4 Barriers to Leveraging Big Data for Operational Innovation

4 Barriers to Leveraging Big Data for Operational Innovation

Barrier #1: Data Silos Within Organization

Applying Big Data to product, service and process innovation will be challenging if data remains segregated in various departmental, systems or channel silos. To create a complete picture, the data needs to come together. For example, the customer service department and marketing department might each uncover information that together would provide deep insights about possible new products and services. However, in the absence of a single source of truth, both departments might have incompatible metrics so they don’t know how to pool their knowledge in an actionable manner. A Modern Data Management Platform moves organizations beyond swamps of segregated data sources and disconnected business processes, to a single platform for data convergence and collaborative curation. This helps break down data and communication silos across departments to foster all kind of innovations - be it incremental, radical or disruptive. Progressive companies build data-driven applications to disseminate information to relevant people on the ground who make decisions, such as identifying product gaps earlier in the production cycle, eliminating features that customers don’t want or adding features they want to pay a premium for!

Barrier #2: Data Silos Across Organizations

By nature, innovation tends to cross organizational boundaries. To only see innovation as “a product” would be myopic. New services, processes, alliances and business models equally contribute toward making a meaningful innovation ecosystem. Yet, many companies are buried in their internal data and systems, and fail to capture the value of externally available data. A Modern Data Management Platform allows businesses to integrate their internal data with third-party data as well as public and social data. Mining the right data sources fundamentally enables companies to gain a competitive advantage and create next-generation products relevant to future market demands. A recent Forrester report “What You Need To Go From Data Rich To Insights Driven” corroborates the above view -  “As firms advance and find new value by integrating internal and exogenous data sources, they will in turn monetize this value by making data products and services available to their customer and partner ecosystems thus pushing the business to pursue new customers, new markets, and new product lines.

Barrier #3: Rigid Data Model

Reducing time to market is extremely important for an organization which values innovation and wants to stay ahead of competition. Applications including CRM, ERP, HR and financial are widely deployed for core business processes. However, legacy design and the process-specific nature of such applications provide rigid data structures that hinders business agility. Modern data management offers a new hybrid space of platform and applications built for a big data and digital enterprise architecture, designed for agility. Free from the shackles of relational data modeling, modern data management completely changes the paradigm by combining data together, without having to first design a data model. It captures and automatically models a variety of structured and unstructured master, reference, transaction and activity data, without any volume restrictions. The end result of this convergence is data with context and relevance--a new way to derive insight from all the data that impacts a business and its innovation roadmap.

Barrier #4: Unreliable & Out-of-Context Data

As the volume of data grows at a much faster rate than can be manually reviewed and rectified, companies are looking for advanced ways to solve the data quality problem. Modern data management leverages machine learning to monitor, manage and improve the data quality to stay ahead of data challenges. Machine learning not only helps determine and improve data quality, but also enriches the data with relevant insights and provides intelligent recommended actions for data quality and operational improvements. Besides, machine learning coupled with graph technology enable a contextual data model. This allows companies to create of a single common data foundation and visualize and present information for the business user in the context of their work, role or department. More importantly, it puts every business user at the helm of the growth, innovation and digital transformation trajectory.