Data Science, Big Data & Analytics, AI and Blockchain, Mobile Technologies are Disrupting the Digital Era CFO's, CEO's and CDO's

We are in Digital "VUCA" world, These digital disruptions are stimulating the CEO's and CDO's of these industries(Space, Airline, Banking, Insurance, Healthcare, Automobile, FMCG, eCommerce, Manufacturing, Telecom, Pharma, Real Estate, Agriculture, etc);

Digital Disruption is a transformation that is caused by emerging digitaltechnologies and business models. These innovative new technologies and models can impact the value of existing products and services offered in the industry.

A Disruptive Technology is one that displaces an established technology and shakes up the industry or a ground-breaking product that creates a completely new industry.

CEO's and CDO's are in conviction on the Digital&Agile transformations&strategies to address the Business Agility and Digital "VUCA"; Indeed the Lean Agile and Agile PMO's are proven and became a mission critical for Digital CEO's and CDO's;

Essentially, data science is an iterative process of searching for statistical insights. In the process of unlocking those insights, data science professionals may build, test and discard one model after another until they find the one that fits the data best and identifies the specific correlations and other patterns of interest. Over the course of a specific data science project, data scientists may explore diverse data sets, evolve their approach for ingesting and sampling, and adjust the specific mix of features, predictors, and algorithms from which their models are built.

How Agile is helping rather fixing the Big Data big problems?

Agility is fundamental to business’ ability to successfully build systems in a world where it’s difficult to predict the future. Data science and new big data business capabilities (e.g., machine learning, AI) are becoming essential to optimally anticipate the future. We are all aware of Agile was established through 4 values, grouped by 12 principles and manifest through many practices with iterative and incremental development through customer feedback as part of the "Empirical Process"with highly customer collaboration and finally with Agile Mindset or Lean Agile Mindset; The Data Scientists, Stake holders and Dev teams has to collaborate to get the desired out come and the below depicts the agile mantra of the working software or PSI.

• Agile methodologies can boost the productivity of complex collaborations among dev teams as well as data scientists and other service providers.
• Considering that data science is increasingly a team-oriented discipline, it’s essential that statistical modellers, data engineers, business analysts, visualisation designers, programmers and others in the development team be able to pool their efforts within open, collaborative environments to common vision.
• Pair programming between Data Scientists and Dev Team will drives the good out come
• Data Base Refactoring is a change at data base schema that improves its design while retaining its behavioural and information
• Agile techniques for data modelling like exploring data structures, evolutionary data models for iterative and incremental development
• Leverage tools - CI/CD for Database regression, integration, etc

Agile empowers the "Vertical Slice"; So the strategy of a Big Data User Story adheres:

• One new data element from a single data source; One new data element from multiple data sources
• A cnahge to an existing report, a nw report, A new sporting view, A new data mart table. etc;

I am envisioned and experienced the below ways of Big Data, Analytics with AI delivery with Agile Frameworks and Methods with Lean Agile Mindset;

1. Usage Driven: how people use or work with the data fulfil the business goals is the true issue, so focus on usage and work closely with your stake holders to explore their intent to develop the stories driven development(reports, charts and predictions, etc); Build multiple use cases to describe each attribute that a user will take in the new report;

• Collaboration is crucial between Data Scientists, Stake Holders & Dev Team to develop he desired usage.

2. Data Driven: If the organisation or CEO's have fair idea about their business agility and what analytics to develop for their business (reports, charts and predictions, etc), If so DDD will be the best fit by leveraging 5w's ( Who, What, Where, When and Why?)

• who is you target audience?
• what you are going to provide?
• where this makes sense?
• when this makes sense?
• why might this idea work?

3. Architecture Centric: Out of 12 principle s of Agile, 11 are fully comparable with architectural practices;Given rapid big data technology changes, risks of doing too much too soon and hence being locked in to a solution that will inevitably need to be modified at significant cost. An architecture centric approach facilitates future rapid iterations with less disruption as well as lower risk with less cost by creating appropriate architecture abstractions, as iterative and incremental model.

4. Lean Startup: If the organisation or CEO's won't have any ideia or not confident what analytics to develop about their business (reports, charts and predictions, etc) At high level Lean Startup allows for testing hypotheses before committing to developing a production solution. For data and analytics, this cycle promotes experimenting with new data sources and analytic techniques while preventing these experiments from becoming unstable “de facto” production solutions.

Conclusion: Agile development is about flexibility, iterative value, rapid course correction, and effective collaboration within and across teams and to over come digital :VUCA" leverage the guided mindset of Agile and Lean to work in together for better results as out comes or desired business hypothesis;

Note - Are you struggling with agile transformations or adoptions to get the desired out comes please let me know or reach me at [email protected]. Am happy to help..

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