Intelligence Augmentation

Computers are good where humans are weak. Therefore, we could concentrate on strategic planning instead of spending so much time on doing calculations, something computers outperfom humans.

How can we implement winning “human + machine” strategies?

The solution is organizational innovation: inventing new organizational structures, processes and business models that leverage ever-advancing technology and human skills.

 
Ubiquity of digital connectivity

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The new global economy will rely on companies that are able to “rethink their business models, identifying new opportunities for creating and capturing value“, and the way to go are the new modes of value creation, based in new data that organizations can accumulate. They not just used the information (data) from the traditional perspective of analyzing segments of the operations data, but were able to accumulate data from each part the customer experience and connect in a optimum “network” database.

 

New efficiencies in technology computation
=
digitization + advanced analytics and algorithms

 

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New business models that derive from an ability to exploit data.

To capitalize on them, organizations would have to make complex process changes and build employees’ skills.

 

  • data-maturity: Companies must be able to identify, combine, and manage multiple sources of data.
    Customer Profiling:  protected personal data, customer logs, online social networks, services data history, surveys.
  • data-science team: They need the capability to build advanced analytics models for predicting and optimizing outcomes.
    Team-building: graduate internships, workshops and courses, professional development path.
  • top- leading management driven: management must possess the muscle to transform the organization so that the data and modelsactually yield better decisions.
    ***Sufficient time and energy in aligning managers across the organization in support of the mission.
  • strategic bussiness architecture design: A clear strategy for how to use data and analytics to compete, and deployment of the right technology architecture and capabilities.-> R.A.D.S.A.

 

Readiness Analysis for Data Science Adoption
 Adolfo De Unánue, 2017

Many organizations might think that adopting data science has to do only with the development of a machine-learning product, but for building data products that supports the decision-making actions over particular organization’s systems requires a deep organization analysis.

As data products are a transverse component in the architecture of the organisation, the analysis of the innovation of an Enterprise Architecture to adopt data science, must define the current State Architecture (or AS-IS), the Future State Architecture (or TO-BE) and the Change architectures.  With this, an hollistic approach of the organization must be taken to set the strategies to achieve this endeavor:

  • Business Architecture
    What does the business do?
    How does the business do these things?
  • Data Architecture
    What are the key pieces of information that the business needs? Which is the Enterprise Data Model?
  • Application Architecture
    What are the key functions that our applications need to provide?
  • Technology Architecture
    What are the key technology that our applications and business need?

 

Things to identify

  • Why they want a Data Science initiative?
  • Who are the main stakeholders? Which are their goals and concerns? Which are the main pains? Are those pains related to data?
  • Define precisely what the different stakeholders understand by “data science”?
    How aligned are the DS initiative and the actual and future goals of the enterprise
  • Which data do they have? How is that data? Where is that data? In which kind of storage? Is accessible?
  • Do the enterprise have the processes to consume and produce data?
  • How reliable is the infrastructure?
  • Taken all this in account, Which are the gaps between the Current State Architecture and the Future State Architecture?

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The Strategy

  • Use a tailor-made TOGAF Architecture Development Cycle: Phases A-E.
  • Identify the stakeholders and their concerns.
  • Apply questionnaires and interview the stakeholders
  • Identify the main business processes.
  • Collect (using templates) the information about Infrastructure, Applications and Data.
  • Organizations Analysis: SWOT / TOWS, Porter 5 competitive forces, Porter Value Chain and Vrio Analysis.
  • Process the information to Archi, an Archimate/EA tool and then import that to a graph database.
  • Establish the Data Maturity Framework (The University of Chicago -DSSG).
  • Gap Analysis.

Legal and Ethical implications

 

Adopting data science into your organization is the same as developing a new business model. New human and technological services will be part of a new transformation. At the same time, you will create new data, solutions, data-products and services. All of them need to be carefully protected and comply with the highest standards in accordance to law, and ethics.

journey analytics-3There is a need for legal advise for the acquisition of new digital and technological services, as well as the design of proprietary information and confidential disclosure agreements for the assurance of your data products development.  You mustcare aout the protection of information privacy for the due diligence of data treatment.  

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