Knowledge Hub

Data Imputation is majorly used in Data Science domain.

‘Imputation means, fill missing data.’

Data Imputation normally comes into play so business rules can be imposed on all data rather on subsets of data. Before Machine Learning Model runs, data imputation is done by either missing data is filled up or the whole row is removed.

Missing value can create many kinds of issues e.g., during categorizing, classifying, clustering etc., if data is missing then results will not be 100%.

Generally, in numeric datasets, missing values are replaced with MEAN values. Having said that, there many ways to deal with missing data.

Few Imputations Techniques

• The first technique is where data is missing at random basis but before using it, it is completely removed from the dataset.
• The second technique is also where data is missing at random basis but here missing values are replaced referring to the other data points available with current dataset.
• Third technique is where data is not missing at random basis and mainly replaced using Mean/Median/Mode values.

‘Disambiguation means to make something clear and understandable.’

We all know the word i.e., Ambiguous which means something which is unclear especially when same words are used for multiple purposes.

In the current era, semi and unstructured data is the norm. Now a days, social media has become one of the major sources for customer profiling. Then audio and videos are another source which can have any kind of data behavior.

‘Data Disambiguation’s biggest use case is to handle Sarcasms.’

As we know, in Sarcasm, dual meaning words are heavily used. For example, there is a common saying i.e., What’s up? This phrase can be used to say greeting to a friend and can also be used to show anger to someone unknown moving around in your restricted area.

The job for Data Disambiguation process is.

• To dig out the right meaning out of confusing statements.
• To understand the right concept of statement.
• Remove anything which can corrupt the behavior of pattern.
• Translate or replace dual meaning word with single meaning words so end user is not confused before taking any decision.

Data Fusion, another term overlapping with many other already in-place terms.

‘Data Fusion is about pulling same nature of data from multiple source systems to co-relate with each other to produce one unified output.’

For example, data for a particular product can come from POS (point of sales), from shops, from online stores etc. At the same time, people can talk about that product on social media, in seminars, webinars etc. Fusion of all those data elements can produce valuable information about that product for sales and marketing teams.

Yes, looking into image you can make out same thing can be achieved in Data Warehouse, Data Mart, Data Lake, Data Lakehouse, Data Vault/ Business Vault etc., and you are not wrong. All above-mentioned Data Store can be used for Data Fusion.

Integration Hub is normally confused with datastores like Data Warehouse, Data Lake, Data Lakehouse etc., which is not correction.

Integration Hub is a single central place from where you can send instructions to internal and external systems to perform some actions. Integration Hub consists of Spokes, each Spoke consists of number of steps for actions.

Generics Steps

• Create actions to be sent out to external systems.
• Run commands on external servers.
• Allow sending messages e.g., Rest, SOAP etc.
• Allow parsers.
• Allow SQL command to databases.

Yes, we do it every day e.g., we create connections with any system e.g., creating a connection with SharePoint then running commands to Delete a file. Consider 100 users want to do the same action, each must go through the same number of steps. Using Integration Hub, Spokes consisting of connections, credentials, and actions to be performed on e.g., SharePoint. Now, all those 100 users can reuse the same Spoke created to delete files in SharePoint.

In this era, social media has surpassed every medium to generate data. Social media is not only about writing messages, it also about sending audio or video messages and the moment audio and video (with voice) comes into picture, NLP comes into action.

‘NLP is about using techniques to understand the way humans communicate.’

Using NLP, code tries to convert audio into text and vice versa. Using NLP capabilities there are solutions which talk to humans, understand what they want and then reply to them. NLP also assists in performing actions e.g., a human can ask ‘I want a Chill Coke Zero’, the NLP will recognize this request and get a Cold Coke Drink for customer. At the same time, if Chill Coke Zero is out of stock, using Machine Learning and AI, it can reply to customer in Voice format that ‘At the moment, we are out of stock, would you like to go for Chill Pepsi Black’.

Steps of NLP

• Understand
• Analyze
• Manipulate
• General Human Language or Text

Initially, I planned to write two separate topics on Data Strategy and Data Management but as both are huge areas and the aim of this book is not to train people but rather to make sure readers understand the topics, so decided to combine.

Data Strategy is about defining many elements to make sure when a data solution is implemented, it covers everything to host data, to manage data, to protect data, to maintain data, to make use of data etc.

‘Data Management is one of the Key Element of Data Strategy.’

As the image depicts itself, Data Strategy is about defining all the features and capabilities before the execution process starts. Data Strategy is about tools, processes, implementation planning, maintaining, planning etc., based on business or organization requirements. Data Strategy covers business strategy, organizational roles, data architecture and data management. Data Strategy goals are to innovate, cater to business requirements and address risk and regulatory challenges.

Hype Cycle of Data Management is confused with Data Management which is not correct as one is about solution and other about enterprise.

Hype Cycle of Data Management is about knowing the pattern of technologies like how past tools and technologies performed, and which are new technologies in trend.

Hype Cycle of Data Management assist organizations to plan its technologies roadmap, it assists to take decisions like which tools and technologies to adopt and which not to adopt.

There are many a times when organizations are reluctant to adopt new technologies and they end up like Kodak and Nokia.

There are many companies which prepare these kinds of statistics and share with CTO(s), CDO(s), CFO(s), CEO(s) etc.

To make it easy, first must understand Traditional Data Stake (TDS). Now, what is TDS lolz 😊. TDS is your On-premises solution which can include any kind of Databases or maybe Hadoop environment as well. Whereas MDS is your tools and technologies on Cloud.

‘MDS vs TDS is more over a comparison between Cloud vs On-premises infrastructures.’

In TDS/ On-premises, if you need new hardware, there is a complete procurement cycle involved. If you need 2 CPU of processing at nighttime and 4 CPU in daytime but you end up paying 4 CPU for 24 hours as you have already bought the hardware 😊.

In MDS, when you are on Cloud, any resource is few clicks away. If you need 2 CPU as night, you only pay when you are using those and same for daytime CPU requirement.

What does "success" mean, and why is it so important to have a clear idea of it when dealing with data solutions?

Success is like reaching a goal or doing something well. When we talk about data solutions, which are ways to manage and use information, it's crucial to know what success looks like. Imagine trying to win a game without knowing the rules or score. Defining success in data solutions helps us understand if we are doing things right, meeting our goals, and making the most of our information. It is like having a map to guide us on the path to doing a great job with data.

Let's break it down further:

• Understanding Success: Success, in general, means achieving a goal or doing something well. It's like hitting a target or accomplishing what you set out to do.
• Applying Success to Data Solutions: In the context of data solutions, we're talking about ways to handle and use information effectively. This could include tasks like collecting, organizing, and analyzing data.
• Importance of Defining Success: Think of it as setting clear rules for a game or knowing the score you want to achieve. Defining success in the world of data solutions helps us clarify what we're trying to achieve and how we'll know when we've achieved it.
• Setting Goals: When you define success for data solutions, you're essentially setting goals. These goals could be related to accuracy, efficiency, security, or other aspects of managing data.
• Monitoring Progress: Knowing what success looks like allows you to keep track of how well you're doing. It's like having a map that shows you where you are on your journey and how far you are from your destination.
• Making Informed Decisions: With a clear definition of success, you can make informed decisions. You know what you're aiming for, so you can choose the best strategies and tools to get there.
• Continuous Improvement: Defining success isn't a one-time thing. It's an ongoing process. You can adjust your goals as needed and keep improving your data solutions over time.

Yes, you are right, you might not have heard about this approach. It is something new which Myself is working on.

Let’s decode it…..

Productionizable Viable Product (PVP): A Revolutionary Approach to reduce TTM (Time to Market) of adopting cutting-edge Data & Analytics Technologies while minimizing CAPEX (Capital Expenditure).

‘PVP focuses solely on new data & analytics initiatives rather than enhancements.’

Productionizable Viable Product (PVP) approach represents a distinctive approach in optimizing IT project execution by employing a singular Production environment throughout the project lifecycle. This streamlined architecture is designed to alleviate the complexities associated with managing multiple environments. To address data privacy concerns, the PVP architecture integrates controlled measures such as data masking or anonymization during the initial phases of development and testing, ensuring adherence to regulatory standards. The incremental provisioning strategy is a pivotal aspect, commencing with the provision of minimum required services or tools in the Production environment. The architecture emphasizes iterative testing cycles within the Production environment, allowing for continuous validation of integrated code sets through incremental Testing. This cyclic Testing in Production ensures thorough scrutiny before the final release to end users. In essence, the PVP architecture provides a structured and controlled framework, optimizing workflows, reducing complexities, and enhancing the reliability of Analytics project execution.