Gone are the days when data was just an IT concern. Now, data analytics and business intelligence (BI) are crucial elements to making decisions across business. Most organizations recognize the value of BI and analytics, yet don’t receive the full benefits of it. That’s because they’re rushing past the first step – establishing data readiness.
Data readiness is data the business and business users can act upon. Employees throughout the company incorporate business-ready data in their work to make informed decisions, whether it’s tracking a customer’s success or onboarding a new employee. However, with larger and larger volumes of complex data, businesses need processes that can mine the full value from their data.
IT teams should focus on five key areas to accelerate data readiness and empower data self-service: developing knowledge graphs, implementing data catalogs, addressing governance issues, streamlining data integration, and finally, modernizing data pipelines.
Developing knowledge graphs
Most organizations have processes in place to organize and gather explicit data. Explicit data is generated by clear actions and provides straightforward conclusions, like when a customer buys a product or likes a post.
In contrast, implicit data is less concrete, but still provides vital information – actions like clicking links on a blog post or viewing a product but not purchasing it reveal important information about customer sentiment and experience. By connecting implicit and explicit data, businesses can make more informed, timely decisions. Knowledge graphs build a visual relationship between these data types. The graph can provide organizations with new insights on customer behavior, sentiment, and experience, and build a more complete picture of business data.
Implementing data catalogs
To build a knowledge graph, administrators need a way to locate both implicit and explicit data easily. Administrators also need a way to inventory and monitor this data, so they can track data lineage, fix data irregularities, and reduce the risks of security issues. This is where data catalogs come in.
Data catalogs are a centralized inventory of data within the organization. Metadata supplements the catalog to help users identify where the data is located. For example, marketing departments can connect customer data and advertising data to determine the best time to advertise to certain groups. Meanwhile, insurance sales can view how different data elements relate to certain policies, then analyze any trends across the data.
Addressing governance issues
Businesses can also accelerate data readiness by identifying and addressing governance weaknesses. Data governance is the processes and technologies implemented within an organization to manage and secure data. Data governance encompasses how data is collected, analyzed, and shared.
Data governance is an essential component of securing and managing data – however, organizations often struggle with governing large volumes of data, especially when this data is stored in both the cloud and on-premises. Without clear data governance guidelines in place, users can doubt their data, affecting confidence in their BI and analytics. With good data governance in place, the overall quality of the data improves as does employees’ confidence in it.
Improving data integration
For large volumes of data, data catalogs can record the data’s lineage, allowing organizations to track its life cycle and who has accessed the data within the organization. Data catalogs also take inventory of data across cloud and on-premises storage.
Implementing master data management (MDM) processes such as information integration models can help organizations organize higher-level data elements. By combining data catalogs and MDM, organizations can use progress mapping to create visualizations of where the data is created, sourced, and accessed throughout the organization. This helps improve data integration across the company, providing everyone access to up-to-date and trusted data.
Modernizing data pipelines
In addition to addressing problems in data governance, organizations must modernize their data pipelines, or the processes that stream data across the organization to a set location. Previously, organizations simply transferred raw data into a data lake, which inhibited data retrieval and created more delays in actually utilizing the data. Now, technologies such as automated data preparation modernize data pipelines.
Data preparation is the process that takes raw data from a source, and then cleans, transforms, and enriches the data to a usable state. Clearly, data preparation is crucial to businesses – it leads to data self-service across the organization, and empowers business users to use available data to make more strategic decisions.
To streamline data preparation, users can use artificial intelligence (AI) and automation. AI can scope errors and inconsistencies across large amounts of data, which is a key part of data cleansing. Automation technologies standardize and speed up data preparation processes, creating a consistent method of organizing and preparing data.
As the uses for data continue to grow, businesses must ensure their data is actually usable. By focusing on these five core areas of data readiness, organizations can effectively use BI and analytics applications to inform business decisions.
About the Author
Ayush Parashar is vice president of engineering at Boomi, a leading provider of cloud-based platform as a service (iPaaS). He brings over 20 years of product development experience to leading the engineering team for Boomi’s Data Catalog & Preparation products. He joined Boomi through the acquisition of Unifi Software, where as co-founder and VP of engineering, he built the product and engineering team from the ground up. Before Unifi, Ayush was part of the founding team at Greenplum, which was acquired by EMC and spun-off with Pivotal.
Sign up for the free insideAI News newsletter.
Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1
Speak Your Mind