Data Management: Will It Remain Clunky Or Can AI Make It Simpler?

If there is no data there is no intelligence.?In this?#HEXATalk?session,?we talked about?some questions particularly around?artificial?intelligence in data management.?There are plenty of ways AI can augment data professionals throughout the data pipeline, from sifting through large data sets for duplicates to easing the preparation process.??

Hear these young and innovative minds share knowledge about data management and how AI can be incorporated to make the process?easier.?

#HEXATalk - Artificial Intelligence in Data Management ?

1. What are the steps involved in Data Management and perhaps what steps can be?augmented?using AI???

Data is increasingly seen as a valuable asset for a business. Because data is so widespread and so valuable, you have to take care of the data you harvest. Simply dumping all your gathered data into an Excel sheet or database is unacceptable. Instead, you have to treat your data properly. For that reason, you need a data management framework that describes how to collect, process and classify data. A data management process helps to raise your organization’s standards and keep them consistently high.?

Data Management includes:?

1. Defining A Data Architecture:?Before even getting into how you want to make sense of the data you first have to define the architecture for your data, so in this step, you actually define how your data is collected, integrated, transformed and stored in such a way that it aligns with your business values.?
2. Data Modeling:?In this step, you basically need to define specific/basic data models which represent the core business concept, their?key?attributes and the relationships?between?them.??
3. Database Administration:?Along with data modelling it's also necessary to manage your entire database so that the data is available to you at all times and this is where database administration comes into the picture, where you monitor the database performance and try to automate it in such a way that you reduce query response time and get quick results.?
4. Data Integration & Inter-operability:?Data is essentially scattered across various sources like manual entries, payment portals, social media?etc.?in various formats. To make sense out of this, we need to consolidate data into one single consistent format.?ETL or Data virtualization is generally performed and data is stored in a data warehouse or data lake for further analysis.?
5. Data analysis:?Now that we have all the data in a consistent format it is ready for us?to?visualize?it, or develop?algorithms?to discover insights and use data for better and informed decision making for the business.?
6. Data Quality Management:?You think that this would be the end of the entire process but that's not true.?It is important to make?sure?that?the quality of data is always maintained based on parameters like Integrity, Validity, Accuracy, consistency?etc.?
7. Data Security And Governance:?Covering all practices that prevents database breach like encryption, access control, tokenization and backup plans. Build a master data management that ensures consistent use, fixing duplicated incomplete data.??

2. How can the tedious and manual process?of data management be?addressed using Artificial Intelligence & Machine learning???

Let's just start with an example consider there are 2 tables that you have to join. A data manager will sit and analyze, and figure out which column is the best to be used to join the two tables but now consider having an AI algorithm that can figure out from all the data entries and data columns, it can match the best column which can help join the tables and if that happens the process will be done in seconds, normally which would take hours of manual work.??

Here is another example of a large firm wherein many employees have different ways of writing data or entering data. Consider they are managing customer data and, every employee has a different way of writing it, some write it as first name-last name, last name- first name etc. So, now how do you figure out there are duplicates? Some reports suggest that 20% - 30% of data is duplicated in large firms. How do you handle this? This is where Artificial Intelligence comes into the picture by using cosine similarity, you will be able to figure out all the data and analyze from the data entries which of the entries belong to the same person and, that's where your database gets rid of duplicate entries in a faster and less-tedious way eliminating manual process.?That is how AI helps in making your database more and more efficient.??

3. Could you give an example of one of the use cases where you used?AI in?the data management process??

Here is a project that we worked on that is building compliance rules using data management.

• The first step involved collecting and sourcing data, this is where we received all the structured and unstructured data in different formats such as pdf, texts etc, and converted them into one particular format and stored it in our database.
• The second step involved the extraction and ingestion process where we used ETL features to normalize and standardize data, so by ETL, we extracted data from the model systems which are not compatible with your infrastructure, transforming it into a compatible form and then feeding it back into your destination system. The main features of ETL include data mapping, data integration, data synchronization and workflow management.
• The third step involved automation where we used some patented algorithms which identified the formats of different attributes and suggested the transformation which is to be applied.
• The fourth step involved simplification, this is a very important step in data management. We can never compromise with data quality. The solution already comes with a default quality check and data profiler. It also allowed us to build rules, in fact, build the rules with a no-code low-code concept. Here we have a different rule engine that gave us mathematical, statistical and logical operations to build business rules, compliance rules and regulatory reports.

---------------------------------------------------------------------------------------------------------------Have thoughts on this week’s trends or questions for me or the Guests? Post your thoughts in the comment section or send a note to?[email protected] .?Please include the hashtag #HEXATalk?and mention me,?Yogesh Pandit ! Until next week.??? ?

About the speakers:

Akshita?Lakkad ?is currently pursuing?Masters?of Science in Computer Science at New York University.?Her concentration there is in CS and Data Science.?Prior to starting her master's, she worked as a Business Technology Analyst at ZS Associates.??

Sree Durga ?is currently pursuing?Bachelors?of Technology in Mechanical Engineering at Bits?Pilani, Hyderabad (completed 2nd?year). Her interests include software development and data science.?

Advait?Varma ?has completed a bachelor's degree in electronics and telecommunication and is pursuing his master's in?Operations Research in Finance and Management from Columbia University.?When?it comes to his financial?ability,?he is focused on problem-solving and?has a desire to pursue a career in fintech.??

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