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LATEST DEVELOPMENTS IN PLANNING AND ANALYTICS TECHNOLOGIES

CORPORATE PERFORMANCE MANAGEMENT (CPM) APPLICATIONS

Specialised software planning systems have been available since the late 1960s. Back then, computer power and the applications that ran on them were expensive and were not widely used. However, as computer technology advanced, computer time-sharing bureaus appeared that were able to offer sophisticated solutions (at the time) that were relatively inexpensive to rent and fairly simple to set up. The software was maintained and hosted by a service bureau, with the companies accessing and using the programs from a computer terminal via a dial-up telephone link—nothing else was needed. (Today’s cloud-based solutions are nothing new. It is just that the technology has become faster, more reliable, more powerful, and cheaper.)

During the 1970s and 1980s, the cost of computing continued to drop, which allowed organisations to develop their own internal information technology (IT) capabilities that were often cheaper than the cost of using a bureau. As a consequence, organisations started to purchase software and hardware in order to bring those bureau-based applications in-house. To meet this new demand, many of the existing planning and reporting products were converted to run on-premise, first on mainframes, then mini computers, and then onto networked micros. The continued fall in price of hardware and software along with the awareness of the new planning technologies greatly increased demand with the result that more software vendors entered the market.

In response to increased competition, software vendors had to find ways of differentiating themselves. One route was to move away from applications that focused on one aspect of management, such as budgeting or financial reporting, and instead expand functionality with features to cover more areas. The rationale behind this was twofold:

• First, it meant customers would get two or more systems for the price of one, which gave the vendor a price advantage, while at the same time allow them to charge slightly more for their suite.

• Second, through conversation with customers, it was recognised that a process such as budgeting was also linked to forecasting and management reporting. However, to support these processes data had to be moved between applications, and duplicate effort was required to maintain business structures in the different systems. Combining these capabilities would eliminate this source of pain.

As functionality grew and the idea of software that supported more than one process made sense, the IT analyst firm Gartner launched their paper on corporate performance management (CPM), which, as previously mentioned, they defined as ‘the methodologies, metrics, processes and systems used to monitor and manage an enterprise’s business performance’.¹ It was not intended to be a description for a software product, but that did not stop vendors from claiming that they had a CPM solution. The trouble was that many of them still offered discreet products with the only level of integration being the label on the software packaging.

To clarify the capabilities of a CPM system, Gartner put forward the following application areas that they felt constitute performance management:²

• Financial and management reporting and disclosure

• Budgeting

• Planning, forecasting, and strategy management

• Profitability modelling and optimisation

Throughout the turn of the 21st century, the market for CPM applications exploded. Surveys by Gartner reported that CPM was the highest priority in business intelligence (BI) tools for organisations. This was the catalyst for the big Enterprise Resource Planning (ERP) and database vendors, in particular SAP, Oracle, IBM, SAS, and Infor, to enter the market. As they typically did not have any products of their own, they either acquired many of the smaller CPM vendors or quickly developed their own. Because of their large installed base of clients, they were able to easily cross-sell the acquired solutions and soon captured a large market share. Some of the database vendors sought to integrate the acquired applications into their own existing database technologies and reporting functionality.

That is where we are today. It is a mature market that is made up of a few mega-vendors offering a broad range of solutions, with a handful of smaller vendors whose opportunity lies in supplying niche applications. However, in the opinion of the authors, the market for CPM is about to change. In recent years, new developments in both hardware and software technologies are starting to bring major changes to the way in which applications are conceived, written, and delivered. In the remainder of this chapter we will look at a few of them.

THE RISE OF BUSINESS ANALYTICS

The Next Competitive Edge

Business analytics refers to the ability to investigate past performance through the use of statistical methods that can then be used to drive business planning. Once thought of as being nice to have, applying analytics, especially predictive business analytics, is now becoming mission-critical and a competitive edge for organisations.

The use of analytics that include statistics is a skill that is gaining mainstream value due to the increasingly thinner margin for decision error. There is a requirement to gain insights, foresight, and inferences from the treasure chest of raw transactional data (both internal and external) that many organisations now store (and will continue to store) in a digital format.

An experienced analyst is like a caddy for a professional golfer. The best ones do not limit their advice to the professional for factors such as distance, slope, and the weather, but also strongly suggest which club to use.

BI Versus Analytics Versus Decisions

Here is a useful way to differentiate BI from analytics and decisions. Analytics simplify data to amplify its value. The power of analytics is to turn huge volumes of data into a much smaller amount of information and insight. BI mainly summarises historical data, ty pically in table reports and graphs, as a means for queries and drill downs. However, reports do not simplify data nor amplify its value; they simply package up the data so it can be consumed.

In contrast to BI, decisions provide context for what to analyse. Work backward with the end decision in mind. Identify the decisions that matter most to your organisation and model what leads to making those decisions. By understanding the type of decision needed, the type of analysis and its required source data can be defined.

Many believe that the use of BI software and the creation of cool graphs are the ultimate destination. BI is the shiny new toy of information technology. The reality is that much of what BI software tools provide, as just described, has more to do with query and reporting often by reformatting data. A common observation is, ‘There is no intelligence in business intelligence’. It is only when data mining and analytics are applied to BI within an organisation that has the skills, competencies, and capabilities that deep insights and foresight is created. This can then be used to create better planning models that assess actions for improving business operations and opportunities.

Data mining that uses statistical methods is the foundation and precursor for predictive business analytics. For example, data mining can identify similar groups and segments (for example, customers) through cluster or correlation analysis. This allows an analyst to frame their analytics to predict how their object of interest (such as customers, new medicines, new smartphones, and so on) is likely to behave in the future, with or without interventions. This allows predictive analytics to move from being descriptive to prescriptive, and as such become the foundation for planning.

To clarify, BI consumes stored information. Analytics produces new information. Predictive business analytics leverages data within an organisational function focused on analytics that possesses the mandate, skills, and competencies to drive better, faster decisions and achieve targeted performance.

Queries using BI tools simply answer basic questions. Business analytics creates questions. Further, analytics stimulate more questions, more complex questions, and more interesting questions. More importantly, business analytics also have the power to answer the questions. Finally, predictive business analytics that are bound up in a business model can display the probability of outcomes based on the assumptions of variables.

The application of analytics was once the domain of ‘quants’ and statistical geeks developing models in their cubicles. However, today it is becoming mainstream for organisations, with the conviction that senior executives will realise and utilise its potential value.

Business Analytics, Big Data, and Decision Management

Much is being written today about big data. Big data has been defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, validate, storage, search, share, analyse, and visualisation. What is needed is to shift the discussion from big data to big value. Business analytics and its amplifier, predictive business analytics, serve as a means to an end, and that end is faster, smarter decisions.

Many may assume that this implies executive decisions, but the relatively higher value for and benefit from applying analytics is arguably for daily operational decisions. Here is why.

Decisions can be segmented in three layers:

• Strategic decisions are few in number but can have large impacts. For example, should we acquire a company or exit a market?

• Tactical decisions involve controlling with moderate impacts. For example, should we modify our supply chain?

• Operational decisions are daily, even hourly, and often affect a single transaction or customer. For example, what deal should I offer to this customer? Should I accept making this bank loan?

There are several reasons that operational decisions are arguably most important for embracing analytics. First, executing the executive team’s strategy is not solely accomplished with strategy maps and their resulting key performance indicators (KPIs) in a performance scorecard and dashboards. The daily decisions are what actually move the dials. Next, although much is now written about enterprise risk management (ERM), the reality is that an organisation’s exposure to risk does not come in big chunks. ERM deals more with reporting. Risk is incurred one event or transaction at a time. Finally, in the sales and marketing functions operational decisions maximise customer value much more than policies. For example, what should a front-line customer-facing worker do or say to a customer to gain profit lift?

Operational decisions scale from the bottom up, and in the aggregate they can collectively exceed the impact of a few strategic decisions.

Predictive Business Analytics: The Next New Wave

Today many business people do not really know what predictive modelling, forecasting, design of experiments, or mathematical optimisation mean or do. However, over the next ten years, if businesses want to thrive in a highly competitive and regulated marketplace, use of these powerful techniques will become mainstream within planning, just as financial analysis is today. Executives, managers, and employee teams who do not understand, interpret, and leverage these assets will be hard-pressed to survive.

When we look at what kids are learning in school, then that is certainly true. We were all taught mean, mode, range, and probability theory in our first-year university statistical analytics course. Today, children have already learned these in the third grade! They are taught these methods in a very practical way. If you had x dimes, y quarters, and z nickels in your pocket, what is the chance of you pulling a dime from your pocket? Learning about range, mode, median, interpolation, and extrapolation follow in short succession. We are already seeing the impact of this with Gen Y and Echo Boomers who are getting ready to enter the work force; they are used to having easy access to information and are highly self-sufficient in understanding its utility. The next generation after that will not have any fear of analytics or look toward an expert to do the math.

There is always risk when decisions are made based on intuition, gut feel, flawed and misleading data, or politics. One can make the case that the primary source of attaining a competitive advantage will increasingly be an organisation’s competence in mastering all flavours of analytics. If your management team is analytics impaired, then your organisation is at risk. Predictive business analytics is arguably the next wave for organisations to successfully compete and not only to predict outcomes, but reach higher to optimise the use of their resources, assets, and trading partners, among other things.

It may be that the ultimate sustainable business strategy is to foster analytical competency and eventual mastery among an organisation’s work force. Today managers and employee teams do not need a doctorates degree in statistics to investigate data and gain insights. Commercial software tools are designed for the casual user.

Game-Changer Wave: Automated Decision-Based Management

What is the next big wave that will follow after analytics? Automated decision-based management. As organisations achieve competency and mastery with analytics, the next step will be automated rules based on the outcomes from applying analytics. The islands of analytics that emerge in an organisation’s various departments and processes will be unified in closed-loop ways. Communications will be in real-time.

This does not mean that an organisation’s workforce will be reduced in size by robot-like decision making. However, it does mean that algorithms, equations, and business rules derived from superior analysis will become essential to managing towards optimisation. Decision-based managerial software will eventually emerge that is independent of, but is integrated with, an organisation’s multitude of data storage platforms and data management ‘stacks’ between the data and decisions. These future software generated decisions will be aligned with the executive team’s strategy and its KPIs. When that day comes, it will be a game-changer and the basis for a book to be written in the future.

Substantial benefits are realised from applying a systematic exploration of quantitative relationships among performance management factors. When the primary factors that drive an organisation’s success are measured, closely monitored, and predicted within an overall plan, that organisation is in a much better situation to adjust, advance, and mitigate risks. That is, if a company is able to know, notjust guess, which non-financial performance variables directly influence financial results, then it has a leg-up on its competitors and delivers real value to its shareholders, employees, and other stakeholders.

For a more detailed explanation on how business analytics can change the way business is perceived and managed, we would recommend the book Predictive Business Analytics: Forward Looking Capabilities to Improve Business Performance by Lawrence Mais el and Gary Cokins.

APPLICATION INTEGRATION

For the past 30 years or more, software aimed at planning has basically been about adding up numbers. However, as technologies such as business analytics become main-stream, there will be significant changes to what those solutions will be able to do in the future.

Most planning and reporting systems are at the stage where transaction systems were 15 or more years ago. Back then, the systems used to record the business were split into sales and purchase ledgers, stock control, sales order books, and so on.

The advent of ERP saw the integration of these solutions into a single system where user interaction was controlled by an encompassing workflow capability. This gave benefits in that it helped automate the reordering of stock and provided warnings should production be out of line with forecasts. End-users, line managers, and senior executives all use the same system to look at the current and future status of production, so clear decisions can be made about production priorities. However, for ERP to be successful, it required organisations to rethink the management processes involved in order to take advantage of the software.

The same will be true of planning and reporting systems in the future. Today, most software vendor solutions are a series of discreet applications that focus on different aspects of performance. However, there is a new category of application emerging that totally integrates the six disciplines of strategic and operational planning with budgeting, forecasting, management reporting, and risk management. These systems will eventually encompass business analytics with traditional BI reporting tools to the extent that, to the end user, they are one application.

With this type of system, users throughout the organisation will be controlled by an encompassing workflow capability that allows them to view the status of strategy, its execution, and their involvement in making it happen. They will be able to gain insight about the immediate past from which decisions can be made concerning what works and what does not work. However, like ERP systems, they will require a re-think of the management processes involved so that they can operate on a continual basis.

CLOUD-BASED APPLICATIONS

Another recent trend that is gaining traction is cloud-based solutions. In a nutshell, a cloud-based solution is one where the software and hardware is not owned or hosted by the client. Instead, customers rent the application from a third-party who then supplies the application’s capabilities as a service (more commonly known as software as a service, or SaaS for short). An early example of such an application is SalesForce.com, which is used by thousands of companies to collect and manage sales forecasts.

In the early days of cloud-based solutions, there was much scaremongering concerning access to the data. Can you trust the vendor not to lose the data or allow unauthorised access? If they go out of business, what happens to the data? How would an organisation continue to operate? For sales forecasts there may not be too much of a concern, but with an organisation’s strategic plans, operating results, and the mechanism by which plans are set, these concerns are very real.

In recent years, much has been done to alleviate these fears. Organisations are now used to handling their financial transactions over the Internet, and most cloud-based vendors have extremely secure installations. Any hint of malpractice regarding data or access would finish them, so they have a vested interest to be as secure as possible. In terms of companies going out of business, most applications are built on standard technologies and, due to fierce competition, most cloud solutions can be replaced quickly.

The biggest catalyst for adopting a cloud-based solution is economic. Cloud solutions are extremely cost effective. In summary, these costs savings include the following:

• The elimination of hardware to run the application The cloud vendor provides this. This means that as the application grows, there is no requirement to upgrade the hardware, maintain it, place it in a secure facility, or have engineers on standby. Savings here alone can be considerable.

• The elimination of software at the client site. All the customer needs is an Internet browser, which comes free with almost any device. As most traditional on-premise applications store data in some form of database, often the software vendor will require the customer to have operating system licenses and database licenses, all of which are extra costs and are not included with the solution software. Cloud-based solutions totally eradicate these hidden costs.

• The elimination of software installation and upgrades. With a cloud-based application, users are always on the latest version. As operating systems change and mobile devices gain more power, it is the interest of the application vendor to sort out how to take advantage of new developments. This way, customers do not have to spend time and effort on this.

• Anytime, anywhere access. It does not matter what device you use or where you are; provided you have Internet access, you will be able to use a cloud-based application anytime and anywhere.

All of these points result in a substantial lower cost of ownership. This ownership typically comes without any upfront capital costs and annual maintenance payments. Instead, these costs are replaced with a much lower rental cost that can be turned off at any time. It also means that customers do not need to be concerned about hardware capability and the subsequent impact on costs that come from increasing CPU power.

Cloud-based planning solutions are still in their infancy, with some organisations still worried about whether the service may disappear overnight without warning. However, the costs involved, or rather, the lack of them, are so persuasive that for many applications it just may be worth the risk.

IN-MEMORY CHIP TECHNOLOGY

The speed and capacity at which microchips can store and process data is rapidly advancing. In the past, data that was being analysed had to be stored on a device that was physically separate from the processor. This was because the processor was limited in its ability to hold data, and even then this type of memory was very expensive. The separate area was often a magnetic disc (or hard drive) that could hold very large amounts of data and was relatively inexpensive. The disadvantage of this design is that it requires data to be constantly written to and from memory, which incurs a time penalty. The more data to be analysed, the more time is required to swap data between the two types of memory. This resulted in analyses that took hours to run.

The new in-memory chip technology replaces the need for a separate physical disc, which in turn eliminates the time taken to read and write data. The result is vastly increased response times and systems that are able to support real-time processing of massive amounts of information.

The implications are significant. Aspects of so many items mentioned in this book, including drill-down queries and refreshing of models, become nearly instantaneous. For analysts, investigations and explorations of multiple ‘what if’ scenarios can be processed at the speed of thought.

Just one final word is needed about trying to predict the future of software: You can be sure that no matter what we see today, something else is bound to appear and disrupt what is regarded as normal. Regardless of the future developments that may arise, it is always important to keep in mind that technology is an enabler, and it needs to be evaluated in line with how the organisation is to be managed.

In the final chapter, we will look at ways in which the planning framework can be introduced into an organisation.

Endnotes

¹ www.gartner.com/it-glossary/cpm-corporate-performance-management

² Magic Quadrant for Corporate Performance Management Suites, Christopher Lervolino, John E. Van Decker, Neil Chandler, Gartner 14 February 2013