The first thing I would like to do is dispel a myth that many people believe. That is, being information-enabled or competing with data means analytics or BI. This is only partially true.
Analytics is one of the methods an organization uses to compete on information. For example, with analytics you can analyze buying behavior and leverage the information to better promote products. To truly be information-enabled, an organization must control the information across operational (transaction) systems as well as analytic solutions.
In the world of analytics, most organizations invest a significant amount of time and effort cleansing data from operational systems before it moves into a data warehouse. Thus, enabling higher quality analytics where reporting can be performed. However, the “cleansing” effort is rarely reflected back into the source/operational systems. This plays into the unwritten rule of IT that bad data doubles at the rate of good data.
All of us have experienced issues with data not being properly managed and cleansed across operational systems. For example, have you:
- Received duplicate mailings/emails from a company
- Received a call from a company prospecting you when you are already a customer
- Had to tell the sales rep which products of theirs you actually own
All of these examples are symptoms of operational systems where data has not been managed (or possibly process issues). These happen to be examples of negative experiences. We should also consider the positive potential outcomes of well managed data such as greater cross-sell/upsell and increased customer loyalty.
To effectively compete, there must be a strategy around your data. Although we all agree that “the business owns the data,” IT must have its house in order to support a company becoming information-enabled.
For IT, it all starts with Data Architecture. Unfortunately, in most IT circles, the function of data architecture is poorly defined and most often does not exist.
In short, the Data Architecture function is more responsible for strategy than execution. Within our team, the execution is managed by the Enterprise Data Management function which I will discuss in my next blog. The Data Architecture function is responsible for both structured and unstructured information. For our purposes, we will focus on structured information. This function defines the strategy for:
- Data Governance – Data Governance is the practice of managing data as a corporate asset across the enterprise. It involves the processes, policies, standards, organization, and technologies required to manage and ensure the availability, accessibility, quality, consistency, auditability, and security of data in a company or institution. This team oversees:
o Business Intelligence
o Master Data Management
o Metadata Management
o Other data management strategies
- Master Data Management – Master data is the official, consistent set of identifiers, extended attributes and hierarchies that play a key role in the core operation of the enterprise.
- Metadata Management – Structured information that describes, locates, and facilitates the access, usage and management of an information asset. Metadata management is an end-to-end process for ensuring that metadata is properly created and maintained.
- Data Integration – The strategy for moving information for an organization regardless of locale or source.
Besides Data Architecture, my next set of blogs will address the question, how do organizations become “information-enabled”.