments.


Data Science – the Ethos and Principle

Data Science can be seen as the fourth cornerstone, but it goes beyond that as it more than just data analysis. It embodies the ethos and principles of collaborative communication and information transparency. It’s about maximising both the efficiency and effectiveness with holistic organisation business and customer performance visibility, actionable insights, and timely decision making.

Empower and engage a responsive, data-driven and value optimisation culture.

People are absolutely crucial to any organisation. Talks of AI replacing humans are nonsense. AI complements and augments human capabilities, but people are still essential to driving strategies and getting things done right.

The idea is to empower and engage a responsive, data-driven, and value optimisation culture. But many organisations suck at empowering the people to reach their full potential and maximise outcomes.

The main reason being lack of communication, and lack of clarity in communication. Two key blames are politics, and data networks.

Whilst politics exist within any organisation, and are necessary, and not necessarily a negative – it does accentuate the negative when resources lack and performances are forced. This push comes to shove and fingers start pointing. People switch to survival and self-preservation mode, a zero-sum game. Leaders who put pressure of senior management, in turn this pressure trickles down the org chart, and everyone feels the impact.


Data network also becomes siloed, and communication slows to a trickle, as agendas get covered up, accountability becomes vague, and information loses transparency as objectives and judgements get clouded.

Job security then trumps job innovation, as people strive to pose and protect their own inner circle at the expense of improving cultures and workflow. Insecure and threatened, thus further pushing out new ideas and real talents.

The key to enabling people to perform well is to provide data network the facilitates better information flow, more efficient / effective use of time and resources, and encourages trust / knowledge share – thus improving productively and output, and better use of politics to manage people towards a positive outcome.

Blame the system, not the people. Respect your people, folks.

Data science enables democratisation and empowerment for the people.


Platforms and DMP > Data Information Network

Data networks comprises of three core modules, that can interchange and interfaced any different stages of needs and upgrades.

For data science, we look at:

  • Analytics Tracking – how data gets tracked, collected, and attributed
  • Database Systems – how data gets stored and accessed
  • Analysis and Reporting Tools – how data gets meaning and presented

At the end of the day, no matter how sexy or sophisticated the latest razzmatazz bling tool is pitched on the marketplace, it goes back to the age old fundamentals of solid data management strategies.

But as technology improves, markets become more connected, and users become more omni-channel savvy – datasets are now also overloading and exploding into big data with increase across volume, velocity, and variety.


Legacy systems are something that still exists across most organisations, nearly two decades into the 21st century. No doubt you experience this in your current workplace, the frustration of working in platforms from the past, and the disappointment in delay in adoption of new platforms that supposedly promises increased benefits.

Legacy system can only be pushed so far before limits are reached and constraints get overstretched in error and scalability. Upgrade to faster and stronger solutions are inevitable in staying competitive against the rising global competition with entrants whom are hungrier and more resourceful to compete.

So the balance is choosing between a hot new racing car, or an ageing old classic – whatever tickles your fancy. Or the company budget, political structure, and business agenda or goals.

Upskill, or get out. There is no other option for long term sustainability.


Reporting and Analysis – Manual > Automation > Machine Learning and What (Reports) > Why (Insights) > How (Recommendations)

The evolutionary stages in any reporting and analysis begins with exploring what something looks like. This first step is a manual step, in learning and making sense of the dataset. But the goal is to maximise the value in this dataset. And this involves two axes of evolution:

  1. First axis – the what, the why, and then the how
  2. Second axis – manual description, automated processes, and predictive artificial intelligence via machine learning.

Data Reporting and Insights - development and continuous improvement stages

The table is self-explanatory. Where most organisation currently sits is in manual reporting of the what – analysts providing data in the most basic descriptive format. Where most, if not all, organisations would love is automated intelligence that provides insights and recommendations in the most timely and efficient manner that would allow them to dominated the competition, increase market share, and win that economic game.


So yes. Machine learning – the current sexiest thing, the hottest chic in town. Strutting the catwalk and striking poses with attitude. Stuck in between AI parents, and competing for attention with deep learning sibling. The new hope and new frontier for academics, speakers, consultants, and yes your one special organisation.

All popular fads have their point. Useful to a certain extent. The problem is style over substance. Empty chatter and all talking but no walking. Not sticking thru the thick and thin.

Data is everywhere. Everyone’s got data. Your data. My data. Their data. Mining, buying, and hoarding up that data store. Big data in the house. Nope, no one’s that special anymore. Now it’s how you use data.

And the end of the day, it’s a tool – a technological platform that enables improved systems and processes, and not just for the sake of adopting a new technology.

Get the what reporting and processes right first, such as classifications and metrics. Then work on the why and insights, such as channels and ROI performance. And finally, the fun begins with the how and recommendations for actions and wins, such as targeting and optimising. Winning.


Quick Wins – Collaboration Mindset, Alignments (projects, data source, KPI), Optimise Platforms (collaborate development projects)

Here’s a few quick wins you can implement in your organisation and departments today. Literally.

1. Create collaboration networks.
Reach out to your peers and like-minded followers. With a data science strategy comes a fruitful initiative with reaching out to all analysts within the organisation. Analyst not necessarily by role title, but by the role skillset in which they add value on a daily basis. What we look for are not just technical skillset, but also analytical mindsets. Starting points are simply saying hello, and sharing ideas / thoughts / knowledge. Initial introduction to discover who their managers and stakeholders are. Get referred to additional contacts to expand this network. Build trust relationships and safe collaborative environments.

2. Align in projects and purpose.
Establish synergy and collaborative goals. Identity key areas of overlap, and how everyone fits within the puzzle of the holistic business viewpoint. Starting points for discussion are mapping out routine reporting cadence, frequent adhoc analysis requests, data sources / platforms, core KPI metrics and end audiences. Build a common ground to work from.

3. Optimise current platforms, processes, and systems.
Do the best with what you’ve got by improving existing solutions. There’s always a better way of doing things – leverage, innovate, and collaborate with current networks. Which touchpoints can be improved, no matter how small. Better to progress 1% improvement on a hundred things, rather than big 100% jump on one thing. Eat the big elephant with one bite at a time.


Get that foundation right first. Nurture and appreciate the green grass on your side of the fence. Before you scale out to sexy thoughts such as big data and new DMP’s.


Business Team Networks – Analysts Networks and Senior Management / Leader Drivers

Who will drive a much improved DSA strategy.

Notice I mentioned “who”, not the “what”. It’s the people who makes things happen. AI takeover? Not for a long while yet. Be amused by robotics cynics and naysayers. Enjoy the blockbuster films for what it is – fictional entertainment.

It begins with you, and your closest peers / colleagues. The mentioned analyst network. The guild of like-minded people, and safe environment to discuss and thrive. Rich in constructive sceptical progress, but void of malicious cynical digress.

It then needs top down support from senior management and leaders, aligned with the analyst network as direct line managers and end audience stakeholders. What’s in it for them, and what’s in it for everyone. This is not a charity agenda, no business is. Any for profit organisation requires solid business cases to implement ideas and strategies, no matter how small. Even non-profit organisation requires a solid business implementation to get things done. And senior leaders and management team are the relationship builders that sells the hardsell to get the right people on board.


Most importantly, it’s people with an abundance mindset. Take on the blue ocean theory, where there is ample opportunities to be created for everyone. A collaborate like-minded focus with win-win outcomes for everyone. Where a teamwork works in synergy and exponential progress.

Rise above the poverty mindset. Avoid the red ocean theory, a competitive isolated silo on zero-sum game theory, constraint by resources, and limited in leadership vision. Where destructive political entities scheme on exploiting resources to fullest advantage at all costs and progressing own selfish gain. We’ve all experienced this. We’ve also all witnessed the ultimate demise end result from it.

Dive in blue oceans. Drive in abundance. Win in synergy.


Partnerships – IT developers / Sales operations / Customer service

With a solid team who can enable and drive a solid DSA agenda, we can then branch out to build allies, or partnerships in the organisation who are crucial within this DSA 3×3 matrix concept framework.

With authority in senior leadership support, and credibility in peer network support, a collaborative business case can be established to demonstrate returns and benefits. A showcase to pitch to potential partners.

The key partners to build relationships can start with:
• IT developers.
• Sales team operations.
• Customer service centre operators.

IT and sales/service operations provide the infrastructure to demonstrate and showcase the DSA strategic initiative. They participate in the holistic customer journey, where data to actionable insights can increase business value and competitive advantage.

This ultimately makes up the people networks of the 3×3 matrix structure with pillars and cornerstones.


Information Architecture – Data Science & Analytics & Process Systems – Modules Layers and Building Blocks

With the right team players and an aligned abundance strategy in place, it is then essential to have the right process and platforms with a solid architecture strategy. Data engineers from the data science discipline provide the technical expertise in this realm.

Think interchangeable interlinking components, with the right modules and building blocks to be flexible and feasible to facilitate implementation. Being able to meet current capabilities, and then also adapt and evolve as per requirements and agendas arose.

To facilitate this initial modules and blocks, we’re not investing in any new tools or resources just yet. We revert to looking at doing the best with what we’ve got.


The key areas of data science output in information flow, we go back to the following reporting and analysis processes:

  • Analytics – how data gets tracked, collected, and attributed
  • Database Systems – how data gets stored and accessed
  • Analysis and Reporting Tools – how data gets meaning and presented


We can then design systems around these key workflows. For example:

  • Analytics – Google Analytics > Adobe Analytics
  • Database systems – offline Microsoft Access > cloud MySQL
  • Analysis and reporting tools – Microsoft Excel > Tableau


DSA 3×3 Matrix Concept – Slide Presentation

Here is a presentation slide outlining this DSA 3×3 Matrix Concept.


What is Data Science?

So what is data science? I’ve dedicated a short sweet separate post to this question. So here’s my simple summary at a snapshot.

What it is: Data science is the art and science of transforming data into useful information and actionable insights, and thus adding value and impactful knowledge to the world. Value-add intelligence is the key takeaway in benefits in data science capability.

What it is NOT: A magic bullet. A corporate fashion show. A quick easy fix. It takes concentrated effort in strategy, planning, collaboration and implementation to establish a successful data science discipline within any organisation. Run far away snake oil salesperson who promises this quick easy fix.


Intro to Science Disciplines

What is science? Again, a separate post in future will be dedicated to address this in detail. So here’s my simple overview.

Science originates from the latin word “scire”, which means “to know”. Simply defined as the study to improve knowledge and understanding of the universe. All the observation-based evidence of the nature nd behaviours across the 3 core branches – formal, natural, and social.

How does it impact the world? Science absolutely improves our lives across many dimensional levels. Most confuse religion with science, and politics with history. The core essence is evidence. Proven evidence that allows iterations and progressive improvements with exponential growth over the last 50 years vs the full humankind history.

Last 50 years technology exponential growth vs humankind history


Where does Data Science sit within the overall Science discipline?

It may sound contradictory whilst some may say controversial, but my take on this is. Data Science is not a discipline or sub-stream of Science itself per se.

How data science relates to science is this. It encompasses the core essence of how science works. Which is it facilitates the processes and principles of various scientific disciplines. As with an organisation, it facilities the overall function of the pillars and cornerstone, and enables data communication and actionable insights that contributes towards the goal.

Data science drives the ethos in everything we do, and is the basis and foundation of this DSA 3×3 Matrix Concept.

Welcome to a new era.


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