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Scope Creep is a known term among Project Management but is worth discussing it among data folk as scope creep can also impact data architecture and data modelling.

‘Scope Creep are those new requirements which are never formally added in project scope or if added, are without estimating impact of project deadline.’

Scope Creep means adding new requirements without analyzing impact on time and cost. I personally follow the thumb rule of TCS.

‘TCS: Time, Cost and Scope.’

Whenever 1 of the above mentioned 3 pillars of project management is impacted, the remaining 2 WILL also be impacted. Scope Creep is referred as e.g., a new requirement is added to the project scope, but the Project Manager didn’t estimate how much more effort is required and will there be any additional cost involving it. At the end of the day, it may impact Project Go-Live date which no one wants.

Reasons of Scope Creeps

• Deficiency of a Vibrant and Thorough Scope
• Absence of a Stakeholders Concurrence 
• Weak Project Sponsor Decision Management 
• Contradictory Business Users Views
• Less participation of all stakeholders during Project Lifecycle
• Bad Management of Risk and Issues Proactively
• Crossing Prioritizing Requirements
• Lack of Change Management Process
• Bad Efforts Estimation
• Bad Evaluation of new Requirements
• Missing Stakeholders
• Non-identification of Out-of-Scope earlier in Project Lifecycle
• Vague Acceptance Criteria(s)
• Poor Assumptions Management
• Poor Constraints Management
• No Clear Project Deliverables

Foresight and Futurology are used interchangeably. And yes you are right this new term is not something we have never encountered before.

Let’s decode it…..

Data: Refers to information, facts, or statistics collected for analysis, reference, or computation.

Futurology: It's the study or soothsaying of possible futures. It involves examining current trends and making prognostications about unborn developments.

Combining these," Data Futurology" suggests concentrating on the future of data, in terms of how data is collected, reused, anatomized, and its impact on colorful aspects of society or technology.

Let go little deeper about conception of" Data Futurology," which involves the study and soothsaying of possible futures related to data.

Let's break it down:

• Study or Forecasting of Possible Futures
• This refers to the practice of examining current trends and making predictions about future developments. It involves analyzing data and information to anticipate how various aspects of a particular field might evolve over time.
• Data Futurology
• This term specifically focuses on the future of data. It encompasses the entire data lifecycle, including how data is collected, processed, and analyzed. Additionally, it explores the potential impact of data on different aspects of society and technology.

POC stands for Proof of Concept. POC normally follows Sales Context of the product.

In POC, output is most important, not technology. In POC, the implementers make sure output is fulfilling customer requirements and at the same time convince business that this is the solution to go with.

The Goal of POC is to make sure whether Idea is Feasible or not.

‘POC is built and run for Business Users.’

Protype is the next stage of POC. In POC, one has test that yes Idea is Feasible, and it will benefit business agenda.

Now, in Prototyping, tools and technologies comes into picture where one starts converting Ideas in Reality, where it will show how the end-result will look like. It will show, how product will look like. It will show the real end solution.

Prototype is not built considering that business users will jump in and start using it. Prototypes are normally for technical folks to evaluate the end results with dumpy but relevant data.

’Prototype is built and run for Technical Users.’

New name of Pilot is MVP 😊. MVP is the next stage of Prototyping where Tools and Technologies are tested, and end solution is tested by technical folks. Now in MVP, subset of actual requirements is considered, subset of real data is used, subset of hardware and infrastructure is used, subset of software is used, and limited number of features are implemented.

Once MVP is ready, it’s released to actual users to start using it. This gives business users a taste of the actual solution. The biggest advantage of this approach is, with small implementation, with small investment, business can taste the blood and take a decision to go to the next stage or drop the solution.

Now a days, many organizations divide complete solution into multiple MVP(s) i.e., by implementing solutions in chunks, this is also supported by Agile approach where product gets ready in shorter timeframes and then keeps maturing with time. In MVP, just like actual implementation, three environments are spin i.e., DEV, UAT and PROD, along with all the steps like DevOps, CI/CD, Metadata Management, Data Quality, Data Governance, Data Lineage, Master and Reference Data Management, and many other steps which are required to go to Production.

The terms "IT" (Information Technology) and "Digital" are related but refer to different concepts within the broader technological landscape.

Information Technology (IT) encompasses the use, development, and management of computer systems, software, networks, and information. It is primarily concerned with the technology used to store, retrieve, transmit, and manipulate data. For example, IT includes hardware (computers, servers, storage), software (applications, operating systems), networking (local area networks, wide area networks), and the management of databases and information systems. IT is essential for the efficient storage, processing, and communication of information within organizations.

Digital is a broader term that extends beyond just technology and includes the transformation of processes, businesses, and society through the use of digital technologies. It is concerned with the integration of digital technologies into various aspects of life and business to improve efficiency, create new opportunities, and enhance experiences. For example, Digital transformation involves not only IT but also areas such as data analytics, artificial intelligence, internet of things (IoT), cloud computing, and other emerging technologies. Digital initiatives aim to leverage technology to bring about fundamental changes in how organizations operate, interact with customers, and deliver value.

In the context of data and databases, loops typically refer to iterative processes where a set of operations is repeated multiple times based on a specific condition. Loops are fundamental in programming for handling repetitive tasks, and in the data world, they are commonly used for iterating through datasets, processing records, and performing various operations.

There are two types of loops:

• For Loops
• While Loops

For Loops:

A for loop is used to iterate over a sequence (such as a list, array, or range) for a predetermined number of times. In the data world, loops are often used to iterate through rows in a dataset or perform a certain operation a specific number of times.

Example (Python):

for row in dataset:

# Process each row in the dataset

print(row)

While Loops:

A while loop continues iterating as long as a specified condition is true. In the data world, while loops are useful when the number of iterations is not known in advance, and the loop continues until a certain condition is met.

Socratic questioning is a form of inquiry and discussion named after the ancient Greek philosopher Socrates. It involves a method of cooperative argumentative dialogue to stimulate critical thinking and illuminate ideas. Socratic questions are designed to encourage individuals to think deeply, consider various perspectives, and arrive at their own conclusions. The goal is not to provide answers but to facilitate a process of self-discovery and learning.

When applying Socratic questioning to data solutions, the goal is to stimulate critical thinking, uncover assumptions, and encourage a deeper understanding of the data-related challenges or opportunities. Here's how Socratic questioning can be applied in the context of data solutions:

• Clarifying Questions: Can you clarify the specific data issue you're trying to address to ensure a clear understanding of the problem or goal in the context of data solutions.
• Probing Assumptions: What assumptions are you making about the quality of the data to uncover any preconceived notions or assumptions that might impact the approach to handling data.
• Exploring Evidence and Reasons: What evidence supports the decision to use a particular data processing method to understand the rationale behind the chosen data processing techniques or tools.
• Considering Alternative Perspectives: How might a data scientist approach this problem differently than a business analyst to encourage thinking about different viewpoints and approaches within the realm of data solutions.
• Examining Implications and Consequences: What are the potential consequences of using incomplete or inaccurate data in decision-making to explore the downstream effects of data-related decisions and their impact on the organization?

A Key Performance Indicator (KPI) is a measurable value used to evaluate how effectively an individual, team, department, or organization is achieving its objectives. If objectives define where you want to go, KPIs measure how well you are progressing toward that destination. They provide evidence of performance and help organizations make decisions based on facts rather than assumptions.

Not every metric is a KPI. Organizations generate many metrics, but only a few are truly critical to success. A KPI must be directly linked to a strategic objective, clearly measurable, regularly monitored, and capable of influencing decisions. For example, the number of emails sent is simply a metric. However, customer retention rate may be a KPI if customer loyalty is a strategic priority. The word “Key” means the metric must focus attention on what truly matters.

KPIs exist at different levels within an organization. Strategic KPIs measure long-term success, such as revenue growth, market share, or profitability. Operational KPIs track daily performance, such as order processing time or system uptime. Financial KPIs measure cost efficiency and return on investment. Customer KPIs measure satisfaction and retention. Process KPIs measure productivity and quality. In modern organizations, KPIs are typically displayed on dashboards and reviewed regularly by leadership teams.

We all have heard about KPI(s), and we use it in our daily work but not the OKR(s).

Let decode these…..

KPIs (Key Performance Indicators) have been a well-established and widely used framework for performance measurement for quite some time, OKR (Objectives and Key Results) gained popularity in recent years, especially in the tech industry. OKRs are not necessarily new, but they have gained significant attention, particularly due to their successful implementation by companies like Google.

Here's a brief overview of the timelines:

• KPIs: Key Performance Indicators have been used for a long time, and they are a more traditional approach to measuring and evaluating performance. KPIs are commonly associated with specific metrics that reflect the success or effectiveness of processes within an organization.
• OKRs: Objectives and Key Results, on the other hand, gained prominence in the 2000s and 2010s. They were popularized by companies like Google, which adopted and refined the framework. OKRs are more goal-oriented and are designed to create alignment and focus on achieving specific, measurable objectives.