There are various reasons why revenue might be falling for your company. Most of these reasons might be in your radar, and your strategy might include improving on those parameters in the next quarter.
But there is one silent killer in your revenue engine you might not be aware of. Ignoring this aspect can cost you heavily.
This is the case of siloed data.
90% of organizations cite data silos as a challenge to growth. And data inefficiencies cost organizations an average of $12.9 million every year.
Dependence on incorrect, incomplete, or stale data leads to erroneous decision-making that hurts revenue. Most of these issues related to data originate from data silos.
What do we mean when we say data inefficiencies? Using incorrect data to drive decisions across the organization or basing a new revenue strategy on the shoulders of stale data in your systems are some examples of data inefficiencies.
How can organizations make data-driven decisions that drive revenue? Is there a need for more data? Definitely not.
Every organization has their own data that can be leveraged to make powerful data-driven decisions. But most of this data is stuck in systems that don’t talk to each other.
Enabling this data to flow freely along the revenue engine can help capture critical information to understand your business better. Data-driven organizations are 23 times more likely to acquire new customers, 6 times more likely to retain them, and 19 times more likely to be profitable.
First Up, What is Siloed Data?
Siloed data is information from revenue-generating activities like sales, marketing or customer success that is stored in isolated tech stacks. This data is controlled by one team but inaccessible to others.
For example, marketing team might create revenue generating strategies with the data within their own tech stack. This data is not related to data that sales or customer success has. As a result, these departments keep working within their own confines, without having access to the same data, or devising ways to support each other.
Such silos hampers organizations as revenue generation is no longer the responsibility of just sales or marketing. It’s a team effort that demands all customer-facing functions to come together and refer to a single source of data. This is where revenue operations thrives as a function.
A data pile-up leaves everyone puzzled, and organizations pay dearly in terms of unaligned goals, internal competition, siloed thinking, and stunted growth.
How is Siloed Data Created?
Most departments don’t have access to unified, strategic data for cohesive planning. For instance, 47% of marketers blame data silos as their most significant problem for extracting insights from data.
Why is it that data silos thrive? Here are three key reasons:
1. Siloed departments
Departments within the organization often have individual goals to achieve. Along the way, they may miss the point of setting these goals – for the organization’s collective growth. As a result, they operate in silos concentrating on their own targets.
It mainly creates problems when customer-facing teams – marketing, sales, and customer support – start functioning in isolation. The common objective is to increase revenue and retain customers, and these teams must collaborate closely for success.
But with differentiated tech stacks for each team, it’s incredibly challenging to use one department’s data for another. As a result, sales and marketing teams often end up at loggerheads and can’t align GTM strategies.
The numbers are shocking because as much as 90% of sales and marketing professionals report misalignment in strategy, among other things. And it’s estimated that this misalignment costs businesses over $1 trillion each year.
2. Cultural resistance
Even as digital transformation sweeps the world, 54% of organizations manage customer-facing operations in silos.
In organizations that don’t follow a culture embedded with data, teams face challenges in deriving valuable insights from data. Only 25% of employees think they can leverage data to their benefit.
Then there’s limited knowledge of new technologies. Sometimes, leaders find upgraded systems challenging to comprehend. Since they hold key positions, accepting this shortcoming and stepping up digital transformation could feel overwhelming. 84% of employees think leaders should move to more modern tech stacks to capture business opportunities.
The result of this cultural resistance? Organizations end up sticking with legacy systems thinking these will perpetually perform well.
3. Separate tech stacks
72% of customer interactions are now via multiple digital touchpoints. Enterprises with data silos store information from individual touchpoints separately, giving rise to irregularities as data collected from each can be vastly different. And companies that don’t keep up with digital transformation risk losing nearly $7 million.
Moreover, as technology evolves, companies may add more tools to their tech stack for various functions and departments. With each clashing effort, this stack keeps growing and creating unmanageable siloed data if integrations aren’t in place.
And while applications are on the rise, integrations aren’t increasing equally. In 2022, an organization had 976 applications on average. But only 28% of these apps were integrated.
5 Ways Siloed Data Is Damaging Your Revenue
Maintaining data silos is doing more harm than good to businesses. While you must uphold customer confidentiality, not sharing a ton of other information will impact revenue adversely.
Below are 5 significantly adverse effects of siloed data.
1. Missed business opportunities
30% of respondents in a survey by CMO Council and Televerde stated they faced challenges in hitting revenue targets because of organizational silos. And that’s not all. 84% of employees are missing business opportunities by not moving to modern data solutions.
Fostering a ‘data ownership’ culture over a ‘data sharing’ one has a far-reaching impact on identifying new prospects and nurturing those in the sales pipeline. Unhealthy competition among teams further motivates them to fulfill internal targets while ignoring external possibilities. It also deepens the rift when leads handed over from marketing to sales don’t come with the right set of data.
2. Unfavourable customer experience
This lead handover also translates into cumbersome customer experiences. Most businesses have multiple customer touchpoints. If data from each is unlinked, sales teams have less visibility on which part of the sales funnel the lead is at. And as a result, they don’t know what serves these prospects the best.
An ineffective handover leaves customers unsatisfied through impersonal emails, repetitive conversations with multiple teams, and inability to make a purchasing decision. Rather effectively, this process creates a friction point by not providing complete clarity on helpful information.
As a result of siloed data, lack of visibility into the contact center for lead attribution also leads to marketers overestimating their customer acquisition costs. For instance, if conversions happen over a call, they may not be attributed correctly. Instead, an email is listed as the point of conversion, which may have higher costs attached.
This is an industry-wide problem, as suggested in a study by Invoca, wherein marketers confessed to overestimating CPA by an average of 46%. CAC is high, but the conversion rate is lower, giving marketing and sales teams incorrect data.
3. Ineffective revenue forecasts
Less than 25% of sales organizations have a 75% or greater inaccuracy in sales forecasting. Siloed data leads to information hoarding by teams. It presents an incomplete picture of performance and forecasts for leaders. In addition, subpar data analysis based on incomplete data, incorrect data, or duplicate entries creates further ambiguity.
Ultimately, the onus falls on the leader’s shoulders to combine insights from multiple departments. However, they can only gather team-level data and don’t understand how to tie it together to extract the correct information. Data silos create a massive and intricate data puzzle for managers, thus, slowing down decision-making on revenue forecasts.
4. Reduced employee productivity
Siloed data also has intangible effects on the organization’s revenue growth capabilities.
One such area is the lack of cross-functional collaboration between teams. For example, marketing and sales teams have historically faced challenges while working together. If the business loses out on revenue opportunities, blame-shifting is common.
Organizational culture that promotes a siloed mentality for goal achievement also leads to departments putting individual growth first and business growth second. Each team wants an initial win for themselves, not the company.
Outdated technologies and siloed data have implications on employee engagement, too. Employees may feel that the enterprise isn’t progressing with modern times. At the same time, the C-suite could be utterly clueless about how employees use data. According to ZDNet, 73% of C-suite leaders say they don’t know how their employees drive change with the help of data.
This deep misalignment between leaders and employees could cost critical revenue for the business.
5. Increased compliance risks
Individual silos work on different data security patches, which leaves them vulnerable to cyberattacks, privacy breaches, and information leaks. Given that each one needs various compliances, they continually pose a risk.
At other times, data stored in the warehouse is not accurate, consistent, or reliable. Consider this. If a salesperson is connecting with 25 leads in a day and entering each lead’s details in a spreadsheet manually, they’re bound to make an error. This threatens data integrity, further giving rise to incorrect predictions, goal-setting, and revenue projections.
The era of third-party data is currently undergoing massive change. Third-party data is a privacy issue for most consumers, and brands are taking note, too. Several major countries, such as the UK and US, have already placed restricting regulations. Moreover, key players such as Google Chrome, Firefox, and Safari are also phasing out third-party cookies.
This presents new challenges for enterprises with siloed data that don’t share data uniformly. According to Gartner, organizations that promote data sharing will outperform their competitors on most business metrics by 2023. But at the same time, less than 5% of data-sharing programs will correctly identify trusted data and locate trusted sources in 2022. It shows that most organizations aren’t prepared to retreat from third-party data.
Solve the Data Silo Problem
You can solve the siloed data problem and improve customer experience by making it better for your internal customers (employees) first. They’ll find it easier to focus on their core functions over administrative, repetitive tasks that aren’t crucial for revenue generation.
So, how can you tackle the key problems?
Problem 1
Poor tech stack integration among revenue teams makes reading data from other departments challenging.
Solution:
Invest in tools that easily integrate with your existing systems
Organizations today apply data-driven tech sporadically for a few teams. The rest either don’t get access or use a different solution. These solutions are not interlinked.
What they need instead are tech platforms that have seamless integration capabilities with existing systems. Therefore, enterprises don’t need to undergo a complete and complex overhaul of data-driven technology. Using these applications, departments can easily access and read siloed data from other warehouses.
These integrations can vary from organization to organization. When choosing tools for your tech stack, consider how easy it is to integrate with the rest of your existing systems. Opt for tools that don’t cost you an arm and a leg to fully sync with multiple systems that you may have.
Problem 2
Ineffective collaboration among revenue teams because of siloed data has a direct, adverse impact on GTM strategies.
Solution:
Provide a single source of truth for your revenue teams
Leaders can build an ‘organization-first’ thinking over a ‘team-first’ approach that ties each department’s efforts to achieve collective goals.
They can set this in motion by deploying data-sharing systems across the enterprise in place of siloed data. A single source of truth (SSOT) aggregates data from all systems within the organization into one platform, enabling compelling business insights.
A centralized, AI-based data repository serves robust revenue growth through deep insights and intelligent predictions. It also motivates customer-facing departments to sell as one team and execute cross-functional collaboration.
Data-sharing also opens up new perspectives for teams as they access information from other departments, which they couldn’t previously. Employees can then use this renewed understanding of the business to craft more unified and improved revenue strategies.
Problem 3
Inconsistent and unreliable siloed data leads to incorrect goal and revenue projections, causing critical compliance risks
Solution:
Improve data compliance
Third-party data may cause hindrances in gathering reliable data from multiple sources. But the move to a privacy-focused, digital world is prompting more users and enterprises to shift to first-party data. First-party data is reliable since users share this directly with you. This information could be their email ID and phone number, which is crucial for nurturing and closing leads.
First-party data has all the benefits that third-party doesn’t – privacy compliance, uniqueness, accuracy, low-cost availability, and marketability.
Another way to improve data quality is by defining methods for feeding information into your data platform. These could be using the same data format or labeling tasks the same way across the organization. It ensures that information in the system is uniform and shareable with any team.
Data also needs regular cleaning to keep it current, contextual, and complete. However, this isn’t just an IT task. Leaders can encourage teams to refresh data as a joint exercise and reward them for finishing the activity successfully. It could also strengthen bonds between employees from different departments.
Problem 4
Legacy systems employ siloed data that isn’t upgraded for automation, further increasing the burden on employees.
Solution:
Deploy AI-based tech
AI-based systems automate repetitive, time-consuming tasks giving teams more freedom to perform their core functions. Functions that bring in revenue for the business.
These platforms have improved further, using intelligent integrations for siloed data to extract hidden insights from across the organization. It gives superior decision-making prowess to leaders.
AI capabilities also enable sales teams to refine the sales funnel, chart out customer journeys for each stage, generate high-quality leads, and, more importantly, plug revenue leaks.
Nektar.ai’s revenue intelligence system works in this direction. It automatically captures first-party data from multiple touchpoints (including phone, Zoom, and email, among others). Which enables the platform to share intelligent and accurate opportunity matches.
It enriches contacts with this information, extends the pipeline by uncovering buried leads, and stops revenue leaks, transforming this data into powerful insights that leaders can use to drive better decisions.
Use Revenue Intelligence to Address Siloed Data Woes
Gartner’s Chief Data Officer Survey shows that CDOs who successfully execute data-sharing initiatives are 1.7 times more effective at displaying business value and ROI from their data strategy.
Organizations that want to be successful today need to embrace digital transformation. Additionally, they must align sales, marketing, and customer service operations using an integrated tech stack.
Today, no single team is responsible for driving growth and revenue, leaving no place for siloed data. Revenue leaders must work in tandem by engaging in continuous, seamless data exchange for a unified view of accessible, available, and actionable revenue generation systems.
To sum up, here’s how you can deploy a revenue intelligence strategy effectively by crushing siloed data:
- Take a comprehensive view of your revenue engine. Including the alignment of data, goals, and strategy across the organization. Unify all data with a revenue intelligence system.
- Consider all departments involved, such as sales, marketing, customer service, finance, and IT.
- Upgrade your tech stack to avoid sporadic technology applications and data governance issues.
- Involve both people and processes for holistic technology, talent, and culture development
Nektar.ai’s platform is purpose built for modern revenue teams to unify all the data into one central data platform. We integrate and capture information from across the sources and intelligently wire it back to your CRM, making it your source of record, so that you don’t have to lift a finger.
Nektar ends up becoming a system of intelligence on top of this collected data to surface actionable alerts and insights on pipeline risks, deviations against organization’s best practices, depth and strength of seller relationship within buyer committee.
