Spotify, the audio streaming giant, recommends a personalized playlist based on your music preferences. On the other hand, Myntra, India’s leading fashion e-commerce platform analyzes your shopping behaviors to send customized WhatsApp messages about their new collections and offers. Meanwhile, if you search for staycations on Google, the latter recommends you top travel and hotels in no time. Have you figured out how these brands know their customers so well? It's the way they have successfully leveraged data to their advantage. Besides being leaders in customer experience management and delivering top-notch market relevant services, they have mastered data intelligence and are one of the most resilient businesses in their respective industries. This proves one thing: Data is THE fuel to business resilience, growth, and glory.

Why is Data Modernization Important?

Did you know that around 68% of data available to organizations remains unused. On average, wasted data causes a loss of $1.7 million per year. If this statistic is alarming, wait till you hear that 67% of business leaders struggle to gather relevant insights from data. This is like dropping the ball, especially at a time when powerful insights and information can keep you ahead of your competitors. Where are most companies going wrong? Major enterprises still rely on a traditional database for data management. Traditional database store data in a tabular format that makes it difficult to run complex queries. Their performance degrades and query response time decrease as the data footprint increases. However, the good news is that these issues are easier to overcome.

The key to becoming a data-driven company is to embrace data modernization on Cloud. According to a report by Known, modernizing data architecture on cloud leads to a 28% increase in business growth, a 13% reduction in incident resolution time and 35% faster time to insight. Safe to say, spending on data modernization can definitely help you reap business benefits.

Data Modernization on Azure: Your Golden Ticket towards Fueling Digital Transformation

Data modernization and the cloud go hand in hand. Shifting data to a cloud platform like Azure can enable advanced analytical, data science, data governance and role-based accessibility, enhance data quality and security - all using Azure Cloud-native tools. Let us discuss the steps of data modernization on Azure in more detail.

1. Ingest Raw Data into a Data Lake

   Given the high volume of data a business generates every day, there is a need for an advanced data storage system that stores data of any shape, size or type. A data lake is a storage repository that stores big data in a raw format. One of the specialties of a data lake is it uses schema on-read. It means you can store data in its native format as it does not follow a predefined schema. You can use the data later as and when the need arises. Azure Data Lake helps you to store petabytes worth of data which is 200 times larger than any other cloud storage system. It removes the burdens associated with storing huge volumes of big datasets, ensuring that it can meet present business changes.

2. Debug Big Data Programs Effortlessly

   Azure Data Lake is integrated with Visual Studio, IntelliJ and Eclipse that lets you deploy tools to debug and tune your code. You can check how your code runs at scale and detect performance bottlenecks, easing the process of tuning your big data queries.

3. Leverage the Power of Analytics to Transform Raw Data at Scale

   Organizing and processing data requires tools that can support large volumes of data. This is where Azure HD Insight comes into the equation. HD Insight leverages open-source frameworks like Hadoop, Spark, Kafka, Microsoft R Server and Storm for big data analytics. You can scale HD Insight when required. This means you only have to pay for the resources you use. If that’s not all, an IDC study reports that HD Insight contributed to 63% lower TCO on a per-terabyte basis.

4. Enable Schema on-Read for More Agile Analytics

   Traditional data warehouse use schema on-write which is usually not flexible in handling unstructured data, big data and frequent schema changes. Azure Data Lake uses schema on-read which is very agile. It works well with all kinds of datasets and lets you introduce new data sources on the fly.

5. Integrate Machine Learning for Advanced Analytics

   You can integrate Azure Machine Learning (ML) to develop and deploy predictive models that help in the analysis of historical data. This helps in better forecasting of your business operations and workflows and identification of future market trends.

6. Process Huge Volumes of Structured Data with Azure SQL Data Warehouse

   If you have to deal with large volumes of structured data, you can set up a SQL Data Warehouse. It will help in processing massive datasets through parallel processing. SQL Data Warehouse stores relational datasets in a columnar format. This structure significantly minimizes your data storage costs and enhances query performance. You can run analytics at massive scale

7. Use Azure Analysis Services to Derive Business Value

   How can you get consistent and business-friendly access to data? Use Azure Analysis Services to integrate data collected from multiple sources into a trusted BI semantic model that enables data discovery and self-service for businesses.

8. Add Data Visualization Tools for Better Control

   Azure Analytics and Power BI enable you to visualize your data, share insights across your teams and embed them into a website or an app. You can develop interactive, business reports for effective data-driven decision-making and integrate these reports into collaboration tools like SharePoint or Teams.

Unlock the True Value of Data with Cloud4C

Tim Berners Lee, the inventor of the World Wide Web, said “Data is a precious thing and will last longer than the systems themselves.” The question still remains: Do we control data or does data control us? For your business, data is like a pot of gold. You have to mine data effectively to stay ahead of market trends and your competitors. This is why you should invest in data modernization on Cloud. At the same time, data modernization is a complex process that demands specialized expertise.

Cloud4C offers an accessible data modernization approach on Azure with cost-effective data management operations and advanced analytics for accurate business forecasting. Our experts will help you deploy an end-to-end data modernization strategy that can guarantee a faster time-to-market and reduce your data storage costs by 40%. Now, you can now drive digital transformation at a much faster pace minus the disruptions! To know more about Cloud4C’s Azure Data Modernization Services, get in touch with us today!