A data catalog is an inventory of data assets within an organization, which can help data scientists and other stakeholders to collect, organize, access and enrich metadata that assists with overall data management. Without a data catalog, many business and IT leaders would struggle to identify the sources of data within their organizations, let alone where all that data resides.

Advanced storage solutions provide a solid foundation for data analytics initiatives. These include next-generation databases, data warehouses and data lakes. Next-generation databases are specifically built for speed and scale, while a data lake is a large pool of raw data. A data warehouse, by contrast, is a repository for structured, filtered data that has already been processed for specific purposes.

Data security should be a key component of any IT initiative, especially one centered on Big Data (where a breach could potentially expose massive amounts of information to cybercriminals). In fact, some observers, have dubbed highly sensitive information “toxic data,” due to its ability to harm an organization if it falls into the wrong hands.

Organizations must transform their stored data into a more easily accessible asset. Data cleansing solutions standardize data formats, and data merge solutions combine data from multiple sources. Master data management (MDM) tools allow business and IT leaders to create a single source of truth across their organizations. Without MDM tools in place, many organizations struggle to answer relatively basic questions.

Data governance encompasses the policies, processes and organizational structures that support data management and analytics. Organizations face governance challenges including where and how long to store data, who can access which data (and how they can access it), how data is shared across the organization and how to cull redundant records that contain identical information.

Data discovery tools, such as data catalogs, help organizations to arrive at a better and more complete understanding of what data assets exist in their environments. Only after engaging in data discovery can business and IT leaders begin to make an informed assessment about what sorts of objectives can be achieved using the information resources they already have.

As they adopt new data analytics tools and processes, organizations must shift their cultures in a way that embraces both employee experiences and data-driven insights. For instance, leaders should be careful not to discount the experience of sales team members, who have intimate knowledge of customers’ needs, and instead enhance that firsthand knowledge with insights derived from data analytics tools.

To power data analysis, organizations must invest in solutions such as data visualization tools and IT operational analytics (ITOA). ITOA can assist with functions such as root cause analysis, and it is frequently an early use case for organizations investing in data analytics for the first time.