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Data is the New Oil. It’s only useful when it’s refined.

Introduction

Think about it – oil is a valuable resource that is used to power machines, create products, and more. However, it’s not much good unless it’s refined into something useful. The same goes for data. It’s only valuable when it’s refined and used to its full potential. In this blog post, we discuss the importance of data refinement, and how businesses can make the most of this new data evolution to succeed in business.

What is Data Architecture?

Data Architecture is a set of guidelines that determines what kind of data is collected and how it is used, processed, and stored in a database system. Data integration, for example, depends on the Data Architecture for instructions on the integration process. The modern computers will be much clumsier if not shifted from a programming paradigm to a data architecture paradigm.

Evolution of data architecture

For the Hint/ Reference

When we talk about the evolution itself… the biologists have observed that the same species existed in different parts of the world with no difference between them. they were identical in every nature. Example Hodgson’s frogmouth and Sri Lanka frogmouth existed in different parts of the Indian continent. How this could been possible. Only answer is… these species evolved when the world landscape was same and go separated when the world mass land divided itself into different continent.

Data architecture has also has a similar phenomenon. In the every beginnings of enterprise data management, the data was stored at a central repository which was available throughout the organization for the usage. | As time went by and changing business climate conditions forced transactional and analytical to isolate to the extent that it seemed very different from each other. | So to again unlock the full potential, the transactional data’s from multiplied points has to be bought into a centralized architecture as a common shared entity.

The first generation data platforms were known as data warehouses were built to make integrated transactional data available for specific analytic purposes. In a decade the data warehouses couldn’t handle the volume in the growth and data and ran out of storage and management needs.

As a result, people turned to the second generation data architecture called data lakes. But both data warehouses and data lakes couldn’t solve the problem. There are three important reasons for it….

  1. Time Lag between the data creation and Data Insights available for business decision making. As the technology grew there was a time lag between the transactional data which got generated in the multiple locations and its ingestion, integration and providing them as useful insights for business decision making. And on the top of it, these transactional data which originated at the multiple points at multiple frequencies has to be processed multiple time creating a greater time lag.
  2. The Difference between Enterprise Analytics and Human expert Interpretation. There was a huge gap in how the enterprise data architecture interpreted and presented the data and how the human experts derived meaningful outcomes from them. This again created a greater time lag.
  3. Need for specialized skill sets. Data architecture and algorithm remain complex and they need specialized skills like application integration, data engineering, machine learning, data ops and ML Ops. Typical business users don’t possess this skill thus widening the gap.

The third generation data platform data lake houses removed the problem of creating a storage layer and processed data real time within the BLOB storage. This architecture adopted well with for the cloud and the AWS S3 or Azure DLS2 can provide the required storage.

We are now moving into the fourth generation enterprise data fabric, transforming data architecture itself. The Data Fabric is a fourth generation of data platform architecture. The purpose of the Data Fabric is to make data available whenever it is needed, by hiding the complexities involved in data movement, transformation and integration so that anyone can use the data.

A data fabric is a network of data platforms that work together to provide more value. The data platforms are spread out across the company’s hybrid and multi-cloud computing systems.| Different nodes in a Data Fabric can be different. A Data Fabric can have multiple data warehouses, data lakes, IoT/Edge devices and transactional databases. Different technologies can be used including Oracle, Teradata and Apache Hadoop to Snowflake on Azure, RedShift on AWS or MS SQL in the on-premises data centre.

The Data Fabric contains all the data needed for the entire data-information-insight lifecycle. One part of the fabric may give raw data to another part that does analytics on it. These analytics can be used by other systems to make decisions.

In the Data Fabric, everything is a node. This means that the nodes can interact with each other in different ways. Some of these ways involve data movement, while others allow data access without movement. The idea behind this is that data silos (and differentiation) will eventually disappear. |Data Fabrics and Lake houses are two types of data architectures that are still maturing. They both have a lot of potential in the future. These technologies may someday make it so people can access data more easily and get insights from it quickly.

Why Data is important and How businesses use data to succeed & what industries are expected to be transformed by data in the near future?

Uber uses data to determine the supply and demand and uses it to increase prices. For example… during the new year eve, when there is more demand for the taxis the distance which costed 200USD can go up to 1000 USD.

Walmart which has 20,000 stores in 28 countries uses data to give real time recommendations to it customer when they are inside the store based on their previous purchase patterns and behavior. this increased their conversion rates and thus increasing the profitability.

Netflix uses real time data to provide recommendations to its users on what type of movies they can watch based on the type of past movies they watched.

Let’s take an example of a Netflix users who is dating someone and has more inclination on watch love stories… Netflix provides movies based on love themes… when the same user switches to movies based on break up and come back… Netflix again understands this shift and starts recommending movies based on dealing with break up and coming back. This increases the retention rate and profitability as well .

Alibaba has so far created more than 20,000 consumer data models based on specific behaviours and demographics. Many of these models have upto 1 Million customers. Using these data points, Alibaba can customize offers, customize products and push them to the consumers and increase their profits. Alibaba uses data to improve not only the online sales but also the offline retail market which consists of 80% market share in China’s retail industry.

The industries affected or transformed by data in the present and in the future cannot be limited to the list we are sharing here below. It is only a comprehensive list.

Banking – Banks use data to understand how their customers use their accounts and to figure out where the security risks are. They also use big data in location intelligence to decide where they should put their branches. In the future, data might help people use money more efficiently too.

Agriculture – Farmers are not usually the first people who come to mind when you think of big data. But data analytics are now very important for agriculture. They will become even more important in the future as we need to predict the weather and get the most out of the land to feed a growing population.

Real estate firms are using big data to better understand the market, their customers, and what is happening with different properties. Property management companies are using data from their buildings to improve performance, find areas of concern, and make it easier to maintain the property.

Telecommunication – The telecommunication industry is using big data to improve in several key areas, including customer experience, fraud reduction, churn prediction, and dynamic pricing. And with the rollout of 5G, data plays a key role in network planning, monitoring and management. With big data, telco can now make better decisions when it comes to impactful analytics.

Health care – Doctors have been using data to figure out what blood pressure range is normal, and how much sugar people should consume each day. Healthcare companies are now using data analytics to answer bigger questions about healthcare. For example, if you want to find out if a patient is at a higher risk of addiction to substances, big data can help you do that. Going forward, data collected from devices like smartphones and wearables will be able to help doctors understand their patients even better. This means they can save money and deliver better care.

Textile Industry – Online purchases play a big role in fashion industry and textile industry is directly connected to it. In the near future the online players will be able to communicate the changing consumer preferences in real time from millions of Point of purchase data points and process them real time. These real time data can be used to entirely customize the production processes in textile industry from season to season. This will lead to increased profitability by avoiding wastages, designing and producing garments which will hit the market in line with the fashion trends.

How IT companies need support in adopting besting refining techniques of the oil? data governance and data management.

If an organization does not have effective data governance, data inconsistencies between different systems can go unresolved. For example, customer names might be listed differently in sales, logistics and customer service systems. Data errors can happen when data is integrated from different sources. This can cause problems with business intelligence (BI), enterprise reporting and analytics applications. These problems might not be identified and fixed, which could lead to even more inaccurate BI and analytics.

What are the common problems faced related to data architecture?

Conclusion

Businesses must be careful with how they use big data. It is changing the way businesses operate and how consumers interact with them. As data becomes more accessible, businesses will need to be even more careful with how they use it. For help navigating these waters, contact us today. Our team of experts can help you find success with big data while protecting your customers’ privacy.

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