Building your data and analytics strategy

When it comes to being data-driven, organizations run the gamut with maturity levels. Most believe that data and analytics provide insights. But only one-third of respondents to a TDWI survey said they were truly data-driven, meaning they analyze data to drive decisions and actions.

Successful data-driven businesses foster a collaborative, goal-oriented culture. Leaders believe in data and are governance-oriented. The technology side of the business ensures sound data quality and puts analytics into operation. The data management strategy spans the full analytics life cycle. Data is accessible and usable by multiple people – data engineers and data scientists, business analysts and less-technical business users.

TDWI analyst Fern Halper conducted research of analytics and data professionals across industries and identified the following five best practices for becoming a data-driven organization.

1. Build relationships to support collaboration

If IT and business teams don’t collaborate, the organization can’t operate in a data-driven way – so eliminating barriers between groups is crucial. Achieving this can improve market performance and innovation; but collaboration is challenging. Business decision makers often don’t think IT understands the importance of fast results, and conversely, IT doesn’t think the business understands data management priorities. Office politics come into play.

But having clearly defined roles and responsibilities with shared goals across departments encourages teamwork. These roles should include: IT/architecture, business and others who manage various tasks on the business and IT sides (from business sponsors to DevOps).

2. Make data accessible and trustworthy

Making data accessible – and ensuring its quality – are key to breaking down barriers and becoming data-driven. Whether it’s a data engineer assembling and transforming data for analysis or a data scientist building a model, everyone benefits from trustworthy data that’s unified and built around a common vocabulary.

As organizations analyze new forms of data – text, sensor, image and streaming – they’ll need to do so across multiple platforms like data warehouses, Hadoop, streaming platforms and data lakes. Such systems may reside on-site or in the cloud. TDWI recommends several best practices to help:

  • Establish a data integration and pipeline environment with tools that provide federated access and join data across sources. It helps to have point-and-click interfaces for building workflows, and tools that support ETL, ELT and advanced specifications like conditional logic or parallel jobs.
  • Manage, reuse and govern metadata – that is, the data about your data. This includes size, author, database column structure, security and more.
  • Provide reusable data quality tools with built-in analytics capabilities that can profile data for accuracy, completeness and ambiguity.

3. Provide tools to help the business work with data

From marketing and finance to operations and HR, business teams need self-service tools to speed and simplify data preparation and analytics tasks. Such tools may include built-in, advanced techniques like machine learning, and many work across the analytics life cycle – from data collection and profiling to monitoring analytical models in production.

These “smart” tools feature three capabilities:

  • Automation helps during model building and model management processes. Data preparation tools often use machine learning and natural language processing to understand semantics and accelerate data matching.
  • Reusability pulls from what has already been created for data management and analytics. For example, a source-to-target data pipeline workflow can be saved and embedded into an analytics workflow to create a predictive model.
  • Explainability helps business users understand the output when, for example, they’ve built a predictive model using an automated tool. Tools that explain what they’ve done are ideal for a data-driven company.

4. Consider a cohesive platform that supports collaboration and analytics

As organizations mature analytically, it’s important for their platform to support multiple roles in a common interface with a unified data infrastructure. This strengthens collaboration and makes it easier for people to do their jobs.

For example, a business analyst can use a discussion space to collaborate with a data scientist while building a predictive model, and during testing. The data scientist can use a notebook environment to test and validate the model as it’s versioned and metadata is captured. The data scientist can then notify the DevOps team when the model is ready for production – and they can use the platform’s tools to continually monitor the model.

5. Use modern governance technologies and practices

Governance – that is, rules and policies that prescribe how organizations protect and manage their data and analytics – is critical in learning to trust data and become data-driven. But TDWI research indicates that one-third of organizations don’t govern their data at all. Instead, many focus on security and privacy rules. Their research also indicates that fewer than 20 percent of organizations do any type of analytics governance, which includes vetting and monitoring models in production.

Decisions based on poor data – or models that have degraded – can have a negative effect on the business. As more people across an organization access data and build  models, and as new types of data and technologies emerge (big data, cloud, stream mining), data governance practices need to evolve. TDWI recommends three features of governance software that can strengthen your data and analytics governance:

  • Data catalogs, glossaries and dictionaries. These tools often include sophisticated tagging and automated procedures for building and keeping catalogs up to date – as well as discovering metadata from existing data sets.
  • Data lineage. Data lineage combined with metadata helps organizations understand where data originated and track how it was changed and transformed.
  • Model management. Ongoing model tracking is crucial for analytics governance. Many tools automate model monitoring, schedule updates to keep models current and send alerts when a model is degrading.

In the future, organizations may move beyond traditional governance council models to new approaches like agile governance, embedded governance or crowdsourced governance.

But involving both IT and business stakeholders in the decision-making process – including data owners, data stewards and others – will always be key to robust governance at data-driven organizations.

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There’s no single blueprint for beginning a data analytics project – never mind ensuring a successful one.

However, the following questions help individuals and organizations frame their data analytics projects in instructive ways. Put differently, think of these questions as more of a guide than a comprehensive how-to list.

1. Is this your organization’s first attempt at a data analytics project?

When it comes to data analytics projects, culture matters. Consider Netflix, Google and Amazon. All things being equal, organizations like these have successfully completed data analytics projects. Even better, they have built analytics into their cultures and become data-driven businesses.

As a result, they will do better than neophytes. Fortunately, first-timers are not destined for failure. They should just temper their expectations.

2. What business problem do you think you’re trying to solve?

This might seem obvious, but plenty of folks fail to ask it before jumping in. Note here how I qualified the first question with “do you think.” Sometimes the root cause of a problem isn’t what we believe it to be; in other words, it’s often not what we at first think.

In any case, you don’t need to solve the entire problem all at once by trying to boil the ocean. In fact, you shouldn’t take this approach. Project methodologies (like agile) allow organizations to take an iterative approach and embrace the power of small batches.

3. What types and sources of data are available to you?

Most if not all organizations store vast amounts of enterprise data. Looking at internal databases and data sources makes sense. Don’t make the mistake of believing, though, that the discussion ends there.

External data sources in the form of open data sets (such as data.gov) continue to proliferate. There are easy methods for retrieving data from the web and getting it back in a usable format – scraping, for example. This tactic can work well in academic environments, but scraping could be a sign of data immaturity for businesses. It’s always best to get your hands on the original data source when possible.

Caveat: Just because the organization stores it doesn’t mean you’ll be able to easily access it. Pernicious internal politics stifle many an analytics endeavor.

4. What types and sources of data are you allowed to use?

With all the hubbub over privacy and security these days, foolish is the soul who fails to ask this question. As some retail executives have learned in recent years, a company can abide by the law completely and still make people feel decidedly icky about the privacy of their purchases. Or, consider a health care organization – it may not technically violate the Health Insurance Portability and Accountability Act of 1996 (HIPAA), yet it could still raise privacy concerns.

Another example is the GDPR. Adhering to this regulation means that organizations won’t necessarily be able to use personal data they previously could use – at least not in the same way.

5. What is the quality of your organization’s data?

Common mistakes here include assuming your data is complete, accurate and unique (read: nonduplicate). During my consulting career, I could count on one hand the number of times a client handed me a “perfect” data set. While it’s important to cleanse your data, you don’t need pristine data just to get started. As Voltaire said, “Perfect is the enemy of good.”

6. What tools are available to extract, clean, analyze and present the data?

This isn’t the 1990s, so please don’t tell me that your analytic efforts are limited to spreadsheets. Sure, Microsoft Excel works with structured data – if the data set isn’t all that big. Make no mistake, though: Everyone’s favorite spreadsheet program suffers from plenty of limitations, in areas like:

  • Handling semistructured and unstructured data.
  • Tracking changes/version control.
  • Dealing with size restrictions.
  • Ensuring governance.
  • Providing security.

For now, suffice it to say that if you’re trying to analyze large, complex data sets, there are many tools well worth exploring. The same holds true for visualization. Never before have we seen such an array of powerful, affordable and user-friendly tools designed to present data in interesting ways.

Caveat 1: While software vendors often ape each other’s features, don’t assume that each application can do everything that the others can.

Caveat 2: With open source software, remember that “free” software could be compared to a “free” puppy. To be direct: Even with open source software, expect to spend some time and effort on training and education.

7. Do your employees possess the right skills to work on the data analytics project?

The database administrator may well be a whiz at SQL. That doesn’t mean, though, that she can easily analyze gigabytes of unstructured data. Many of my students need to learn new programs over the course of the semester, and the same holds true for employees. In fact, organizations often find that they need to:

  • Provide training for existing employees.
  • Hire new employees.
  • Contract consultants.
  • Post the project on sites such as Kaggle.
  • All of the above.

Don’t assume that your employees can pick up new applications and frameworks 15 minutes at a time every other week. They can’t.

8. What will be done with the results of your analysis?

A company routinely spent millions of dollars recruiting MBAs at Ivy League schools only to see them leave within two years. Rutgers MBAs, for their part, stayed much longer and performed much better.

Despite my findings, the company continued to press on. It refused to stop going to Harvard, Cornell, etc. because of vanity. In his own words, the head of recruiting just “liked” going to these schools, data be damned.

Food for thought: What will an individual, group, department or organization do with keen new insights from your data analytics projects? Will the result be real action? Or will a report just sit in someone’s inbox?

9. What types of resistance can you expect?

You might think that people always and willingly embrace the results of data-oriented analysis. And you’d be spectacularly wrong.

Case in point: Major League Baseball (MLB) umpires get close ball and strike calls wrong more often than you’d think. Why wouldn’t they want to improve their performance when presented with objective data? It turns out that many don’t. In some cases, human nature makes people want to reject data and analytics that contrast with their world views. Years ago, before the subscription model became wildly popular, some Blockbuster executives didn’t want to believe that more convenient ways to watch movies existed.

Caveat: Ignore the power of internal resistance at your own peril.

10. What are the costs of inaction?

Sure, this is a high-level query and the answers depend on myriad factors.

For instance, a pharma company with years of patent protection will respond differently than a startup with a novel idea and competitors nipping at its heels. Interesting subquestions here include:

  • Do the data analytics projects merely confirm what we already know?
  • Do the numbers show anything conclusive?
  • Could we be capturing false positives and false negatives?

Think about these questions before undertaking data analytics projects Don’t take the queries above as gospel. By and large, though, experience proves that asking these questions frames the problem well and sets the organization up for success – or at least minimizes the chance of a disaster.

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Most organizations understand the importance of data governance in concept. But they may not realize all the multifaceted, positive impacts of applying good governance practices to data across the organization. For example, ensuring that your sales and marketing analytics relies on measurably trustworthy customer data can lead to increased revenue and shorter sales cycles. And having a solid governance program to ensure your enterprise data meets regulatory requirements could help you avoid penalties.

Companies that start data governance programs are motivated by a variety of factors, internal and external. Regardless of the reasons, two common themes underlie most data governance activities: the desire for high-quality customer information, and the need to adhere to requirements for protecting and securing that data.

What’s the best way to ensure you have accurate customer data that meets stringent requirements for privacy and security?

For obvious reasons, companies exert significant effort using tools and third-party data sets to enforce the consistency and accuracy of customer data. But there will always be situations in which the managed data set cannot be adequately synchronized and made consistent with “real-world” data. Even strictly defined and enforced internal data policies can’t prevent inaccuracies from creeping into the environment.

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Why you should move beyond a conventional approach to data governance?

When it comes to customer data, the most accurate sources for validation are the customers themselves! In essence, every customer owns his or her information, and is the most reliable authority for ensuring its quality, consistency and currency. So why not develop policies and methods that empower the actual owners to be accountable for their data?

Doing this means extending the concept of data governance to the customers and defining data policies that engage them to take an active role in overseeing their own data quality. The starting point for this process fits within the data governance framework – define the policies for customer data validation.

A good template for formulating those policies can be adapted from existing regulations regarding data protection. This approach will assure customers that your organization is serious about protecting their data’s security and integrity, and it will encourage them to actively participate in that effort.

Examples of customer data engagement policies

  • Data protection defines the levels of protection the organization will use to protect the customer’s data, as well as what responsibilities the organization will assume in the event of a breach. The protection will be enforced in relation to the customer’s selected preferences (which presumes that customers have reviewed and approved their profiles).
  • Data access control and security define the protocols used to control access to customer data and the criteria for authenticating users and authorizing them for particular uses.
  • Data use describes the ways the organization will use customer data.
  • Customer opt-in describes the customers’ options for setting up the ways the organization can use their data.
  • Customer data review asserts that customers have the right to review their data profiles and to verify the integrity, consistency and currency of their data. The policy also specifies the time frame in which customers are expected to do this.
  • Customer data update describes how customers can alert the organization to changes in their data profiles. It allows customers to ensure their data’s validity, integrity, consistency and currency.
  • Right-to-use defines the organization’s right to use the data as described in the data use policy (and based on the customer’s selected profile options). This policy may also set a time frame associated with the right-to-use based on the elapsed time since the customer’s last date of profile verification.

The goal of such policies is to establish an agreement between the customer and the organization that basically says the organization will protect the customer’s data and only use it in ways the customer has authorized – in return for the customer ensuring the data’s accuracy and specifying preferences for its use. This model empowers customers to take ownership of their data profile and assume responsibility for its quality.

Clearly articulating each party’s responsibilities for data stewardship benefits both the organization and the customer by ensuring that customer data is high-quality and properly maintained. Better yet, recognize that the value goes beyond improved revenues or better compliance.

Empowering customers to take control and ownership of their data just might be enough to motivate self-validation.

Click her to access SAS’ detailed analysis

The Future of CFO’s Business Partnering

BP² – the next generation of Business Partner

The role of business partner has become almost ubiquitous in organizations today. According to respondents of this survey, 88% of senior finance professionals already consider themselves to be business partners. This key finding suggests that the silo mentality is breaking down and, at last, departments and functions are joining forces to teach and learn from each other to deliver better performance. But the scope of the role, how it is defined, and how senior finance executives characterize their own business partnering are all open to interpretation. And many of the ideas are still hamstrung by traditional finance behaviors and aspirations, so that the next generation of business partners as agents of change and innovation languish at the bottom of the priority list.

The scope of business partnering

According to the survey, most CFOs see business partnering as a blend of traditional finance and commercial support, while innovation and change are more likely to be seen as outside the scope of business partnering. 57% of senior finance executives strongly agree that a business partner should challenge budgets, plans and forecasts. Being involved in strategy and development followed closely behind with 56% strongly agreeing that it forms part of the scope of business partnering, while influencing commercial decisions was a close third.

The pattern that emerges from the survey is that traditional and commercial elements are given more weight within the scope of business partnering than being a catalyst for change and innovation. This more radical change agenda is only shared by around 36% of respondents, indicating that finance professionals still largely see their role in traditional or commercial terms. They have yet to recognize the finance function’s role in the next generation of business partnering, which can be

  • the catalyst for innovation in business models,
  • for process improvements
  • and for organizational change.

Traditional and commercial business partners aren’t necessarily less important than change agents, but the latter has the potential to add the most value in the longer term, and should at least be in the purview of progressive CFOs who want to drive change and encourage growth.

Unfortunately, this is not an easy thing to change. Finding time for any business partnering can be a struggle, but CFOs spend disproportionately less time on activities that bring about change than on traditional business partnering roles. Without investing time and effort into it, CFOs will struggle to fulfill their role as the next generation of business partner.

Overall 45% of CFOs struggle to make time for any business partnering, so it won’t come as a surprise that, ultimately, only 57% of CFOs believe their finance team efforts as business partners are well regarded by the operational functions.

The four personas of business partnering

Ask a room full of CFOs what business partnering means and you’ll get a room full of answers, each one influenced by their personal journey through the changing business landscape. By its very variability, this important business process is being enacted in many ways. FSN, the survey authors, did not seek to define business partnering. Instead, the survey asked respondents to define business partnering in their own words, and the 366 detailed answers were all different. But underlying the diversity were patterns of emphasis that defined four ‘personas’ or styles of business partnering, each exerting its own influence on the growth of the business over time.

A detailed analysis of the definitions and the frequency of occurrence of key phrases and expressions allowed us to plot these personas, their relative weight, together with their likely impact on growth over time.

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The size of the bubbles denotes the frequency (number) of times an attribute of business partnering was referenced in the definitions and these were plotted in terms of their likely contribution to growth in the short to long term.

The greatest number of comments by far coalesced around the bottom left-hand quadrant denoting a finance-centric focus on short to medium term outcomes, i.e., the traditional finance business partner. But there was an encouraging drift upwards and rightwards towards the quadrant denoting what we call the next generation of business partner, “BP²” (BP Squared), a super-charged business partner using his or her wide experience, purview and remit to help bring about change in the organization, for example, new business models, new processes and innovative methods of organizational deployment.

Relatively few of the 383 business partners offering definitions of a business partner, concerned themselves with top line growth i.e. with involvement in commercial sales negotiations or the sales pipeline – a critical part of influencing growth.

Finally, surprisingly few finance business partners immersed themselves in strategy development or saw their role as helping to ensure strategic alignment. It suggests that the ongoing transition of the CFO’s role from financial steward to strategic advisor is not as advanced as some would suggest.

Financial Performance Drivers

Most CFOs and senior finance executives define the role of the business partner in traditional financial terms. They are there to explain and illuminate the financial operations, be a trusted, safe pair of hands that manages business risk, and provide s ome operational support. The focus for these CFOs is on communicating a clear understanding of the financial imperative in order to steer the performance of the business prudently.

This ideal reflects the status quo and perpetuates the traditional view of finance, and the role of the CFO. It’s one where the finance function remains a static force, opening up only so far as to allow the rest of the business to see how it functions and make them more accountable to it. While it is obviously necessary for other functions to understand and support a financial strategy, the drawback of this approach is the shortcomings for the business as a whole. Finance-centric business partnering provides some short-term outcomes but does little to promote more than pedestrian growth. It’s better than nothing, but it’s far from the best.

Top-Line Drivers

In the upper quadrant, top line drivers focus on driving growth and sales with a collaborative approach to commercial decision-making. This style of business partnering can have a positive effect on earnings, as improvements in commercial operations and the management of the sales pipeline are translated into revenue.

But while top line drivers are linked to higher growth than financial-focused business partners, the outcome tends to be only short term. The key issue for CFOs is that very few of them even allude to commercial partnerships when defining the scope of business partnering. They ignore the potential for the finance function to help improve the commercial outcomes, like sales or the collection of debt or even a change in business models.

Strategic Aligners

Those CFOs who focus on strategic alignment in their business partnering approach tend to see longer term results. They use analysis and strategy to drive decisionmaking, bringing business goals into focus through partnerships and collaborative working. This business benefit helps to strengthen the foundation of the business in the long term, but it isn’t the most effective in driving substantial growth. And again, there is a paucity of CFOs and senior finance executives who cited strategy development and analysis in their definition of business partnering.

Catalysts for change

The CFOs who were the most progressive and visionary in their definition of business partnering use the role as a catalyst for change. They challenge their colleagues, influence the strategic direction of the business, and generate momentum through change and innovation from the very heart of the finance function. These finance executives get involved in decision-making, and understand the need to influence, advise and challenge in order to promote change. This definition is the one that translates into sustained high growth.

The four personas are not mutually exclusive. Some CFOs view business partnering as a combination of some or all of these attributes. But the preponderance of opinion is clustered around the traditional view of finance, while very little is to do with being a catalyst for change.

How do CFOs characterize their finance function?

However CFOs choose to define the role of business partnering, each function has its own character and style. According to the survey, 17% have a finance-centric approach to business partnering, limiting the relationship to financial stewardship and performance. A further 18% have to settle for a light-touch approach where they are occasionally invited to become involved in commercial decision-making. This means 35% of senior finance executives are barely involved in any commercial decision-making at all.

More positively, the survey showed that 46% are considered to be trusted advisors, and are sought out by operational business teams for opinions before they make big commercial or financial decisions.

But at the apex of the business partnering journey are the change agents, who make up a paltry 19% of the senior finance executives surveyed. These forward thinkers are frequently catalysts for change, suggesting new business processes and areas where the company can benefit from innovation. This is the next stage in the evolution of both the role of the modern CFO and the role of the finance function at the heart of business innovation. We call CFOs in this category BP² (BP Squared) to denote the huge distance between these forward-thinking individuals and the rest of the pack.

Measuring up

Business partnering can be a subtle yet effective process, but it’s not easy to measure. 57% of organizations have no agreed way of measuring the success of business partnering, and 34% don’t think it’s possible to separate and quantify the value added through this collaboration.

Yet CFOs believe there is a strong correlation between business partnering and profitability – with 91% of respondents saying their business partnering efforts significantly add to profitability. While it’s true that some of the outcomes of business partnering are intangible, it is still important to be able to make a direct connection between it and improved performance, otherwise those efforts may be ineffective but are allowed to continue.

One solution is to use 360 degree appraisals, drawing in a wider gamut of feedback including business partners and internal customers to ascertain the effectiveness of the process. Finance business partnering can also be quantified if there are business model changes, like the move from product sales to services, which require a generous underpinning of financial input to be carried out effectively.

Business partnering offers companies a way to inexpensively

  • pool all their best resources to generate ideas,
  • spark innovation
  • and positively add value to the business.

First CFOs need to recognize the importance of business partnering, widen their idea of how it can add value, and then actually set aside the enough time to become agents of change and growth.

Data unlocks business partnering

Data is the most valuable organizational currency in today’s competitive business environment. Most companies are still in the process of working out the best method to collect, collate and use the tsunami of data available to them in order to generate insight. Some organizations are just at the start of their data journey, others are more advanced, and our research confirms that their data profile will make a significant difference to how well their business partnering works.

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The survey asked how well respondents’ data supported the role of business partnering, and the responses showed that 18% were data overloaded. This meant business partners have too many conflicting data sources and poor data governance, leaving them with little actual usable data to support the partnering process.

26% were data constrained, meaning they cannot get hold of the data they need to drive insight and decision making.

And a further 34% were technology constrained, muddling through without the tech savvy resources or tools to fully exploit the data they already have. These senior finance executives may know the data is there, sitting in an ERP or CRM system, but can’t exploit it because they lack the right technology tools.

The final 22% have achieved data mastery, where they actively manage their data as a corporate asset, and have the tools and resources to exploit it in order to give their company a competitive edge.

This means 78% overall are hampered by data constraints and are failing to use data effectively to get the best out of their business partnering. While the good intentions are there, it is a weak partnership because there is little of substance to work with.

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The diagram above is the Business Partnering Maturity Model as it relates to data. It illustrates that there is a huge gap in performance between how effective data masters and data laggards are at business partnering.

The percentage of business partners falling into each category of data management (‘data overloaded’, ‘data constrained,’ etc) has been plotted together with how well these finance functions feel that business partnering is regarded by the operational units as well as their perceived influence on change.

The analysis reveals that “Data masters” are in a league of their own. They are significantly more likely to be well regarded by the operations and are more likely to act as change agents in their business partnering role.

We know from FSN’s 2018 Innovation in Financial Reporting survey that data masters, who similarly made up around one fifth of senior finance executives surveyed, are also more innovative. That research showed they were more likely to have worked on innovative projects in the last three years, and were less likely to be troubled by obstacles to reporting and innovation.

Data masters also have a more sophisticated approach to business partnering. They’re more likely to be change agents, are more often seen as a trusted advisor and they’re more involved in decision making. Interestingly, two-thirds of data masters have a formal or agreed way to measure the success of business partnering, compared to less than 41% of data constrained CFOs, and 36% of technology constrained and data overloaded finance executives. They’re also more inclined to perform 360 degree appraisals with their internal customers to assess the success of their business partnering. This means they can monitor and measure their success, which allows them to adapt and improve their processes.

The remainder, i.e. those that have not mastered their data, are clustered around a similar position on the Business Partnering Maturity Model, i.e., there is little to separate them around how well they are regarded by operational business units or whether they are in a position to influence change.

The key message from this survey is that data masters are the stars of the modern finance function, and it is a sentiment echoed through many of FSN’s surveys over the last few years.

The Innovation in Financial Reporting survey also found that data masters outperformed their less able competitors in three key performance measures that are indicative of financial health and efficiency: 

  • they close their books faster,
  • reforecast quicker and generate more accurate forecasts,
  • and crucially they have the time to add value to the organization.

People, processes and technology

So, if data is the key to driving business partnerships, where do the people, processes and technology come in? Business partnering doesn’t necessarily come naturally to everyone. Where there is no experience of it in previous positions, or if the culture is normally quite insular, sometimes CFOs and senior finance executives need focused guidance. But according to the survey, 77% of organizations expect employees to pick up business partnering on the job. And only just over half offer specialized training courses to support them.

Each company and department or function will be different, but businesses need to support their partnerships, either with formal structures or at the very least with guidance from experienced executives to maximize the outcome. Meanwhile processes can be a hindrance to business partnering in organizations where there is a lack of standardization and automation. The survey found that 71% of respondents agreed or strongly agreed that a lack of automation hinders the process of business partnering.

This was followed closely by a lack of standardization, and a lack of unification, or integration in corporate systems. Surprisingly the constraints of too many or too complex spreadsheets only hindered 61% of CFOs, the lowest of all obstacles but still a substantial stumbling block to effective partnerships. The hindrances reflect the need for better technology to manage the data that will unlock real inter-departmental insight, and 83% of CFOs said that better software to support data analytics is their most pressing need when supporting effective business partnerships.

Meanwhile 81% are looking to future technology to assist in data visualization to make improvements to their business partnering.

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This echoes the findings of FSN’s The Future of Planning, Budgeting and Forecasting survey which identified users of cutting edge visualization tools as the most effective forecasters. Being able to visually demonstrate financial data and ideas in an engaging and accessible way is particularly important in business partnering, when the counterparty doesn’t work in finance and may have only rudimentary knowledge of complex financial concepts.

Data is a clear differentiator. Business partners who can access, analyze and explain organizational data are more likely to

  • generate real insight,
  • engage their business partners
  • and become a positive agent of change and growth.

Click here to access Workiva’s and FSN’s Survey²