Consolidate enterprise data
Because most data gathered and stored by an organization is captured for a specific purpose by a particular organization, many enterprises have incomplete knowledge of the data they already possess and the state
it’s in. So a key first step is to use an enterprise-wide survey to do a complete inventory of the data within the organization. Then map the data, its location and its format. Over time, the enterprise
should create a system that tracks the status and movement of data—automatically updating so the enterprise data map is always up to date.
“Potentially, you can take all of these processes and make them cognitive,” Bhandari said, “automating discovery of data, tracking updates, learning, and preparing the data pipeline for your applications.”
Most business executives are familiar with the data warehouse, a repository that contains and organizes data from disparate systems, integrates data of different types, and makes it available to a diverse set
of users via a variety of analytics tools. In this era of big data and cognitive systems, a new type of repository is emerging—the data lake. These structures are capable of scaling massively and handling
both structured and unstructured data.
When the data is brought into the data lake, it is curated, annotated and, in some cases, reformatted so analytics tools can process it efficiently. Initially, much of the curating and annotating will be done
manually by subject matter experts and data scientists but, over time, a cognitive system can do this work with little or no human assistance.
A wide variety of data can be stored in a data lake, but none will be more strategic than what we call master data. Master data is information about the people, places and things that are critical to a business.
Master data is connected to transactions, but while transactions live in particular business units and functions, master data cuts across many of them.
Contextual information is also important data to place in a data lake. For instance, retailers want to know not only what a customer bought, but also where they bought it, what the weather was like when they
bought it, and what they tweeted when they bought it. By connecting master data and transactional data with this contextual information, businesses can get a 360-degree view of a customer. They know who
each individual customer is and can better predict what that person might want or do in the future.
Since most enterprises use significant amounts of data purchased from third parties, it’s critical to consider this material when setting up systems for consolidating and managing data. Business units and functional
organizations may subscribe to the same or similar data—sometimes from the same providers. Consolidating enterprise-wide licenses will make third-party data easier to find, reduce costs and, potentially,
enable the licensing of some of those data sets to clients.