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Understanding Your Data Landscape

Posted by Micheline Casey

2

Have any of you ever played the arcade game, Whac-a-Mole? For those of you who are unfamiliar with it, a typical Whac-A-Mole machine consists of a large, waist-level cabinet with five holes in its top and a large, soft, black mallet. Each hole contains a single plastic mole and the machinery necessary to move it up and down. Once the game starts, the moles will begin to pop up from their holes at random. The object of the game is to force the individual moles back into their holes by hitting them directly on the head with the mallet, thereby adding to the player’s score.

I bring up Whac-a-Mole because anytime I start down the path of what is supposed to be a new data integration project, inevitably, a number of other ‘like’ projects pop up, seemingly out of nowhere. Trying to control them is often nearly impossible. It’s always interesting to me to watch the amazement from project team members when they realize that if there had been communication and coordination across the company, that they would have know about these other projects. But usually there’s not, and so what is happening across an organization’s data landscape is often little understood by the business areas to whom having access to high quality data is critically important.

Visibility into, and transparency of, an organization’s data landscape – from an enterprise perspective – is critical to the success of everything from data integration/SOA to data governance to master data management to data quality to security and privacy compliance. You can’t govern what you don’t know you have; you can’t secure and protect what you don’t know you have (or you spend too much on security because you can’t properly take a risk-based approach); you can’t integrate and optimize what you don’t know you have; and, it’s hard to develop master data if you don’t know where all the data of a certain type lives in the organization. And in a world where customer intimacy and experience is ruling the day, if you aren’t aware of and connecting all the data you have on your customers, could you do more harm than good?

Capturing information about your organization’s data landscape – and its inter-connected ecosystem – is a fairly straightforward process, though time-consuming the first time out of the gate if it’s never been done before. But it’s more than just a straight-forward inventorying of assets. There are lots of other metadata that should be collected and known about the landscape to bring the game-changing value.

Other important items to know about your data landscape include:

  • The alignment of data assets to the organization, functional areas, processes, and services
  • Assignment of stewardship responsibilities
  • Authoritative data sources
  • Data quality metrics – accuracy, integrity, currency
  • Data security information – criticality, integrity, availability, access rights
  • Definitions, rules, policies, standards, compliance environment
  • Relationships and upstream/downstream impacts
  • Information exchange mechanisms

Strong enterprise architecture practices and metadata repository can assist in linking data assets to the business and services that those assets support. This allows for slicing and dicing of data in ways to support DG, standards, policies, DQ, portfolio management, and performance management. It also supports understanding of the interconnectedness of data assets across the environment, including externally.

Increasing the agility and speed-to-market of an organization are just a couple benefits of having full visibility into the enterprise data landscape. Other benefits include:

  • Identify opportunities and gaps
  • Understand risks
  • Optimize data and identify and reduce redundancies
  • Understand impact risks of changes ‘to the assembly line’
  • Define authorities and responsibilities
  • Improve overall return on investment in enterprise data assets

If your organization hasn’t done an inventory of its data assets, it can be overwhelming to know where to start. Start small, in manageable chunks, in ways that make sense for your organization, based on need and priority.

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Posted in Enterprise Information Management

Tagged Assets, Data management, Data quality, Data security, Enterprise architecture, Metadata

Jun·04

Metadata – Much more than ‘Data about Data’

Posted by Micheline Casey

0

Metadata has been on my mind recently in a number of areas. I happen to think that a strong metadata tool is one of the most important component pieces of an overall data strategy, and can deliver a lot of value beyond what people generally think about in terms of metadata. Yes, it’s information about the data that an organization has. But a sophisticated metadata tool that is well implemented can provide value that goes beyond this.

If the right tool is implemented well, what you should get out of it is a central application for the following:

  • enterprise business model
  • enterprise business terms and definitions
  • enterprise business metrics
  • enterprise conceptual and logical data models
  • enterprise architecture
  • technology portfolio management
  • security classifications
  • compliance reporting
  • user self-service and self-discovery

In looking at tools, two most critical items that go beyond the normal capabilities model are 1) the flexibility and adaptability of the tool (how customizable will it be in the your environment, as you want to add new metadata elements or get into new lines of business, etc.) and 2) the ease at which data can be extracted (not just viewed) out of the tool for integration into other systems (via open APIs, CSV files, etc) or imported from another system (via the same) into the tool. As a self-service/self-discovery model for internal users, integration with internal collaboration tools such as sharepoint or wikis is important.

If your organization is starting from scratch, don’t feel overwhelmed about populating the tool. Throughout your organization, there is already tribal knowledge about systems, applications, and databases. Outside of the traditional IT group, the cyber security teams usually have a lot of great information that can also be leveraged to populate the tool once it is implemented.

Lastly, strong processes and policies are needed to ensure that the metadata tool is populated with the right information, by those stewards who have the authority, and that the information is kept current.

Posted in Data Governance, Enterprise Information Management

Tagged Data governance, Enterprise architecture, Metadata

Mar·24

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About Me

Micheline Casey is Principal at CDO, LLC, a boutique consultancy supporting the development of large scale, enterprise information management, identity and access management, and data governance strategic plans and implementation efforts. Prior to CDO, LLC, Ms. Casey was the first state Chief Data Officer in the country, and part of the Governor’s Office in the State of Colorado. Ms. Casey’s emphasis is on large scale, enterprise-wide, policy rich projects, and she brings strengths in business strategy, data management, data governance, and data security. Her work has been profiled in publications such as Public CIO magazine, and she was recently named on the 2011 Top 25 Information Managers list by Information Management magazine. She has almost 20 years experience in the technology industry, working in strategy, marketing, and business development roles.

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