Vast amounts of data leads to analysis paralysis among many marketers and business leaders. How can they navigate these treacherous waters?

Over the last decades data collection and analysis has grown exponentially. This has resulted in shifting roles for business leaders and marketers alike. On the surface, more data implies that businesses will run more efficiently, increasing profits in return. But, oftentimes, teams will be left with more questions than answers. 

New data raises new questions, while others wonder how accurate the data is to begin with. Organizational structures can hamper proper interpretation, leading to decisions that alter the course of the company, for better or for worse. How can business leaders manage and navigate the ever expanding black hole of performance tracking whilst their companies grow at a rapid pace?

Marketers experience analysis paralysis

In April 2016, Marketing Dive observed the changing role of marketing in corporate settings due to the emergence of data driven digital marketing. The rise of Big Data has drastically changed the field of marketing, which is now more integrated into the day-to-day operations within an organization. Marketing departments now work alongside sales, IT, business intelligence, customer care among others. 

During The Economist’s Marketing Unbound, attended by Marketing Dive, Director at the MIT AgeLab, Joseph Coughlin, said that Big Data isn’t a replacement for Big Ideas. Big Data serves a strategic purpose, helping to tailor consumer offerings. However, Coughlin warned that the technology doesn’t create visions for consumers to get excited for a brand. A brand afterall, is a collection of assets bundled in an abstract idea.  

As encouraging as the plea to keep Big Ideas alive is, marketers are finding themselves in a world that is dominated by results, nullifying what little imagination is left to add magic and flare to their brands. CMO at Avention, part of the commercial data analytics company, The Dun & Bradstreet Corporation, explained to Marketing Dive that data paralysis has become a real issue for marketers. Adding that while marketers recognize the value of data-driven decision making, the sheer volume of data fuels uncertainty. 

Marketers feel trapped, unable to draw sound conclusions from the massive insights at hand. This phenomenon requires marketers to work more closely together with IT-departments to collect, measure and retrieve relevant data to drive business results. Ample data can manifest in different forms, however. Data paralysis is but one of them. It can also be accompanied by anxiety and disengagement as organizational complexity increases. 

Mapping the customer experience

Marketers struggle particularly in mapping the customer journey and finding the right KPIs to meet their financial targets. The 2019 CMO Survey conducted by Deloitte found that monitoring the customer experience was one of the top challenges for marketing executives. Developing the necessary capabilities inside the organization to deliver and monitor the customers experience ranking first, with 13.4 percent of respondents citing this as the number one challenge within their organizations. 

This was followed by determining which touchpoint contributed most to the overall customer experience, representing 12.1 percent. Deloitte found that this was especially true within organizations with a heavy focus on internet sales, or ecommerce. At these companies, 20.6 of respondents indicated attribution as their top challenge, compared to only 7.2 percent at companies with no internet sales.

That marketers at ecommerce companies in particular cite attribution as their primary challenge isn’t surprising when weighing it against budget allocation and a strong dependency on IT departments to enhance customer journey monitoring. Multiple forces battle within the same space over a finite budget. Hence every digital marketing department, from programmatic display to search, demands optimal tracking to prove its contributions are contributing the most to overall financial performance. 

At the core of discovering the different touch points and designing an optimized customer experience is finding the most relevant KPIs that link the customer journey to financial results, with 9.1 percent of respondents indicating this being one of their top challenges. On top this sat the coordination with other departments such marketing, IT, HR and Sales. A point noted by Marketing Dive just three years prior. 

Poor metrics equals poor outcomes

However, having all the data doesn’t necessarily mean guaranteed positive outcomes. In 2017, McKinsey wrote that performance management can unlock a business’ hidden potential, but many companies run into problems when tapping into their data streams. The team at McKinsey notes that mapping informal and formal processes to align company operations to run more efficiently is crucial to meet strategic objectives. McKinsey cites GE as one of the primary examples of how, under the leadership of Jack Welch, a company was able to thrive by harmonizing data and operations.

While not much is left of General Electric’s status as an American icon, and many workers will disagree about the effects of the corporation on work in the US as a whole, GE was able to align its 250,000 workforce and put it on a growth trajectory. Thousands weren’t able to witness it however, as they were highly likely relieved of their duties due to Welch’s aggressive budget cuts. But that is beyond the point. GE remains one of the corporate standards when it comes to performance management, with many failing to emulate it. 

McKinsey cites several reasons why companies today fail to properly integrate performance management into their operations. The primary cause of faulty performance management returns is the metric selection itself. The team at McKinsey noted that business leaders may select metrics that don’t contribute to the actual performance. Leaders should only track metrics that allow for process optimization. 

McKinsey highlighted that manufacturing plants track performance per shift, rather than total output of shifts combined.  This has a psychological ripple effect, as it incentivizes unit workers to either complete tasks within the shift or stall to ensure the next team won’t make its target in time. Managers therefore should focus on total combined output to promote collaboration between the different teams who depend on each other’s performance.   

As an extension to poor metric selections, over-ambitious targets can hamper performance just as much. Leaders gravitate toward targets that are either overly ambitious or just out of reach for employees. While this might seem sensible from an executive’s perspective to further improve productivity, McKinsey warns that when targets seem unattainable, staff members will be reluctant to achieve them before they have even tried. 

Targets often aren’t clearly defined, further fueling a sense of unattainability with the employee. McKinsey found that many targets aren’t formulated in a manner that emphasizes the contribution of the worker to the overall business objectives. When formulated in broad business terms, the targets have little to no impact on the overall company performance. Instead, targets should be formulated clearly, verifiable and be communicated properly across the department.  

Visualization prevents analysis paralysis

Communicating targets and results is part of the larger framework of storytelling. Proper storytelling contributes to better data-driven decision making. In September 2022, Chief data and analytics officer at global technology research and advisory firm ISG, Kathy Rudy, explained to CIO, that storytelling through data is difficult. However, the skill is necessary to turn data into actionable insights. 

Rudy notes that enterprise leaders should keep their audience in mind, ensuring the data that is communicated is relevant to them. Proper structure should be able to anticipate audience questions. This means leaders shouldn’t get overly complicated, displaying their expertise on the subject at hand. The data presented should be digestible and answer questions. Frequently results presented during company-wide meetings are irrelevant to those not at the board table. 

Once the right data is selected, and its origin validated, it should be presented in a fashion that engages the audience. Director and analyst on the business analytics and data science team at Gartner, Peter Krensky, told CIO that visualizing data can create context and drive the message home. This rings especially true in settings that review business performance, which can either elevate workforce motivation and drive fear of job loss. Leaders should anchor the emotional component during the visual representation.     

Decrease analysis paralysis

In September 2019, Michael Harris and Bill Tayler at the Harvard Business Review argued that obsession over metrics shouldn’t harm business results. In the piece, both noted that in the era of data, leaders, and employees alike, have become paralyzed by the abundance of numbers. The abstraction of strategy has been replaced by hard metrics. 

This phenomenon is called surrogation, with the prime example brought forward was Well Fargo, where one day executives decided they wanted to track cross-sales to customers. Tracking this metric, the managers believed, would result in long-term customer relations. In practice this turned into one of the biggest fraud cases in the bank’s history, with overzealous sales-representatives opening 3.5 million accounts without customer’s consent. The failed strategy has resulted in reputational damage that would haunt Wells Fargo for years to come.

Harris and Tayler argued that by focusing on metrics alone, employee behavior was negatively influenced, as they would confuse the metric with the object being measured. Companies can prevent surrogation by engaging front-line workers with target formulation and ensure incentives should be anchored in the strategic metrics. Consequently metrics should work in tandem and not operate as a single entity. This ties in with McKinsey’s recommendations where metrics should be achievable and communicated with those responsible. 

Industrial-era thinking

Data however doesn’t exist in a vacuum. All evidence available, can easily be sabotaged by the structures within the company itself. In February 2021, in a post for Forbes, Steve Denning, commented that Key Performance Indicators (KPI), a measuring technique to track a team performance and contribution to the overall business performance, should be linked to the overall business’s objectives. Denning notes that while this seems fairly obvious, many organizations have lost sight of their own objectives. This effectively renders KPIs pointless. 

Corporations are oftentimes taken hostage by their own objectives, which in the most realistic sense, is generation shareholder value. But as this is considered a faux pas, resulting in bad publicity, they dilute their objectives into smaller chunks that appear to be decoupled from generation shareholder value, while at their core have the same outcome. Denning also notes that KPIs, due to hierarchical structures within corporations, are created top-down. 

Executives believe that the targets set will lead to increased worker productivity. In reality however, many employees don’t have the means to navigate through the many bureaucratic layers within an organization. As information travels down, portions reach the employee, further decreasing the ability to achieve one’s own targets. At the end of the race, managers in turn are left dissatisfied with their employees, which is a monster of their own creation. 

Denning argues that the poor translation of business objectives into achieving KPIs stem from companies still operating in frameworks designed during the industrial-era, which is centered around maximizing shareholder value, within rigid hierarchical organizations. Meanwhile, a small section of companies have transitioned into digital-era leadership. They are centered around delivering maximum value to customers, instead of shareholders.

Small teams are able to deliver great results, with hierarchical structures making way for unleashing individual talent. Denning’s argument sounds plausible, however, we might argue how long this system of digital-era management can last as organizations grow. The same has been witnessed at Google who had to give up many of its employee perks to boost productivity, falling into the same industrial-era thinking. Something it was so vehemently opposing during its early days. 

Improve decision making

Denning however touches on an important point, namely breaking down rigid structures and reframing the organization to focus on achievable results. Hierarchies and bureaucracy can be destructive when trying to turn metrics into actionable insights that enable the different entities within an organization to move in the same direction. In 2017, McKinsey argued that the only way to optimize decision making within an organization is breaking down the hierarchical structures that prevent collaboration and integrate accountability and authority within each layer. 

This however is easier said than done, McKinsey notes. Large corporations are complex and as they grow, accountability gets diluted. Managers are unable to delegate properly and with each expansion of the workforce, more decision makers get involved. Additionally, communication democratization through easy to use channels like email, Slack and shared workspace have added unnecessary clutter in a previously well-orchestrated machine. As information travels at unprecedented speeds through an organization, employees get disengaged and leaders loser oversight. 

Hence McKinsey recommends organizations to break down decisions into four separate categories, varying in scope and impact and their frequency. Big-bet decisions are broad and can drastically alter the course of an organization. These decisions are so big they can skyrocket growth or run a company into the ground.  The smartphone manufacturer Blackberry or the mishandling of Nokia by Microsoft are primarily examples of big-bet decisions that have upended the future of those companies. A more recent example is the struggle of Disney to turn its streaming service Disney+ profitable.   

Big-bets are followed by cross-cutting decisions, which are frequent high risk decisions, but are more compartmentalized in nature. Cross-cutting decisions are chains of smaller decisions that can alter the course of a company, but are collaborative efforts. At the layer below lies the delegated decisions. These are low-risk, frequent decisions cascaded to teams or individuals within an organization, not requiring close attention of top executives. At the bottom of the decision hierarchy are ad hoc decisions, which are infrequent and have low stakes. 

For the sake of this piece, we’ll focus on Big Bets decisions, as they have the most impact on the workforce. In order to prevent fatal miscalculation, McKinsey recommends that Big Bet decisions should always include an executive sponsor. The executive member oversees the project with the manager and ensures it remains within scope. Big Bet decisions should be broken down into smaller decisions that are digestible and during interlocks, progress can be made.  To facilitate progress, a decision-making approach should be established. Frameworks should be created to have a level-playing field that fosters discussions. 

It must be noted that the rigidity of the framework will differ per region, as different hierarchical structures exist. In order to mitigate top-executives taking the project hostage solely through their seniority, a rulebook should be created that allows for enough counterbalance to dispute decisions that might risk a successful completion of the entire project, as it slides away from its initial scope. A strong decision making framework will ensure that teams can move fast and have ample input at hand during each interlock to drive the project forward. 

OVIS Framework

One of such frameworks is the OVIS framework, highlighted by the Boston Consulting Group (BCG) in 2021. This framework, the BCG notes, is designed to improve transparency around decision making, including all the stakeholders involved and their roles within the project. The frame, which stands for “Own, Veto, Influence, Support”, clearly defines the role of each stakeholder within the project. 

The model behind the framework distills the observations made by McKinsey and allows for less abstraction. The owner of the project, as the name implies, is the final authority figure and is wholly accountable for the decisions being made within the project. Despite this large responsibility, they have to ensure all stakeholders are able to partake. They are also responsible for resolving conflict, creating an environment where ideas can be discussed openly. 

The Veto Holder can block the project owners decisions, but shouldn’t exercise their authority frequently. Vetoes can only be passed if the owners are bound to jeopardize the outcome of the project. The Veto Holders serve as points of escalation and can only be used to make critical decisions. This will be familiar territory for those working in corporate settings. Escalating to the highest authorities within an organization is a last resort when all other means of collaboration have failed. 

Influencers in turn implement the decisions from the owners or consult owners through their expertise on how to progress the project. Owners of the projects have to ensure the influencers can voice their expertise and be able to counter decisions made among the stakeholders. Whether or not BCG wanted to lean into buzzwords, having a committee of experts, who might not necessarily be involved with the project, can be a valuable addition toward a successful delivery. 

The last group in the framework are the supporters, who provide information to decision makers, but aren’t part of the decision making process itself. The support role cannot be trivialized, as they are essential for owners who need to stay up to date about the project’s progress. BCG’s framework is but one of many and one might argue whether it doesn’t add more unnecessary complexity. However, it can serve as a blueprint for project structuring and solicit better engagement from group members. 

Organizational complexity

We purposely framed this article beyond data collection and interpretation, as data exists and flows within complex and large organizations. Along this road, there are many decision makers who interpret the data differently from their organizational counterparts. In some cases, they’ll even skew the data or the optics of it, to serve their message. This can be as simple as changing the values along the axis of a graph, or leaving out certain portions that would hurt the overall message. 

Case in point remains that as a marketer you exist within this tightly knit web, may it be in projects, or trying to achieve your targets. Marketers will have to distill the data at hand through visualization or include only that is relevant to the story. We are big proponents of collecting as much data as you can, but only use what is relevant to the story and complement it with additional data when necessary. 

Business leaders in turn have a duty to create the necessary conditions where data is openly discussed to allow for new interpretations that otherwise would be subject to biases. As a leader, tracking metrics shouldn’t invite unsolicited behavior, however. They should be grounded in reality, achievable and not cannibalize on existing revenue streams. This is easier said than done, as within larger organizations, a certain form of hierarchy needs to exist and as many operations run simultaneously, determining which suit best for organization optimization can be tricky. 

That doesn’t mean no efforts can be made to drive egalitarian frameworks. A good example is the Dutch game developer Guerilla games, who used a studio-wide pitch process, where all employees could pitch their ideas for a new game. The forum concept led to one of the most successful franchises in the studio’s history.