Breaking the barriers to financial planning with exploratory analytics

A disruptive approach to financial planning and management

SUMMARY

Catalyst

An ongoing evolution in data analytics infrastructure, processes, and techniques has created a perfect

storm that is disrupting the way the office of finance thinks about (and engages in) enterprise

performance management (EPM). We believe that financial planning and analysis (FP&A) will be one

of the first areas to be on the path to transformation with the emergence and adoption of cloud and

exploratory analytics – that will not only help users explore new frontiers in performance management,

but also make planning and analysis, ‘viral’. The next million users of planning will likely be from

outside the finance function, and they will sign up at a much faster rate than ever before. This

research provides our view on the forces shaping the evolution of these technologies, and how they

influence those organizations that use, or are planning to acquire FP&A solutions.

Key messages

Traditional planning processes suffer from a range of issues, including the lack of flexibility,

timeliness, and collaboration

Three trends that are gaining strong traction in analytics – cloud, exploratory analytics, and

embedded predictive analytics – are set to disrupt traditional planning practices

The new paradigm of planning will thrive on the edges of finance and lines of business and

will be relevant to all sizes of enterprises, from very large corporations to start ups

A key aspect of new age planning solutions will be agility – allowing customers to start small

and then switch to a hyper-growth mode in short timeframes

Self-service exploratory analytics and cognitive abilities will help FP&A professionals focus on

holistic analysis of root causes and deliver actionable insights instead of mundane reporting

and data collection

Ovum view

The worlds of FP&A and business intelligence (BI) are converging fast. This is a profound

transformation which is both technology and business pain-point driven; it is apparent that there is a

rising wave of discontent against traditional performance management solutions which focus too

narrowly on financial metrics and defined processes. An overwhelming majority of CFOs aren’t happy

with the quality of financial planning and analysis and are looking for ways to make finance a more

‘inclusive’ and ‘insightful’ function. On the other hand, the role of the CFO itself is changing from a

custodian of accounts to a driver for business, which necessitates that there needs to be a unified

analytic future across departments and an end to a "data myopic" mentality, a common malaise that

has affected finance for too long. For the modern enterprise, EPM can no longer exist as an analytic

silo, isolated from the rest of the enterprise in every sense – in data, analytics, and skills. To forecast

and plan better, the finance organization of tomorrow needs to go beyond general ledgers and into the

lines of business, and make decisions faster. Using exploratory analytics, modern cloud architecture,

and embedded predictive smarts can help speed up that process and ensure that finance

professionals are data aware without having to invest heavily in new skills or new IT resources. We

term this new paradigm of financial planning and analysis (FP&A) as ‘exploratory planning’, which

combines the rigor of planning with the discovery and visual analytics aspects of data exploration, as

well as cognitive abilities which allow systems to learn, understand, and reason.

RECOMMENDATIONS FOR ENTERPRISES

What does this mean for me?

We believe that the adoption of cloud and exploratory analytics in planning processes is inevitable as

they are user driven. Enterprises need to ensure that they do not impede these movements as it will only help

increase employee productivity and allow for more informed decision-making. It will likely encourage a higher

level of engagement and collaboration among employees across departments and enable faster problem-solving.

Exploratory analytics and cloud will not benefit uninitiated users only; even finance specialists, data scientists,

and data analysts will be able to use cloud and exploratory analytics to focus on understanding and analyzing

data without having to learn additional skills or shift from their "comfort zone" applications.

However, we also urge enterprises (and IT) to pay close attention to the data framework behind cloud and

exploratory analytics. IT will still need to manage how their users access, work with, and act upon

data. Topics like data governance and data quality will need a mix of traditional approaches as well as

novel, point approaches to ensure that there is a balance between complete data lockdown and self-

service: an elastic architecture founded on rigorous principles is essential. Organizations that handle

sensitive customer data should be extra mindful of how and who they allow access to, while keeping

in mind that breaches of customer data are rarely a result of cloud deployment but more so from poor

or nonexistent governance practices.

Finance systems are mission critical. Why change?

The problem with status quo

Figure 1 illustrates the key pitfalls and pain-points enterprises frequently associate with the planning,

budgeting, and forecasting process.

Figure 1: Key issues with FP&A

Source: Ovum

FP&A is a key component of managing and driving business performance. However as a process it

continues to be one of the most difficult to execute. A majority of plans are still too conservative; this is

partly due to inflexible planning systems that do not allow business managers to readjust their

budgets against plan frequently. As a result, line of businesses and finance professionals assign

conservative targets and later 'over-achieve' on their plans to book profit. In the current financial

climate, the office of finance needs to play a much more aggressive role in expanding business. This

is a difficult transition for most CFOs and requires analysis and scenario planning backed by hard

data.

Selecting the right technology platform can solve half of the problems with a lengthy and disjointed

planning cycle – the other half is process.

•Multiple iterations

•Long analysis cycles

•Costly

•Resource intensive

Extremely time consuming

•Cannot accomodate frequent changes to

•process

•assumptions

•data

Inflexible

•Data is frequently outdated, inaccurate, non-transparent

•Lack of ownership and accountability for data quality

Based on unreliable data

•No motivation to cooperate and/or participate; Complicated process/requirements

•Difficulty in getting inputs from all users on time

•Lack of checks and balances to avoid budget padding

Not collaborative

•Siloed operational, financial, and strategic plans

•Departmental myopia

•Difficult to arrive at common semanticsDisjointed

Three key trends that will reshape FP&A – cloud, exploratory analytics, and predictive With the proliferation of easy-to-use, visual, and exploratory analytics solutions, we believe that the

barriers to entry for analytics, and by extension, planning, are considerably lower than before; this is a

significant trend that we suggest will lead to significant acceleration in the adoption and usage of

planning software. On the technology front, it is our view that exploratory analytics will finally become

the norm rather than the exception, and user preferences will likely dictate that enterprises adopt a

hybrid model that allows these older systems to coexist with newer solutions. In 2016, we also believe

that the cloud will make its biggest impact on planning, as has been predicted for some years. There

are favorable tailwinds for cloud already, with several mega-vendors facilitating the mass exodus of

core financial applications to the cloud. However, it will not be the traditional advantages of the cloud

but more of its softer benefits, such as faster time to market, ability to fail fast, and technology

abstraction with cognitive abilities that will make or break the case for cloud based planning. These

trends are starting to gain traction, especially in areas where finance intersects with functions such as

sales and marketing, and in verticals such as retail, where businesses are largely web native and

B2C.

The cloud will be the primary ‘engagement model’ for planning Planning in the cloud offers new ways to consume, collaborate on, and turn content into actionable

insights. Cloud helps reduce the time and expense of developing new ideas that help increase

customer engagement. In the age of austerity, cloud planning makes it possible to invest scarce

organizational resources to engage customers and have employees working towards a common

enterprise goal, rather than in keeping the lights on and producing non-differentiating analysis.

Cloud-based applications provide a natural place to collaborate

The cloud solves the accessibility problem and helps lines of business tune in to financial goals by

abstracting technological complexity, making software more user-friendly (fewer training

requirements) and improving collaboration. It provides ready-to-use, relatively inexpensive

infrastructure and resources for creating, developing, testing, deploying, and refining new ideas that

improve customer engagement. With the cloud, organizations can test and build financial applications

faster.

Cloud will help finance move closer to data and other lines of business

Across the board, enterprise perceptions about the cloud are changing fast. Cloud computing no

longer elicits adverse reactions from most CXOs, and CFOs are no exception. Figure 2 illustrates

cloud adoption across a global enterprise base.

Figure 2: Cloud has made substantial inroads into finance and business applications

Source: Ovum 2014 Enterprise Insights

It is evident that finance and accounting find the cloud invaluable. In addition, there is also very high

adoption of cloud services analytics and lines-of-business applications (as shown by the blue arrows).

With the increasing adoption of cloud CRM, HR, and payroll systems, cloud planning becomes a

natural progression for most enterprises. As more data is located off-premise and inside cloud PaaS

applications and databases, we believe that the case for cloud planning becomes stronger.

Cloud will help planning get more robust by providing access to external data

Business changes and uncertainties in the economic environment are leading factors for planning

variability from actual performance. In the future, the importance of external data to planning and

analysis will only increase and solutions that base their forecasts on internal data only will fail to

account for the market's impact on the business.

While external data can be used in on-premise planning systems as well, in practice, the cloud is

better suited to analyzing streams of data whose business value cannot be ascertained before

analysis. With a cloud solution, the result is an elastic infrastructure that can be provisioned,

managed, and scaled up and down at will, without creating any additional overheads for the customer.

A good example of using newer technologies to harness external data can be found in IBM Planning

Analytics, an offering which combines the self-service visual exploration capabilities of Watson

Analytics and the rigor of IBM Cognos TM1 planning. Using the solution, users can easily blend

external data from (flat files, CSVs, etc) to their curated data sources and analyze the blended data to

find meaning. The self-service nature of the solution doesn’t add gamut of new work to the finance

and IT departments' mandate in supporting big data initiatives, but instead uses automation to help

non-initiated users carry out most tasks on their own.

Cloud favors a faster release cycle

A key benefit of cloud-based planning applications is a rolling release cycle without broken workflows.

Typically, cloud vendors release new features more frequently – at least quarterly. On-premise

applications, on the other hand, have release cycles of 12–18 months. Cloud-based applications also

make version-transition automatic and do not require the customer to plan for and spend resources on

the upgrades. A few vendors now offer options for customers to switch off automatic upgrades if they

would like to stick to a particular version.

Cloud will reduce IT overhead(ache)

The absence of hardware and systems management in cloud planning reduces a lot of the grunt work

that typically inundates the IT department. For example, by using a cloud-based planning platform, all

of the maintenance work – keeping the development tools and the software stack up to date, fixing

bugs in the platform, installing new patches, monitoring utilization, adding new hardware – is now

handled by the vendor. In addition, modern cloud planning platforms also focus on task automation

and self-service, which means users are more capable of servicing break-fix and incremental analysis

requests themselves without coming to IT. This helps free IT developers from mundane tasks so that

they can focus on new initiatives.

However, cloud cannot be a forced choice The cloud is a significant endeavor for most enterprises and needs to be adopted at a pace that

organizations are comfortable with. Many enterprises, large and small, may need to mix and match

on-premises and cloud deployments to suit their needs or comply with external regulations. Therefore,

an enterprise solution (or vendor) that underscores customer flexibility should ideally be able to

support both a cloud model as well as on-premises. This deployment model itself may be offered

directly from the vendor or through a third party, but ultimately it is helpful for customers to have both

options. Larger enterprises frequently need a hybrid solution, where on-premises is a logical choice to

satisfy local data security or data regulatory requirements governing storage and use. A hybrid

solution gives organizations the option of scaling resources for specific workloads and running

applications on the most appropriate platform for a particular given task.

Exploratory, self-service analytics will rekindle the appetite for discovery driven financial analysis

What do we mean by "exploratory, self-service planning"?

We define "exploratory planning" to be a method or philosophy of analysis which places maximum

emphasis on ad hoc querying, testing and re-testing of hypotheses for problem or query definition and

data set selection, and dynamic visual representation, irrespective of underlying data structure or

platform (e.g. "small" versus big data). The central idea of exploration is to enable users to start their

analytics journey without a predefined starting point and/or path and provide the flexibility to travel in

any direction from their initial query. Exploratory planning that supports self-service concepts is ideal

for nontechnical users as most of the query is visually driven. It is different from traditional planning,

where the data sets, schema, and queries are already predetermined; a large part of the process is

identifying the right data set(s) to use. This has huge implications on how data is sourced, wrangled,

governed, or stored. Given that almost anything can be queried in any way, any exploratory approach

needs an extremely capable data back end that can blend data from multiple sources at run-time,

while adhering to all organizational compliance and governance directives. IBM (Watson Analytics)

has been one of the key players in the exploratory analytics this market, differentiating itself on the

cognitive insights it brings to the table – which don’t force the user to start from a blank slate. Figure 3

shows some of the cognitive smarts of IBM Watson Analytics.

Figure 3: Cognitive elements of IBM Watson Analytics

Source: IBM

Why do we need exploration and cognitive technologies in planning?

Traditional FP&A systems often tend to take a rigid, process-oriented approach, leaving very little

scope for ad-hoc exploration of data. As a result, FP&A largely becomes a planning and reporting

exercise with very little analysis of underlying factors.

There are three key reasons why planning is progressively failing to achieve meaningful objectives; an

increasing variety of data sources (revenue, costs, forecasting, exchange rates, risk, and operations),

the resulting increase in the volume/size of data, and the high skill requirements for managers to

achieve a holistic understand of this data. As a result, managers are not able to perform even routine

analysis of root cause factors. As a result, there’s a widening gap between BI and EPM that no

traditional tool is currently addressing. This leads to a greater reliance on disjointed MS Excel like

tools which perpetuate data silos and end up exposing the organization to greater operational risk. A

recent article in CFO.com covers a survey by APQC, a member-based nonprofit business

benchmarking entity, showing that only 40% of 130 finance executives polled from very large

organizations rated their FP&A capabilities as effective. Responses to several other survey questions

underscored that executives consider their company to have low FP&A maturity. Two thirds of survey

participants said their finance teams are always swamped by basic financial management duties such

as periodic forecasting of how performance is trending versus annual budget targets. They have little

time for, say, investigating cost drivers or testing the probable outcomes of bundling and pricing

options.

Enter exploratory, self-service analytics. A combination of exploratory analytics with traditional EPM

provides a sandbox where enterprises are able iterating their queries, and identify which data sets to

interrogate for the answers they are looking for. It’s different from traditional analytics, where the data

sets, schema, and queries are already pre-determined. At the exploratory phase, the user is expected

to look for answers that explain why KPIs have changed, or whether they are looking at the right KPIs

at all. Exploratory analytics does not replace an existing regimen of planning, analytic, query, or

reporting tools – it complements it. Simply put, in planning, exploratory analytics:

Shows the big picture: it shows the forest. Traditional analytics and planning tools give you

the precise picture, where you get down to the trees.

Lives in the ad-hoc: may be used for quickly getting a fix on some unique scenario, where

the user might run a query once and move on; or it can be used for recalibrating where you

should do your core data warehousing analytics – which means that it is a preparatory stage

for feeding new data to the data warehouse.

Provides the context for making decisions. Data warehousing analytics and planning are

where final decisions (for which the organization may be legally accountable) are made, but

the underlying parameters for making decisions can be derived from exploratory analytics

tools

What should an exploratory/cognitive planning system be able to do?

Figure 4 shows a broad classification of the features we believe are key to exploratory planning

solutions.

Figure 4: Capabilities desirable in new planning solutions

Source: Ovum

We believe that new tools that combine exploratory analytics and traditional planning features

solutions will be able to access a wide variety of data sources, both internal and external. For internal

data sources, standard data governance approaches will still work. Big data stores such as HDFS

(Hadoop Distributed File System) or cloud data stores like Amazon S3 or Microsoft Azure BLOB

(binary large object) storage will also be candidate data sources. It will also be possible to upload flat

files through dedicated interfaces.

For external data, a lot of pre-processing, including parsing, profiling, and some data quality, will be

done from ingestion to analysis, in stages. This is an area where exploratory analytics vendors such

as IBM Watson Analytics excel, with automatic data preparation capabilities. Techniques here will

range from entity extraction, ontology matching, and probabilistic matching to pattern identification.

Using these techniques in planning will lead to the uncovering of newer dimensions of planning, such

as an analysis of budgets, but as they relate to operating expenses, billings, creditors, and debtors.

Similarly, it could help executives spot trends as they emerge without overly relying on IT.

Ambient predictive analytics will spur smarter, in-time decisions

Ovum has always believed that predictive analysis will not grow as an end-user tool market, but as an

analytic function embedded within a specific business process/context. In the context of planning,

predictive capabilities are key as they help organizations use historical data to derive insights that are

actionable in the future. While most organizations appreciate the value of predictive analytics, a key

impediment to broad adoption has always been the skill levels required. On an average, predictive

analytics – involving modeling, what-if analysis, and scenario planning – is used by less than 1% of

the organization. To be effective, predictive capabilities need to be embedded into line of business

and finance applications and automated to a large extent, so that uninitiated users can use them

without having to switch their operating environment or having to learn new skills (either math or

coding).

What will the future of FP&A and EPM look like?

What should an aspirational FP&A system allow you to do?

An aspirational FP&A system should allow users to go beyond planning to analysis, beyond bean-

counting to actually driving business. The rising expectations from the FP&A function should serve as

a wake-up call for finance executives; finance needs to be much more holistic with its analysis, which

means that skills and metrics once deemed inconsequential or tertiary will now be critical to financial

success. This does not mean that finance executives will have to learn programmatic languages such

as SQL to do their day jobs or that they would need to be marketing or sales specialists. Tooling and

technology should be able to automate that to a large extent. Instead, they must be able to think

critically about data, convert data into insightful analysis for corporate financing strategies, revenue,

and cost structures. Revenue analysis will transcend superficial revenue variance monitoring and

require a working understanding of the actual product or service. This appears evident, but is

surprisingly absent in many finance organizations. Product knowledge and metrics will be integral to

efficiently (and accurately) analyze the revenue flow from the top of the funnel to the bottom. In effect,

finance will stop producing a mega model built on departmental proxies; instead, a modular series of

line of business models will feed into the expanded finance organization.

As more and more data becomes available and usable, finance will be able to analyze not only the

harder metrics around P&L numbers but also the transaction, cost, and sales metrics. The role of the

financial analyst will accordingly need to blend finance, statistics, accounting and business

analytics/strategy.

How will technology influence vendor selection?

A careful selection and integration of emerging technologies can help bridge the gap between finance

and lines of business, as well as the gap between performance management and business

intelligence/analytics. While selecting an EPM platform, Ovum advises that the primary considerations

still center around the core capabilities of EPM platforms around planning, budgeting, financial

consolidation, and forecasting – such as the ability to design rolling budgets, combine top-down

planning with bottom up planning, and perform driver-based planning (planning focused on business

drivers and enabling KPIs). In addition, Ovum advises users to look for differentiation around the

following parameters:

Cloud strategy: Vendors with a clear cloud strategy that encompasses a range of

options, including a public cloud offering as well as the options for private cloud. Given

industry trends, any vendor that is yet to support any cloud initiatives, or ones that have

yet not spelled out a clear cloud EPM strategy (includes pure hosting), or ones that have

a strategy but are yet to show any significant customer traction, despite a few quarters of

market exposure, should be rated lower.

Exploratory analytics: Vendors with a definite focus towards making it easy for users to

perform ad-hoc, exploratory analytics. This is different from pure reporting and visual

analytics in the sense that it relies on automation and self-service which is tuned to the

perspective of the office of finance. Therefore, vendors that offer pure self-service

analytics without any packaging, content, or blueprints for the office of finance will not be

ideal. On the other hand, vendors that only offer packaged reporting and dashboards for

the office of finance, but do not allow external data, or ones that do not allow a true free

form blending and exploration of data, will not fit the bill.

Predictive capabilities: Vendors that can embed predictive capabilities into planning

processes with a focus on automation will be best suited to deliver in this category. This

is distinct from providing a disjointed set of tools that need data replication and a separate

environment to work with prediction. Only vendors that embed predictive capabilities

inside planning processes while providing the user the capability to tweak the basic

prediction parameters will be best suited to deliver on this front.

How can we get started?

Exploration can start anywhere

Exploratory planning is as much about new technology as it is about the way users reimagine and

interact with technology. The technology aspect of cloud or exploratory analytics, for example, is

indeed novel, but in isolation neither cloud nor exploratory analytics embody the true spirit of

exploratory planning. Instead, when multiple technologies are combined with a view to serve the end-

user interest, it is truly exploratory planning.

Getting started with the notion of exploratory planning will require a concerted effort from finance as

well as IT. It is best poised to succeed if it is built on an understanding of enterprise pain points with

the current process and key objectives of decision makers. Like every other technology that is

adopted by finance, exploratory planning should be targeted at existing pain points of current planning

processes and be rolled out in smaller test deployments before going enterprise wide. This approach

of finding solutions to existing issues and avoiding big bang deployments is similar to agile

development methodologies which demonstrate a much higher success rate in situations where the

project scope changes often, as is the case with planning and analytics.

For exploratory planning to work effectively, IT departments must be open to creating an environment

amenable to exploration. A great exploratory planning environment will likely fail if it does not allow

open and transparent access to rich data and content. On many occasions, such data lies inside the

enterprise but is poorly used because people do not know about it or access is prevented by overly

restrictive policies.

Overall, we believe that exploratory planning can start at any point in the planning cycle. It can start

either from the finance department or from a line of business. Since exploration is more about

developing a new approach to planning which goes beyond rigid KPIs, there is no pre-defined

technology path/roadmap. Figure 5 shows a probable sweet spot for exploratory planning.

Figure 5: Exploratory planning to tie finance and lines of business

Source: Ovum

Conclusion For most enterprises, self-service, cloud, and exploratory analytics will not be a binary "yes we do" or

"no, we don't" discussion. While it is inevitable that these technologies will impact almost every

organization, it is important that they be managed and rolled out at a pace that organizations find fit.

To ignore the prevalence and virally expanding popularity of free-to-try analytic tools and point

planning solutions may be foolhardy; shadow IT usually thrives in the most locked down of IT

environments. Instead, it is best to understand the pros and cons of these solutions and work self-

service into the overall planning and analytics strategy. Doing so not only helps prevent compliance

mishaps but also helps organizations discover newer insights that could positively influence business

metrics.

APPENDIX

Methodology

The research is based on Ovum data plus ongoing consultations with Ovum clients, discussions with

industry vendors, and extensive scanning of technical references.

Further reading

2016 Trends to Watch: Analytics, IT0014-003065, October 2015

Cloud EPM: A Metaphor for the Modern Office of Finance, October 2015

Technology Best Practices to Shorten Planning, Budgeting, and Forecasting Windows, March 2015

Author

Surya Mukherjee, Senior Analyst, Software – Information Management

surya.mukherjee@ovum.com

Ovum Consulting

We hope that this analysis will help you make informed and imaginative business decisions. If you

have further requirements, Ovum’s consulting team may be able to help you. For more information

about Ovum’s consulting capabilities, please contact us directly at consulting@ovum.com.

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