Are You a Self-Learning Enterprise?

By Ajay Khanna published in CMSWire at https://www.cmswire.com/digital-experience/are-you-a-self-learning-enterprise/

We now live in a self-learning era. In our digital economy, the most successful companies know how to harness their data and learn from it. Be it Google, Airbnb or Amazon, the learners in the digital economy are leading the industry with new revenue models, innovative business processes and more effective customer engagement.

What sets these enterprises apart from others? It’s how they have embraced the use of data to improve their business and make more informed decisions. 

A self-learning enterprise does the following better than their competitors:

1. Puts data at the heart of every decision

Self-learning enterprises are data-driven. For example, a key motivation for Amazon’s acquisition of Whole Foods was the data. With Whole Food’s consumer data about habits and patterns of the in-store experience, Amazon can tailor the shopping experience to the individual. What better way to learn about consumers than at grocery stores, where they shop almost daily? 

The same is true for Apple’s recent acquisition of Shazam. It wasn't about the technology but about the years of user behavior data that Shazam possessed. Such enterprises see data as a competitive advantage and use the data to design the right product for the right customer and offer it at the right time using the channel of choice.

2. Organizes data of all types at unlimited scale

Self-learning enterprises are equipped to collect and organize data from all internal, external, social and third-party sources. They can correlate various forms and types of data, connect transactions, omnichannel interactions, master data and reference data to create a picture of their customers, products, suppliers or employees that help them make the right decisions at the right time. They understand that it is not just about collecting petabytes of data in data lakes or managing the data, it’s about organizing it in a form that can be used for decision management.

3. Unifies data sets to deliver personalized views

Leading digital enterprises use unified data sets to create consistent single-source-of-truth of data across applications, systems, and functional groups. Moreover, they also realize that each user of data needs the information in the context of their business role and objective. Self-learning enterprises use data-driven applications that are personalized, contextual and provide the most relevant information to sales, marketing, customer service, field service and call centers, stemming from the same consistent data foundation.

4. Infuses analytics into every business process

Pretty visualizations are eye-catching and may help with decision making but getting reliable information is critical and can get expensive. Beyond the initial data modeling and mapping required, companies also struggle with the inability to make real-time changes that reflect their business needs across data management and analytics environments. Seventy-five percent of Netflix users select programs based on the recommendations, and Netflix wants to make it even higher. Netflix is another example of a self-learning enterprise that is turning to consumer data to learn about their behaviors and preferences and then using that data to deliver the desired experience.

Self-learning enterprises bring together all data they need for analytics and machine learning in one place, accessible in real-time. Reliable and accurate data from master profiles, interactions, transactions, third-party, public and social media sources is consolidated for more in-depth analytics. When analytics runs on a reliable data foundation, organizations can make better and informed decisions. Moreover, when machine learning is applied to the specific business and industry, with a focused set of benefits for each business users' role, the ROI can be measured, so it does not get labeled as yet another failed science project.

Just as the process of aggregating data to perform historical or predictive analytics is a cumbersome and expensive process, gathering and blending all the right data that guarantees the effectiveness of machine learning must be the in the DNA of a self-learning enterprise.

5.  Continuously learns about customers, products and their relationships

Self-learning enterprises go beyond typical business intelligence tools to directly correlate and measure insights and recommendations with actual outcomes through a closed-loop cycle and keep improving. As companies bring together analytics and operational execution, results are continuously measured and compared with projections, to find areas of improvement. Self-learning enterprises continually learn about customers, products and their relationships. As their knowledge about customer behaviors and preferences improve, they can offer increasingly relevant products and services. Engagement becomes personalized, and analysis of data improves recommendations through this self-learning. Having a reliable data foundation is critical not only for relevant insights and the effectiveness of machine learning to refine recommendations, but also to quantify actual ROI through captured metrics.

Companies can begin the journey towards becoming self-learning enterprises by starting with the organization of data of all types and sources at scale to form a trusted data foundation. The next step is to bring in analytics for operational execution — it can be simple business rules or machine learning algorithms tuned by data scientists to deliver relevant insights and recommended actions. At full maturity, a self-learning enterprise will be able to measure the outcomes of those actions and use data in a continuous cycle of improvement. The journey to becoming a self-learning enterprise follows an evolutionary path and allows businesses to take advantage of shifting trends and unexpected market opportunities.

About the Author

Ajay Khanna is Vice President, Marketing at Reltio, the creator of data-driven applications. Prior to joining Reltio he held senior positions at Veeva Systems, Oracle and other software companies including KANA, Progress and Amdocs.