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Modern data platform strategy

Establishing a Strong Data Strategy for Modern Data Platform

Building a Winning Data Strategy to create a Modern Data Platform

Modern data platform strategy

To start-off with some brainstorming, let’s ask ourselves how fundamental a Data Warehouse (DW) is to get the most of your enterprise data. Is data warehouse still relevant? A technology that enterprises used to adopt about 30 years ago, and since then there has been a lot of changes in this technology space. With the heavy influx of modern technologies, it makes us think if the principles of data warehouse still relevant.

In this article, we have covered some important concepts enterprises can follow to develop a data platform modernization strategy on Microsoft Azure. These concepts will help you follow the best practices and solutions to build a modern data analytic platform. Let’s see how the world of data has changed.

Importance of Data Analytics in Modern Businesses

Today all businesses are data businesses. An enterprise that does not manage, govern, and secure its data, will simply not be able to compete in the market. It is one thing to gather and manage data, but it is another to make decisions based on it. This is where analytics comes in, which helps businesses develop dashboards or reports that supports current state of the business and future decision making. Data analytics helps businesses perform complex analytics for better, faster, and more automated insights. Data science enables automated insights with depth and scale that are beyond the human capabilities. Even small business today can gain scalable insights into social media, digital interactions, and market behavior. Machine Learning (ML) and Artificial Intelligence (AI) drive autonomous processes and decisions for smart organizations.

Framework for Building a Modern Data Platform

This above framework can be used to build a cloud data platform and consume data from disparate data sources to create a data-driven business. This above framework can be used to build a cloud data platform and consume data from disparate data sources to create a data-driven business. Business processes accumulate data from variety of different sources and as an enterprise system of records, the data warehouse is mostly built over line of businesses data sources. A user can analyze data from sales systems, CRM systems, general ledger, accounting inventory, supply chain, and many other sources to generate insights. There are data elements in external systems like web data, click stream data, call records from the call center and the feedback data, and data coming from IoT sensors. Whether the data is structured, unstructured or semi-structured, a modern data platform handles data from volatile sources and can be used for data science.

There is a lot of data coming from a variety of different sources but is all the data captured important to the business? One way to leverage all data is through data pipelines using Azure Data Factory (ADF) which schedules and orchestrates data transformation and bulk loads. And with Azure Databricks, a user can perform transformation specifically for data science which can also build and execute data models. It will score these models and load it into the data warehouse which allows a user to integrate data coming from ADF pipelines or unstructured data coming from IoT sensors which will be consumed through Azure Databricks.

So, Azure Databricks can be used to transform data, wrangle, and consume it and load it in data warehouse that can be built with Azure Synapse Analytics. Synapse offers high performance, highly scalable data storage for enterprise data models with security, governance, and manageability. To make right utilization of the data stored, Microsoft Power BI consumes curated data enterprise data that’s coming from the data warehouse in Synapse for reporting and analytics. Databricks transforms the data stored in data lake and uses Power BI to run dashboards for data visualization. A user can build business applications such as automated loan approval or fraud detection using any other cognitive services in conversational UI that consumes data through business services. These are some of the ways an enterprise can build a modern data platform that is capable of consuming data from variety of sources and perform data analytics for decision making purposes.

Cloud Enablement for Modern Data Analytics

Now, let’s understand how cloud enables building a modern data analytics platform. The cloud enables projects that are easier to start and easier to scale such as ERP, CRM and operational applications that are increasingly cloud native. Collaboration platforms and file sharing have been transformed by cloud flexibility and scale. In today’s world especially when we are dealing with connected data that’s coming from internal systems and external assets, the need for robust security is present and cloud service layers allows us to implement it.

On a cloud data warehouse one can implement encryption, sensitive data classifications, and row & column level securities along with threat detection and integrated Identity Management. Resilience and availability are not just good objectives but vital security features for global always-on services. All these services take years to implement on on-premises environment and still it is not possible to achieve the level of success as compared to implementing these services on cloud.

Cloud-based modern data platform architecture eliminates the need for large CAPEX investments made in purchasing hardware and building build a data platform. Deploying data warehouse in the cloud is faster, easier, and more flexible than a traditional data solution running on-premise. Iterating designs and handling diverse data is far more efficient then an on-premises solution as Azure Synapse Analytics optimizes a lot analytical and official workload with elastic scaling. As the volume and variety of data continue to expand, a modern cloud-based data platform can serve all data needs. Enterprise models, business intelligence or analytical sandbox for predictions, can all be stored, managed, and provisioned together.

All these things come together as Microsoft Azure combines Power BI and Azure Synapse Analytics. Power BI is a market-leading platform for data visualization, and it offers incredible capabilities when combined with Azure Synapse Analytics.

Azure Synapse Analytics was previously called as Azure SQL data warehouse. Microsoft has just not rebranded it but has added brand new capabilities that provide seamless data ingestion and data transformation along with the features of the secure data warehouse. And the data warehouse query performance is no longer a matter of just IT because with Power BI consulting services, an enterprise can analyze and visualize large volumes of data self-service user queries and offer exploratory on-demand services. Power BI developers that wish to analyze real-time unstructured data need a scalable platform which can be queried easily with familiar syntax and connectivity. The modern data platform serves enterprise data function as it can consolidate business data and machine learning into the mainstream business and provision vital resources for self-service governance.

This is one of the strategies and one of the ways one can build a modern data platform on Azure. Organizations mainly build a modern data warehouse because they want to integrate all their data sources and make it available for decision making. Other want to perform predictive analytics because they want to understand the customer churn, why customers leave their businesses, and understand the customer loyalty with real-time analytics.

Modern data warehouse is at the center of enterprise analytics as a source of sanctioned data, system of record, and as hub serving models for business intelligence and data science. Modern data warehouse brings together data at any scale and delivers insights through analytical dashboards, operational reports, or advanced analytics. It combines all structured, unstructured, and semi-structured data on a data Lake storage from various sources and different tools can be used for ingesting of data. The idea is to bring in the data from all these sources into Azure SQL data warehouse and use it for servicing consumption needs such as building dashboards and reporting using Power BI.

Enabling predictive analytics is one of the ways to build a platform that is more suited towards predictive analytics. It transforms data into actionable insights using best-in-class ML tools and the above architecture allows combining any data at scale to build and deploy a custom ML model at scale. Use machine learning and deep learning techniques to gain deeper insights from your data using the language of your choice as Azure Databricks supports Python, R, Spark, and some other languages too. You can build Notebooks using Azure Databricks that is seamless like Jupyter Notebooks that organizations have been using in the past. A user can leverage native collectors between Databricks and Azure SQL warehouse or Synapse analytics to access and move the data at scale.

Moving into the real-time analytics, above architecture represents a scenario that can utilize data as it is generates and analyzes it in real time offering actionable insights at scale. Let’s look at some examples relevant to this scenario.

A person using a credit card experiences a fraud and the banking company will notice it and will be able to detect the fraud in real-time and notify that the customer right away. Connected cars like Tesla and Volvo that have sensors embedded on them offer real-time details about oil and coolant temperature, tire pressure, vehicle speed, etc. In an ecommerce example, consider clickstream analysis for the customers visiting a website and displaying certain promotions and offers to them. In a healthcare scenario, real-time patient monitoring is an example. And in supply chain management or manufacturing scenario, it can be used for real-time demand and inventory management. A user can get insights from the data as it streams, and this happens through real-time ingestion of the data with some technologies like Azure IoT hub or Apache kafka that brings the data in the system.

These are some of the strategies and ways one can follow to build a modern data analytics platform on Azure. The growing needs of enterprises regarding data analysis and big data management has encouraged them to build and design data collection solutions with data engineering consulting services. Data experts can help develop cloud data platforms that can also lay the foundations for data analytics and empower enterprises to leverage data for informed decision making.

Tarun Agarwal, Team Motifworks

Tarun AgarwalTarun Agarwal

VP- Cloud Solutions ​​​​​​​​​​​​​| Motifworks

Known as a Data Analytics thought leader who fuels data-driven transformations for Fortune 500 firms, Tarun’s passion is to tell the “story” of the data that is hidden in an enterprise’s data assets. He does this flawlessly by leveraging Big Data, Machine Learning, AI, and cloud platforms. Tarun’s expertise lies in modernizing data platforms through cutting-edge technology solutions and at Motifworks, Tarun leads the Data & AI practice.

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