Data Management

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Data Management is a critical element of any organization and the fuel for Planning and Budgeting. Data relavance and quality must be maintained in order for the crucial business elements (people, processes, and technology) to be properly linked and aligned with core capabilities.

Viewpoint A

Summary: Data is the fuel for planning and budgeting, and the management and relevance of data is critical to creating an actionable, timely Plan/Budget that is understood and supported by the participants. Data Management should consider: level of detail, relevance, quality, quantity, uses within the process, choices and trade offs, collecting and validating, and lastly consolidation for decision making. Building a connection between data gathered and resulting Plan/Budget is key to insure participates and managers are vested in the process and the results. More...

Viewpoint B

Summary: Data Management encompasses the people, processes and procedures required to create a consistent, enterprise view of an organisation's data in order to increase consistency and confidence in decision making and increase the value of data to the organization. Effective Data Management is critical to help the organization focus on core capabilities and to build linkages across the crucial elements of business (people, processes and technology). More...

Recommendations, Benchmarks, Resources, Implications


Viewpoint A

Data is the fuel for planning and budgeting. Decisions about resources and targets are based on having relevant and sufficient data as inputs. Those decisions are, themselves, data that determine the success or failure of the enterprise during the fiscal year.

Past operations present an obvious and important source of data for planning and budgeting. Very often such data is highly relevant and of good quality – those who generated the data on operations are often the direct beneficiaries of the planning and budgeting decisions and so they have an incentive to get the numbers right. Sometimes, however, the data from direct operations is not directly observable (such as when costs can not be metered), and this presents a challenge to planners. Either future conditions have to be forecast, which presents a large set of challenges to officials, or indirect costs have to be calculated and allocated. The allocation of costs may be widely accepted, but often there are contentions as to the appropriate basis for allocations based on historical or operational factors.

Level of Detail:

Plans and budgets can appear to be page after page of numbers with numbing detail. The best plans and budgets, however, contain data that is meaningful for managers to operate the organization. If the organization collects more data than is used to make meaningful decisions, then the planning and budgeting process, and its associated analysis, is overly burdensome on managers. Alternatively, if planners and budgeters do not insist on collecting timely and sufficient data, then the decisions reflected in the plan and budget will lack value. In addition, managers will benefit from a plan and budget that contains sufficient detail to support decisions they will make during the cycle.

Level of Relevance:

Almost no individual data point is, in and of itself, significant in the planning and budgeting process. It is the relationship among data points – trends and correlations – that makes data relevant for drawing conclusions. If there is too little data to make confident interpretations of data, then its use in planning and budgeting may result in conclusions that are not reliable. Therefore, detail matters. So, too, does some determination of how certain the plan and budget are based on the reliability of the conclusions drawn from data. To the extent that conclusions are uncertain is the degree to which the plan and budget needs to be flexible. Furthermore, the organization’s tolerance for risks/unknowns in its plan and budget will come into play in decisions that are reflected in the plan or budget. In this way, then, budgets are not merely functions of data, but also of the organization’s belief in the quality of the decisions made based on the data. If members of the organizations believe that the data does not support choices made because of too much uncertainty, then the budget will lack value as a guide to operations.

Data may be insufficient because it does not relate to expectations about the future, or because there are too few data points to draw reliable conclusions, or because technology or the environment has changed in such a way to make past observations less relevant. Even with these or other weaknesses, if managers are willing to overlook the difficulties, either because of the ability to make course corrections or because of the expectation that the organization is able to overcome lack of data with robust operations, then the fact of this level of “buy-in” may make the data problems inconsequential.

Quality of data:

There are times, such as with formal forecasting models, that the quality of data is important. However, there are other times when data needs to be good enough only to make threshold decisions. More data suggests to many that the data itself is accurate, or that analysis based on that data is reliable. That conclusion may or may not be true.

The lack of certain types of data may make forecasts and other analysis less reliable or relevant. Data from outside sources, such as suppliers, customers, or other stakeholders, may be insufficient, biased, or gamed, and so must either be used with caution or discounted appropriately. Also, data may need to be used that is somewhat outdated because the planning and budgeting cycle may not cleanly align with the cycle of business. If the data in question is critical to the operation of the organization, then the process will need to be sufficiently flexible to make mid course corrections.

Quantity of data:

For the analysts and forecasters there is rarely “too much” data available to them. For these “number crunchers” the challenge is when there is too little data to make reliable forecasts of the future. However, for those who produce the data, often the operational managers within the organization, “too much” data means that they believe they are spending too much effort to provide information to planners and budgeters. Often, such managers are not shown the connection between their contribution and the decisions that are made based on that data. When this happens, the managers perceive the costs of providing the data, but are not made aware of its value. Therefore, successful planners and budgeters provide transparency and insights to those in the organization to justify their call for data.

Uses of data in planning and budgeting:

Analysis and forecasting is intended to support decisions about planning and budgeting. Very often, such analysis is highly consequential for decision makers, and so there is a strong perception that the analysis and underlying data add value to the planning and budgeting process. Certainly, those who make the decisions perceive that value, but others, who have the burden of only providing data, may not perceive such value. Moreover, to the extent that members of the organization may question the reliability of the data used in planning and budgeting, they may also question the decisions that are based on it. For example, some data may be important but not irrefutable or certain, such as conclusions about full costs and where they are generated. In this example, the planners and budgeters may make important assumptions about cost allocations, or other variables of importance, but some managers in operating areas may resist such interpretations. Similarly, some data that would be valuable is not available to analysts because of confidentiality due to competitive sensitivities or other concerns.

Choices made based on conclusions from data:

Planning and budgeting decisions are typically made with analysis based on data provided within the organization. However, since decisions are influenced by considerations beyond the data – typically called “politics” – the link between data collection and final planning and budgeting decisions may not be robust. When data is not seen as driving budgets, managers are sometimes discouraged when providing future data or in operating under the resultant budget. Managers and other data providers may respond to a perception of politicized budgeting and planning by gaming how they provide critical data. For example, the allocation of costs is typically an important consideration for planners. Managers who account for those allocations, therefore, have significant influence on the process. Their input is critical to driving meaningful analysis and plans.

Even if managers are sincere in the provision of data, there are still challenges in making use of the resultant analysis. Often, the analysis can generate a false sense of precision in the face of uncertainty about the future. Moreover, in the rush to complete the planning and budgeting process, analysis may be hasty, and other niceties, such as documenting decisions and analysis, may receive insufficient attention.

Since analysis is almost never fully accurate, and operations do not reliably follow from prediction, budgets are not “done” as much as they are overcome by events and must be adjusted or abandoned as tools. However, for many decisions the original budget may well be “good enough.” Some activities, like hiring or capital investment, may require different levels of resolution in the analysis than other decisions, such as whether to close part of the enterprise.

Collecting and validating data:

Much of the data used in planning and budgeting is contributed by managers within the organization. Data such as costs, demand, and other variables are often best provided by managers who are most knowledgeable about the operations of the organization. Such data is most likely to be accurate, timely and relevant. When decisions are based on the analysis of such data, those decisions have a good chance of being reasonably correct. Therefore, it can be concluded that the budget should reflect, in some significant way, the data provided by the organization’s managers. However, the managers who are subsequently subject to the budget may not see how their insights and contributions influenced it. This is due to a lack of traceability of the assumptions and conclusions in the plan relative to the underlying data. In order to enhance the credibility of the budget, and the confidence of managers subject to it, it should provide sufficient detail to allow managers to understand how their data was used and how conclusions were reached or changed as a result of their contributions.

The process of collecting data from the organization should be considered by those responsible for planning and budgeting. When managers fail to see a connection between the information they provide and the resulting plan or budget, then they may not feel vested in the process and may not respond to calls for data with due care. On the other hand, when managers see a very strong connection between their own contribution of information and the results of the process, then they have an incentive to game the process – hoping to influence the plan or budget in a predictable way based on their input. These considerations of either failing to feel connected to the process or feeling that they can selectively influence the outcome, may generate a great deal of inaccurate or selective information that may seem more like noise to planners than useful data.

Manipulating data:

Data must be transformed in some way in order for it to be analyzed. Very often, large amounts of data must be used in order for the analysis to be meaningful to the organization. Therefore, being able to store and access large amounts of data is a prerequisite for effective planning and budgeting. Since the data in question often changes over relatively brief periods of time, a system must be instituted to keep data current. However, planners and budgeters typically use off-line data stores and non-dynamic software that can neither appropriately track the obsolescence of data or update it automatically. Complicating matters further is the fact that data may be combined from a number of sources, and may have different meanings to different observers. Unless the assumptions underlying the data and analysis are recorded, and are able to be maintained in the system, analysts can be faced with many possible interpretations of reality in the organization.

Consolidating data:

Plans and budgets are built up of analysis of data. They serve to consolidate and transform information about operations in the past or currently under way into predictions about the future. For some organizations and environments, there is little difference between the next plan and the last cycle’s reality. But, other organizations experience a great deal of change, and their plans and budgets can contain the essence of truth about how the organization performs that can be valuable to managers. However, there are a variety of ways in which plans and budgets can abstract away the reality that some organizational managers perceive. For example, by consolidating and transforming information, managers may not be able to see the link between what they do and the effectiveness of the larger organization or component. Also, the manager may have control over only a small component while the budget may call for a level of performance from a larger organization that the individual manager can not control. Likewise, the budget may reflect dimensions of operations, and targets for them, that a manager may not be able to control or even monitor, such as in matrixed organizations. The aggregated nature of many budgets can make it difficult for managers to understand how high level decisions should influence their own decisions. If a budget calls for a change in suppliers, costs, or revenue, for example, individual managers may not be able to understand whether they are expected to do anything differently. The fact that such managers may have fiduciary control over resources means that they are being held accountable for performance, but the plan and budget may assume that managers are able to coordinate their efforts and outcomes when the managers believe that those plans and budgets do not give them sufficient guidance. (dz)

Viewpoint B

Data management encompasses the people, processes and technology required to create a consistent, enterprise view of an organisation's data in order to increase consistency and confidence in decision making and increase the value of data to the organization. It is the key to an organization's total Performance Management and integration of planning and budget. Figure 1 sets out the connection and tools that make this possible.

Data management initiatives improve data quality by focusing data's accuracy, accessibility, consistency, and completeness, among other metrics. Effectiveness usually is driven by executive leadership, project management, line-of-business managers, and data stewards, along with methodologies for tracking and improving enterprise data.

Figure 1
Data management initiatives are usually driven by desires to comply with the law, or the to use enterprise data to improve knowledge-worker efficiency. Most large companies have many applications and databases that can't easily share information. Therefore, knowledge-workers within large organizations often don't have access to the information they need to best do their jobs. When they do have the data, the data quality may be lacking. By setting up standards for data management and governance, these problems can be mitigated.

In the Planning and Budget world, data is critical and the quality and management of that data for use in decision-making is essential to an organizations success. Figure 2 sets out the movement and transition from Data to Action.

Data management issues revolve around a series of issues that need to be addressed to drive success and value to organizations. Some of those issues might be: 1. Lack of Executive Buy-in: A key point is to keep executive sponsors up-to-date and engaged by having a communication plan. One can do this through traditional status reporting as well as drop-in meetings and updates when there is success.

Figure 2

Figure 2
2. Not Having a Proper Foundation: Starting the process of better data management before having the proper foundation will likely lead to failure. A proper foundation includes proper data management for the organization, data models, metadata, etc.

3. No Metrics: You need to know where you are, where you have been, and where you are going. Keep metrics on the scope, progress, maturity model, dollars saved, dollars earned, risk mitigated, and anything else that can be measured -- the more tangible, the better. Metrics keep people interested, both the stewards and management. 4. Poor Planning: Proper planning is key to initiating effective data management. One can start by putting together a communication plan, framing the scope, talking with members of the effected business units, etc. Each organization is different, but it is important to get all the red tape out of the way initially. 5. Being Viewed as ‘Overhead’: It is very important for data management to be viewed as a necessity - an integral part of the organizations growth and stability.

Figure 3

Figure 3

Recommendations

  • A strategy for data storage and access is a prerequisite for effective planning and budgeting.
  • Use benchmark information as an input into the budgeting/planning process.
  • Create a consistent, enterprise view of the organisation's data as a foundation for Planning & Budgeting.
  • Leverage operational, financial and process data for Planning & Budgeting.
  • Ideally, data should have operational uses beyond the Planning & Budgeting process.
  • While, ideally, the Planning & Budgeting process should not create new data collection, it may highlight the need for additional data collection that has significant operational value.
  • Continuously evaluate the data being utilitzed to ensure ongoing timeliness, relevance, and value.
  • Quantity of data should not be viewed as synonymous with quality of data and a large quantity of data can be viewed as being “precise” when it is not.
  • Have a willingness to develop a budget that is usable and robust despite a lack of “complete” data and a willingness to accept a budget that is not “done” because it lacks certain data elements. Don't let perfect be the enemy of good.
  • Include documentation of the meaning and sources of data elements. This will promote a common understanding of data elements and promote data collection standards.

Benchmarks

  • Your input is welcome here

Resources

  • Your input is welcome here

Implications

1) Level of planning is too detailed 2) Budgets contain lots of detail 3) It includes allocations that I can’t control 4) Need to keep track of lots of changing data 5) Too little relevant detail 6) Too much effort 7) The budget should reflect submitters numbers 8) Inability to link individual and organization performance 9) Too little value 10) The appearance of value being added 11) Allocations are important to control or influence 12) More data communicated does not increase usefulness 13) Consumption rates are of value in budgeting 14) Allocations are important to budgeting 15) Lack of buy-in on numbers 16) Allocations of non-controllable expenses 17) Shared services allocations 18) Implies accuracy 19) Creates noise in the organization 20) Implies the budget is “done” 21) Too much data 22) Detail matters 23) Detail is available that is relevant 24) Lack of demand data/customer forecast 25) Budget contain truths that are valuable 26) Data that is difficult to get is valuable 27) Lack of true end to end cost accountability 28) Implies knowledge of connection between cost and accountability 29) Implies knowledge of accountability measures 30) Lack of ownership/buy-in 31) By the time it’s done, I don’t even recognize my numbers 32) Traceability is relevant and important 33) Matrix organization may not control all 34) Too complex 35) All budgeters should influence allocations 36) Budgets do not reflect functional detail 37) Managers held accountable for things beyond their control 38) Too much data can confuse 39) Don’t confuse me with the facts!! 40) Data does not drive budgets as it should 41) Too little data 42) There are many versions of budgets 43) Boundaries not clear on “budgeting” 44) Budgeting needs demand data and forecasts 45) Lack of translation from high level driver 46) Poor tools 47) Too much game playing 48) Multiple versions of the truth 49) Budgets need an appropriate amount of detail and supporting information 50) Communications needs increased dramatically 51) Too little information 52) Lack of automation 53) Money driven down low, but decisions made higher 54) The process is different for different decisions 55) Confidentiality of decisions, i.e., classified, closure, capital 56) All budgeters can influence allocations 57) Budgets consolidate information 58) The submitter should understand adjustments made 59) Lack of documentation in creation 60) Complex accounting system 61) What should we include in costs? 62) Access to data is difficult 63) Information changes overtime 64) Budget time line is not aligned with available data 65) Lack of consumption rates 66) False precision in the face of uncertainty 67) Forward pricing rates/frequent updates 68) Has too much detail 69) Consumption rates are available at some point 70) Data needs to be relevant and obtainable 71) There is a correct amount of data 72) There needs to be a flexible budget



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