11 Enterprise Performance Management Best Practices - Planning Phase

This article continues where we left off discussing the eight performance management best practices in the defining phase of the Lifecycle Performance Management Model. The Lifecycle Performance Management Model is an enterprise framework that is centered on 35 best practices. These best practices span across the five phases of the performance life-cycle: defining, planning, executing, monitoring and reporting. This article is the second of a series of five discussing the performance management best practices within Lifecycle Performance Management, and will focus on the planning phase.

The focus of the planning phase is to start the buzz and get your organization prepared for the cultural changes that will take place during your successful performance initiative. Best practices in the planning phase enable you to gain employee acceptance into the performance initiative and put employees into a high performance mindset. They also include base-lining current performance and setting future goals, breaking down functional barriers, identifying key processes that drive business success, and ensuring a successful performance management implementation through training.

1. Employee Acceptance Management

Employee Acceptance Management is the process of gaining employee buy-in by emphasizing performance expectations from the top level down. Employee Acceptance Management involves transforming employees into a high performance mindset, communicating employee expectations and enabling them to understand the impact that their specific role has on the success of the organization.

2. Performance Management Planning

Performance Management Planning is the practice of defining the performance strategy and
prioritizing activities according to that strategy-to ensure operational alignment with organizational goals. Performance Management Planning involves planning, budgeting, forecasting and allocating resources to support strategy and achieve optimal execution. The Performance Management Plan includes consolidating, monitoring, and reporting on performance outcomes for management, regulatory, and statutory purposes. The ultimate goal of Performance Management Planning is the ability to plan and budget in real-time with dynamic plans that provide real-time feedback to everyone who is part of the process.

3. Time Management (Planning versus Implementing)

Planning is an essential item on the critical path of every project. Our studies have shown that cutting corners on planning can triple the cost and time to implement enterprise level projects. Planning requires adequate information about the current and target states and accurate estimates of the time and financial investments required to perform all the steps necessary for change.

Planning also involves putting together a team of committed and motivated individuals with defined team roles, outlining all tasks, assigning responsibilities, and proactively managing and mitigating risks. The planning process should include the development of a vision/scope
document so that each team member understands the project vision, goals, objectives, schedule, and risks. The planning team should allow adequate time for team members to understand, investigate, document, and communicate prior to design and implementation.

4. Leadership Development

Leadership Development is the strategic investment in, and utilization of the human capital within the organization. The practice of Leadership Development focuses on the development of leadership as a process. With the rapid rate of change in our global economy, leadership has taken on the critical role of adaptation and innovation in the workplace. As companies restructure their business processes and employees, they need solid leadership training to communicate effectively, influence others, maximize creativity, and analyze your business. How leadership is demonstrated within an organization will determine how successful that organization will be and how successful those who follow will become.

5. Employee Training

Employee training is one of the most powerful cost reduction drivers. Our research shows that the under-trained employee consumes two to six times the amount of technical support (including peer support) than an adequately trained user. Employee training should be performed on systems and applications, being careful to match the training that is delivered in relation to the employee's job. Training should include a mix of instructor-led classroom training, computer-based training, and just-in-time training to help increase user productivity and reduce support costs.

6. Staff Motivation

A motivated staff is one that will operate as a team and will pitch in when needed to solve any problem or challenge at hand. They will often exceed expectations and provide critical back up for each other. A motivated staff works harder to meet the goals set by the organization.

7. Automated Asset Management

Electronically supported life-cycle driven asset process. Automated asset management consists of electronically supported procurement, automated inventory, and centralized data repository that are available to financial, administrative, technical planners, system administrators, and the service desk. Managed data within the asset management system consists of contract terms, hardware inventory, software inventory, accounting, maintenance records, change history, support history, and other technical and financial information.

8. Systems Scalability

Systems Scalability is a technology infrastructure that can logically and physically increase in performance and capacity with continuity to meet reasonable growth and change over time. A scalable architecture contains a strategic migration plan for continuous growth and progress. Commitment to scalable architectures enables the roll-out of homogeneous hardware and application platforms across users and departments with different processing requirements, while providing technical staff with a common platform to support.

9. Capacity Planning

Capacity planning is a process by which the capacity of the network and assets is measured, compared against requirements, and adjusted as appropriate. The process of capacity planning involves mapping new initiatives to existing infrastructure, understanding the cost
dynamics of network bandwidth and storage, memory, and other system resources.

10. Enterprise Policy Management

Enterprise policy management is a managed user environment in which a network or desktop administrator can control, with rules-based logic, which applications, settings, network resources, databases, and other IT assets a user can use. This environment is defined by user ID and is not necessarily machine specific. It is typically implemented by user profiles maintained at the server and synchronized with the client device that a user is logged onto.

Enterprise policy management precludes the user from making changes to the system; such as introducing unauthorized software or changing settings that may cause conflict with other system resources. As well, a managed environment controls the ease of use of the desktop, providing a common set of applications and access for groups of users or individuals. In this manner, the user is presented only with the tools they have been trained on and need for the job, and assures that changes are managed. This process, integrated with a system management and change management policy, can reduce service desk calls and unplanned
downtime, as well as create a more predictable platform for system upgrades.

11. IS Training

IS professional training is critical in preparing the IS staff that are delivering support and service to users to confidently plan and implement initiatives and solutions, and resolve user issues quickly and effectively. IS professional training should be obtained for all staff members on the systems, tools, and applications that are utilized in their daily jobs. Training should include instructor-led training classes,certification courses, seminars, and computer-based training.

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About Victor Holman

Victor Holman is a performance management expert who provides fast, simple and inexpensive ways to transform organizational performance.

Check out his FREE performance management kit, which includes several templates, plans, and guides to help you get started with your next initiative.

Victor’s Complete Lifecycle Performance Management Kit is a turnkey organizational performance management solution consisting of a web based organizational performance analysis, 7 guides, 39 templates, 600+ metrics, 35 best practices, 48 key processes, a performance roadmap and more.

Learn all about performance management at The Performance Portal

Eight Enterprise Performance Management Best Practices - Defining Phase

Have you ever tried looking up performance management best practices? If so, then you probably discovered the same things I did, that there is very little documentation or standard best practices dealing with performance management. You may find human resources best practices or IT best practices, and even best practices dealing with various departments within an organization. But chances are you will have little luck finding a comprehensive set of enterprise performance management best practices. The truth is, performance management is a complex process that affects every aspect of your organization. Even with a detailed plan on how to reach your organizational goals this can be an overwhelming task and take years to fully understand. I've put together a set of enterprise performance management best practices that drive organizational success and help you avoid the obstacles that can bring a performance initiative to a halt.

The Lifecycle Performance Management Model is an enterprise framework that is centered on 35 best practices. These best practices span across the five phases of the performance life-cycle: defining, planning, executing, monitoring and reporting. This article is the first of a series of five discussing the performance management best practices within Lifecycle Performance Management, and will focus on the defining phase.

The defining phase is where preliminary management processes are performed. These preliminary processes are those outside of traditional performance management, but which are critical to the success of your performance management initiative. Defining phase best practices are the executive processes that don't necessarily include participation from all levels within the organization.

1. Organizational Mission and Goals Management

Mission and Goals Management is the practice of ensuring that organizational mission and goals are well documented and communicated throughout the organization. Identified by executives and executed by management and staff, Organizational Mission and Goals Management is a process that includes participation at all levels and requires continuous validation throughout the maturation and growth of the organization. Organizational Mission and Goals Management includes identifying objectives throughout all business units, personnel, processes and systems and monitoring the progress of meeting those objectives. The objective is
to control costs by having people, processes and systems within the organization working toward supporting the mission and goals of the organization.

2. Performance Scope Management

The practice of defining the outcomes, documenting assumptions, and defining the scope of your performance initiative. Performance Scope Management can be approached in several ways such as defining deliverables, functionality and data, technical structure, and enterprise/organizational structure. Performance Scope Management involves setting the high level processes for which the performance management team will approach divisions, support teams and individuals in order to align performance to business objectives. Performance Scope Management ensures that expectations are met by clarifying roles, processes and expectations.

3. Performance Team Development

Performance Team Development is a critical process in Lifecycle Performance Management. It involves ensuring that the performance team is well aware of the issues facing the organization from the customer, employee, senior management and key stakeholders perspectives. Performance Team Development includes ensuring that there is support and commitment from the CEO, a direct reporting line to executive management, access to systems, data, organizational charts and processes, and liaisons form each of the business units to bridge the gap in communication and operational knowledge.

4. Vendor Performance Management

A low risk vendor conforms to the Gartner Group vendor suitability models. The vendor/service provider model assesses the viability of vendors against a set of characteristics that have been proven a low risk, high quality purchase. An organization that utilizes low risk, as well as high quality vendors and providers, will be less likely to encounter quality, reliability, or supply issues. This practice compares vendors and service providers on their financial viability, organizational stability, quality control, stringent testing for compatibility, independent market support for technology differentiation, and responsiveness to field service issues. We believe that vendors that have best in class capabilities will reduce the risk and associated costs compared to vendors that may offer lower priced products without sound testing, field support, or management practices.

5. Vendor Standardization

Vendor standardization limits the number of vendors that an organization purchases from. For given assets, an
organization selects a limited set of vendors from which products or services can be purchased. Vendor Standardization usually consists of a primary and secondary vendor. By standardizing on fewer vendors, an
organization can gain purchasing leverage and reduce incompatibility issues, support issues, vendor liaison requirements, testing of new technology, and administrative costs of vendor management. While it may limit the available selection of technology and features somewhat, it enables larger discounts with volume purchasing. Vendor standardization is part of a comprehensive asset management process that includes establishment of procurement procedures and policies, and compliance monitoring and management.

6. Organizational Stability

Stability of an organization is critical to keeping the staff members and teams consistent and focused. It enables the maturation of processes, procedures, and talent. Constant reorganization, management changes, and political infighting take a toll on moral, turnover, costs, risk and progress.

7. IT Cost Management

IT Cost Management is the financial management of your network that measures the total cost of IT services on a regular basis, compares the costs to industry benchmarks, and makes decisions on changes that include financial, not just technical, objectives. The process, policies, and tools are continuously and regularly applied to track progress and optimize spending. With IT Cost Management frameworks, such as TCO Lifecycle Management, proper technology refresh cycles can be established and investments can be verified as having positive financial impact and returns prior to implementation.

8. Performance-Based Budgeting

A results focused planning and budgeting framework which focuses on three elements: the strategy (how to achieve outcome), outputs (activities to achieve final outcome), and the result (final outcome). Performance-based budgets use missions, goals and objectives to justify funding. Through the allocation of resources, performance-based budging achieves specific objectives based on program goals and measured results. As a result, it is possible to understand which activities are cost-effective in terms of achieving the desired result.

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About Victor Holman

Victor Holman is a performance management expert who provides fast, simple and inexpensive ways to transform organizational performance.

Check out his FREE performance management kit, which includes several templates, plans, and guides to help you get started with your next initiative.

Victor’s Complete Lifecycle Performance Management Kit is a turnkey organizational performance management solution consisting of a web based organizational performance analysis, 7 guides, 39 templates, 600+ metrics, 35 best practices, 48 key processes, a performance roadmap and more.

Learn all about performance management at The Performance Portal

Twelve Basic Predictive Analytics Techniques

Predictive analytics is a solution used by many businesses today to gain more value out of large amounts of raw data by applying techniques that are used to predict future behaviors within an organization, it's customer base, it's products and services. Predictive analytics encompasses a variety of techniques from data mining, stastics and game theory that analyze current and historical facts to make predictions about future events.

Predictive models examine patterns found in historical and transactional data to identify opportunities and risks. Predictive models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

There are some basic and more complex predictive analytics techniques. Three basic techniques include:

Data Profiling and Transformations
Sequential Pattern Analysis
Time Series Tracking.

Data profiling and transformations are functions that analyze row and column attributes and dependencies, change data formats, merge fields, aggregate records, and join rows and columns.

Sequential pattern analysis discovers relationships between rows of data. Sequential pattern analysis is used to identify frequently observed sequential occurrence of items across ordered transactions over time. Such a frequently observed sequential occurrence of items (called a sequential pattern) must satisfy a user-specified minimum support. Understanding long-term customer purchase behavior is an example of the sequential pattern analysis. Other examples include customer shopping sequences, click-stream sessions, and telephone calling patterns.

Time series tracking tracks metrics that represent key behaviors or business strategies. It is an ordered sequence of values of a variable at equally spaced time intervals. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for. Examples include patterning customer sales that indicate product satisfaction and buying habits, budgetary analysis, stock market analysis, census analysis, and workforce projections.

More advanced predictive analytics techniques include:

Time Series Forecasting
Data Profiling and Transformations
Bayesian Analytics
Regression
Classification
Dependency or Association Analysis
Simulation
Optimization

Time series forecasting predicts the future value of a measure based on past values. Time series forecasting uses a model to forecast future events based on known past events. Examples include stock prices and sales revenue.

Data profiling and transformation uses functions that analyze row and column attributes and dependencies, change data formats, merge fields, aggregate records, and join rows and columns.

Bayesian analytics capture the concepts used in probability forecasting. It is a statistical procedure which estimate parameters of an underlying distribution based on the observed distribution. An example is used in a court setting by an individual juror to coherently accumulate the evidence for and against the guilt of the defendant, and to see whether, in totality, it meets their threshold for 'beyond a reasonable doubt'.

Regression analysis is a statistical tool for the investigation of relationships between variables. Usually, the investigator seeks to ascertain the causal effect of one variable upon another-the effect of a price increase upon demand, for example, or the effect of changes in the money supply upon the inflation rate.

Classification used attributes in data to assign an object to a predefined class or predict the value of a numeric variable of interest. Examples include credit risk analysis, likelihood to purchase. Examples include acquisition, cross-sell, attrition, credit scoring and collections.

Clustering or segmentation separates data into homogeneous subgroups based on attributes. Clustering assigns a set of observations into subsets (clusters) so that observations in the same cluster are similar. An example is customer demographic segmentation.

Dependency or association analysis describes significant associations between data items. An example is market basket analysis. Market basket analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.

Simulation models a system structure to estimate the impact of management decisions or changes. Simulation model behavior will change in each simulation according to the set of initial parameters assumed for the environment. Examples include inventory reorder policies, currency hedging, military training.

Optimization models a system structure in terms of constraints to find the best possible solution. Optimization models form part of a larger system which people use to help them make decisions. The user is able to influence the solutions which the model produces and reviews them before making a final decision as to what to do. Examples include scheduling of shift workers, routing of train cargo, and pricing airline seats.

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About Victor Holman

Victor Holman is a performance management expert who provides fast, simple and inexpensive ways to transform organizational performance.

Check out his FREE performance management kit, which includes several templates, plans, and guides to help you get started with your next initiative.

Victor’s Complete Lifecycle Performance Management Kit is a turnkey organizational performance management solution consisting of a web based organizational performance analysis, 7 guides, 39 templates, 600+ metrics, 35 best practices, 48 key processes, a performance roadmap and more.

Learn all about performance management at The Performance Portal

Three Basic Predictive Analysis Models

It used to be that basic data was enough to make successful decisions within an organization. A CEO could look at common key performance indicators such as net profit margin, debt to income ratio, and return on investment and be able to make the best decisions available at the time.

For the past several decades, companies have collected large amounts of data in order to evaluate why they performed the way they did and to understand their customer's needs and preferences. They built data warehouses and advance reports to improve accuracy to improve key processes, and optimize performance.

As time went on, companies learned that they could use historical data and trends to predict future behavior, and to make decisions. This was seen in examples as when a call center manager uses call volume by hour statistics to staff a call center for peak and non peak times.

Then organizations moved beyond reporting capabilities and began gathering even larger amounts of data to apply statistical analysis to further predict future trends and behavioral patterns. This was seen in examples like the banking industry using credit history, residential information, job information, debts, etc to calculate a credit score to determine if a person is likely to pay off a loan. This is an example of predictive analytics, and organizations in all genres are learning to apply it to their reporting capabilities. Predictive analytics applies large volumes of data to capture relationships between explanatory variables (variables used in a relationship to explain or predict changes in the values of another variable) and predicted variables from past data, and applying it to predict future outcomes.

Predictive modeling is the process by which data is modeled and diagnosed to try to best predict the probability of an outcome. In many cases the model is chosen on the basis of detection theory to try to guess the probability of a signal given a set amount of input data. Models can use one or more classifiers in trying to determine the probability of a set of data belonging to another set.

There are three main types of models associated with predictive analytics: predictive models, descriptive models, and decision models.

Predictive models predict future behavior and anticipate the consequences of change. Predictive models are comprised of a number of predictors (factors likely to affect future behavior or results). For example, in marketing a customer's age, sex and income can be used to predict the likelihood of buying.

Predictive analytics' central building block is the predictor, a single value measured for each customer. For example, 'most recent', which is based on the number of weeks since the customer's last purchase, has higher values for more recent customers. This predictor is usually a reliable campaign response predictor: you will receive more responses from those customers more highly ranked by 'most recent'. That means that if you contact your customers in order of 'most recent' - first, call the most-recent customer; next, call the next-most-recent customer; and so on - you will improve your response rate. For each prediction goal, there are an abundance of predictors that will help rank your customer database. For example, consider a customer's online behavior: Customers who spend less time logged on may be less likely to renew their annual subscription. In this case, retention campaigns can be cost-effectively targeted to customers with a low monthly usage predictor value.

Descriptive models quantify the relationships between data in order to classify customers into groups. While predictive models focus on predicting one customer's behavior, descriptive models identify relationships between several customers or products. Descriptive models do not predict a target value, but focus more on the intrinsic structure, relations, interconnectedness, etc. Descriptive models are used in our earlier example of the financial industry and credit scores.

Cluster analysis is a descriptive modeling technique that identifies clusters embedded in the data. A cluster is a collection of data objects that are similar in some sense to one another.

Another descriptive modeling technique is the k-means algorithm. K-means algorithm is a distance-based clustering algorithm that partitions the data into a predetermined number of clusters (provided there are enough distinct cases). The k-means algorithm works only with numerical attributes. Distance-based algorithms rely on a distance metric (function) to measure the similarity between data points.

Decision models describe the relationship between all decision elements and predict the results of decisions, allowing you to try different scenarios, and optimize results. Clinical Decision Support Systems use predictive analysis in the health care industry to determine at risk patients and sometimes to determine which course of action would be best given a multiple array of variables.

Rational decision models are based around a cognitive judgment of the pros and cons of various options. It is organized around selecting the most logical and sensible alternative that will have the desired effect. The decisions are normally organized through a detailed analysis of alternatives and a comparative assessment of the advantages of each. Weighted criteria scoring is an example of rational decision models.

Hopefully this has given you a better understanding of the basic predictive analysis models that drive predictive analytics. Check out my article on predictive modeling techniques to learn about 12 common techniques used to predict future behavior.

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About Victor Holman

Victor Holman is a performance management expert who provides fast, simple and inexpensive ways to transform organizational performance.

Check out his FREE performance management kit, which includes several templates, plans, and guides to help you get started with your next initiative.

Victor’s Complete Lifecycle Performance Management Kit is a turnkey organizational performance management solution consisting of a web based organizational performance analysis, 7 guides, 39 templates, 600+ metrics, 35 best practices, 48 key processes, a performance roadmap and more.

Learn all about performance management at The Performance Portal

The Challenges of Introducing Predictive Analytics to Your Organization

Predictive analytics is a solution used by many businesses today to gain more value out of the large amounts of raw data by applying techniques that are used to predict future behaviors within an organization, it's customer base, it's products and services. Predictive analytics encompasses a variety of techniques from data mining, stastics and game theory that analyze current and historical facts to make predictions about future events.

The benefits of implementing predictive analytics is undeniable. There are countless documented case studies and success stories where predictive analysis yielded a substantial return on investment, helped companies optimize existing processes, provided a better understanding of customer behavior, identified unexpected opportunities, and anticipated problems before they occurred. But with all of the benefits associated with predictive analytics, there are many challenges that accompany becoming an analytics-driven organization.

The perceived complexity is the largest challenge facing executives today. The cost of implementation is a close second. While these are legitimate fears, many tools are being developed to simplify the process and establish transparency from the complex formulas and statical modeling. It is, however, up to organizations to educate themselves on the basics and concepts of predictive analysis in order to fully utilize these tools.

Another challenge, which is more technical, is the traditional approach of having analyst explore data sets by saving data and manually applying relationships in order to make predictive assumptions. While this can work at a basic level of predictive analytics, predictive analytics at it's most effective application requires extremely large amounts of data and thus is best suited for analytics platforms wih parallel processing, which support custom analytical applications that query data using SQL.

This brings us to another challenge with implementing predictive analytics in your organization, and that is managing the enormous data volumes associated with it. Some organizations known to apply leading edge analytical techniques, are gathering perabytes (that's approximately 1000 terabytes, or 1 million gigabytes) of data. While these amounts of data require costly data warehouse upgrades, it enables organizations to form very comprehensive analytics and it enhances visitor/customer experience by providing targeted, customized marketing and services.

But with these large amounts of data and data storage comes the challenges of producing the platform for processing this data with complex formulas at fast rates. Because of this, analytic platforms often run off massively parallel processing (MPP) databases. MPP databases coordinate processing of a single program by more than one processor by dividing up parts of a program into several processors with their separate memory and operating systems. But many organizations that cannot afford MPP databases, instead implement analytical platforms as data marts to off-load complex processing.

While these challenges to indeed appear to be complex, the important thing to know is that if you have the architecture to support it, there are several tools out there that take out the complexities and applying predictive modeling.

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About Victor Holman

Victor Holman is a performance management expert who provides fast, simple and inexpensive ways to transform organizational performance.

Check out his FREE performance management kit, which includes several templates, plans, and guides to help you get started with your next initiative.

Victor’s Complete Lifecycle Performance Management Kit is a turnkey organizational performance management solution consisting of a web based organizational performance analysis, 7 guides, 39 templates, 600+ metrics, 35 best practices, 48 key processes, a performance roadmap and more.

Learn all about performance management at The Performance Portal

What is Predictive Analytics and Why Are We So Afraid of It?

The other day I got into a discussion with a panel of executives and the topic of predictive analytics became a main area of discussion. Most of the executives were aware of predictive analytics, and many of them had implemented predictive analytics in some form or another, whether it be through CRM, decision support systems, marketing, etc. One thing they all had in common was they all were interested in increasing the value of their data investment. But the feeling that permeated from the group most was intimidation.

Most executives, while extremely interested in implementing predictive analysis techniques and strategies, felt overwhelmed about the perceived technical nuances that accompany them. So why are we so afraid of entering the informative world of predictive analytics? The truth is, predictive analytics can be very complex, combining advanced data mining and data warehouse solutions to transform large data volumes into meaningful decision making information. This article will address some of the predictive analytics fears facing executives today, and hopefully will ease some of those fears you may have about implementing an predictive analysis solution.

The main reason most execs fear predictive analytics is because it is driven mainly by statistical analysis. Predictive analytics applies statistics, advanced mathematics, artificial intelligence and data management that many business and IT professionals view as extremely complex. What they probably don't realize is that there are several tools that are out today that are dedicated to taking out the complexities that drive people away from predictive analysis. It used to be that you had to hold a PhD in statistics to create and run analytical computations, which was extremely costly to retain. When combined with the costs of specialized analysis programs and hardware, it was very difficult to justify costs. Today however, with a strong understanding of the business processes and the data your business generates, combined with some SQL skills, anybody can perform sophisticated analysis.

Another fear businesses have when it comes to implementing a predictive analysis solution is the high costs that are associated with it. They are skeptical about the numerous case studies and success stories where predictive analysis yielded a substantial return on investment, helped companies optimize existing processes, provided a better understanding of customer behavior, identified unexpected opportunities, and anticipated problems before they occurred.

Some of the fears executives have when it comes to implementing a predictive analysis solution simply come from the fact that most only have a vague concept of the many areas that predictions can be applied to deliver additional value throughout the entire organization.

Lastly, most people fear stepping into the world of predictive analytics because it requires a lot of skill and creativity. When utilizing a platform that can manipulate such vast amounts of data, the sky is the limit as to what kinds of insights your company can gain when combined with creative professionals that truly understand the data, the business, and the organizational goals. But until a framework is created that walks these businesses through the stages of planning, manipulating and evaluating data in order to make predictions and drive decision making, there will be a large number of executives that remain reluctant to enter predictive analytics.

The other day I got into a discussion with a panel of executives and the topic of predictive analytics became a main area of discussion. Most of the executives were aware of predictive analytics, and many of them had implemented predictive analytics in some form or another, whether it be through CRM, decision support systems, marketing, etc. One thing they all had in common was they all were interested in increasing the value of their data investment. But the feeling that permeated from the group most was intimidation.

Most executives, while extremely interested in implementing predictive analysis techniques and strategies, felt overwhelmed about the perceived technical nuances that accompany them. So why are we so afraid of entering the informative world of predictive analytics? The truth is, predictive analytics can be very complex, combining advanced data mining and data warehouse solutions to transform large data volumes into meaningful decision making information. This article will address some of the predictive analytics fears facing executives today, and hopefully will ease some of those fears you may have about implementing an predictive analysis solution.

The main reason most execs fear predictive analytics is because it is driven mainly by statistical analysis. Predictive analytics applies statistics, advanced mathematics, artificial intelligence and data management that many business and IT professionals view as extremely complex. What they probably don't realize is that there are several tools that are out today that are dedicated to taking out the complexities that drive people away from predictive analysis. It used to be that you had to hold a PhD in statistics to create and run analytical computations, which was extremely costly to retain. When combined with the costs of specialized analysis programs and hardware, it was very difficult to justify costs. Today however, with a strong understanding of the business processes and the data your business generates, combined with some SQL skills, anybody can perform sophisticated analysis.

Another fear businesses have when it comes to implementing a predictive analysis solution is the high costs that are associated with it. They are skeptical about the numerous case studies and success stories where predictive analysis yielded a substantial return on investment, helped companies optimize existing processes, provided a better understanding of customer behavior, identified unexpected opportunities, and anticipated problems before they occurred.

Some of the fears executives have when it comes to implementing a predictive analysis solution simply come from the fact that most only have a vague concept of the many areas that predictions can be applied to deliver additional value throughout the entire organization.

Lastly, most people fear stepping into the world of predictive analytics because it requires a lot of skill and creativity. When utilizing a platform that can manipulate such vast amounts of data, the sky is the limit as to what kinds of insights your company can gain when combined with creative professionals that truly understand the data, the business, and the organizational goals. But until a framework is created that walks these businesses through the stages of planning, manipulating and evaluating data in order to make predictions and drive decision making, there will be a large number of executives that remain reluctant to enter predictive analytics.

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About Victor Holman

Victor Holman is a performance management expert who provides fast, simple and inexpensive ways to transform organizational performance.

Check out his FREE performance management kit, which includes several templates, plans, and guides to help you get started with your next initiative.

Victor’s Complete Lifecycle Performance Management Kit is a turnkey organizational performance management solution consisting of a web based organizational performance analysis, 7 guides, 39 templates, 600+ metrics, 35 best practices, 48 key processes, a performance roadmap and more.

Learn all about performance management at The Performance Portal

Why Your Organization Needs a Performance Improvement Strategy...

It is believed that having the right metrics can only get you 80% of the way to an effective performance metrics program. The last 20% comes from deploying the metrics, seeing how they affect performance, and then adjusting them accordingly. The same can be said about the areas which these metrics guide. One of the main reasons we measure performance is so that we can identify weaknesses and areas of improvements. What we do once we identify these weaknesses and areas of improvement is what determines how effective our performance initiative, and in turn, organization will be.

Learn How to Identify Your Areas of Improvement

The purpose of performance improvement is not to point fingers and place blame on a group or individuals that are not performing well, nor is it intended to solve problems. Performance improvement is simply a way of looking at how an organization can perform better. The difficulty with performance improvement, especially in an enterprise organization, is understanding which processes are working well and which aren’t and knowing what to tackle first when key processes are interconnected.

Learn How to Approach Performance and Leverage Your Organizational Strengths

Other challenges of implementing a performance improvement plan enterprise-wide occur when performance management teams try to implement change on a large scale. Performance improvement is best accomplished by implementing small changes, mastering a particular process to achieve those changes and identifying the next change that will lead to further performance improvements.

Learn How to Apply a Custom Performance Roadmap to Transform Organizational Performance

The best way to ensure that your organization is constantly improving and identifying relevant areas for improvement is by involving all employees, from top management down. Most often, negative performance is a result of one or more of the following factors, and is best resolved when all levels of the organization participates:

· Unclear team/job responsibilities

· Unclear or lack of performance feedback

· Inadequate physical environment, including improper tools, supplies, or workspace

· Lack of motivation and incentives to perform as expected

· Skills and knowledge required for the job

· Ineffective processes

Take control of your organization’s performance and join the elite group of high performing organizations.

About Victor Holman

Victor Holman is a performance management expert who provides fast, simple and inexpensive ways to transform organizational performance.

Check out his FREE performance management kit, which includes several templates, plans, and guides to help you get started with your next initiative.

Victor’s Complete Lifecycle Performance Management Kit is a turnkey organizational performance management solution consisting of a web based organizational performance analysis, 7 guides, 39 templates, 600+ metrics, 35 best practices, 48 key processes, a performance roadmap and more.

Learn all about performance management at The Performance Portal