**Disclaimer – This post is simply an introduction to SharePoint governance software, Riadenia, which will be released shortly. The software will be released shortly after being QAed.**

SharePoint governance has been a subject that people have discussed forever, but it really didn’t seem to become such an important buzzword until the 2007 platform was released. I don’t know why that was, but I never heard it come up previously. There has been a lot written about it, from scripted guidance to tooling. Interestingly though, SharePoint governance, as well as computing governance, is for the most part super arbitrary so standards that attempt to define any “best practice” tend to fall woefully short. They don’t even make sense most of the time in terms of pragmatic application. For any meaningful progress in regards to SharePoint governance, the objective of reform must firstly be defined having regard to the standards that an organization wishes to achieve. As I see it, any undertaking would only be of value to an organization if its ultimate aim were to be the establishing of a framework that would allow for rules of governance. By this I mean a system in which everyone is subject to however remains sheltered from arbitrary governance standards.

In this way it should be stressed that firms should look at governance tooling and guidance not really ever as a completed solution but as a means of enabling them to better apply a SharePoint governance framework. This framework, importantly, would need to remain mutable. SharePoint governance, and it’s related tooling, is neither a project nor a technology. It sponsors a control framework for safeguarding your organization at a level that strikes a balance between business needs and protection needs. Basically your firm needs to have a solid framework in place before any governance automation technology could make a difference. These tools are created to enhance your systems not develop them.

Some might argue that implementing governance in SharePoint is as simple as setting basic IT SLA’s in place, pointing to the existence of some of the inherent features that constitute the wider system of the administration of collaborative (SharePoint) software, an honest and objective assessment would make it patently clear that this is no longer the case. Serious doubts have been cast over the competence and integrity of leveraging such basic features. No less significant is the very low level of confidence in the system as a whole, such confidence being a necessary prerequisite to its effectiveness. In view of this, an intention to address those factors have led to the belief that governance is arbitrary for the sake of there being no effective governance system. It also becomes apparent that the governance reform initiative must be approached on at least two equally important levels; the SharePoint framework and, for the sake of a better term, the human resource.

So what’s the problem? The real crux of SharePoint governance issues arise from the fact that people take canned SharePoint governance advice, and attempt implementation without tailoring towards very crucial enterprise aspects such as SharePoint deployment intention, company culture, and industry. Rarely will SharePoint governance aspects, outside of the most generic counsel, translate well. These needs are not met satisfactorily by a method tailored around informal recommendations behind closed doors. These factors underscore a need for a mechanism that in my view would best be embodied in an independent commission, automated and managed within the framework itself and ultimately in an automated fashion.

So what does all this esoteric crap actually mean? It entails balancing the practical, with the not so practical. There must be pragmatic objects for each object being governened, and this must in turn contains relevant thresholds that define the characteristic, and in a larger sense the limits, of that object. In terms of SharePoint, this is pretty easy to graft what this should be shooting for, for each object, within the context of the Riadenia – SharePoint Governance Automation, this means sites (**SPWeb**‘s) limits must be placed. The reason that **SPWeb**‘s are a practical target is because they represent a good middle tier proxy object, it isn’t as vast and untargetable as a **SPFarm**, **SPWebApplication** or **SPSite** but it isn’t as specific and narrow as a collection of governance-worthy objects like an **SPList**. Roping this back in, this problem, and the overall advised approach has nothing to do with the version of the software. Rather, this problems spans multiple version of it, even the objects being mentioned in the above are consistent with those present in the current, and last release (2003 didn’t have, for example, the **SPWebApplication** and **SPFarm** objects).

The thresholds themselves are nothing fantastic and mind-blowing. Ideally, to build profiles a definitive model can be built that tells you, for example, to set your thresholds **x** site administrators / securable site object is “good” in which would allow you to average these metrics and add (eg) 1 standard deviation above the average to help you identify better. Through application of the central limit theorum (conditions under which the mean of a sufficiently large number of independent random variables, each with finite mean and variance, will be approximately normally distributed) you can adjust your threshold metrics to select different sets. However, this is beyond the scope of my simple application! I will take it there one day though when I get the chance to get some feedback on current state.

One important piece of adaptive governance procedures is the introduction of some methods of forecasting. The main problem with this effort in large-scale SharePoint projects is the existence of optimism bias and strategic misrepresentation with project promoters. A consequence of such bias is a high incidence of cost overruns and benefit shortfalls in projects. Thus number of measures aimed at eliminating, or at least reducing, optimism bias and strategic misrepresentation in governance development must be introduced. The measures include changed governance structures and better planning methods. The aim is to ensure that decisions on whether to build projects or not are based on valid information about costs and benefits, instead of being based on misinformation as is often the case today. This is not to say that costs and benefits are or should be the only basis for deciding whether to build large projects. Clearly, forms of rationality other than economic rationality are at work in most projects and are balanced in the broader frame of public deliberation and decision making. But the costs and benefits of large-scale projects often run in the hundreds of millions of dollars, with risks correspondingly high. Without knowledge of such risks, decisions are likely to be flawed. When contemplating what planners can do to improve decision making, we need to distinguish between two fundamentally different situations: (1) planners and promoters consider it important to get forecasts of costs, benefits, and risks right, and (2) planners and promoters do not consider it important to get forecasts right, because optimistic forecasts are seen as a necessary means to getting projects started. The first situation is the easier one to deal with and here better methodology will go a long way in improving planning and decision making. The second situation is more difficult. Here changed incentives are essential in order to reward honesty and punish deception, where today’s incentives often do the exact opposite. Thus two main measures of reform will be considered below: (1) better forecasting methods, and (2) improved incentive structures, with the latter being more important.

Thus there are four types of forecasting for each SharePoint object under the governance umbrella introduced.

**NaÃ¯ve / Bayes**

The Naive Bayes algorithm is based on conditional probabilities. It uses Bayes’ Theorem, a formula that calculates a probability by counting the frequency of values and combinations of values in the historical data.

Bayes’ Theorem finds the probability of an event occurring given the probability of another event that has already occurred. If B represents the dependent event and A represents the prior event, Bayes’ theorem can be stated as follows.

Prob(B given A) = Prob(A and B)/Prob(A)

To calculate the probability of B given A, the algorithm counts the number of cases where A and B occur together and divides it by the number of cases where A occurs alone.

**Simple Moving Average**

A simple moving average is the easiest and most popular technical indicator.

The simple moving average is calculated by taking the arithmetic mean of a given set of data values. For example, the basic 5-day moving average of 5, 6, 7, 8, 9 is (5+6+7+8 +9)/5 =35/5 =7.0

As new values become available, the oldest data points must be dropped from the set and new data points must come in to replace them. For example, the basic 5-day moving average of 4, 5, 6, 7, 8, 9 is (4+5+6+7+8)/5 =30/5 =6.0

4 is the newest data point that has come to replace 9. Thus, the data set is constantly “moving” to account for new data as it becomes available. This ensures that only the current information is being accounted for.

**Weighted Moving Average**

A weighted moving average is simply a moving average that is weighted so that more recent values are more heavily weighted than values further in the past.

The commonest type of weighted moving average is exponential smoothing. The calculation is quite simple:

*P _{0} + Î±P_{1} + Î±^{2}P_{2} + Î±^{3}P_{3} + *

*â‹…â‹…â‹…*

*+*

*Î±*

^{n}*P*

_{n}+*â‹…â‹…â‹…*

where *Î±*, the smoothing factor, is more than zero and less than one, *P _{0}* is the latest value on which the moving average is being calculated and

*P*is the value

_{i}*i*periods previously (usually

*i*days ago).

**Exponential Smoothing**

This is a very popular scheme to produce a smoothed Time Series. Whereas in Single Moving Averages the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as the observation get older.

In other words, recent observations are given relatively more weight in forecasting than the older observations.

In the case of moving averages, the weights assigned to the observations are the same and are equal to 1/N. In exponential smoothing, however, there are one or more smoothing parameters to be determined (or estimated) and these choices determine the weights assigned to the observations.

This smoothing scheme begins by setting *S*_{2} to *y*_{1}, where *S _{i}* stands for smoothed observation or EWMA, and

*y*stands for the original observation. The subscripts refer to the time periods, 1, 2, …,

*n*. For the third period,

*S*

_{3}=

*y*

_{2}+ (1- )

*S*

_{2}; and so on. There is no

*S*

_{1}; the smoothed series starts with the smoothed version of the second observation.

**Adaptive Rate Smoothing**

Statistical forecasting technique that takes variations into account through a coefficient. This coefficient is allowed to fluctuate with time to reflect significant changes in the pattern of the activity or phenomenon being studied. Adaptive exponential smoothing is an extended version of exponential smoothing.

All these types of averages, for software to be complete, must and are baked into the final code. By keeping the forecasting approaches ambiguous, each of the concerning SharePoint governable objects can be targeted.

Next Post In Series >> Leveraged Metric Constraints And Building Governance Profiles **(coming soon!) **

Upcoming Posts In Series >> Using Riadenia™SharePoint Governance Automation **(coming soon!)**

Read More About Intial SharePoint Governance Software Experiments