Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...Full Bio
Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.
Know the Dual Roles of A Data Scientists?
Substitution, Extension & Transformation: The Three Keys Of Digital Transformation
- Substitution represents basic IT modernization; such as leveraging new consumption models (e.g., cloud, "as a Service") to directly replace functions and costs that already exist in an enterprise. Shifting from on-premises to cloud can generate tangible cost savings for an organization; however, it does not have a large impact on how an organization goes to market, better serves their customers or optimizes their key business processes.
- Extension is where disruptive technologies (e.g., data science, artificial intelligence, machine learning, data lake, IOT, blockchain) are folded into an environment to provide an organization with capabilities not available otherwise. For example, analytics frameworks are folded into existing applications to enhance the velocity and visibility of data to managers and deliver recommendations that facilitate increasingly rapid and defensible decision-making.
- Transformation is the Holy Grail; hence it represents the overarching goal of Digital Transformation. Truly transforming one or more business processes is a complex effort that requires a process centric to transforming an organization's business models, coupled with scale-out and elastic foundational technologies.
- Identify Key Business Decisions. Identify and understand the decisions that the key business stakeholders need to make to support an organization's key business initiatives
- Create Analytics Sandbox. Provide an analytics environment that allows the data science team to rapidly ingest data, explore the data, and test the data for its predictive capabilities in a fail fast environment.
- Deploy Predictive Analytics. Leverage predictive analytics to uncover individuals' relevant behaviors (e.g., tendencies, propensities, preferences, patterns, trends, interests, passions, affiliations, associations).
- Deploy Right-time Analytics. Create "right time" analytics capabilities that can flag anomalies and behavioral changes that might be worthy of analysis.
- Train Business Users. Train business users to "Think like a Data Scientist" in identifying variables and metrics that might be better predictors of business performance.
- Capture Analytic Insights. Capture and catalogue analytic insights that are being uncovered about your key business entities.
- Evaluate Insights Business Relevance. Assess the potential business value of the Analytic Insights captured in the Insights phase using the S.A.M. (Strategic, Actionable, Material) methodology.
- Deploy Prescriptive Analytics. Build prescriptive analytics to deliver actionable recommendations to the key business entities that support key business decisions and use cases.
- Deploy Data Lake. Build a Data Lake that supports the capture, refinement and sharing of the organizations data and analytic digital assets (collaborative value creation platform).
- Leverage Application Development. Operationalize the recommendations by leveraging modern application development techniques to integrate the results into web sites, mobile apps, dashboards, and reports.
- Measure Decision Effectiveness. Tag the recommendations in order to determine their effectiveness. Use the results of the effectiveness measurements to fine-tune the analytic models.
- Operationalize Analytic Insights. Capture, catalogue and operationalize the captured customer, product, operational and market insights in analytic profiles (stored in the data lake) that can then be shared across multiple business use cases.
- Identify Monetization Opportunities. Run envisioning exercises with key business stakeholders to identify and assess the value of insights as they relate to new revenue opportunities.
- Prove ROI. Conduct a Proof of Value where the data science team can collaborate with the business stakeholders to determine if the analytics can be turned into new revenue opportunities.
- Operationalize New Products/Services. If there is a compelling ROI and the analytic models can generate the necessary lift, then push the new revenue opportunities to market. Instrument the rollout to monitor the monetization effectiveness and make right-time course corrections.
- Create New Business Models. Consider your customers' reasons for doing business with you; that is, what are they trying to accomplish from a more holistic perspective retirement, health, funding college, vacation, meals, entertainment, buying a house, transportation, etc. Leverage your customer, product and operational insights to transform your business model to more easily integrate or embed into the life, or business model, of your customers and partners.
- Create Analytics Platform. Extend your analytics platform to incorporate customer-facing interactivity where customers and partners can develop new apps that integrate into their business operations.
- Enable Third-Party App Developers. Determine how to enable, scale and secure the analytics platform so that third-party application developers can develop, market, sell and support new value-added applications.