Analytics, Meet Operations. Operations, Say Hello to Analytics
Originally published at InsideBigData http://insidebigdata.com/2016/01/18/analytics-meet-operations-operations-say-hello-to-analytics/
In this special guest feature, Manish Sood, CEO of Reltio, examines how business operations and data analytics can meet to obtain competitive advantage. Manish is the CEO of Reltio, the creator of data-driven applications. Prior to founding Reltio, he led Product strategy and management for the Master Data Management (MDM) platform at Informatica and Siperian. He is the co-author of the patent that revolutionized MDM through a global business identifier. During his career, Manish has architected some of the largest and most widely used data management solutions utilized by Fortune 100 companies today.
It’s so simple: We have all this data (with more coming in every second), and so we need data analytics. And as those analytics reveal so many previously clouded assumptions, they have a huge impact on operations. Again, it’s so very simple.
Now, welcome to the real world.
All organizations regardless of industry have a perennial chasm between analytical data warehouses and operational applications. It’s not anyone’s fault, it’s just an example of how technology had been unable to meet the different data volume and latency characteristics at reasonable price points, until now.
Of course, it doesn’t have to be this way, and given the obvious benefits to be accrued from greater insights, it absolutely shouldn’t be this way. In today’s business environment, a new breed of data-driven applications (DDA) makes it much easier for operations and analytics to be combined seamlessly in a single application. A closed loop ties back analytic-derived insights, that are directly relevant, to the execution of specific business tasks. But getting to that point requires a change in data management philosophy, and that in turn will only come when we understand the problem.
Exploring the Chasm
So why is there such a gap between these two disciplines, when it’s clear that they should be closely linked?
A lesson in technology history shows us that enterprises have added a variety of diverse systems to address specific business challenges, making for an alphabet soup of acronyms: CRM, ERP, SCM, CPQ, etc. These disparate systems differ greatly in the basic foundation—many have rigid configurations, some exist on-premises while others operate in the cloud, and so on. What they do have in common is that they collectively generate massive amounts of data. This data is collected and stored so that business teams can make more informed decisions about improving processes, identifying competitive opportunities, and improving customer experience.
But then there’s a catch: Operational systems are typically designed to provide process efficiency and data collection, not data aggregation and analytics. They fall short when used to build a full picture of business entities—customers, products, partners, locations and their inter-relationships. That only comes with byzantine levels of integration, with cooperation required from every department. And as if that wasn’t complex enough, there are now reams of data from external sources like social media or industry-specific data services that is required to complete a true 360 view. The task of merging all this data and delivering it to business teams through operational applications is beyond challenging.
Traditionally—a word guaranteed to set off red flags in this dynamic world—companies have tried to solve the issue by merging profile and reference data from multiple operational applications in Master Data Management (MDM) tools. A data warehouse has then been the repository of choice to co-mingle the transaction data with curated profile dimensions for insights. But this process is flawed in both accuracy and efficiency. Analytical insights, delivered outside the business applications, lose its immediacy, context and operational relevance; and data inside MDM tools essentially starts to decay without fulfilling any useful business purpose.
Bridging the Gap
This is why the data-driven applications previously referenced play such a critical role. They enable organizations to both capture data, make it reliable, and extract relevant insights by combining data –both structured and unstructured—from multiple sources, operations, third-party data subscriptions, and social media to offer a complete picture to business leaders and line managers.
So what exactly is a data-driven application? Built on the foundation of MDM and connected to internal and external data streams, DDAs create a comprehensive picture of business entities such as customers, products, places, channels, and activities by combining data from all sources and revealing relationships across these entities. Business professionals leverage this complete data profile to instantly draw analytical insights and make better-informed decisions that have an immediate impact. Indeed, DDAs have built-in, Big Data scale, real-time analytics for personalized relevant and contextual insights, right within the application. And rather than using the same prism, the apps enable data analysis from multiple perspectives that reflect dynamic market conditions to unveil new information.
And yes, they come with graphics. DDAs provide user-friendly visuals and also guidance in the form of intelligent recommendations, all within the operational application. These suggestions improve not only operational efficiency but also consistency—visual graphs present relevant information based on the most up-to-date data for deep and meaningful analysis. DDAs also incorporate closed-loop feedback for immediate actions, such as alerts for a compliance risk, as well as, to continuously improve business processes, and customer experience.
Best of all, these tools are not for software specialists only—they belong comfortably in the generation of technologies exemplified by LinkedIn and Facebook (both of which, of course, seamlessly draw data from multiple sources to offer cohesive profiles, just like a good DDA should).
The Secret Sauce
It’s not really hard to understand why this generation of technologies present such an advantage: All data is in one place, it’s current, and it’s complete. Now, it’s up to us to focus on what’s relevant.
All relationships between people, companies, products and places are available in visuals that front line business users can easily consume. Insights are offered within operational applications, and in context, rather than deferred to a separate reporting or analytics tool. And just like today’s most popular social applications, where data, content, insights and relationships are all presented in one place and in context, enterprise B2B data-driven business applications offer the same quality of user experience and depth of information.
Data arrived as a blessing but can often seem like a curse—high on volume, low on insight. But that’s set to change: A new generation of enterprise data-driven applications can do the work of converging multiple data silos into a relevant and manageable streams for operational excellence that serves up a competitive advantage.