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Information Management

 

 

 

 

 

 

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1        ELT Involvement in Information Management

An organization cannot consider embracing analytics-based performance management methodologies without the involvement of its executive team. But what type of involvement from them is needed?

InfoManagement Direct, June 2, 2011 Gary Cokins

An organization cannot consider embracing analytics-based performance management methodologies without the involvement of its CEO, COO, CFO, CMO and other members of its executive team. But what type of involvement from them is needed?

I have been fortunate to have met with executives from more than 100 commercial and public sector organizations. Earlier this year, I visited with executives and/or their project teams from Canada, Belgium, the Netherlands and throughout Scandinavia. After these meetings, I reflected upon each executive’s attitude toward using business analytics and performance management methodologies, such as balanced scorecards with key performance indicators, strategy maps, customer profitability analysis, risk management, driver-based budgeting and rolling financial forecasts.

These experiences have led me to believe there are three types of executives when it comes to involvement in performance management methodology implementations: deep thinkers, pragmatists and motivators. I nickname them C-Sweet, C-Savvy and “Sea” Level executives.

1.1    What all Three Types Have in Common also Sets Them Apart

While each type of executive has a different approach to analytics-based performance management, all have one thing in common: planning horizons. In general, all executives look to the future with three planning horizons: near term (now or soon), intermediate term (the next three years) and long term (beyond three years).

However, each type of executive tends to focus on a different planning horizon, leading to the difference in whether they are considered a deep thinker, pragmatist or motivator.

1.1.1   Deep Thinkers: C-Sweet

These types of executives attempt to be visionaries and strategically plan for their organizations far into the future. The risk they take is that their governing boards of directors may not be patient or tolerant enough to allow the wisdom of their long-term plans to be realized. As a result, they may be placing themselves in jeopardy – especially if shorter-term financial results fall short of their board’s expected profits or the expectations of Wall Street analysts and the investment community.

These types of executives are admirable because they delegate strategy implementation responsibilities down to their line managers. That is, they muse about the future and rely on their team to connect operational processes and execution to their strategic objectives. It is their middle-level managers who implement and use the performance management methodologies. I like this type of leadership; I think of them as “sweet” because they exhibit caring and confidence in their management teams.

1.1.2   The Pragmatists: C-Savvy

These types of executives are typically in industries with shorter product life cycles (e.g., telecommunications) where technology is moving so quickly that they cannot risk thinking too far into the future. An example is the change from VHS tape to DVD to now Internet video downloads. Their risk is that they may miss an unforeseen turn in the road and spin out, causing a surprise decline in financial earnings. As a result, they manage closer to the operations and focus on the shorter term more than the deep thinkers.

However, the pragmatists do understand the value of implementing and integrating performance management methodologies. They realize the need for better organizational steering and control due to a complex, volatile and wired world that requires the ability to quickly react with great agility. Ideally, they prefer to be proactive and increasingly promote the use of business analytics in their organization.

I refer to them as the “C-savvy” executives, because their savvy is demonstrated through shrewdly observing that the combination of business analytics and enterprise performance management methodologies is the next big wave for organizational improvement.

1.2    The Motivators: Sea Level

Motivators are not necessarily micromanagers. They are big believers in the use of performance metrics, accountability and behavioural change management.

These types of executives are typically attracted to strategy mapping, balanced scorecards and dashboard components of the enterprise performance management framework. Strategy and performance measures are communicated to front-line employees, including performance measures with targets to motivate employees. This type of strategy communication allows motivators to monitor progress toward achieving strategic objectives.

I refer to this group as the “sea level” executives. They understand the importance of motivating their organization’s workforce not from a mountain top perch, but at the organization level where work gets done – at the organization’s sea level.

1.3    Some Realize Performance Management Value - And Some Don’t

Not every executive team sufficiently recognizes the value of and benefits from implementing analytics-based performance management methodologies. Forward-thinking executives view strategy formulation as magnitudes of importance above just having organizational effectiveness and good processes.

In contrast, there are other executives who believe that simply muddling along with conventional management methods (e.g., lean/Six Sigma, ERP, standard cost accounting) is enough to be a high-performance organization. Such executives don’t fall into any of the three categories of performance management-minded executives I’ve discussed.

Will the executives who simply use conventional management methods eventually regret this judgment? I believe their organizations will fall far below their potential, especially when compared to organizations led by deep thinkers, pragmatists or motivators.

Similar to Maslow’s famous hierarchy of needs, you start with basic needs – storage and protection – and progress to the stage where information becomes actionable and drives revenue growth

Information Management Newsletters, August 15, 2011

2        What is Information Management

T.M. Ravi

It used to be that “information” meant file boxes or filing cabinets containing paper-stuffed manila folders, stacks of slides or spools of microfiche. Today, however, information comes in all types, sizes and formats – everything from paper invoices to IM chat files to online videos and Tweets – some of which is created in a traditional manner (through enterprise applications) and some of which is user-generated both inside and outside the walls of the organization. This information moves in and out of the organization like the tides, surging across both public and private networks, created at both the core and the edge of the enterprise.

Many enterprises have embraced the rapid pace of technology, staying on the cutting edge. In adopting these hot new technologies from different vendors as they become available, they have benefited from process automation, workflow efficiencies and improved knowledge worker productivity. For many, however, information management has been an afterthought and addressed in an ad hoc manner. This results in increased data privacy risks, high costs of storing and managing information, and information liability due to poor compliance and discovery readiness.

Good information management combines technological innovation and intelligent processes around that information. This marriage of technology and processes delivers cost savings, compliance and data protection. Similar to Maslow’s famous hierarchy of needs, you start with basic needs – storage and protection – and progress to the stage where information becomes actionable and drives revenue growth. Following this methodical approach up to the pinnacle of information management nirvana calls for a step-by-step approach. And just as Maslow suggested in his hierarchy, organizations can meet higher-level needs only after they’ve satisfied the more foundational ones. Failure to follow these steps will result in, at best, only short-term gains and leave those larger goals unrealized.

The following is an overview of those needs – starting with the most basic (storage) and progressing to the aspirational (value) – to help organizations articulate their path to information management nirvana.

2.1    Storage

An enterprise should first focus on building a strong base of secure storage. Building a secure storage foundation with easy and reliable access to information when and where it is needed is a cornerstone of any successful information management strategy.

This foundation should be secure, scalable, and available and provide distributed coverage for today’s global enterprises. The key capabilities that the foundation has to provide are: capture, transport, retention, access and disposition. Information security and privacy must be assured by this foundation. Not just the storage of data but also the movement of data has to be secure. Secure transport of data is essential for verifiable and reportable chain-of-custody for both physical and electronic information – key metrics for demonstrating compliance.

In building the foundation, one must also consider scale. The explosion of information means the need for storage will always be bigger than what an organization can dream of. So the challenge becomes how to build for scale without needing to make a large investment in capacity that will go unused in the immediate future. Cost-effective scalability can be achieved by leveraging storage services that scale as the business needs grow. In addition to being cost-effective and scalable, storage should also be protected to reduce the risk of loss. Information has to be available and recoverable in the midst of various human and system errors, failures and catastrophic situations.

Storage may be on-premise or off-premise (cloud storage or offsite record centres) depending on the needs, maturity and size of the organization, as well as the type of storage application and the nature of information being stored. It should also address the storage of information across different media and formats, including tape and paper.

2.2    Efficiencies

Once enterprises have a solid foundation of storage, the focus can shift to how it can be managed by a more efficient, system-driven process. A process approach is essential for the repeatability and consistency needed to realize gains – lower costs, quicker access – over the long term. Processes should be system-driven to enable the automation that is necessary when dealing with large volumes of data, helping to reduce operational risks.

Information management in this step is “operationalised” to deliver on the business goals of improved efficiencies and reduced cost. The focus in this step is on processes that leverage both technology and the people.

Major efficiency improvements come once you understand what information you have, how it is utilized and how best to invest in the infrastructure to support it. When it comes to its value to the business, not all information is created equal; cost and risk play a central role in how information should be managed. Understanding the inherent types and categories of information will go a long way in reducing the cost of managing that information.

2.3    Governance

At this point, it becomes tempting to start focusing on the top line value of information. Without the appropriate controls, however, information can become a liability and not an asset. Leveraging the aforementioned secure foundation and cost efficiencies, an enterprise can now address the risks associated with their information. The risk of information is managed through the development of policies.

Policy control delivers information governance and helps organizations proactively manage information liability. When delivered at the global level, across the organization and the systems within, policy control can help pave the way toward realizing the value inherent in information while eliminating any risk factors.

All information in an organization should not be kept forever; doing so can put the business at risk. Proper classification of information – by format, by use, etc. – enables the development of granular policies for access, retention and destruction. Many organizations do not have defensible policies for unstructured information in place because of the complexity of classification and unrealistic policies that are impractical to enforce due to the sheer volume and speed of this information. Broader classification by application, department or user is easier to develop and automate than finer-grain classification that can be difficult to develop and implement. Relying on user-driven classification is unrealistic in most cases, given the large data volumes that users are dealing with. Developing policies prior to the introduction of a new class of information ensures that business risk can be proactively managed.

Some organizations launch into massive software integration projects to achieve policy integration, producing an unwieldy Frankenstein monster of integration. This leads to unrealized value and wasteful spending. What is critical at this step is holistic policy control across all information, applied consistently across information types, media and formats. This does not necessitate tight integration of systems but rather uniform policies across systems. A practical approach is to have integration as light as possible and as late as possible that still allows the enterprise to achieve policy goals. In other words, do not make policy control so cumbersome that organizations are tempted to skip to the realization of business value. A practical approach with prioritization to reduce risk is far more likely to succeed.

It’s important in this stage to involve multiple stakeholders in the organization to help drive needs. In addition to the IT staff, the key stakeholders are legal departments and record managers. In this stage, organizations also have the opportunity to implement best practices and technologies for discovery, compliance monitoring and supervision, archiving and records management.

2.4    Value

Information technologies have long struggled to demonstrate how they increase revenue. Automating the information workflow and enabling efficient information access can help make business processes more efficient. Improving business processes, whether customer facing or internal, can result in top-line value and competitive advantage for the business.

is to gain business insight and make information actionable. As an organization’s ability to analyse information grows, it gains deeper business insight. As this information analysis becomes more real time, the business has an opportunity to dynamically transform itself using that information. As a result, businesses learn how to create new revenue models and develop business agility.

While technologies for data warehousing and business intelligence have been around for a while, there is a fresh wave of interest in the industry for extracting value from data. What does an organization do with the large repository of data that they store? What about the data that organizations don’t bother to capture because they don’t know how to extract value from it? More and more, organizations are beginning to see that this vast amount of data should be used to unleash value for the business. Information management in the past helped IT reduce cost and mitigate risk; now it has a business purpose: to deliver top line value.

The innovation in collecting, processing, analysing and realizing value from large amounts of data has come not from the quantitative analysts on Wall Street, but from social media and search companies. They have shown that data is good – the more the better.

Every company can be a data-driven business. Companies can leverage their customer data, operational data and supplier data to move faster and make better decisions. Data will help improve customer-facing business processes, reduce time to market and make businesses agile.

In considering the hierarchy of needs for information management, an organization will never completely satisfy the lower-level needs of storage and efficiency. In fact, these needs will continue to evolve and grow with the evolution applications and infrastructure. However, an organization that goes about strategically addressing the lower-level needs will have the processes, culture and architecture in place to better adapt and evolve.

2.5    Key Findings

·                   performance management is what users want from BI investments

·                   business intelligence teams that have the biggest impact on performance have a shared

·                   teamwork-based relationship between IT and the business

·                   business intelligence and performance management initiatives need to align with data

·                   management and integration, and enterprise information management initiatives

2.6    Recommendations

·                   Create a BI competency centre that promotes a single architecture to manage

·                   performance and provide decision support across the enterprise

·                   Establish BI and PM platform standards by not only picking the right tools but also

·                   defining how they will be used and by whom

·                   deploy an enterprise wide metrics framework identifying a comprehensive set of

·                   performance metrics across all areas of the business, creating a workable plan to

·                   calculate these metrics and to provide transparency of the metrics to all stake holders

Today, a BI and PM initiative should take into account more than just the traditional use of analyst-driven BI applications; it should also include multiple initiatives to measure, manage and improve the performance of an individual, process, functional team or a business unit, or even the entire organization.

Recognizing where you are in the model can help you understand the incremental changes that are needed to raise your level of maturity and increase the value to the business of your BI efforts. We also encourage clients to evaluate their overall business maturity, and to use this model to assess the maturity of diverse departments or lines of business. Many may find that different parts of their business are at different levels of maturity, and this model can help identify these gaps and spur discussions among departments.

2.7    Level 1: Unaware

Companies at this level have not identified their vision, investment and commitment to BI, nor have they identified the use of information and analysis to help drive their business. We call this level "unaware," because no real BI initiative is in place, only one-off projects to satisfy individual requests for data. This level of maturity is also often viewed as "information anarchy," because, in most cases, there is a lack of internal control — resulting in inconsistent data across departments, which is often misinterpreted or has to be constantly modified to meet individual and departmental needs. There is heavy use of spreadsheets for analyses, and limited use of reporting tools. At this level, metrics are not effectively identified, defined or used. The business value of formalizing metrics and managing performance metrics is not understood. The IT- entric reporting team supports the administration of BI and information management initiatives without an identified sponsor. Funding comes from the IT budget and is charged to a cost centre.

The primary challenges at this level are to identify the business drivers and needs for supporting BI, getting commitment and resources to move forward, and understanding the current information management structure — including data sources, data quality, architecture and systems.

2.8    Level 2: Tactical

Companies at this level have started to invest in BI efforts. Users are generally a subset of managers and/or executives who mainly rely on data to drive tactical decisions, with the drivers generally being process efficiency or cost reduction. Although a subset of users may provide input into the requirements definition, generally, these users don't work directly with the team that is leading BI initiatives. There isn't a true sponsor for BI efforts, but senior IT executives, such as CIOs, that need information and/or provide information are the focal point for management and decision-making activities. In most cases, employees and managers use their own metrics to run their parts of the business or perform their job. These metrics are shared within a department, and may be used in management meetings to explain departmental performances, but they are not formally shared. Also, where departments may use similar or even common metrics, these are calculated and defined inconsistently. At this level, the need for a data inventory may be recognized and may be in process, but there is still often data inconsistency. Some systems are in place that enable a small number of users to receive standard reports and data online, but most tools, applications and data are in "silos," with insufficient formal skills training for users. Often, at this level, companies are using off-the-shelf software, with few or no modifications. Senior executives frequently lack confidence in the quality and consistency of the data, leading to many arguments over "whose data is right" in management meetings. This often results in insufficient funding and support for BI initiatives.

A major challenge that often faces organizations at this level is the lack of organizational structure and processes that provide business input, and the absence of a methodology for ensuring that the business remains involved throughout the entire evolutionary cycle of their initiatives. In addition, many organizations have huge infrastructure issues, stemming from having many disparate systems (data sources, tools and applications), which create concerns about the relevance and consistency of data and analysis.

Organizations also often find themselves continuously in response mode. Each time a new manager wants a new report, IT scrambles to develop one, which is generally not leveraged across the organization and/or driving business transformation.

2.9    Level 3: Focused

At the "focused" level, we start to see a stronger focus by senior executives on commitment to BI and PM. But the primary focus is on driving specific business initiatives, such as improving business effectiveness, aiding in marketing decisions and supporting financial reporting. The sponsor for these efforts may be a senior IT executive, such as the CIO, but it is more likely to be a business executive, such as the CFO or head of sales. At this level of maturity, metrics are formally defined to analyse specific departmental or functional performances. The IT department is tasked with creating a repository of metrics to enable senior management to analyse departmental performance, without relying on business managers. This most commonly occurs when demands arise for a management dashboard. The goal is to optimize departmental performance, but there is no formal linkage to broad enterprise objectives, which often results in inconsistent goals and metrics among departments.

Although the sponsor has a defined initiative for each specific department objective, the scope of the BI initiative still extends across multiple departments. Users are trained on the basics of the systems to access data. Often, funding will come from one or more business units or is charged back to other business units. At this level, we often begin to see the organizations forming a BICC that comprises business and IT professionals (see "Organizational Structure: Business Intelligence and Information Management"). However, BICCs are generally focused on specific applications or the use of information.

The most common scenarios at this level include those companies that have invested in financial consolidation and reporting systems, but that have not made equivalent investments in nonfinancial data, such as sales and product data. However, this data is not integrated and resides in "stovepiped" applications. These efforts are largely supported by specific analytic applications, such as corporate PM suites, marketing analytics and product PM that are implemented independently of each other. At this level, the company has achieved some solid success, and has realized business value from its BI and PM efforts. But these successes are generally focused on very specific parts of the business. They may support cross-functional decisions, but, again, are highly specific. The challenge is to extend this success more broadly, rather than it remaining one part of an organization's strategy. This, in turn, means evolving a BI and PM initiative across systems and architecture, and expanding the scope of the application and user base.

2.10   Level 4: Strategic

Companies at this level have a clearly defined business strategy with senior executive sponsors. They are driving their BI and PM strategy according to these overall strategic objectives. Often, at this level, companies focus on integrating BI and PM into critical business processes that support analytic applications. Information is made available to employees across the company, including senior financial planners, marketing executives, supply chain managers, HR managers, brand and product managers, promotion specialists, advertising and marketing communication managers, and marketing operations directors, based on their impact on overall strategic objectives. Often, the scope starts to expand outward to include suppliers, business partners and customers.

The sponsor is a senior business executive, such as the CFO, COO, the chief strategy officer (CSO) and, in rare cases, the CEO. These executives have the ability and the vision to directly influence overall business objectives. The CIO may also be the sponsor for a strategic BI and PM initiative, as long as the CIO is recognized and is able to directly influence the overall business objectives. The organization has formed a BICC, comprising business and IT professionals that work as a team, and has sufficient resources and funding to accomplish its goals.

At the strategic level, an enterprise framework of metrics has been deployed or developed that links financial goals and other strategic objectives to departmental, functional or operational metrics (see "Tutorial for Creating an Enterprise Metrics Framework"). This is where a cause and effect linkage model may be used (see "Tutorial for Using the Gartner Business Value Model to Create an Enterprise Metric Framework"). These metrics are consistently defined across the enterprise and allow various PM applications to be linked together through the metrics framework (see "Understand Performance Management to Better Manage Your Business").

The BI initiative encompasses business applications, IT operations tools, and desktop applications and has been expanded to encompass a wider range of PM applications. Data governance policies have been defined and are being managed. Data quality metrics are defined and data quality is monitored for consistency. Strategic data is trusted and acted on at the executive level to drive strategic change. Processes are in place to continue to evolve data quality as new and more data becomes integrated into BI and PM applications. Users are trained to access data and to apply it effectively when making strategic and tactical decisions.

The main challenge at this level is to execute and develop a balanced organizational structure, given the company's evolving business objectives and strategy. Many organizations also face challenges in building agility into BI and PM systems so that they remain effective as business needs change.

2.11   Level 5: Pervasive

At this level, BI and PM are pervasive across the business and across part of the entire corporate culture. Users at multiple levels within the company have access to the information and analysis needed to help drive business value and impact. Information architecture and application portfolios are implemented and managed, and BI and PM systems are integrated into business processes. Agility is also built into BI systems, so that they can adjust with changes to the business and in the delivery of information. As with the strategy level, the sponsor for BI efforts is a senior business executive, such as the CFO, COO, CSO or CEO, who is linked directly to and drives overall business objectives. The organization has also formed a proactive and dynamic BICC, comprising business and IT professionals (see "Toolkit: Eleven Best Practices for Supporting a BICC"). Not only is data governance in place, but information is trusted and acted on at multiple levels. Users are trained and measured on their ability to support data quality and data governance policies. At this level of maturity, unlike the strategic and focused levels, users at multiple levels of an organization have access to information and analysis that enables them to lead, discover, manage, innovate and decide to impact business performance. As a result, metrics are linked directly to individual performance goals/objectives. Closed-loop systems allow employees at all levels to make real-time business decisions, based on their potential impact on strategic goals. The scope of BI initiatives extends outside the organization to include suppliers, business partners and customers. Metrics are also shared in real time with customers and suppliers to strengthen business relationships. The challenge at this level is in continuing to be a best practices leader despite constant changes, such as merger and acquisition activity, executive reorganization or other major business disruptions. Companies may also face challenges in keeping their strategy up-to-date with evolving user needs and emerging technology. Organizations that are at or moving toward this maturity level need to evaluate and understand the costs and support the impact of their BI applications, tools and infrastructure becoming mission-critical. For example, the additional costs of supporting highly available systems and the availability of support services and resources to respond to diverse u

1.     Three Types of Executives in Performance Management

2.     The Path to Information Management Nirvana

3.     Maturity Model

 

3        Data Governance Maturity

3.1    Level 1 Data Governance Organisation – Aware

An Aware Data Governance Organisation knows that the organisation has issues around Data Governance but is doing little to respond to these issues. Awareness has typically come as the result of some major issues that have occurred that have been Data Governance-related. An organisation may also be at the Aware state if they are going through the process of moving to state where they can effectively address issues, but are only in the early stages of the programme.

3.2    Level 2 Data Governance Organisation – Reactive

A Reactive Data Governance Organisation is able to address some of its issues, but not until sometime after they have occurred. The organisation is not able to address root causes or predict when they are likely to occur. “Heroes” are often needed to address complex data quality issues and the impact of fixes done on a system-by-system level are often poorly understood.

3.3    Level 3 Data Governance Organisation – Proactive

A Proactive Data Governance Organisation can stop issues before they occur as they are empowered to address root cause problems. At this level, the organisation also conducts ongoing monitoring of data quality to issues that do occur can be resolved quickly.

3.4    Level 4 Data Governance Organisation – Managed

A Managed Data Governance Organisation has a mature set of information management practices. This organisation is not only able to proactively identify issues and address them, but defines its strategic technology direction in a manner focused on Information Development.

3.5    Level 5 Data Governance Organisation – Optimal

An Optimal Data Governance Organisation is also referred to as the Information Development Centre of Excellence. In this model, Information Development is treated as a core competency across strategy, people, process, organisation and technology.

 

4        Assigning Data Ownership

One of the tenets of Data Governance is that enterprise data doesn't "belong" to individuals. It is an asset that belongs to the enterprise. Still, it needs to be managed. Some organizations assign "owners" to data, while others shy away from the concept of data ownership. Let's look at both approaches.

4.1    Approach #1: Assigning Data Ownership

This approach acknowledges that enterprise data is "owned" by the enterprise rather than individuals or silos within the enterprise. However, accountabilities for working with that data must be assigned to roles in the organization, and individuals (or teams) fill these roles. So it is often convenient to designate "Data Owners" (typically from business groups rather than technical teams) who help coordinate those accountabilities.

In some organizations, this approach is used for Compliance and Access Management purposes. Data Owners are given the right to decide who can have access to enterprise data. They are involved in a process that is something like this:

4.     A person (staff member, contractor, partner, supplier, etc.) requests access to information

5.     A business resource (the Data Owner, the person's manager, etc.) gives the OK

6.     A technical resource (usually a DBA) physically grants permission to an application, database, or other data store containing the data. Often, the permission follows a CRUD schema (create, read, update, delete)

Meanwhile,

A) This process is aligned with enterprise compliance and data management efforts (privacy, information lifecycle management, data retention, eSecurity, controls management, etc.), and

B) Request/permissions documentation is collected and retained to satisfy compliance and operational goals

In some organizations, this Access Management function is administered by Data Governance. More often, it is managed through Information Security, Compliance, Privacy, Risk Management, or other groups. Before you decide to use the term "Data Owner" in your Data Governance and Stewardship program (or Enterprise Information Management program), you should understand your organization's approach to Access Management, whether it employs the concept of Data Owners, and what responsibilities they have.

If you do decide to employ the concept of Data Owners, it's critical that you clearly define responsibilities for Data Owners versus those for Data Stewards. Click here to see a list of responsibilities that might be assigned to Data Owners, Data Governors (if you use that term) Enterprise/Strategic Data Stewards, or Operational/Tactical Data Stewards.

4.2    Approach #2: Federated Responsibilities

Enterprise data, by its very nature, flows through an organization, touching many business and technical processes and being stored/moved/transformed by many IT systems. It can end up in uncounted numbers of reports, online displays, data feeds, and information products.

In small enterprises - or organizations with simple data flows - it may be possible to trace a type of data from creation/acquisition/collection through all its data flows. If so, it may be feasible to assign accountability for that data to an individual who would be responsible for ensuring that steps are taken to secure the data and to enforce quality rules and other types of data-related standards and rules.

However, this is not feasible for most large or complex enterprises. For them, the concept of Data Ownership may not be useful. Instead, they take another approach: federated data-related accountabilities.

In this approach, they first document data lineage (the path data has taken from its creation/acquisition to a specific system or report). Then they assign data-related accountabilities for a manageable number of segments to Data Stewards, SMEs, and/or Data Custodians (technical resources).

The federated accountabilities approach to stewardship brings several challenges:

1.      Documenting data lineage / data flows can be hard work. What do you do if this hasn't been finished? Sometimes you have to assign accountabilities first - and these include researching lineage.

2.     Assigning accountabilities may be difficult (just as it can be difficult to assign Data Stewardship accountabilities in general). If these accountabilities are in addition to other responsibilities, resources can feel over-burdened.

3.     Managing and coordinating the responsibilities of groups of responsible parties takes time and effort. You'll need documents to map roles & responsibilities to data.

4.     Designating a "point person" (a Data Owner, Data Steward, or other resource) may become necessary. This person would need to be familiar with data flows, would need to be able to discover who is assigned responsibilities for each segment of that flow, and would need to be able and willing to involve those resources in research, issue resolution, and impact analysis.

The federated accountabilities approach to data stewardship has a disadvantage: it's more complex. But it also has a significant advantage: it works.

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5        Data Ownership- Is there such a thing

Are you a Data Owner? Do you know any Data Owners? Do you know people who think they are Data Owners when you think they are not? Just what does it mean to be a Data Owner, anyway? These questions are on the minds of many people lately, but the answers are still being formulated.

Data Ownership was the focus of a Forum and Keynote address at the 2003 Annual Seminar of the Insurance Data Management Association (IDMA), held April 14 and 15 at the Westin Hotel in Philadelphia. The Forum preceded the Annual Seminar and was open to all seminar attendees. Data Ownership was considered from 3 perspectives: the company, the data collection organization, and the individual.

Tracy Spadola of Teradata kicked off the Forum with a discussion of the company perspective. She made a distinction between Data Ownership and Data Stewardship. Ownership suggests that an individual is empowered to make decisions alone, and that he can act to address the needs of his business unit. Owners design and control their own processes.

Stewardship, on the other hand, connotes a facilitation role. A steward uses a consistent, repeatable process to achieve alignment across the organization, and the needs of all areas are considered when making decisions.

A third role is that of Custodian. The Custodian is responsible for the physical security of the data. It is a role often played by IT people who work closely with Business Data Owners. Custodians administer the password access systems and provide backup and disaster recovery capabilities. These are people who create and enforce data standards and implement the physical data architecture.

 

Sara Schlenker, Published in TDAN.com July 2003

 

6        Value Proposition: Why do I need to do Data Governance now?

Recently an associate commented that perhaps we could better gain buy-in by answering the question “why now?” Adding the dimension of time was an interesting twist on spelling out the value proposition. After all, many businesses operations have been running fine for years with only ad-hoc data governance processes.  What would be the risk of delaying implementing Data Governance for one more year?

The obvious answer is the missed opportunities. These are two fold. First, you miss the opportunity to reap the benefit of a leaner and more agile information system environment. So your organization will continue to cobble together point solutions and struggle to fully integrate your data, systems and more importantly your business processes.

Second is the opportunity to implement Data Governance now while it is more manageable. What makes it more manageable is the lower number of people that need to adjust their behaviors to meet an enterprise standard. As the economy recovers, and your business grows, so will the staffing needed to meet the increased demands.

If you chose the path to invest inward deploying Data Governance now, you will have done so for less and prepared your team for growth. If you delayed, the existing problems you have due to the lack of Data Governance will persist and cost you more for each future occurrence. This impacts your profitability. More resources resolving the issues resulting from the lack of Data Governance implies fewer resources to implement new products and features. This impacts your efficiency.

Looking at the impact of delays from a risk management perspective, the delay significantly increases the risk to your future business. Not correcting bad practices today increases the risk of a significant adverse event in the future. This is analogous to living an unhealthy lifestyle. The cumulative effect of years of neglect will eventually take its toll and lead to a significant data breach or system malfunction, both of which are associated with real losses on the books.

If it helps your business case, adopt the perspective that the cost of Data Governance is on a par with the cost of insurance, where you spend a little now to mitigate the probability and the cost of a future failure. This is no different than the logic that drives the need for a project management office. So be a strategic leader, look at this lull in the economy as an opportunity to position you and your company for the future growth that will accompany economic recovery.

http://www.primedataconsulting.com/blogPDC/data-governance/2010/12/13/value-proposition-why-do-i-need-to-do-data-governance-now/

 

 

7        Why Data Governance? 

Organizations collect, process and store enormous amount of data,  A growing number of applications/systems, which support various lines of businesses, have been collecting more and more data through various channels. Mergers and acquisitions have made the situation even more complex and confusing when it comes to the management of data and business processes. 

Figure 1 shows an example of the way data assets grow over time.

A lack of data management practices (or data standardization processes) results in challenges that are faced by both IT and business. Often, individual applications or systems maintain and manage their own data. This results in data silos or data hubs (logical or physical). The solutions around data redundancies, metadata management and related data issues are always tactical in nature, whereas data anomalies are fixed by temporary patches or left unprocessed. Thus, issues around data integrity, consistency and accuracy make the data unreliable; typical factors impeding clean and conformed data include lack of standards, typos and duplicates, applications being ported from different platforms/languages, lack of standardized quality processes, historical/outdated data, unknown data and many others. 

Businesses need clean and conformed data to make better decisions. That’s why data and information quality has become an important factor in making better business decisions.

Such decisions are critical for the following areas:

1.      Customer services. 

2.      Product optimization.

3.      Regulatory compliance.

4.      Managing operation costs and risks.

Without  reliable data, business intelligence generated over a period of time is questionable. Inaccurate financial reports and audit reports will not only receive penalties but will have financial implications as well. 

Thus, processes/policies and rules that ensure enterprise-wide data asset management are needed. It becomes difficult to maintain and manage data without policies, strategies and dedicated efforts by the team across business functions. Data governance seeks to solve these problems, through information policies, data rules, guidelines for managing key data elements and assigning roles for accountabilities and responsibilities.

7.1    What is Data Governance?

Data governance is the processes or policies which guarantee that important data elements that can be trusted. A framework or set of processes is implemented throughout the enterprise, empowering the right people to take control of data and processes. A data governance program also includes technology, which helps identify and fix  data issues, resulting in fewer negative events due to poor data. It’s also about the communication, identifying common language that will bridge the gap between IT and business managers. 

In short, data governance is about management of the availability, usability, integrity and security of the data. Some of the key focus areas are data quality, data integration, policies around privacy, compliance and security, the data warehouse and BI, architecture integration and analysis and data access, in terms of archival, retrieval and storage. 

A data governance program will have certain drivers, such as:

• Identifying data anomalies and fixing them, particularly with regards to  key data assets around important business processes.
• Optimizing business processes and defining data rules. 
• Designating the right people responsible for information quality and security. 
• Creating policies for handling data, in case of initiative changes. 
• Coordinating with key business stakeholders to ensure that information policies support business objectives. 

The data governance program and initiatives around information quality management need to involve stakeholders representing a cross-functional team to fulfill the objectives. 

7.2    Stakeholder Involvement and How to Start Data Governance Initiatives

Issues around data management and information quality can be addressed using data governance initiatives. These initiatives need business and IT support, which means stakeholder involvement across the teams. These initiatives start with bringing people together for mutual understanding and educating them about doing the right things in the context of information quality.

Figure 2 shows how different stakeholders within business and IT see data governance differently

Starting a data governance initiative requires answers to three questions around benefits of the program:

1.      How will the program increase company revenue?

2.      How can the program lower costs?

3.      How can the program reduce the risks and address compliance issues?

It would be wise to start with initiatives where data needs to be fixed to minimize the risks or where business users have voiced complaints. Additional initiatives that can be considered may include a CRM implementation, a new data warehouse, BI initiatives and analysis of complaints management.

Information quality surveys can be launched across the line of businesses, with questions about data related to important business processes. For example, the billing and dispatch departments can be asked about the validity of the addresses of the customers, in terms of format. Does the customer get the communication or shipment in time or it is lost or returned? The inventory department can be asked whether the data always reflects the correct inventory amounts and types. The marketing department can be asked about their confidence in contacting potential customers using the email or phone numbers listed in the system. 

Prepare a case study for analysis of each problem statement, followed by a detailed business impact assessment. With this, information quality ROI can be calculated, which will significantly help make the case for whether data governance initiatives will be beneficial. 

Generating awareness among the business stake holders about latest trends, risks and competitors’ initiatives regarding information quality may also help in selling the importance of data governance program. 

To show ROI and get immediate attention, you need to start with small initiatives and share the results and a detailed impact assessment with key business users or stakeholders; this will help to sell the importance and need for a data governance program.

To fulfill business objectives, a data governance program needs to have a roadmap. This roadmap should clearly reflect the high-level approach and iterative nature of future engagements with business and IT. 

7.3    Data Governance Roadmap 

A data governance roadmap outlines the guidelines for its initiatives. It starts with identifying short-term or long-term business objectives around the initiative, which requires input from business and IT stakeholders to asses business and IT processes. (See Figure 3.)

A data governance approach lists phases and high-level activities, as shown inFigure 4.

A data governance forum receives input and assesses the impact on business processes, with the help of a cross-functional team. The forum  offers insight on policies, standards, metadata management and control. Information quality scorecards can also help assess the data governance initiatives, point out success factors and provide an executive management summary, thus attracting funding for more initiatives.

7.4    Practical Approach for Information Quality Scorecards 

An information quality scorecard is a tool used by the data governance team. It involves aggregating technical metrics with business metrics, thus helping business stakeholders remain aware of problems around key business processes and prioritize resolution. The scorecard helps the data governance forum analyze the impact of initiatives on enterprise-wide information policies, as well as compliance and regulations. (See Figure 5.)

7.5    Overview of Data Governance Team Structure

Data governance program initiatives can fulfill objectives only when the right people get involved with defined roles and responsibilities. Key roles  are:

1.      Project Sponsor: These can be C-level executives or business leaders who are driving the program. In the case of a financial services company, a CFO who has faced challenging situations related to risk and compliance due to poor quality data may be involved. A CMO may be involved when customer data is at stake and customer data standards are not met. The initial executive sponsor may be a business manager  when the program starts with small initiatives, and senior business leaders may get involved down the road when the program is implemented enterprise wide.

2.      Business Manager: Key business managers or subject matter experts get involved to provide the context of business process and data. They report on the impact of the data on related business process and provide recommendations about the key data elements. They also provide input on the scenarios that can be validated against the data. 

3.      Data Stewards: These people include programmers, data analysts, data architects and database or system administrators. Their key activities include designing metadata mappings, understanding business processes, defining data rules, data mining, creating data assessment reports, cleansing data issues, and managing and maintaining the related infrastructure. 

4.      Project Manager: The primary role of a project manager is to deliver the finished project. This role involves managing all the resources, communication, coordination, and risks and issues management.

Data governance programs consist of cross-functional teams for data issues management. 

1.      A team of business/IT analysts  manage and log  data issues, categorizing issues by line of business and coordinating with  respective business SMEs for resolution.

2.      A data governance forum looks into policies and standards, aligning business leaders on managing risks and compliance, approval for strategies and funding for projects that involve data cleansing/transformation or incorporating new systems/applications for the information quality assessment.

3.      A data team involves business analysts, data profiling analysts and programmers for the extraction of source data and infrastructure support.

7.6    Important Considerations around Key Challenges

The data governance program will face key challenges that may impact project timelines in large transformation programs.

1.      Access to data: Basic groundwork for data assessment starts with the challenges, such as identifying key data elements for assessment and their best source. Extracting data from a source and getting the access to live data is another big hurdle. Transformation programs in large organizations may require access to 100 percent live production data.

2.      Data assessment: Business SMEs and application SMEs should be aligned on metadata management, defining data validation rules and additional data analysis required to support data cleansing or data transformation during the resolution of issues. Care should be taken that the resolution, tactical or strategic, supports the policies and is signed off by the data governance forum. Tracking and monitoring  progress on these issues should be done with care.

3.      Workshops: Alignment of cross-functional team for data governance workshops is another big challenge. Availability of SMEs is important and can be a major issue. Business managers should be informed of resource requirements for workshops. Carefully planned workshops should have a clear agenda and involve key decision-makers. Clarification of queries and issues should be well-documented and signed off by key business stakeholders.

4.      Infrastructure (hardware platform and software licenses) management and support: It is necessary to coordinate with the IT team for the availability of the hardware platform, installation of required software licenses and application of latest patches. Vendor support can be another big challenge and must be closely managed. If care is not taken in mitigating these risks, data governance program timelines can be impacted. 

5.      Program management: It is necessary to inform the stakeholders about their roles/expectations upfront. The data governance program needs to align with key business stakeholders and business leaders regarding the roadmap, approach, high-level plan, scope and key dependencies.

Organizations worldwide have been facing tremendous challenges in information management. Information quality can be assured only through data governance initiatives and processes, which provide insight on data issues, resolution, standards and responsibilities. Forming cross-functional team within a data governance forum empowers the right set of people to take control of data assets and make the right decisions in the context of quality data. Clean, confirmed and complete data enables a business to make better decisions in order to achieve their business objectives. 

As we move along this journey, we need to assess and audit the current situation by referring to a data governance maturity model. This model does not criticize existing practices but provides guidance. (See Figure 6.) 

There is no single formula which will organize the enterprise perfectly or quickly. Enormous efforts across cross-functional areas need to be put in, and those efforts need to be rewarded to keep up momentum in the long run. Success only comes through personal responsibility and careful planning.

8        Five (5) Key Considerations for Data Governance

8.1    Accessing the Data

Basic groundwork for data assessment starts with identifying key data elements for assessment and their best source. Extracting data from a source and getting the access to live data is another big hurdle. Transformation programs in large organizations may require access to 100 percent live production data.

8.2    Assessing the Quality

Business SMEs and application SMEs should be aligned on metadata management, defining data validation rules and additional data analysis required to support data cleansing or data transformation during the resolution of issues. Care should be taken that the resolution, tactical or strategic, supports the policies and is signed off by the data governance forum. Tracking and monitoring progress on these issues should be done with care.

8.3    Workshop your Requirements

Alignment of a cross-functional team for data governance workshops is another big challenge. Availability of SMEs is important and can be a major issue. Business managers should be informed of resource requirements for workshops. Carefully planned workshops should have a clear agenda and involve key decision-makers. Clarification of queries and issues should be well-documented and signed off by key business stakeholders.

8.4    Support and Manage the Infrastructure

It is necessary to coordinate with the IT team for the availability of the hardware platform, installation of required software licenses and application of latest patches. Vendor support can be another big challenge and must be closely managed. If care is not taken in mitigating these risks, data governance program timelines can be impacted.

8.5    Outline Stakeholder Expectations

It is necessary to inform the stakeholders about their roles and expectations upfront. The data governance program needs to align with key business stakeholders and business leaders regarding the roadmap, approach, high-level plan, scope and key dependencies.

 

 

9        What is an Open Methodology Framework?

An Open Methodology Framework is a collaborative environment for building methods to solve complex issues impacting business, technology, and society.  The best methodologies provide repeatable approaches on how to do things well based on established techniques. MIKE2.0′s Open Methodology Framework goes beyond the standards, techniques and best practices common to most methodologies with three objectives:

1.     To Encourage Collaborative User Engagement

2.     To Provide a Framework for Innovation

3.     To Balance Release Stability with Continuous Improvement

We believe that this approach provides a successful framework accomplishing things in a better and collaborative fashion. What’s more, this approach allows for concurrent focus on both method and detailed technology artefacts. The emphasis is on emerging areas in which current methods and technologies lack maturity.

The Open Methodology Framework will be extended over time to include other projects. Another example of an open methodology is open-sustainability which applies many of these concepts to the area of sustainable development.

10  Revision History

Records of formal review shall be maintained for each approved version (integer value) listed below. The document Custodian shall maintain copies of each version listed below.

 

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