What Is The Data-Driven Organization?


Data-Driven Organizations execute strategic change using massive amounts of people supported by data, algorithms, and unprecedented processing power. They give us the strategic choice to swap between our human capital, our data capital, and our artificial capital; allowing us to lower our overall financial investments across the organization.

With Big Data, it’s now possible and financially feasible to do capital swapping in the Data-Driven Organization to better optimize our strategic change, culture change, and revenue growth. But to be able to do this swapping we must standardize our models.

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Models are the bedrock for our Data-Driven Organization. They are abstractions used to understand our reality. They can be conceptual, textual, graphical, numerical, and statistical. Models provide us with the rules for our behaviors, calculations, tasks, decision-making, data collection, and automations; helping us with our business planning, business development, operations, and training.[/message][su_spacer]

[su_spacer]To fast track our capital swapping, we need our models standardized to build and align our Data-Driven Organization. (M)anpower, (A)utomation, (D)irection, and (E)ducation, MADE for short, are good guidelines for us to follow.

Here are the MADE Guidelines:

Manpower. Manpower includes our hard skills, soft skills, and behaviors.

M1 Data collection for Hard Skills

Hard Skills are for solving problems. We have SLAM problem solving skills, using SEAD approaches, aligned with SLIDE methodologies, emphasizing SIT analysis:

  • SLAM – (S)trategy, (L)eadership, and (A)nalysis, (M)anagement
  • SEAD – (S)tatistics, (E)ngineering, (A)ccounting, (D)ata Algorithms
  • SLIDE – (S)urveying, (L)and-marking, (I)ncluding, (D)ropping, (E)levating
  • SIT – (S)ame/Different, (I)mpacted/Non-Impacted, (T)argets/Non-Targets

M2 Data collection for Soft Skills

Soft Skills are how we interact with others. We have HARD Wired Soft Skills and PACES work styles:

  • HARD – (H)ammers / Tools, (A)utopilot, (R)einforcement, (D)eep Specialization
  • PACES – (P)artnering, (A)dvising, (C)onceiving, (E)xecuting, (S)torytelling

M3 Data collection for Behaviors and Cultural Elements

Behaviors and Cultural Elements are how we think, prioritize, and act as a group. We follow TRAP orientations:

  • TRAP – (T)hings, (R)elationships, (A)ftereffects, (P)eople

M4  Data collection for how Skills and Behaviors support each other

M5 Data collection for how Manpower supports Automations, Directions, and other Manpower

Automations. Automations complete a series of pre-defined approaches and steps to accomplish specific goals with or without human interaction. Automations include machinery, software, algorithms, Artificial Intelligence, and robotics.

A1 Data collection for Automation Steps, Timings, and Interactions

A2 Data collection for how Automations support Manpower, Directions, and other Automations

Directions. Directions encompass the tools we use to stay on track to meet our objectives.

D1 Data collection for Health Ratios and Measures

Health Ratios gauge the level of our success for what we’re doing. Measures feed into our Health Ratios so we narrow our focus.

D2 Data collection for Mind Maps

Mind Maps are our mental pictures and inner voices that guide us on how we think, act, and collaborate. Mind Maps include PRICE positioning, 5-C leadership, and SHELL methodologies:

  • PRICE – (P)riorities, (R)isk, (I)mpact, (C)omplexity, (E)motional Triggers
  • 5-C – (C)ommunication, (C)apacity, (C)apability, (C)redibility, (C)aring
  • SHELL – (S)pectrum, (H)ierarchy, (E)nd-to-End, (L)ifecycle, (L)anguage

D3 Data collection for Feelings and Feeling Patterns

Understanding feelings and feeling patterns helps us anticipate and react to our customer and employee sentiments. Sentiments can range from enthusiastic to fearful to apathy.

D4 Data collection for how Directions support Business Outcomes,  Manpower, Automations, and other Directions

Education. Education is our coaching, mentoring, and training to develop, coordinate, and align our Manpower, our Automations, and our Directions.

E1 Data collection for Education Approaches

Education Approaches range from class room, self-taught, coaching, and mentoring.

E2 Data collection for Education Content

Education Content includes diagrams and text that allows us to effectively understand and support our Model.

  • Diagrams reduce complexity and explain from a higher level such things as strategies and roadmaps.
  • Lists increase complexity and explain from a lower level such things as concepts, processes, algorithms, and Artificial Intelligence.
  • Matrices are used for comparison analysis.

E3 Data collection for Principles

Principles are the foundation concepts to best understand the Model. Principles also include specific languages, jargon, and notations.

E4 Data collection for Interpretations

Interpretations are the guidelines coaches and mentors follow when incorporating their own priorities and work styles into the Education.

E5 Data collection for how Education supports Manpower and Automations

MADE allows us to build interconnected models to establish our Data-Driven Organization and fast-track our capital swapping between our human capital, data capital, and artificial capital. When MADE is placed at the right level, our Data-Driven Organization is designed for strategic change, culture change, and revenue growth.

It wasn’t too long ago that MADE was financially impossible. But today with our technologies and our Data-Driven Organizations, investing in approaches like MADE not only makes financial sense, it’s now a competitive advantage.


Chris Pehura
Chris Pehura
DATA-centric Executive Management. Chris is a management consultant with a data emphasis helping Fortune 100/1000 companies strategically evolve and reinvent their businesses to maximize their revenue growth. Through realignment, to overhauls, to rebuilding things from the top down and ground up, he integrates and solidifies leaders, strategies, and solutions into all aspects of the organization. Strategies and solutions leverage Big Data, MDM, business intelligence, competitive intelligence, data science, chief data officers, and data offices. Chris is a coach, trainer, and the voice for how data is the new capital that drives, multiplies, and maximizes revenue growth.

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  1. Thanks John. It took a few iterations to make it meta.

    Though I didn’t call it out, models typically interconnect data to find patterns, so I agree connecting data is important. I used collection in the guidelines to simplify the concept.

    Interested connecting about connecting. What are the characteristics of your data?

  2. Thanks John. It took a few iterations to make it meta.

    Though I didn’t explicitly call it out, models typically interconnect the data within them to determine patterns, so I agree connecting data is important. I used collection in the guidelines because it simplified the concept.

    Definitely open to connecting about connecting. What are the characteristics of your data?

  3. whoa !!

    META !!

    will be plagiarizing – I mean ‘borrowing’ – thanks Chris

    that said – I would rather organizations think more about data CONNECTION, rather than COLLECTION – much more in keeping with the future – it is after all my data – not the corporations …. and I would be more than happy for you to connect to my data – if we can agree mutually beneficial terms that I am privy to in advance.

    … you see where I am going …. right ? #VRM