Ecosystems and Personal Healthcare

To reach a stage where healthcare will be personal, there is a fundamental step: designing a medical treatment considering individual variability. For such purpose it is necessary to combine and curate to existing EMR records with data about the patient’s societal environment, lifestyle, and their genomic data. This can assist the doctors in identifying which approaches, treatments, and preventions will be effective for the patients, what also can be called personalized healthcare, on the prevention side, would also allow identification of genetic variants that increase a patient’s chances of developing diseases. Treatment could begin earlier, helping reduce risk through behavior modification (healthier diet or increased exercise) or leading to placement of patients on drug therapies.

Such an outcome relies of connecting and sharing data within a particular ecosystem. The ecosystem of me (ties back to the earlier concept of the internet of me). On one hand phone, wearables, health sensors, connected clothing—and combining that with clinical tests, scans, and check-ups—will allow much-improved analysis and monitoring and focus on health optimization. On the other hand, sharing such data in medical doctor community, academia, healthcare think tanks will improve healthcare foresight and shape healthcare public policy design, but most importantly will support via the existence of such dataset to create models of detecting effectively symptoms, understating treatment impact, find new ways of treatment and assist medical doctors with assisted reasoning.


Personalized healthcare is about shifting from volume to value. Revealing the unknown and creating a healthcare ecosystem continuum. The purpose of the transformation journey is to shift from normalized healthcare to personalized healthcare.



Personal Healthcare Ecosystem


With this type of engagement, patients are likely to be more cooperative, and comply with procedures and treatment plans. There are of course other kind of benefits, like ease to schedule an appointment and track medication time, decrease healthcare costs, however, the biggest benefit and value of such an approach allows patients and citizens to look after themselves better and have better quality of life, because healthcare become, personal, anchored on a personal health record, which data sharing consent is managed by the individual.

Technology and healthcare always had an uneasy relationship, and the latest announcements that Apple and Google did entering into healthcare may bring the argument of massive surveillance, however, countries like Singapore, are aggressively progressing towards the creation of a shared nationwide healthcare data lake, by law. It should give us some thoughts about how healthcare is fast becoming personal.

Ecosystems and Capabilities as a Service

7 years ago, I got an invitation from Prof. Wil Van der Aalst to participate in a research project lead by the Fraunhofer university, related with using process mining on process event logs, from different companies. The idea was to benchmark and compare how the a similar process was being executed (e.g.: order to cash) in a way that all the companies involved could learn how its process was behaving vis-à-vis the others. There was also another technical research challenge related on data preparation that allow balanced, not biased, process performance comparison. Data sources were being managed and stored by ERP’s and even on the case the ERP used was the same, challenges related with version, database schema or process flow design were also relevant in the research domain.

In those days, companies were not prepared to share such kind of data, it was considered a loss of competitive advantage to expose and compare performance with competitors. Digital transformation was a strategic concept that was not born yet, that resides on how value chain separation fades and starts to intersect with other value chains of different industry sectors, that enables new business models that were not even possible (in todays environment a oil company can provide electricity poles to recharge electrical vehicles, from distributed energy source provided by an utility company) . To another extent, a company is no longer as a member of a single industry sector but as part of a business ecosystem that crosses a variety of industries.

I am working lately with e-commerce companies in Asia. Despite they still pursue a growth strategy in terms of average customer spending, merchants onboarding, sales increase, they are also looking to transform themselves into a software company. For example an e-commerce company wants to stop to transport an order to the customer, to evolve the shipping capability to streamlining sourcing, planning, execution, settlement and end-to-end transport optimization and introduce responsive packaging systems (critical for food safety) or Just in Time supply integration via real time condition monitoring, meaning, the e-commerce company can start exploring direct supply integration with electronics companies or automotive industry. The more they penetrate into new industries by the evolution of the capability, that can include among others: Collaborative Commerce, Product accountability, Supplier Risk Management, Demand Forecasting, Warehouse Management, Smart Contracts, they are able to grow outside their core business and can start providing Retail-as-a-Service for companies that want to enter in the market they operate, connecting local factories where the products are produced to the front end commerce portals and the underlying delivery to the consumers. The capability is not a property and competitive advantage of the e-commerce company is now shared and sold as a service to other companies.

Other area that I see looming is to re-use technologies that were crafted and used in multiple companies. Some start-ups that operate in the artificial intelligence arena are making of their business model to keep the IP and redeploy it in other customers, in particular A.I. models. Like in the example of the Fraunhofer research project, the objective is to improve the models used – for example: for credit risk scoring, fraud, asset integrity management. Customers require that data keeps private, but they do not mind to reuse models that are being evolved in other companies if they fit and they will seek to substitute for the next version as it progressed and continue to deliver better results.

In this kind ecosystems, companies coevolve capabilities around a new innovation: they work cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovations, as ecosystem evolve and originate other ecosystems, consumers or business users can interact, transact,  for a wide range of offerings, without leaving the ecosystem. For the companies that operate in the ecosystem, this means having access to new revenue sources. To the companies that are served via these new kind of ecosystems, it is also a way of lower cost of ownership, speed to market and access more evolved technologies to operate.


Blogs worth reading 2018

Here it is the anual list of blogs which I normally consume on a random basis for research purposes and to be inspired by others point of view.

On Digital Transformation at large:

  • Cape Gemini blog – A good source to look for trends on digital transformation.
  • Column 2 – Independent source on digital transformation by Sandy Kemsley.
  • IEEE Spectrum – Future technologies brought by IEEE.
  • Jim Sinur – Digital transformation trends by Jim Sinur.
  • PeterVan – A 1.0 version type of blog that curates interesting articles from Peter Vander Auwera.
  • On Web Strategy – Dion Hinchcliffe writes about the human side of digital transformation.
  • Internet of Things and Services Blog – By Bosch Software on IoT.

On Complexity:


If you are interested in 2017 list click here.

A year in review 2018

In 2018 I saw the rise of the ecosystem and the commoditization of the IT platform. The IT platform still continues to be the first step or the common denominator to enable digital transformation. It brings interoperability in hybrid models that provides the foundation for not just an Internet of things, but an Internet of things, people, and services working together as well as new kind of technologies like mixed reality and cognitive services by bringing together human, machines, and artificial intelligence. It allows to process the complexity of machine-generated signals, large amounts of customer data, with better and faster analysis of product performance and reach. Nevertheless, a business ecosystem, gradually moves from a random collection of entities that interact or transact to a more structured community, adding more partners, more products, more offers and enlarging the customer base. Of one key success of Amazon is how they grow in terms of product offerings. They started as book store. Today, they sell virtually anything. In business ecosystem, a company manages information and its flows. In terms of data, this is not about our view of customer, that can be expanded how each partner that belongs to ecosystem increases that view of the customer and how it detects new opportunities by having access to a bigger customer based and detailed customer profile customer profile, because data is shared with other companies together. Companies will monetize from each transaction occurred as all the other connected partners, the more the customer buys, the ecosystem grows as the business opportunities. On the other hand, this will allow to evolve business capabilities. If a highly efficient logistics company – that manages a fleet X fold in size and it is serving a wider customer base – joins the ecosystem, all the merchants may if they want, benefit from the new innovative added capability, if you attract fintech to support new ways of doing payments, they will drive new interactions with millennials or digital savvy customers. In a business ecosystem, companies coevolve capabilities around a new innovation: they work cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovation.

In online retail, vertical e-integration is a new paradigm. e-commerce players are already expanding their own brand business into more categories. At the same time, specialized start-ups are selling products such as razor blades or functional foods directly to consumers, often on a subscription or membership basis. By bypassing distributors, these start-ups are able to offer lower prices and sell even small volumes profitably. The music industry is a great example. Today, artists are posting their songs directly onto internet-based music platforms, and in the process avoiding consolidators, distributors, and intermediaries of any kind. Internet of things, edge / fog computing like connected product – toothbrush, kettle, toaster or appliances, can create new business models and revenue models as it is happening with household appliances. Audi, just released an electrical SUV (e-tron) which the new revenue source is selling electrical energy  to charge the batteries that will be billed the customer later.

In manufacturing, new digital operational model are being deployed under the cyber-physical systems concept. Which is the fusion between operational technology and information technology, data created by the manufacturing equipment, the warehouses, the trucks that deliver the final products, the signals broadcast by the smart packages, that allows to manage a network of factories across multiple locations, in terms of being adaptable in real time managing manufacturing capacity, based on customer’s orders or comparing performance and expanding continuous quality improvement circles based on the data that is made available from the shop floor. The hype related with self-healing systems is a reality. Instead of humans constantly fine tuning machines when product is being produced out of specification, it is possible to implement runtime procedures that will act on machine parameters when it is predicted that quality specifications will be missed. Using Machine Learning combined with IoT to understand how an equipment operates and how to fine-tune it’s operation to become more efficient or to ensure product quality features, to start to prescribe machine interaction in terms of automatically recommend actions in terms of machinery parameters if it is necessary to put product features back to specification is also being part of the shop floor digital journey transformation. Despite there are challenges related with data interchange, low latency access to cloud services, there is progress with the emergence of each asset, each machine, component is able to initiate an exchange of data to different levels of the organization ensuring integrated, planning, control, execution, optimization of the value chain, this probably one of the most silent areas which progress is not being reported, but I see finally some real value apearing and being materialized.

2016 entry can be found here – I did not write in 2017 about this.

The Man and the Machine and the impact on the Future of Work

Many people wonder the reasoning of Microsoft’s [1] mission which is: “Empower every person and every organization on the planet to achieve more” . It can sound a cliché, but I could never be more important to reinforce the message, once we are coming out of repetitive work and normalization of processes, which are step by step being totally automated – by the use of technologies like robotic process automation – shifting to the knowledge work space.
I’ve seen based on my experience working with Oil & Gas companies, that the idea of an industry that monolithically crystalized is not a reality anymore. This type of industry sector is being enabled by a set of technologies that took time to mature, combined with challenges related with network infrastructure availability and latency (oil production occur in remote areas of in the middle of the ocean where connectivity is a scarce resource), but today are becoming mainstream. For example using drones to make inspections, mixed reality for executing field inspections, collaboration remotely with engineers in the control center or using cameras for detecting safety (lack of protection equipment) or security (facility intrusion) with image pattern recognition. These new technologies  are also creating a job and career change  widened by the adoption of new technologies and talent scarcity. Two reports from World Economic Forum, named, Towards a Reskilling Revolution and Future of Jobs, highlight:
Companies, “expect to hire wholly new permanent staff already possessing skills relevant to new technologies; seek to automate the work tasks concerned completely; and retrain existing employees. The likelihood of hiring new permanent staff with relevant skills is nearly twice the likelihood of strategic redundancies of staff lagging behind in new skills adoption. However, nearly a quarter of companies are undecided or unlikely to pursue the retraining of existing employees, and two-thirds expect workers to adapt and pick up skills in the course of their changing jobs. Between one-half and two-thirds are likely to turn to external contractors, temporary staff and freelancers to address their skills gaps.”

I was invited as a speaker to the Halliburton Life conference in Abu Dhabi. One of the trends I noticed – as part of the reinforcement on how intrusive artificial intelligence is enabling a new set of possibilities, like for example, improving decision making in terms of financial impact while making seismic data interpretation in terms of addressing exploration and production viability – is how hard is for individuals with strong domain expertise to work. I heard during sessions people expressing frustration on manipulation data sets, being not able to work of the same updated version of a data set, becoming unproductive waiting weeks for results and deal with uncertainty.

I am having a series of meetings with HR function and I’ve been told that the way people work is becoming frustrating and unproductive, despite all ubiquity of cloud, social, bring your own software / device or putting into a different perspective, it is not achieving the expected results. There was definitively productivity improvements, mostly in terms of mobility, meaning individuals can work anywhere at their pace, have access to information they commonly use on every day. However, mirroring self-expression, self-development (be digital ready), achieving a true networked work environment – far from being reached, companies with workforce size higher than 5.000 individuals find how difficult it is to reach out experts, knowing what is happening across domain of expertise which people operate, be part of working groups to construct new identities to replace old ones, avoiding to become redundant as a way of personal fulfilment – and ultimately, how technology ubiquity is bringing lack of integration, distraction shifting our attention on meaningful activities. As an example, people envision a future which individuals participate in a meeting, don’t need to search for meeting content in “teams workspace”, don’t need to connect to a projector, don’t need to write meeting notes, spark and track action items.

Studies carried in an Oil & Gas major using Microsoft’s workplace analytics reveal that in an investment venture appraisal that took 1 year to complete, more than 2.000 individuals were involved with a strong presence of financial departments. As a result, this contributes to:
  • Slow decision making and limited agility;
  • Employees being over-managed;
  • It was possible to gain more than 300 FTEs worth of time by rethinking how work and management is distributed;
  • Cross-functional processes becoming bloated and expensive.
People aspire a future which individuals participate in a meeting, don’t need to search for meeting content in “teams workspace”, don’t need to connect to a projector, don’t need to write meeting notes, spark and track action items. This is not a futurist idea. This a desirable outcome.


3 pillars of Human Led Design
To address this, it is necessary to rethink how technology should pursue a human led design strategy:

  • Help people achieve what is important to them. This something that during all the years companies have worked in operational improvement did not fully deliver. For example, can’t we let people organize and define their own User Interface in a way they will focus on what is really most important to them?
  • Building relationships. There is an intense debate on methods to discover how to get to know individuals and understand their real needs and wants based on trust, re-quoting Elizabeth Tunstall that provides an interesting perspective, as our universal individuality is cannot separated by the sacred, profane and spirituality, instead of creating personas framed on a stereotypes which we must be forced to fit.
  • Design technology that seamlessly integrates into the human world. Design for the capabilities and limitations of the human body. Adjust to the human needs dictated by the physical conditions of an environment or device. For example, designing an application to be operated in an explosive and noisy environment is different for designing the same solution to be operated in a control center.
Future of Work scenarios
The future of work is deeply related with employee upskilling and retainment. Being able to make the change in terms of employee experience will contribute to retain talent for next generation of workers.
Below is a spectrum of possible scenarios to explore in terms of what the future of work can be.
Some are more easy to productize, other’s, like augmented knowledge reasoning aren’t, but it is still worth trying – I remember previous engagements related with public policy enactment as an example – i.e. government agency wants to increase taxes and wants to recommend a scenario that is going to maximize tax collection and minimize negative population sentiment, using A.I. for that purpose.

Collection of Future of Work Scenarios

In summary, guiding principle is focusing less on jobs descriptions and more on the nature of the work produced and then determining how to help employees accomplish those tasks enabled by scenarios like these:
  • Digital assistants may include a digital shadow that will observe behaviors, build and maintain work preference rules, search information, predict and remind you about important actions to accomplish based on the nature of the work you are working on.
  • Unified interactions allows employees to take actions on tasks, gain access to the data feeds they follow, collaborate with other individuals, all without ever leaving a single customized interface according his needs and wants, instead of jump from application to application.
  • Digital upskilling is about training the workforce, with content hyper-personalization based on a role definition execution requisites and performance review feedback. In a world that is becoming digital, the workforce is must be digital ready instead of digital impaired.
  • Collaboration (at large) as a digital extension of individuals senses, contextual knowledge support it serves as an interface for humans to the physical world. Enhance the user’s surroundings with relevant, and/or actionable information, using as an example, mixed reality to augment our ability to perform.
  • Reasoning leveraged on recommendation agents based on past interactions and other sources of information to jump-start and kick-off knowledge-oriented activities. Incorporate specialized Artificial Intelligence technologies processing functions tuned specifically to answer questions, create models, to support decision making.

You can’t optimize for everyone, but it is worth making a profound change happen in our working habits this will help organizations that they cannot digitally transform unless people do.

[1] I am Microsoft employee when this post was written.


Talent Management Digital Transformation in Oil & Gas Companies

Technology adoption is pushing the workforce to re-skill

Oil & Gas companies like utilities, mining and other natural resources industries, were challenged with barrier of ageing workforce. This actually have an impact. Project overruns, lower asset availability and low production targets. But during this last 5 years, what become some experiments around IoT projects, big analytics about operational performance and most recently, real time collaboration on seismic data studies and reservoir simulation, increased relying on technology – shifting from operational technology to information technology.

Oil & Gas companies need more and more personal with computer science background to keep operations going . Therefore, there is a rise in demand of data scientists, statisticians, individuals that know how to use Artificial Intelligence technologies. This requires a different breed of human talent.

Companies need to deliver to a multi-generational / cultural workforce that demands more flexible work options. And that means it is necessary to be agile in adapting human resource practices to meet the needs of the business and the workforce.  Practically speaking, HR’s role is expanding beyond its traditional focus.

From the organizational side, a new approach to education, and especially up-skilling and re-skilling, is necessary to support digitally enabled workforce and the evolution of new business operations that require with latest technology developments. For example, today it is possible to automate pipeline inspection using drones and combining edge computing solutions [1]. As such, this presents a huge growth area of new jobs opportunities that candidates could apply for or existing workers could take new training go gain role readiness.

From the labor market side, there is the ambition of have much more flexibility in the terms work is performed, mimic the work pattern habits, like in personal life – utilization of social media style tools for internal communications and bring your own device. And a part of that, new generational workforce like the Millennials believe oil & gas companies is lacking innovation, agility and creativity, as well as, opportunities to engage in meaningful work. Strategically “employee value proposition” and pull-factors will attract the most talented. Improving brand awareness captivating candidates to apply for a job opening. Attracting the next wave of the workforce can also include using new approaches like “Recruitainment” – gamification in talent management, that includes among others, recreating a real work environment and understand how well the candidate fits into role execution [2]; [3].

Creating the workforce of the future involves to consider the combination of the following dimensions:

  • Modern workplace – which the workforce can collaborate across multidimension kind of data, using the device and the platform of choice. Contrary to other industry sectors, oil & gas companies are data intensive and data tasks oriented that use neural deep learning network algorithms, fuzzy logic, and others to improve data analysis.
  • HR departments, must rethink how core processes are designed. Artificial Intelligence, when combined with other available technologies can free up considerably of a recruiter’s job and can then use time more efficiently attract the best candidates. Video interview is on the loom, as well as, application and job description matching, eliminating unconscious bias. Plan and predict workforce capacity, upskilling needs and employee disengagement can also be supported by AI technologies.
  • Workforce safety will see a significant leap forward due to digitalization. With digital oilfields and the automation of dangerous tasks, fewer workers will be placed at risk. In addition, Artificial Intelligence will reduce the possibility of human error, contributing further to workforce safety. An example of using AI in field inspections is Equinor (formerly Statoil) – Statoil digital field worker a company that is pushing boundaries in terms of digital transformation.

Contrary to the power shift that occurred towards the customer related with consumer industries, in labor market – unless there is talent shortage – the bargaining power is still much on the recruiter side. However, HR workforce, is aware that it is needed to be investing more in recruiting and retaining the best talent, due to the fact that oil & gas companies are becoming IT companies with a necessity of becoming, fast, a data driven company. For example, in scenarios related with  optimizing drilling, by using historical drilling data, it is feasible to quantitatively identify best and worst practices that impact the target. Advanced analytical methodologies would ultimately develop a model that would provide early warning of deviations from best practices, lessons learned or other events that will adversely impact drilling time or costs. Hence, on one hand there is technological dimension related with what IT solutions are best fit to build the models and make the analysis.

New set of technologies for the evolution of Talent Management

On evolving HR function from hire to retire, increasing hiring quality, contributing to knowledge diffusion and retain talent, HR professionals should consider to invest in these new set of technologies taking in to consideration their priorities.


Identifying digital enablers for improved talent management

  • Social Sourcing. Storing a static talent pool is not sufficing. Managing a dynamic talent pool that continuously is aware of the activity the candidate is broadcasting in terms of career achievements, new roles or professional activity is becoming ultimately important to spot the changes and trends of the prospective labor market. Social Sourcing is about make it visible a pipeline of sourcing with internal / external candidates integrated with social workforce analytics and from employee service providers (e.g.: LinkedIn). It also includes identify automatically candidate profiles that match your job description requirements.
  • Intelligent Application Screening. Despite of the evolution related with managing job applications in digital mode become a standard, processing an immense volume of applications is not practical and is time consuming. Robotic Process Automation can help with text extraction, candidate profiling and role matching and workflow automation.
  • Automated Candidate Interviewing. This is one of the trends that is moving very fast, in some cases, is becoming fully automated that with the combination of cognitive services (e.g.: image pattern recognition and natural language processing), it is possible to analyze emotions and enhance candidate profiling, makes a pre-screening of the interviews before they are short-listed to the recruiter.
  • Neuroscience Assessment in combination with advanced analytics. Supports decisions about candidate’s degree of fit, matching candidates to positions and at the same time removing recruiter / hiring manager unconscious bias.
  • Mixed Reality. Is able to replicate in the digital world a natural performance environment. It can be used in “Recruitainment”, as well as in training (e.g. safety, operations performance and optimization, geomodeling).
  • Social Business. Provides a platform for finding relevant information, collaborate, ideate, innovate and reach out to colleagues and SME’s. Knowledge diffusion is improved combined with integrated collaboration and productivity tools. – Social Business is a platform that allows to :
    • Know about the Human: Who the worker is, how the worker identify himself and what you pretend to be, the person’s social graph.
    • Human Interactions: What the worker does. Whim whom the worker engages with. How he react to participation in company forums . What kind of work activities are pursued.
    • Search and Analytics: Search for knowledge, gather feedback, get trends, spot patterns, sentiments, learn.
  • e-Learning.  Enable organizations to train and upskill the workforce at scale, with content personalization, and curation, based on particular role execution requisites and performance review feedback.
  • Social performance analysis. Enables workforce analysis in order to improve performance measurement. It also includes, alignment between the nature of work performed and the type of social network configuration the employee is fitted.

The set of technologies uniquely referred above are supported enhanced by a foundational layer composed of:

  • Data Analysis Services. Supports all the moments which decision making is executed, by combining a multitude of data sources. Enables the exploration of data that go beyond those available in spreadsheets. Visualization is possible by direct connection to data sources.
  • Machine Learning. Like in operational improvement scenarios, Machine Learning can be used from predict employee disengagement and the propensity of leaving the organization, to recommend personalized training, and benefits packages;
  • Cognitive Services. Multiple set of Artificial Intelligence technologies supporting workforce planning, automated conversational applications, emotional analysis, recommendation engines and intelligence search:
    • Role description matching and skills / candidate experience.
    • Recommendation engines, used to propose personalized training program, career path, perform workforce impact analysis in terms of future role needs and new skills to be acquired and ultimately;
    • Business Process automation, by the use of chatbots, for candidate or HR staff self-servicing.

Evolving the HR mission and processes by the adoption of Artificial Intelligence, Big data, analytics, and gamification in the hiring process is picking traction, nevertheless, HR professionals also consider the employee experience if talent must be retained and keep low the propensity of leaving the organization.



[1] – Gartner defines edge computing solutions as: “facilitate data processing at or near the source of data generation and serve as a decentralized extension of cloud, data center or campus networks. Typical sources of data generation in the context of industrial Internet of Things (IoT) include sensors and control devices such as programmable logic controllers (PLCs) and distributed control systems (DCSs)” – Market Guide for Edge Computing Solutions for Industrial IoT – Gartner 2018. Microsoft’s Azure – IoT Edge is such kind of solution.

[2] – This is not a new trend only in Oil & Gas companies. Marriott hotels, who created a virtual reality Sims-like game in which players have to juggle all the responsibilities of a hotel kitchen manager, was one of the first to try a gamification approach.

[3] – Building the Next Generation of Petrotechnical Professionals through Gamification – Paul Ugoji (Total Automation Concepts Ltd) | Akii Ibhadode (Federal University of Petroleum Resources Effurun, Delta State) | Anslem Amadi (Federal University of Petroleum Resources Effurun, Delta State).



Retail Digital Transformation

In the last 7 to 5 years an unprecedent shift occurred in the way consumers interact with retail companies. Consumers complete transactions either online or in-store. Consumers still buy goods at physical stores and switch from physical to digital and back to physical. Contrary of what we may think – and this is also confirmed by retail customers with international presence I am working with – stores continue to contribute almost half of all retail growth; brick-and-mortar sales are not shrinking, but actually growing [1].

Hence, the big question is, where it relies the difference on how we interact and sell, if everyone is doing the same and copy the approaches? Where it lies the competitive advance of doing it differently?
Make a difference means mastering the art of:

  • In-store experience: is related with virtual shopping assistants, with sensing emotions by analyzing expressions related with prices, promotions and the products offered, mixed reality, which the customer can experience the product virtually or get more features by connecting to his personal profile and discover if there is a match. By recognizing and interpreting facial, biometric and audio expressions, new artificial intelligence technologies can identity customers emotions, reactions or mood and deliver contextualized products, recommendations or support – by the use of bots, that use natural language processing to help customers effortlessly navigate questions (“is this size or color available?”; “Can I ship back home the products while I am going to watch a movie?” — improving the customer experience.
  • e-commerce: providing a mobile app is not enough, actually, there is being sensed that there is a point of saturation about using apps because all of them do exactly the same which most of the time is a simplified version of the web site with a different form format. The next wave of innovation is related with prescription and recommendation engines. The customer will see the products and the offers that really matter to him based on the customer profile and combining other sources of external data which he expresses his needs and wants. Recognizing customers and customizing the e-retail experience to reflect their current context, previous purchases and shopping behavior. As customers look to build confidence in a purchase decision, automated assistants can help narrow down the selection by recommending products based on customers needs, preferences and lifestyle.




Capture II

The data focused organization


The art of mastering data in the retail sector will be dependent on the collection of in-store data , product data , and customer data new data sources coming from experiences expanded by the art of the possible using artificial intelligence.
Other key challenge is how to create and expand an ecosystem, a business ecosystem [2], like its biological counterpart, gradually moves from a random collection of elements to a more structured community, adding more partners, more products, more offers and enlarging the customer base. In a business ecosystem, companies coevolve capabilities around a new innovation: they work cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovations [2]. Of one key success of Amazon is how they grow in terms of product offerings. They started as book store. Today, they sell virtually anything.
In business ecosystems, a smart company manages information and its flows. In terms of data, this is not about our view of customer, that can be expanded how each partner that belongs to ecosystem increases that view of the customer and how it detects new opportunities by having access to a much more bigger customer and customer profile, alone or even partnering with other companies together. Retailers by being the anchor of such immense data lake, will monetize from each transaction occurred as all the other connected partners, the more the customer buys, the ecosystem grows as the business opportunities. On the other hand this will allow to evolve business capabilities. If a highly efficient logistics company joins the ecosystem, all the merchants can if they want benefit from the new innovative added capability, if a Retailer attract a fintech company to support new ways of doing payments, they will drive new interactions with millennials or digital savvy customers.

[1] – The great retail bifurcation – Deloitte Consulting 2018

[2] – Predators and Prey A New Ecology of Competition – James F. Moore – Harvard Business Review