value insights

Financial Analytics Transformation- Valutrics

Every company has huge amounts of data and knowledge. However, only those businesses able to transform their disparate data streams into timely, relevant, and coherent information will ultimately achieve real competitive edge. Today, companies want finance to be proactive – advising managers on the financial impact of decisions, helping them to simulate alternatives, and pushing operations to support strategic goals.

How the finance function has changed! Consider the challenges that CFOs face today: synchronizing joint-venture operations. Managing risk and options planning. Overseeing e-Procurement. Twenty or even fifteen years ago, the list of a CFO’s key responsibilities at a leading company would have been far different. Since the late 1990s, driven largely by the Internet, finance’s role has evolved as dramatically as that of any other internal business function – perhaps even more so.

Finance departments have become smaller and physically dispersed. Transaction processing, once a core function, now is often handled by remote shared services groups or outsourced either partially or totally.
Decision support, value creation, new business planning, and overseeing systems development and integration – these are the pivotal activities that finance is now engaged in and its performance is measured by.

Financial analytics is about far more than financial reporting. It is about using data to influence bottomline results.
Take customer profitability, for example. Most organizations report on contribution margins for each customer or customer groups. Financial analytics goes beyond contribution margin reporting to show what drives those margins. How? By providing a 360-degree view of all customer- related activities and costs. It uses data from customer touch-points to find out which customers respond to which marketing, sales and service activities. It uses the information on order fulfillment gathered through SCM (supply chain management) to determine which customers are over- burdening your logistics processes. And finally, it monitors payment behavior to find out which customers make your accounts receivable staff work extra hours.

Financial analytics “explains the numbers” and how they affect your bottom line. Today, many marketing and service activities are still treated as period expenses. By integrating financial analytics and customer data, you can nurture your most profitable customer relationships or renegotiate service agreements with marginal customers.
In other words, financial analytics provides real decision support – not a list of numbers, but the ability to get behind the numbers and simulate alternative approaches. It also lets you manage costs dynamically. By the time a product goes into production, for example, 95% of its costs are fixed. Concurrent costing helps you assess product costs much earlier, when you can still redesign components or processes to reduce costs. This requires simulating a number of options – such as choosing new suppliers, remapping transport cycles, or sharing product components.

Financial analytics involves mapping the allocations between functional areas as well as products, customers, and service channels. It also provides the transparency that old allocation tools lacked. You can use it to start small – introduce shared service pricing for IT services or HR – or apply activity-based management (ABM) methodologies across the board.
Financial analytics starts where controlling once left off by letting you use Activity based Management to increase efficiency. After using Activity based Management, you will quickly see the depth and breadth of financial analytics tools it puts at your command.
Whether you are pricing external goods or internal services, you will see in graphic detail the costs that all parties are incurring and why.

But financial analytics is more than controlling on a new platform. It goes beyond profit-and-loss statements to include data from accounts receivable and accounts payable systems. Indeed, it uses the business information warehouse to bring together relevant data from all the ERP applications – and external data providers as well.
This opens the way to new, more profitable solutions, such as how best to manage working capital. Financial analytics lets you accelerate time- to-cash by applying Activity based Management methodologies to your payment processes. And of course, modeling payment processes isn’t just about reducing working capital. Invoicing costs also mean administration costs.

Financial analytics tools help your credit managers analyze payment behavior, letting them adjust payment conditions based on day sales outstanding for each customer. Tools for liquidity and cash-flow analytics let you manage short- and medium-term payment streams with several financing alternatives.
Besides being tightly integrated with transactional systems, financial analytics is linked to strategic enterprise management .  This gives you access to tools like the balanced scorecard and management cockpit. As you translate strategy into action, you can map performance measures to the processes modeled in financial analytics. The result?
A tighter focus on both strategy and your bottom line.

Getting there from here will take time. Consider the results of a recent survey by the Aberdeen Group on spreadsheet analytics. Excel rules! Despite a host of high-powered new solutions, 88% of those surveyed still use spreadsheets. Today, in fact, they may be doing “double-duty.” With the ability to support Excel-based views and input formats, advanced financial analytic solutions appear to be supplementing – rather than supplanting – spreadsheets as a stand-alone tool. Consider these findings from the Aberdeen Group showing how survey respondents ranked, in preference order, their top-five spreadsheet uses:
1. Consolidation/financial reporting
2. Financial performance metrics
3. Cash-flow optimization
4. OLAP-driven planning, budgeting
5. Enterprise risk management

How respondents defined the top-three benefits of spreadsheets:
1. Facilitates better decisions
2. Introduces greater granularity in data
3. Sharpens perspectives on profitability

How respondents defined the top-three disadvantages of spreadsheets:
1. Little or no integration with transactional and other systems
2. Increases maintenance and cost of other systems
3. Unable to update software business models

As these survey results suggest, adoption of new integrated analytic applications has been slow. Why the sluggish response? According to survey respondents, they are reluctant to adopt new business analytics tools because:
• Integration benefits have been understated: managers don’t understand the benefits of integration between financial analytic software and their enterprises’ transactional systems.
• The spreadsheet format is well-liked: most end-users continue to cling to the comfortable, familiar spreadsheet format – even if it used in conjunction with more sophisticated financial analytic solutions.
• Established suppliers still reign: many buyers are rejecting newer, more innovative suppliers, who provide integration, and continuing to opt for the safety offered by better-known suppliers whose products are in danger of becoming obsolete. improving gross margin reporting across a complex supply chain is a demanding, but rewarding process. Many of the

CFOs are asking some tough questions:
• How do we estimate the value added between single entities and groups?
• How do we break down cost and profitability data by product and customer?
• How do we estimate transfer prices in a heterogeneous systems landscape?
• How do we deal with exceptions (joint ventures, tax and legal requirements)?
• How do we improve data quality while speeding up the group costing process so we can avoid the usual “end-of-the-month” panic? new business analytics applications are making it far easier to manage and control the group costing process. Integrated analytics vendors now offer group costing applications which provide new flexibility and features without forcing you to jettison what you already have. These analytical applications build on a heterogeneous, reconcilable ERP infrastructure. They offer built-in currency conversion to group currency plus conversion to standard units of measure.

To achieve the full benefits of group costing applications, be sure to build in:
• Extensive syntax and plausibility checks on different levels
• Alert-based error detection linked to correction processes
• User-friendly navigation and interface capabilities
• A manual intervention mechanism at the group level (with audit trail)
• Fault tolerance through alternative valuation rules (previous period/ product group level/% share)
As your group costing solution evolves, it should provide drill-down insight to the lowest level. It should let you easily view item level and customer data with all linked characteristics. To ensure group-wide implementation, resist the temptation to go for a big bang. Instead, opt for a smooth run-up with incremental improvement.

Consider the following 10 critical success factors in designing and implementing a holistic and responsive analytics environment:

1. Be aware that there is an important infrastructure behind analytics. The first critical success factor in building the analytics environment is recognition that an infrastructure in support of analytics is required – and that it is harder and more costly to build than it is to obtain the analytic application itself.

2. The analytics infrastructure has a standard framework and features.
The world of analytics has some very predictable needs. These include: uniformity of meaning and definition, especially when it comes to important subject areas such as customer, product, and transaction. Completeness of data is also key. Limited data greatly limits the type of analysis that can be done. Ease of access is vital. If data is hard to access then analytical efficiency will be hampered.
Flexibility is also key: limited analytic processing capabilities reduce data usage.
In short, data that resides in the data warehouse provides an ideal basis for analytics. But the data warehouse does not exist in a vacuum. It is at the center of a larger architecture – the corporate information factory. The corporate information factory supports many types of informational processing, such as real-time processing, statistical processing found in exploration processing and data mining, departmental processing, and so forth.

3. Business analytics must evolve. Analytics needs to be developed in an iterative manner. The inevitable result? Requirements are not discovered until the user understands what the possibilities are. Once users see what can be done, they unleash a new set of requirements.
By staging development, results are put in front of the user on a reasonably regular basis.

4. Don’t expect to get all the requirements in the first iteration. Gather and analyze user community requirements as a starting point. It is a mistake to build analytics in isolation from end-users. If analytics are built in isolation, then the final results may prove irrelevant to user needs.

5. Link analytics to other corporate data. An analytics environment that stands alone has a limited life span and limited usefulness. Nevertheless, it is a real temptation to view analytics as a stand-alone application. Remember, however, that a superficial attempt at integration always yields disappointing results and leads to faulty decision- making. Thorough integration of data is required, for example at data structure, data code, and reference table, calculation, and semantics levels. This integration is necessary for compatibility, consistency, and uniformity.

6. Business analytics depends on historical, as well as current data.
Historical corporate data is very important for providing insight and perspective, especially in the area of customer service. History about customers is important because consumers the world over are creatures of habit. The habits they form early in life are the key to their lifetime buying patterns. Understand a customer’s history and you are in a position to predict the future. And once a corporation can anticipate the tastes and dislikes of a customer, then it can be proactive, not reactive.
All this depends on historical data being readily available for analytic applications. Here, volume is a huge issue. As a rule, inactively used data far exceeds actively used data. Because of the cost of storage, be prepared to store inactive data in an inexpensive location. Usually, historical data has little or no documentation. Metadata describing the historical data needs to be stored for future analysis.

7. Transaction performance must be factored into the analytics environment before the system is built. Systems performance is always a key issue. In the online environment, performance means two to three seconds response time. In the data-mining world, responses may take longer. Performance expectations need to be spelled out at the start of the analytics exercise so that there will be no surprises at the final delivery of the system. The performance expectations of the organization are usually outlined in what is called a service level agreement (SLA). SLAs often include system availability parameters as well as performance parameters. It is noteworthy that the SLA for analytics will be different from the SLAs for other processing initiatives.
Merely adopting the SLA found in the online environment will not suffice because the analytics workload is fundamentally different.

8. Analytics can be purchased, developed internally, or both. It is tempting to say that analytics software developed by an external vendor can be purchased off the shelf. There is much to recommend this approach. First, there is no development time. Second, the software is maintained by the vendor. Finally, the talent for developing such software is not usually available internally.
But there are some problems with purchasing one-size-fits-all analytics solutions. The first problem: what competitive advantage is there in buying software that everyone else in the industry has access to? Another disadvantage: if a third-party vendor builds and maintains the software, how responsive to changes will the vendor be? One alternative is to purchase some portions of your analytics software and develop other parts internally. Internal development may be as simple as having information put on a spreadsheet.

9. Analytics are intuitive and may defy standardized systems design. Try as the systems developer may, a certain (and important!) part of analytics is informal. This is the area in which creative minds that use the analytics system branch out and extend its functionality. To a limited degree, spreadsheets capture this aspect of analytic processing. And those spreadsheets are found in the desks of a few individuals. This could be where you find real competitive advantage.
There are several problems with trying to incorporate informal analytics with formal analytics. For example, the statement of the problem being addressed by the informal part of the system is fluid.
By the time the informal aspect is formalized, it has changed. Information that is useful today may not be useful tomorrow.

10. Analytics must address both current and future requirements. In order to be successful, analytics must not be locked into a rigid set of requirements. When analytics software becomes static, the solutions it provides quickly lose their immediacy and value. The technical and design foundation of analytics must accommodate change – and rapid change at that.
Analytics for operational functions are linked through financial processes to strategic analytics. It is in analytics that the payoff of information processing becomes apparent, unlike the data warehouse environment, where cost justification is difficult to achieve. The analytics environment has two levels – a micro level and a macro level. Both levels have important value to decision-making.

So, analytics are flexible, actionable, real-time, accessible and integrated. They transform data into information into knowledge into action. Recent experience of implementations show by following these five steps you can help ensure success:

Step 1: Start with a business case
The challenge: to define the specific value proposition and the business benefits, based upon best practices and research. Focus on value-creating analytics across the value chain. The benefits should link the analytics investment to corporate value drivers and ROI.

Step 2: Plan your information strategy
Define with users (internal and external) their information requirements. Outline your target technical architecture. Formulate a clear process for mapping information needs to technical solutions. Identify common usage patterns and align them with the proposed solutions.

Step 3: Agree on governance and structure
Seek best practices for defining and implementing internal governance structures to guide business user and technology communities. Governance issues include: accountability and collaboration, and information governance – data stewardship, information consistency, security, and communication.

Step 4: Draft the architectural blueprints for conceptual and logical design
This blueprint serves as a framework and set of best practices to aid the definition of an integrated data model, set of information management processes, integration services, and analytical application architecture.
This will support vendor and technology decisions.

Step 5: Take action!
Appoint a highly skilled and motivated project team. Deliver in small, high-impact, end-to-end solutions. Follow up quickly with timely training and skills development. Follow a solid methodology for sound project execution. Pay particular attention to the integration requirements of your ERP, CRM, and related applications.

These steps sound very formal. They are! Don’t forget, many of these steps become iterative. For those parts of the analytics suite which are hard- wired into the organization and which support business processes on which others depend, structure and development discipline is essential.
However, do leave scope for the individual to experiment and innovate. These one-off initiatives could be the secret to your future competitive advantage!