Understanding the Data Discovery Framework
A data discovery platform is business user-friendly: Typically, the data scientists work in the back offi ce assessing and monitoring volumes of data and produce reports, based on a hypothesis, such as, the success or failure of a product launch, customer satisfaction, campaign effectiveness, etc. for the benefi t of the key decision makers in an enterprise.
With data discovery, this is changing. An intuitive interface enables users to explore data without much training or expertise in any analytics tool. The proprietary platform is structured to store and model datagathered from disparate sources (like customer, production, supplier, location, market research data, etc.) while the built-in layers (as shown in Figure 1. – data collection, event hub, event correlation and syndication engine and visual front-end) obviate the need for aggregates, summaries and pre-calculations.
A visual dashboard provides an interactive interface that empowers the frontline users to make important decisions related to a business operation much faster than usual. This greatly improves an enterprise’s ability to perform analytics and engage in rapid decision making.
A data discovery platform creates a foundation for data security and protocols: Every enterprise has a set of enterprise security standards that are very rigid in terms of their data usage, data access by employees, types of data that shouldûbe accessible and more. However, as the new paradigm of decision-making evolves, components of data security and control, such as data governance, in an enterprise need to evolve as well.
The primary and most important requirement for building a strong foundation for enterprise data security is knowing and understanding the enterprise data and setting up data governance rules. This helps classify data and create data identity, which is the missing link for creating actionable data security and control policies.
Defi ning data within an enterprise (both internal and external) is crucial. It is important to know what kind of data an enterprise has, where it is stored, how and why is it stored there. This helps application of appropriate policies and controls for data protection.
Data discovery tools and software for visualization, integration, data migration, etc. help enterprises identify and locate sensitive structured and unstructured information and classify them. The entire process is automated, thus preventing anomalies.
BI Vendors Need Rethink
Current vendors who offer traditional BI systems, which comprise structured data warehouses and databases, time-consuming and expensive delivery models, will have to re-think about continuing to offer these products only as these are primarily IT driven and restrict deployment by specialised consultants.
The latest data and analytics tools are expected to provide opportunities to analyze data for the benefi t of customers and the market. It is not only about making changes in internal processes, but also about evolving the products and services model in a positive manner.
In short, data discovery tools are affecting business directly at decisionmaking levels unlike their predecessors that involved processing huge quantum of data, both structured and unstructured, to enable data specialists in making recommendations to business leaders.
Conclusion: Early Adopters
A few enterprises that recognized the importance of customer and market-facing analytics, pioneered the march into adopting data discovery tools. This helped in better understanding the relationships among various types of data as an essential core of a business process.
The two key roles that data discovery tools play are:
- They help determine the new products and services to be developed and launched in the market
- Leverage data and analytics for decision making at speed and scale
Early adopters of discovery tools, while launching products and services, aided by data-driven insights, are also experimenting and learning to make more complex and data-driven decisions at speed and scale. Before implementing a new decision-making process at the production level, enterprises need to run pilots to check the feasibility of the process. This will help them acquaint themselves with an effective discovery environment that would enable them to make rapid decisions and implement new offerings quickly in the longer run.