14 Mar, 2016
Building an Insight-Driven Organization

Among the C-suite and boardrooms of almost every large organization, there is a strong conviction and consensus that they need to build an insight-driven organization to stay competitive and relevant in the marketplace. Organizations, which have already embarked on their analytics journey, are taking a pause and reflecting on their maturity against the standard analytics maturity frameworks. An agile and iterative approach towards deriving insight from data analytics can be instrumental in thriving in an increasingly digital world.

Trianz is a well-regarded name when it comes to advising clients on how to harness the vast potential of big data and analytics to address their critical business issues. Having assisted our clients with their pressing business analytics challenges, we believe following are some of the most important building blocks for the making of an insight-driven organization.

Data Foundational Layer

A fundamental requirement for developing business insight is to have a robust data foundation layer where data is available in real time for the business users for their analytical purposes. In a traditional data warehouse architecture, transactional data is usually available with a time lag owing to the ETL batch processing. However, with in-memory database management, distributed computing, and data virtualization technologies becoming mainstream, both transactional and analytical data can co-exist in the same environment without any performance issues (e.g., SAP HANA).

Data Governance

A business-led and IT-supported data governance council can help set the tone for ensuring data quality, privacy, and security. A matured set of data governance policies can substantially improve the value of insight that businesses derive from the data. Also, a data governance council can establish key performance metrics against which it can measure itself and make continuous improvements to reach a steady state of maturity.

Master Data Management

Having a quantum of unstructured or semi-structured data could be more of a bane than boon since poor quality data in planning and execution phase of any project often lead to missing the intended business outcomes. Efficiently managing the master data can help an organization quickly gain insight, turning data into dollars. Improving the maturity of master data management (MDM) program including archiving, backup and e-discovery should, therefore, be a key enterprise-wide priority as this can bring significant benefits to the organization across the value chain.

Common Business Definitions

It is important to have a common business definition of outcomes and standard KPIs across different divisions and geographies of an organization. Organizations often build some form of enterprise data warehouses / local data marts over the years without common business definitions of outcomes, making corporate offices struggle to assess the health of their global operations.

Building Business Views on Data Lakes for Self-Service BI

While an enterprise data warehouse is usually built based on the business requirements, it is important for IT to enable a platform for the business users to do discovery analysis to uncover hidden business insight. Therefore, it is important to have an Operational Data Store (ODS) or Data Lake environment where all the business-related transactional data from various applications is centralized. Self-service BI capabilities can help different business users quickly analyze master data (synthesized data coming from disparate sources) and gain valuable insight.

Integrating Analytics with BPM Applications

One of the core objectives of information management and analytics is to provide right data, to the right user, at the right time. Unfortunately, metrics and KPIs are often displayed in standalone BI tools, rarely integrated with business process management applications. In order to have truly actionable analytics, it has to be in conjunction with the business processes, providing a clear sense to the leaders in assessing issues, trends, and growth prospects of their businesses.

Establishing Incubation (Startup) Center

For organization starting to foray into the realm of data and business analytics, a modular approach would do just fine with setting up an incubation centers consisting a nimble team of 5-7 resources comprising business strategists and technology evangelists, with a mandate to bring incremental improvements in the current enterprise architecture as well as harness disruptive technology forces. Once the successful outcomes are realized, organizations can replicate this approach in different functions and business units by infusing analytics into the decision-making process and underpinning it to the operating model to leverage the power of analytics.