Connect. Learn. Share

Blog > February 2018 > ASG’s Predictions for Trending Topics at 2018 Gartner Data and Analytics Summit

ASG’s Predictions for Trending Topics at 2018 Gartner Data and Analytics Summit

As the 2018 Gartner Data and Analytics Summit approaches, attendees are beginning to buzz with curiosity about which industry trends will dominate discussion. For a preview of what we expect experts to say around the data management, analytics and governance event tracks, here are the hot topics that will likely drive the summit dialogue:
Data Management

Master data and why it’s important
Master Data Management (MDM) may be in trouble. At the least, it’s due for a significant rethink.
“Master data” is a buzz term circulating the IT sphere, but what does it really mean and why is it important? Master data encompasses the key business entities (people, places and things) across any given organization — including employees, customers, suppliers and products, office locations, charts of accounts and more. Because each of these are vital to the business, data quality and standardization are critical, and enterprises will need to ensure master data is accurate for systems to work together and for data to be shared. The challenge is that as the variety and complexity of data sources explode, it’s increasingly difficult to rationalize master data — in fact, the process may take so long and be so costly that it delays decision-making and puts compliance deadlines at risk.
Business benefits of a successful master data management program
When it’s working, MDM eliminates redundancy and redundancy-related costs by providing a “golden copy,” or a single reliable description of data, as well as a single place for changes to be made. By establishing one source of data with one structure, master data ensures consistency across content and analysis and simplifies data sharing between systems and across enterprises. With these capabilities in place, enterprises must outline new expectations for both digital and business exchanges.
Key discipline and technology components of a successful MDM program
A fully articulated Master Data Management (MDM) program comprises organization, strategy, business process and technology components. Technology components may be delivered through a single integrated solution or by the assembly of elements for a custom implementation — though businesses will need to determine which better suits their needs. Either approach must provide for loading, synchronizing and sharing master data.
Many required elements of an MDM program rely on a foundation of metadata management and data intelligence, including data modeling, business glossary and semantic support, information stewardship and data governance and data quality support. Without this underlying data intelligence platform, MDM would fail. To ensure MDM program success, businesses need to determine what other sub-components are most critical to analysis and reporting (e.g., reference data management, which manages slow-moving data) and find ways to implement them in the platform. This is where big data’s new metadata capability – the data catalog – will become a vital component in MDM programs. The ability to recognize master data in data lakes, tag it, associate it with all related data and make it available for use will be a key to MDM success.

The evolving role and practice of analytics
Analytics are quickly changing as the volume of available data grows beyond what people can practically work with. To help analysts find actionable insights, business leaders need to consider how increasing automation with artificial intelligence (AI) and machine learning can define what is available and identify associations of data elements.
The future of self-service data and analytics
Citizen data scientists are on the rise as data leaves the hands of IT and becomes more accessible to business analysts honing their analysis capabilities. Marketing departments will use these skilled business analysts to explore data lakes and other resources for insights into customer behavior, identifying opportunities to increase sales and create new revenue from data — a process that will be made easier as data management and reporting tools evolve. For now, data catalogs represent an important intersection where business and IT people can work together to define and describe available data — but the dialogue must shift to how it can be best used by the business.

The state of data and analytics governance today and where it is headed
The data and analytics landscape is diverse, consisting of some point solutions (addressing, for instance, governance but not data discovery), tools with exclusively internal governance capabilities and a select few that integrate governance, discovery and data intelligence. As the data estate becomes increasingly complex — and compliance with legal requirements such as the GDPR becomes more onerous — industry experts must anticipate and prepare for how risk-based data governance will inevitably become part of the data fabric.
To gain further insights on the Gartner Data and Analytics Summit expected dialogue, learn more about what ASG has to say about data management, analytics and governance. For more information or to register for the 2018 Gartner Data and Analytics Summit, visit the website.
Posted: 2/21/2018 7:16:54 AM by Rob Perry | with 0 comments

Blog post currently doesn't have any comments.