What Is The Data-Driven Organization?
Tags: Americans, Business Intelligence, Andreessen Horowitz, Algorithm, Bizspectrum

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.

[bctt tweet=”Artificial capital includes algorithms, Artificial Intelligence, non-traditional machinery, and robotics.” via=”no”]

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

— ( trategy, ( eadership, and ( nalysis, ( anagement SEAD — ( S) tatistics, ( E) ngineering, ( A) ccounting, ( D) ata Algorithms

— ( tatistics, ( ngineering, ( ccounting, ( ata Algorithms SLIDE — ( S) urveying, ( L) and-marking, ( I) ncluding, ( D) ropping, ( E) levating

— ( urveying, ( and-marking, ( ncluding, ( ropping, ( 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

ammers / Tools, ( utopilot, ( einforcement, ( 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

— ( riorities, ( isk, ( mpact, ( omplexity, ( motional Triggers 5-C — ( C) ommunication, ( C) apacity, ( C) apability, ( C) redibility, ( C) aring

— ( ommunication, ( apacity, ( apability, ( redibility, ( 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.

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