Proposals & Recommendations

Start modestly, develop incrementally

To succeed in the process of becoming digital:

  • Advanced technologies should be introduced initially in a small scale and with a limited scope –e.g.: in a process not working properly or currently inefficient–.
  • And the motivation related to the transformation process will remain high.
  • At the very first moment you gain some efficiency, you already have the first quick win: your first small investment in Digital Transformation (DT) will have been a success.
  • If you innovate by engaging in a big, complex and strategic (drawn out) process, with very wide scope, and it fails, the company will lose confidence in the transformation process and it will come to a halt, maybe definitely, thus missing the DT benefits.
  • If you don’t achieve any improvements, the consequences will be small in scale.
  • For Industry 4.0 workforce transformation, shift from traditional concept of work: prepare workers for jobs that rely more on technology and data skill sets.
  • Companies should build “collaboration instead of control, trust instead of week-long authorization and release processes”.
  • Collaboration entails progressively dismissing the “project manager” role.
  • Micro-management is not the best approach.
  • Instead, facilitators or coaches should be put in place, so teams are empowered and allowed to think and try new ideas (and make mistakes).
  • Give enough time and room to Digital Transformation teams…

Otherwise, innovation will not take place…

To perform Edge Computing Digital Transformation successfully, companies need talented staff and a high level of automation:

  • New professionals with technical skills related to aggregation of data generated from sensors;
  • User experience designers to adapt new interfaces between humans and artificial intelligence.
  • System architects and engineers to design and build technical architectures to process data on-site (at the robots processors) and in the cloud coordinately.
  • Security experts around all these communications.

Implementing Artificial Intelligence (AI) and allied Deep Learning (DL), Predictive Analytics (PA) and Digital Twin (DT) technologies, business leaders will face key changes in the way data are managed:

  • Machine Learning and Artificial Intelligence need labeled data, which must be collected, delivered, filtered, stored and combined.
  • Data lineage, data catalog and data governance are important for increasing data trustworthiness.
  • Tracking data is necessary for security, compliance, and auditing.
  • We cannot launch these new technologies without implementing these Data Governance activities.
  • Thus, the need for new professionals: Data Curator, Data Engineer…
  • All of them under the leadership of the Chief Data Officer.

Impact on healthcare...

Example:

AI, DL, PA and DT for the personalized care medical industry will be worth 6.6 billion $ by 2021, ushering in the era of “personalized care”. Artificial Intelligence has the potential to reduce health care costs by half while improving patient outcomes by 50%.

  • We are witnessing the “shrinking” of Big Data: the aim is to use as few data as possible to obtain useful results.
  • The less data, the faster and cheaper Data Management solutions.
  • There is no Digital Transformation strategy without a Data Protection strategy.
  • We should work under the Agile approach to harness Artificial Intelligence by introducing new bots, cobots, even Digital Twins, supported by technologies like 5G and Edge Computing.
  • More and more companies are already adopting these new possibilities, in every business sector.
  • Build trust and transparency in your team by implying Agile: like all-team weekly meetings, planning and retrospectives.
  • Set matrices and KPIs (Key Performance Indicators) together with your team.
  • Set iterative matrices, so the team can introduce the next data.
  • Set short term goals to guide the team, especially juniors.
  • Experiment with clients to know if a feature solves their problems.
  • Make your team understand the company’s needs and timetables.
  • Set milestones and short-term tasks to make transparent information possible.
  • When considering several research directions, set a time frame for each option, and be ready to stop one and move to the other.
  • Build trust with your management team (explain your work clearly, learn to meet the deadlines, report about problems quickly).
  • Introduce Design Thinking:
    • Empathize with your customers and understand their needs.
    • Identify problems clearly and think over possible solutions.
    • Focus on the desired outcome, not on the current obstacles.
    • Use potent prototypes to study possible solutions.
    • Inspect the solutions and reflect the results.
    • Improve the process.
  • Start collecting data as soon as your project begins.
  • Visualize data.
  • Analyze it.
  • Collect feedbacks and react in real time.
  • Collect more data to get more objective insights.