With over 2.5 quintillion bytes of data created each day, modern enterprises have more information upon which to build their business strategy than ever before.
But despite the widespread adoption of Big Data and data science initiatives, the team at Hanu has watched many organizations struggle to implement systems that reliably produce actionable business insights.
It’s with this in mind that we wrote a short guide to succeeding with Big Data, specifically with the tools offered by Microsoft Azure.
Begin by building a common language
Before you can produce meaningful business decisions and implement data science and analytics strategies, you need to agree on exactly what Big Data is. By defining a common language, you can ensure the best ideas are brought to the surface immediately, without wasting time on anything that is superfluous to your goals.
With this in mind, there are several overarching concepts that most experts agree fit into the definition of Big Data. Big data tends to include data that is too large, fast-moving, or complex for traditional data processing tools. It also tends to encompass data in a variety of structures that require new forms of processing for the purposes of business insights.
Choose a data integration and storage model
As previously mentioned, Big Data comes in many forms. The diversity of Big Data poses a challenge for choosing the right technology to both integrate with your data sources and store that data in a way that makes it easily accessible and usable.
At Hanu, we recommend Microsoft Azure’s HDInsight solution. Based on Apache Hadoop, HDInsight is a fully-managed Azure Cloud service that makes it easy to process huge amounts of data using open source frameworks like Spark, R, Hive, and Hadoop.
One of the primary benefits of HDInsight is that it combines both data integration and data storage services into a single solution, which is part of what makes it so easy to use. A cloud-native service, HDInsight leverages Azure’s built-in security and compliance controls while integrating with other Microsoft tools such as IntelliJ, Eclipse, and Visual Studio.
Run a test project
As with most things IT, it is always to your advantage to test a small project before investing in a large scale implementation. Testing also lets you examine the results of a new system for any unexpected effects.
Your Big Data test project should combine internal data with external, third-party sources. Given that this test is meant to simulate larger initiatives, there are several things you should do to ensure your test is an accurate indicator of full-scale Big Data success. First is to implement security and privacy policies in your test project, as this is the optimal time to ensure your Big Data system is secure. Next, set up contingency plans in case of any discrepancies. Finally, ensure you are capturing ROI data measurements in both qualitative and quantitative forms. This means embedding analytics into every process that you can.
Take your time choosing an analytics platform
After selecting a Big Data storage and integration solution, it is time to begin analyzing your data. This is where you should start to see a return on your investment.
Unlike with data integration and storage, there are as many analytics services available as there are unique Big Data use cases. Azure offers over fifty services dedicated just to analytics. Obviously you won’t use all of them on every dataset – your choice will be dependent on the data you have collected and the results you are looking for. For instance, Azure Log Analytics lets you collect, search, and visualize machine data, whereas Stream Analytics analyzes and reports on real-time data streams from IoT devices. For Apache Spark analytics, Microsoft customers have access to Azure Databricks. If you’re looking for something more all-purpose, Azure Analysis Services is Microsoft’s enterprise analytics engine-as-a-service.
The key takeaway is that Big Data is a diverse field that requires organizations to tailor their analytics platform to their specific requirements. Azure provides an abundance of options from which to create endless variations.
Have more questions about using Big Data? Contact the data modernization experts at Hanu today. We’re happy to answer any questions you might have regarding Big Data and choosing an analytics strategy that will help you drive business outcomes. We will provide FREE estimation if you want to implement Azure for Big Data.
