Take business intelligence to the next level by converging analytics and accelerating time to insight

There is no doubt that the digital transformation age is here. Thanks to trends accelerated by the pandemic, every company is now a digital company. The question is which ones are riding this transformational wave to achieve new levels of success and which are lagging behind.

According to TechNative, 70% of companies presently aren’t hitting their business objectives, and less than a third believe they have a cogent and well-articulated data strategy to get there.

It’s notoriously difficult to incorporate data into a business’ decision-making processes. At the core, the challenge is moving from a descriptive and diagnostic paradigm to a predictive and prescriptive one. This is the difference between storing data – and even having access to it – and being able to use data to fuel your decision-making now, instead of after the fact.

In other words, having data is great, but unless you can use it to generate business intelligence and make mission-critical decisions from it, you’re stuck at the entrance to digital transformation. Those gates will eventually close. To avoid being shut out, you need the right analytics platform and component tools to generate and act on business intelligence in near real-time.

To take the next step towards predictive and prescriptive analytics, you have to understand business intelligence on a curve. You can think of the flow of information on a line from hindsight to insight to foresight. Hindsight tells you what happened; insight tells you why something happened; and foresight tells you what will happen or what you can do to make something happen.

To gain the full breadth of real-time insight, you have to master all three. And you should begin the process by identifying the questions you have and then looking for the right data and analytics that will answer those questions in the way you need.

Business intelligence delivers insights all along this curve, including descriptive analysis that tells you what happened. The type of data you need for descriptive analysis includes things like sales numbers, website traffic, and customer feedback. Then, when you add an analytics layer, you’re able to use diagnostic analysis to understand why those things happened. Understanding the “why” reflects maturity in your business intelligence.

The next step up on the curve is predictive analytics to tell you what’s going to happen in the future, and then the final step is prescriptive analytics that help you know what to do to make that future happen.

Both require machine learning and artificial intelligence in combination with historical data to answer questions about things like demand forecasts, equipment failure rates, and consumer behavior.

As you glean insights closer to real time, you can begin to deliver recommendations to customers just as quickly.

It’s easier to understand the need for various types of analytics and insights in context. Say you’re a large intelligent manufacturing enterprise that makes hundreds of products, like medical and electrical equipment, under a variety of brands. There are billions of dollars in revenue at stake.

The primary business challenges you need to solve for include ensuring minimum downtime for your machinery, forecasting the raw materials you need, and forecasting customer demand for the products you make.

To meet those challenges, you need real-time insights into how well your IoT devices and other equipment are functioning. If their performance is declining, or they’re malfunctioning, that’s going to impact not just hardware lifecycle management but also your overall operational efficiency.

Those insights drive predictive maintenance. Connected devices produce data. When you collect, store, and analyze that data, you can drill down to any core problems the machines are having. Then, by applying machine learning, you can have notifications about needed maintenance sent to your team, so they can jump in quickly and fix the problems.

By proactively addressing any issues, you can ensure production quality and eliminate costly delays like unplanned downtime. You’ll also be increasing the life of your equipment, which saves costs in the long run.

From the supply chain side of the operation, you have to be able to act on data quickly. If operational data like orders, shipments, and transactions isn’t in a usable, accessible format rapidly, it’s not of much analytical use. Supply chains are typically linear, in the sense that each step in the process happens serially, and that often results in totally reactive responses to disruptions.

But in a networked supply chain in which all parts are connected through a digital platform, issues across any point – be it logistics and transportation, manufacturing, assembly, or product consumers – can be flagged and ameliorated at any time. That lets you understand the complexity of customers’ demands and be savvy with planning for resources, leading to less waste, reining in inventory and delivery costs, and still ensuring customer retention.

Still, a digital platform is only useful if clean data can be analyzed to provide insights. For many organizations, there may be a bottleneck in the ETL (extract, transform, load) process that hampers speed. There must be a solution in place to solve that pain point.

The next step up on the curve is predictive analytics to tell you what’s going to happen in the future, and then the final step is prescriptive analytics that help you know what to do to make that future happen.

Both require machine learning and artificial intelligence in combination with historical data to answer questions about things like demand forecasts, equipment failure rates, and consumer behavior.

As you glean insights closer to real time, you can begin to deliver recommendations to customers just as quickly.

Managing a large retail organization has become a greater challenge than ever because of how revenue comes from both brick-and-mortar locations and online sales. Marketing has accordingly become more eclectic, comprising not just traditional ad opportunities like TV spots, billboard, and display ads, but also a savvy and engaging social media strategy and email marketing campaign.

In addition to the logistical complication of managing inventory and forecasting demand for in-person and online sales, you have to think hard about how to create personalized experiences for your customer and find opportunities for cross-selling and upselling. You need supply chain optimization and real-time personalization.

To personalize those experiences, you need a 360-degree view of your customers, linking multiple accounts from a variety of systems to understand the customer persona. From there you can be more targeted in your marketing and serve up recommendations that will appeal to your buyers. From those actions, you can apply machine learning to uncover patterns to predict end users’ behaviors and monetize them.

For example, perhaps your customers are doing their browsing online but are coming into the store that same day to make the actual purchase. That’s a fairly nuanced pattern of behavior that will affect the way you think about the individual customer experience.

In order to optimize the supply chain for an intelligent retail operation, you need to increase efficiency, reduce costs, and ensure the best performance. Start with a complete, unified view of your business that lets you turn operational data like point-of-sale transactions, purchase, and social media sentiment analysis into actionable insights. That will allow you to be smart about capital investments and improve business operations in areas like managing in-store inventory.

As with the prior example, a slow ETL process makes adjusting to these insights quickly all but impossible.

Practically speaking, then, how do you remove those roadblocks and achieve those outcomes?

To start, you need a modernized data environment that pulls information from multiple sources and joins systems together to establish a single source of truth. Then, you need processes to prepare that data to be analytics-ready, as well as the database, applications, and analytics to glean actionable data insights.

To prepare for analysis, you have to remove barriers between operational databases and analytics databases, and they need to sync with business applications. And you need the pipelines to get the analytics to their destination.

Standing in the way of all of that is an ungainly ETL process. Microsoft’s Azure Synapse Link for Azure Cosmos DB, a cloud-native hybrid transactional and analytical processing (HTAP) capability, is the key that unlocks all of it. It obviates the need for the ETL process by creating a seamless integration between the databases and Azure Synapse Analytics.

Microsoft offers products and services for every part of the business intelligence systems, including Cosmos DB, Azure Synapse Analytics, Synapse Spark, and Power BI. Azure Synapse Link stands in the middle of all of it.

There’s no need for data integration pipelines, and latency is <90s to deliver insights quickly without impacting performance. Operational data is stored in a transactional store and auto syncs with an analytical store. Through Azure Synapse Link, it hooks into Azure Synapse Analytics – databases like Apache Spark and SQL – and then via machine learning, big data analytics, and BI dashboards, you get all the insights you need.

Setup is simple. Turn on Azure Synapse Link for new Azure Cosmos DB containers and create a Synapse workspace and connect it with Cosmos DB to grab near real-time data analysis.

Among the many component parts of a modernized business intelligence platform, Azure Synapse Link plays a crucial central role: It removes the ETL bottleneck, allowing all parts of the system to flow, and in that way it becomes a catalyst for achieving near real-time business insights.