Value of a business with data: A top-down view.
The vision and company purpose must be such that it is difficult to mimic what the company stands for.
It is capable of creating a universe around itself.
It is independent of others for its survival and existence.
It manages its systems and ecosystems efficiently.
It is a fortress that stands on its own.
The sportswear and equipment company Nike speaks in data ecosystems language. On the surface, it appears like selling shoes and sportswear serving a wide range of customers keeping customer-centric focus like any other company. Observing closely, Nike’s products are geared towards its mission and purpose “to bring inspiration and innovation to every athlete in the world”. Nike’s ties with iPod, sports apps MOVE and Running, gear like sports watch and sports band, Nike+ training system speak of Nike not just a business, but a smart data ecosystem connected and listening to it customers. The feedback system speaks of the needs of the customers put to reality every day.
Therapeutic ecosystems are placing digital health in the hands of the patient and the physician. Both healthcare and non-healthcare systems are contributing to this revolution. Several personalized, patient care applications, tools for the physician are making healthcare more manageable on a massive scale. Caregiving has become more specialized, attending to the patient’s emotional, physical healing along with medical assistance with the help of applications built around the theme of service and assistance. Complying with HIPAA regulations, ecosystems with data, smartphones, sensors, registered users of these systems. Value is what the patients and healthcare providers do with the platform rather than the identity of the registered user. Partners in the ecosystem include leaders in health, tech, sports, fitness... Mango health for managing patient medical doses, Practicefusion.com for electronic health record platform for doctors and patients, mylifesoftware.com for dementia care providers by care providers are just the beginnings.
What makes these businesses ecosystems serving a higher purpose and stand out among their counterparts. This quest brings to the following questions:
How is the analytical apparatus different and unique to make the experiences special with data for customers?
To answer this, let's begin with a few basics heard often in experiments with analytics.
- Mass customization - leveraging smart data and internet of things, awareness, sense (intelligence), respond, and connection forms the basis (defining characteristics) for smart products. Power of configuration and connection, not profile updates and behavior patterns, addressing the issues that were not previously addressed and as required by the coach – Nike, Adidas. Adidas smart shoe 2.0 is a research project, and its MiCoach running app does not have a developer community I think.
- Becoming personal with data.
This is the market of the individual consumer. Participating and Socializing with the customer and his tribe (friends) for understanding the influencers/ proximate causes. To monitor and respond in real time (social). Vendor motivates, sponsors, and “pulls” customer with customer-centric marketing messages frequently
This is the market of the individual consumer. Participating and Socializing with the customer and his tribe (friends) for understanding the influencers/ proximate causes. To monitor and respond in real time (social). Vendor motivates, sponsors, and “pulls” customer with customer-centric marketing messages frequently
- Data helps create multiverses (contexts) for customer meeting expectations “in context needs”.
Context is explained along the dimensions of space, time, and matter. It explains the reality as of experience. Smartphone (location services) with Mobile apps have expanded the services in real time to the customer. More the dimensions added, more specific the services become. Knowing what is searched, the weather, time of the search, services change – say offer a hot coffee coupon on a cold evening as opposed to a cold drink on a hot day. The shift is towards becoming proactive and predictive to the customer wants (levels of maturity). From transactions to preferences, awareness, and participation.
Context is explained along the dimensions of space, time, and matter. It explains the reality as of experience. Smartphone (location services) with Mobile apps have expanded the services in real time to the customer. More the dimensions added, more specific the services become. Knowing what is searched, the weather, time of the search, services change – say offer a hot coffee coupon on a cold evening as opposed to a cold drink on a hot day. The shift is towards becoming proactive and predictive to the customer wants (levels of maturity). From transactions to preferences, awareness, and participation.
- Ontology models
Classifying data objects based on their relationships with other data objects and not on their similarities is a simple form of ontology. Using resource description frameworks is a standard method to bring structure to unstructured data. It is not so simple as stated in the world of recognizing each object as unique, to classify objects in groups. Knowing the strengths and pitfalls of classifying data based on data objects types is part of this exercise, which is not yet perfected.
Classifying data objects based on their relationships with other data objects and not on their similarities is a simple form of ontology. Using resource description frameworks is a standard method to bring structure to unstructured data. It is not so simple as stated in the world of recognizing each object as unique, to classify objects in groups. Knowing the strengths and pitfalls of classifying data based on data objects types is part of this exercise, which is not yet perfected.
- Importance of Metadata
Several definitions prevail for Metadata - data about data. There are at least three features of Metadata - content related to the intrinsic knowledge, context for the extrinsic knowledge, and structure related to forming associations. Numerous applications of Metadata include preserving the data trails, versioning, rights management, and accessibility of digital resources.
Several definitions prevail for Metadata - data about data. There are at least three features of Metadata - content related to the intrinsic knowledge, context for the extrinsic knowledge, and structure related to forming associations. Numerous applications of Metadata include preserving the data trails, versioning, rights management, and accessibility of digital resources.
- Data as a Service
Processing large amounts of data to publish on-demand information for real-time analytics and visualization is the purpose behind data as a service. Disease analysis, customer behavior models, tracking fraud and detection benefit from the patterns derived with time series analysis.
A few prime examples of organizations using data as a service are UPS with optimized travel routes correlating with the weather and traffic conditions, Amazon's data-driven recommendations based on customer personal support systems, Kayak’s flight price trend forecasts, Target’s predictive clickstream analytics to track customer purchase patterns to adjust pricing are a few examples
Processing large amounts of data to publish on-demand information for real-time analytics and visualization is the purpose behind data as a service. Disease analysis, customer behavior models, tracking fraud and detection benefit from the patterns derived with time series analysis.
A few prime examples of organizations using data as a service are UPS with optimized travel routes correlating with the weather and traffic conditions, Amazon's data-driven recommendations based on customer personal support systems, Kayak’s flight price trend forecasts, Target’s predictive clickstream analytics to track customer purchase patterns to adjust pricing are a few examples
- Data democratized.
Simply said democratizing data is to enable access to data across tiers, unlocking from the IT department of an organization. Concerns include security risks, misrepresentation and integrity.
Simply said democratizing data is to enable access to data across tiers, unlocking from the IT department of an organization. Concerns include security risks, misrepresentation and integrity.
- Presentation and visualization.
Visualizing results of data mined enable grasping insights, derive patterns. It is about using technology tools to present data. What is interesting about interactive visualization is a change in parameters helps to visualize the effect of the changes. Gapminder tools, Tableau software tools have become popular since the beginning of the Internet years.
Visualizing results of data mined enable grasping insights, derive patterns. It is about using technology tools to present data. What is interesting about interactive visualization is a change in parameters helps to visualize the effect of the changes. Gapminder tools, Tableau software tools have become popular since the beginning of the Internet years.
How does data from the current state move to the realms of artificial intelligence, machine learning, cognitive knowledge, and behavior design?
Learning with data and evidence, building the knowledge without the need for reprogramming is the foundation for cognitive computing. The techniques of machine learning involve all the cognitive functions in assimilating data from several possible sources, retain the memory, analyze, discover patterns, advise and continue to learn. IBM’s Watson based applications demonstrate the capabilities.
What are the data level contentions organizations face when moving to smart data initiatives?
- Governance – balancing the privacy of data and the usage – who owns the data. Aligning governance with strategy. How to deal with massive amounts of data? Who maintains the validity of data? For data products – the technical API is the most valuable – competitive advantage.
- Governance – balancing the privacy of data and the usage – who owns the data. Aligning governance with strategy. How to deal with massive amounts of data? Who maintains the validity of data? For data products – the technical API is the most valuable – competitive advantage.
