The rise of cloud computing has led to an increase in complexity for businesses. Your company probably has data in a wide variety of locations: on-premises and a couple of different cloud sources. How do you create a unified strategy for data spread across all those locations? What about empowering individuals and teams to pull insights out of that data? How are you handling live data sources with constantly updating streams of information?
These questions might have your head spinning. It’s hard to answer just one, let alone all of them in conjunction — especially if you’re already in the midst of a digital transformation or cloud migration (or both). Trying to keep your business running smoothly during these monumental, years-long evolutions while simultaneously laying the foundation for a stronger tomorrow is a huge task. The right analytics and business intelligence (BI) platform can help with all these issues and leave you in the strongest possible position post-transformation, opening the door for incredible new insights from advanced analytics with Python and R.
In this ebook, we’ll dig into digital transformations and cloud migrations, connecting to high-performance data warehouses, using a semantic layer for interacting with your data, building materialized views and optimizing them for analytics. In the end, we’ll look at using materialized views to build advanced models with Python and R.
The business world is constantly changing and the pace of those changes is accelerating faster than ever. You need to evolve to keep up. If your business isn’t already undergoing a digital transformation, you’d better be gearing up for one. For a mature enterprise, a digital transformation is a multiyear process that includes a cloud migration to help make data more accessible and portable.
Digital transformations typically encompass a few key elements: putting analytics in the hands of everyone throughout the company, building analytics into the product offering, and monetizing data. The right analytics and BI platform can help immensely with this process: serving as a semantic layer over the data (both while it’s being moved and once it’s at the destination), then allowing for in-database preparation and optimization for speedier standard analytics and even advanced analytics with Python and R. Your analytics and BI platform lays the foundation for the future of your data-driven business.
Organizations know that they need to be data-driven to beat their competition. This means giving every team member access to on-demand analytics to make smart decisions based on timely data — including live data. Users from every business team need to be empowered to dig into the KPIs that matter most to them via an interface that anyone can use without typing code. Inserting analytics and actions directly into existing workflows will also improve adoption rates and ease the burden of learning a new system.
If you don’t have analytics in your application, you’re behind the curve. Users want to quantify the ways in which they’re using your service. Adding analytics to your product, no matter what it is, increases stickiness, keeps users engaged, and helps them understand the value they get from your product. It also differentiates your product in the marketplace and lays the groundwork for monetizing that data. Analytic apps aren’t just for customer-facing apps, they can also change the way your in-house teams work by putting insights and actions right next to each other. Individual decision-makers should be viewing data on the same platforms they’re already utilizing to act on that information. These analytic apps will be powered by your data warehouse and other cloud sources.
What does your company do? Whatever the first answer was to pop in your head, the real answer is: you are a data company (or you will be, very soon). Adding analytics to your product is a necessary step if you want to monetize that data. Data is the new oil, meaning your company is probably sitting on a vast reserve of this valuable resource and needs to create processes to turn it into revenue. Selling users enhanced insights based on their data, as well as leveraging that data in other products or in partnerships with other companies are all powerful ways that modern companies are finding new sources of recurring revenue.
Your company’s cloud migration can be a part of a larger digital transformation, but it’s also vital to your company’s success in the competitive modern cloud-based environment. Having all your data in the cloud lets anyone access the information they need from anywhere, control costs by only paying for the storage and processing power you need when you need it, and seamlessly autoscale your core application software.
Your analytics and BI platform is the key to making this migration smooth, keeping information accessible throughout and laying the foundation for your business’s future. The platform is especially helpful when serving as a semantic layer over the data as well as connecting to your high-performance data warehouse and allowing you to perform complex analyses with live and cached data in the same dashboard. If your platform offers materialized views, it can take your analytics to new heights by allowing your team to prep and optimize data in-warehouse while also priming it for advanced analytics (more on that later).
You’ve got data, lots of it, and you want to get it onto the cloud so that you can do more with it. How do you keep your frontline business users empowered with the data they need while you’re busy moving that data? The right data and analytics platform will serve as a semantic layer over your data, allowing your team to run the queries they need, no matter where the data is. It also allows you to build durable, versatile data models that everyone in your company can use to perform their analysis. The data in these models is already cleaned and modeled. Secure access can be programmatically controlled so that no one can see anything they’re not cleared for and no one can break the model.
Another really powerful semantic layer function is allowing users to perform complex analyses and create dashboards that include live data from your warehouse alongside cached data from any source. The business world moves incredibly fast and you can’t afford to wait for data availability to make decisions. Going strictly off of cached, historical data puts you at a disadvantage. A semantic layer that gives you access to live and cached data squares this circle. It can even save you money, since you control when and how often you query the live data warehouse (very useful if you’re paying per compute).
If the semantic layer sits above your data, empowering users across your organization with analytics and insights, then the materialized view lives in your data warehouse and sets the stage for optimized analytics. These views leverage the skills of data engineers and other cloud data experts who can use SQL to clean and transform the data in-warehouse. Your materialized view can even preemptively perform certain aspects of your analysis and keep the results handy, speeding up and optimizing future queries. Once you’ve migrated all your data to your high-performance cloud data warehouse, a materialized view will help you get insights even more quickly. It even speeds up the sharing of those insights wherever you need them: to in-house users or customers via embedded apps in your software. That’s just the beginning of the game-changing nature of the materialized view.
High-performance data warehouses are great. They store all your data, however it comes in, and connect easily to modern analytics and BI platforms. However, all this data is not useful in its raw form. That’s where your materialized view becomes so vital: once the data is cleaned and prepped, it can be modeled for more complex and useful analysis with help from machine learning (ML) systems.
In your materialized view, data engineers and data scientists can use Python and R for advanced statistical modeling like answering predictive questions. These languages even set the stage for machine learning capabilities like sentiment analysis on text (consumer reviews, social media, etc.). Since all the data cleaning and prep has already happened in the data warehouse, you don’t need to pipe the data to a new location; you can use Python and R as a data frame and model your data via libraries and packages you’re already familiar with.
What form this next level of analysis takes will depend on the questions you’re trying to answer: Do you want to dig through tweets aimed at your customer service account and see what the most common complaints are so that you can head them off in the future? Or maybe you have a deep well of sales data and you want to understand where you’re getting the most value and how your customer base is changing so that you can focus your efforts on a growing client segment. Whatever questions you decide to answer, your analytics and BI system helps turn your newly modeled data into visualizations that you can send directly to dashboards or analytic apps. Materialized views with Python and R are your gateway to answering game-changing questions.
We live in a cloud world. As you migrate your data and undergo or finalize your digital transformation, you’re not just looking to carry on with business as usual. This is your chance to build something new and different to set the stage for your company to own the future of your industry. Choosing a cloud-native analytics platform, storing your data in high-performance data warehouses, and using materialized views to enable advanced analytics on your ever-increasing stores of data are just a few ways that the right technology choices at this crucial time in your company’s development will make a huge difference down the road. You can’t build something that will last if your foundation is shaky. Make Sisense part of your foundation and build boldly, knowing you have the tools and support you need to make real whatever you can dream up.
Mondelio Worldwide is an Australian company having commenced operations in 1982. Mondelio's single focus is to provide predictive data modelling and data analytics services to organisations throughout Australia and overseas.
Mondelio has partnered with Stonebridge Consulting, Naveego and Sisense expanding our product offerings to the wider APAC market.