Actionable Information Governance

 A Practical Approach To
Corporate Information Governance

It sounds obvious that deciding what it is that you want to govern is essential - but information is a complex beast with many facets. I find it useful to think about the capabilities that I need to have. These are the basic parameters that determine my scope - and the nature of the challenges in delivering them. In my world of corporate business intelligence I believe that there are 26 capabilities related to information management. I like to group them into 8 categories:

 

Now you don't have to include all 26 capabilities into your scope. For many reasons it is possible to exclude one or more. You would generally do this because:

• The organisation is not mature enough to develop the capability. An example may be where business intelligence is new to the organisation and the strategic decision is made to concentrate on governing only structured data important to management reporting. In this case (for example) the Content Management capability may be out of scope.
• Budget constraints.
• You outsource parts of your corporate BI. An example may be where all application development is outsourced to another company. In this case the Data Integration capability may be out of scope.

 To explain what each of these capabilities are, here is a brief definition in the following sections.

IM Capabilities

Strategy Group

1. Strategy Development

 Create and execute the information management strategy in the organisation. The strategy includes the processes, procedures, policies, principles, technologies, and architecture that manage data from definition to destruction, which includes transformation, governance, quality, security, and availability throughout its life cycle.

Definitions of service-level agreement (SLA) requirements are in scope.

2. Information Architecture

 An architecture improves our use of information across the full information life-cycle. In the long term, an architecture reference model will focus on the flow of data through various data management layers. These include infrastructure, data sources, data rationalisation and data movement, as well as data usage.

The architecture supports the organisation of data across various databases and applications, based on business requirements. It enables data standardisation and integration across the enterprise, not just for one or two databases or data sources. A formal data management reference architecture will deliver higher service levels and support newer applications and platforms more easily. It includes comprehensive data definitions, data structures, and data integrity rules across the enterprise. It ensures that businesses use data in a consistent manner throughout.

Trust Group

3. Data Quality Management

Test data quality using a consistent national data quality framework and ensure remediation is done when required. Data quality management ensures that enterprise data used by business stakeholders supports critical business processes and decisions with no reservations as to its relevance, freshness, accuracy, integrity, and other previously agreed-on aspects of quality.

4. Data Governance

Create a successful organisation that leverages analytics for competitive advantage by providing business insight, enabling better decision making, and driving strategy.

5. Master Data Management

Create and manage information about business data, especially the national business glossary as a critical business capability. This delivers trusted, reconciled, and consistent views of master data to a wide variety of mission-critical investments.

6. Reference Data Management

Create and manage reference information (Sales, Publishing, Revenue Group and Classification) hierarchies as a critical business capability. This delivers a consistent view of data across business units and the enterprise as a whole.

Develop Group

7. IM Tool Selection & Information Life-cycle Management

Determine business requirements for information management and select the business tools that best satisfy these. Encourage standardisation of tools and techniques across the enterprise to minimize costs and improve consistency of our key numbers used in management reports.

Data quality software that provides the enabling technology for implementing many of the data quality rules, policies, and processes defined through these efforts are also included.

Information Life-cycle Management (ILM) consists of policies to deliver effective management of information throughout its useful life.

8. Training and Accreditation

Deliver training for business and project users of the national business intelligence tools, standard management reports and key measures.

9. Data Integration

Data integration is the process of combining data from various sources and making it more meaningful to the business. Integration happens in intervals from batch to real time depending on requirements.

Data integration is to support business objectives that include a single version of the truth, loading of a data warehouse, application migrations or upgrades, BI, and other business requirements.

10. Data Warehousing Platform

IT development and management of the main management reporting data repositories. This includes management of data integration activities, the content and organisation of the business data stored. Data Warehouses deliver actionable information to business users.

11. Database Design and Data Classification

Classify data into broad categories so that critical business information is identified and protected.

12. Data Modelling

A data model is used to structure data in a manner that’s easily understood by data architects, data analysts, DBAs and application developers. The objective is to have the data leveraged by applications and processes.

Business experts must be able to understand and manage the use of conceptual, logical, and physical models because they are a key determinant of any data’s definition.

13. IM Application Development

Promote enterprise apps standardisation, migration, and consolidation; delivering business insights through data warehousing, business intelligence, and advanced analytics; reducing compliance risk through improved financial reporting, transparency, and auditability; and optimizing business processes to improve efficiency.

Manage and promote use of enterprise analytic database engines.

Many divisions across an enterprise run critical business functions on Excel spreadsheets and Access databases. IT builds and operates many IM applications such as data warehouses, data marts and reporting suites.

IM application development is to support (where appropriate) business developers through guidelines that the business use to determine the costs and benefits of IT or business developed solutions.

A business developer is a businessperson, not a professional application developer, who develops or configures an application to support business functions.

Where business development is not appropriate, IM applications are to be developed using existing BI tools and national analytic database engines. IT and their 3^rd party partners will deliver all non-business developer IM applications.

Secure Group

14. Information Security

Ensure that derived data and calculated measures are kept secure throughout the entire information lifecycle.

15. Data Security

Ensure that data is kept secure throughout the entire information lifecycle.

16. Database Security

Ensure that databases are kept secure throughout the entire information lifecycle.

Operate Group

17. Database Management

Ensure that the design, implementation, maintenance and repair of the national analytic database engines meet the agreed SLAs.

18. Infrastructure Management

Ensures that the BI tools and IM applications are available for use by the business, perform as required by SLAs and incidents are managed appropriately.

Organise Group

19. Content Management

Improve the capture, management, and storage of unstructured and semi-structured content so that a greater number of users can extract greater value from content.

Create and encourage the use of a content analysis process to summarize, add metadata, or detect patterns in content collections, so that people (and systems) can search and categorize content more effectively and manage it more efficiently. Content that lacks descriptive metadata (i.e., unstructured information) is hard to isolate, find, promote, and control.

Identify functionality or other opportunities for improving integration with systems to support content targeting, mobile distribution, social interaction, integrated analytics and optimization.

20. Document Management

Improve the capture, management, and storage of electronic documents, digital assets (especially images and multimedia) and/or images of paper documents.

Analyse Group

21. Advanced Analytics

Similar to Data Mining but where end users have access to application interfaces that answer business questions without the need for the user to understand advanced analytic techniques. These techniques are largely hidden from the end users and are a 'black box'.

22. Business Intelligence

Monitor and basic analysis of operations by creating and managing regular management reporting and applying standard analytic techniques that answer management questions faster and with greater insight than currently. It also includes controlling access to data and reports.

23. Data Mining

Discover deep insights and Explore big data by creating and managing ad-hoc management reporting and analysis using where appropriate standard analytic techniques that answer management questions faster and with greater insight than currently.

Act Group

24. Decision Management

Aims to improve decisions across the organisation to impart precision, consistency, and agility in decisions made.

25. Performance Management

To ensure that organisational goals are consistently met in an effective and efficient manner.

26 Collaboration and Change

Deliver platforms that encourage and support the sharing of information and improve decision making across the enterprise.

Choosing Your Scope

The above capabilities are a lot to manage if IM is something of an emerging discipline in your organisation. The good news is that prioritising and setting some out of scope is a common approach.

Below is an example of how one organisation has restricted the scope of IM and how they split responsibilities between business and IT. This is just one example and there is no 'right' answer. The right fit for your organisation depends on people, processes, technology, maturity, industry and culture. 

 

In the coming days I will round off my thoughts on information governance by talking about 

• Organisation Models
• Governance Processes. 

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[Misha]

HI. Thanks. Yeah as the motor spins up, even for an individual track read, you get uslluay one byte of garbage right at the beginning, and then IIRC about 13K bytes of zeros. This translates to roughly 208 milliseconds of delay. With the way I handle the data, garbage at the beginning is OK anyways. I basically search for the very first sector header, and since reads always (well, almost always) starts in the middle of the data portion of the sector, any initial data whether it be good data from the middle of a sector or startup data from the spin-up is treated the same. ie not used.My earlier code (two sources of interrupt) used the idle-handling code to virtually eliminate this. Since there are no edges happening during all the zeros, the code handles it as if the drive was seeking to the next sector, for instance.David's idea, the latest version of my code, is edge-activated , and it measures the distance(time) between edges. Once again, since there are no edges, the very next edge of valid data will result in the time being huge or at least just plain incorrect. So a long run of no activity is easy to deal with.My next plan is to embed the actual errors into the real data as illegal patterns, so at least I know WHERE and WHEN the errors occurred. Pick two patterns, one that means time between edges was too small, and one that means it was too large. This way I'll be able to see if the problem happens consistently in one area, or follows or trails common bytes.I was also thinking about having the SX do the work of making sure the data is byte-aligned before sending it to the PC. It would do this by detecting a bit-shifted SYNC, and fixing both the sync and the following data. I have plenty of time between edges, minimum 4us, and my routine is currently tiny. This gives me +/- 3us of extra processing time. This shouldn't be too hard. I'd just have to start storing an extra byte or two. Then I'd need to check those bytes against an array, which would be indexed by the number of bits it needs shifted by.

[Steve Bennett]

Hi Monica. These capabilities have been slowly forming themselves in my mind over the last year. There are a number of related posts in my blog and you can search them using rollyo search engine that appears on the right hand of the browser window (just above the tweetstream).

Thanks for the feedback.

[Monica Cerna Rodriguez]

Hmmm... That's interesting. Where did you first hear about this? Do you have other blog posts I can take a look at?Monica Cerna RodriguezMK Partners IncSalesforce Consulting Experts