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Data & Analytics Strategy for Digital Transformation

By: Ramu Gowda, Director Product Management, SAP Leonardo

Analytics has come a long way, from being a front-end tool for business users to visualize and track pre-delivered reports and key performance indicators to self-service for business users helping them discover insights from varied data sources at their fingertips. That said, it has also created a disconnect between different personas involved, i.e., IT trying hard to maintain trust and governance of data coming from different applications and business users accessing data from all over the place, replicating and maintaining data silos and spending their time on discovering insights.

Besides that, providing a single view of the complete picture has become seemingly challenging due to fragmented analytics offerings; with different tools for Business Intelligence (BI), planning, and predictive not being able to connect what happened in past to what would happen in the future and outcomes based on hidden insights in the data.

Adding to this, the need for implementing, maintaining, integrating and continuously updating these different product landscapes has been a major challenge for IT and increases cost for the organizations.

Though we have seen some amazing innovations in the areas of data warehouse and management, Big Data, In-memory processing, agile BI, Predictive, Machine Learning (ML) and Artificial Intelligence (AI) along with increased focus to transition to Cloud, due to factors highlighted above, organizations haven’t been able to truly and completely run with data-driven insights.

So now let’s try to capture what does it mean and take for organizations to be insight-driven to succeed in the digital economy. Keep in mind, digital economy is a ‘Decision Economy’: The smarter, more timely decisions organizations make, the stronger the business will grow.

First and foremost, digital transformation isn’t just about applying new technology. It requires a new way of thinking backed by technology, which of course should result in delivering timely and relevant insights to business users, helping them to drive innovation and create business value.

Embrace One Analytic Solution on Cloud

Let’s start with Cloud, it is very important to embrace Cloud to gain most out of the latest technology innovations while avoiding total cost of ownership (TCO) on implementing and maintaining solutions on-premise. Cloud can help IT spend more budget and effort towards other important business-enablement topics.

Consider this as an opportunity to think about how we can connect historical data with future plans and outcomes and plan based on simulations and modelling; with automated machine learning algorithms to uncover hidden insights, which are delivered to business users by the technology based on the data instead of business users spending time on discovering insights.

This would require getting BI, planning, and predictive together on one platform powered by ML and AI.

Look out for a solution building analytics on Cloud from scratch covering all analytics scenarios, supported by the technology innovations instead of simply ‘lift and shift’ their existing on-premise solutions.

Tackle Data proliferation challenges with BIG (Modern) Data Warehouse Platform

And for this to work seamlessly for the volume of enterprise-wide data and deliver trusted insights, it’s very important to have a right Data Management strategy. A Big Data warehouse (Data warehouse and/or data orchestration) platform, which can let one address the challenges of data proliferation from enterprise data, Big Data, IoT, and beyond. And help transform all the data, the foundation of a digital enterprise, into insights to drive innovation and create business value.

These Modern Data Warehouse platforms are powered with in-memory technology for faster processing along with the capability for Data-Tiering enabling them to be efficient by segregating data to Hot, Warm, and Cold store depending on the frequency of access, agile data operations with modern user experience and governance of all data in the connected landscape with no replication.

So, these modern data warehouse platforms can help build governed single source of truth; combining data across the organization to fuel analytics powered by ML and AI to deliver trusted and meaningful insights to business users.

Embeddability & Mobility will be Key to be Insight-Driven

It’s not enough to turn data into meaningful insights unless business users can consume them and act on it within the context of their business task and within the business applications. That’s why it is very important to consider an Intelligent Enterprise platform that embeds these analytics capabilities in business applications and provides the capability to extend it beyond for analysis and insights.

What’s better than a Mobile device to deliver notifications on insights to business users and prompt them for immediate analysis and action!

This would mean the underlying platform should not only be capable of finding insights but also deliver them as Push Notifications on mobile devices, allowing users to access and analyze further from within the device and to ACT in the moment in the business application.

Mitigate Risk with Hybrid

Approaching this transition could mean quite a lot of disruption and this phase may result in significant risks for established businesses. To mitigate this, we should identify what to keep and what to change, where to start and how fast we can go without breaking things.

For this, it’s important to consider a solution supporting hybrid landscape which lets us not only bridge but also to extend and take advantage of our years of efforts on Data and Analytics during this transition, seamlessly.


Consider this transition as an opportunity to move to cloud for analytics with a platform combining BI, Planning, and Predictive together in one solution and delivering data-driven insights and notifying end users to further analyze and act within the context and in the moment.

Data is the foundation of Digital Enterprise. Governed and Single truth of data is important for delivering trusted and meaningful insights to innovate and deliver business values. It’s important to have in place a right Big Data warehouse platform to address the challenges of data proliferation from enterprise data, Big Data, IoT, and beyond.

Approach this transition with a hybrid strategy, go solution by solution while bridging and extending existing efforts in the new landscape.

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