Pre-Decision Support

The industry of data warehousing has single-handedly raised the awareness of the need for corporate data management. Data warehousing has also raised the awareness of the need for meta data
management (which has become an integral part of data management) and more precisely meta data management that supports the data warehouse.

Decision support databases are often called data warehouses. Companies put themselves through the rigorous tasks of creating data warehouses to, simply put, provide the ability to make better
decisions from corporate data. Less attention is paid to the need for a second (earlier) level of decision support that is very necessary. This level of decision support can be called pre-decision

Pre-decision support is a name that I use to label “support for decision makers before they make decisions” from the data in the data warehouse. Much of this support comes through the use of meta
data about the data warehouse. Let me explain further…

In most cases, warehouse end-users ask specific questions about warehouse data that, if left unanswered, can jeopardize the success of a decision support development project. Most individuals want
to know simple information about the warehouse data, such as, what types of data exist, how the data is named, defined, and referenced, and how the data can be selected. There are also end-users
that want to know more complex information about warehouse data, such as, what business rules were used to select the data, how the data was mapped, transformed, cleansed, and moved to the data
warehouse, and how the data is audited and balanced. Perhaps, THIS information may be equally or more important to some of the warehouse end-users than the actual data itself. All of this
information is contained in meta data.

Without some level of pre-decision support through the use of this meta data, warehouse end-users are left guessing on what data exists and what data should and can be used to help them to make
better decisions. Without this support, these same users spend more time performing ‘data-hunting’ activities, or at a minimum, they become less inclined to take full advantage of the data in the
warehouse. This dampens the results of the work and expense that goes into building the data warehouse. In these situations, return on investment in the warehouse suffers and the decision support
environment falls short of addressing the business and warehouse developer’s expectations and requirements.

Before decision makers and data analysts make decisions from data, they are required to make decisions on selecting data for their analysis. Before decision makers can select the data for their
analysis, they must understand and trust the data. One way to improve trust is through improved documentation.

Enterprise data asset catalogs (meta data repositories or databases) are a logical place to store documentation about warehouse data. Companies often shy away from producing data asset catalogs
(and thus pre-decision support) because of the expense in time, money, and people involved in developing such a catalog. Many of these same companies also end up asking questions about why the data
warehouse is not being used to its greatest potential. Many data warehouse consultants will tell you that the two are clearly related.

Warehouse meta data that populates an enterprise data asset catalog can come from a variety of sources. Tools that contain meta data include data modeling tools, data extraction tools, data
cleansing tools, data reporting tools, data base management tools, data security tools, and so on. Almost every tool in the warehouse development process (and application development process) makes
use of meta data and stores it in some type of tool-based repository.

Tool based repositories can be proprietary in nature (meta data format is not made available to the purchaser) or they can be provide an open architecture (meta data structure is known to the
purchaser). It is recommended that this determination be made prior to selecting the tools for your organization.

These repositories provide adequate support for the activities of the specific tools, but often do not address the broad scope of meta data required to supply the level of pre-decision support
defined earlier. In most companies, more than one tool is used in the development of a data warehouse. Progressive warehouse and meta data managers understand the meta data of each tool and toolset
and make the required meta data available to warehouse end-users in the form of pre-decision support.

Uses of meta data are often rooted in the data warehouse development process. However, uses of meta data reach beyond the warehouse into areas of data quality, data accountability, data security,
application development, object management, and year 2000 initiatives.

The statement is often made that “I wish I had better data from which to make my decision”. Do not be surprised if those around you begin saying “I wish I had better meta data to know which data I need to use to make better decisions”. Pre-decision support through the use of meta data will enable most organizations to ease the decision maker’s pain and provide the company with better decision making power.

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Robert S. Seiner

Robert S. Seiner

Robert (Bob) S. Seiner is the President and Principal of KIK Consulting & Educational Services and the Publisher Emeritus of The Data Administration Newsletter. Seiner is a thought-leader in the fields of data governance and metadata management. KIK (which stands for “knowledge is king”) offers consulting, mentoring and educational services focused on Non-Invasive Data Governance, data stewardship, data management and metadata management solutions. Seiner is the author of the industry’s top selling book on data governance – Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success (Technics Publications 2014) and the followup book - Non-Invasive Data Governance Strikes Again: Gaining Experience and Perspective (Technics 2023), and has hosted the popular monthly webinar series on data governance called Real-World Data Governance (w Dataversity) since 2012. Seiner holds the position of Adjunct Faculty and Instructor for the Carnegie Mellon University Heinz College Chief Data Officer Executive Education program.

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