Modern business intelligence (BI) requires businesses to look outside of their typical sources of customer data so they can better understand customers' needs. Whether businesses realize it or not, customers are producing data about their experiences with brands and products in a variety of ways such as through social media channels, chats, community, email, meetings, helpdesks, product usage, and more. But identifying, gathering, and analyzing this information to help make informed business decisions is easier said than done.

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In the past, enterprise business intelligence has been executed by a combination of people and software, with both playing a critical role in aggregating, understanding and interpreting data that ultimately aims to inform better business decisions. However, one main issue with business intelligence software of the past is that it required the technical expertise of data analysts, engineers, and architects to be able to connect the data, normalize it, and extract useful insights. 

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This necessity for technical expertise in customer data management is driving a movement for low code/no code solutions to sort customer data, help nontechnical experts leverage their customer data, and make more informed business decisions based on customers’ expressed challenges and needs. With these advancements in technology, business intelligence is no longer siloed to just the technical experts, but rather it can be extracted by anyone with customer data aiming to get a deeper understanding of their customers.

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The cost of traditional business intelligence

To understand how new technologies are shifting the way business intelligence is produced, let's first define BI in the traditional sense. Business intelligence is any effort to leverage software and services to transform data into actionable insights that inform an organization’s strategic and tactical business decisions.

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Traditional business intelligence is a costly process for businesses to execute in both people and software. The cost to purchase a Customer Data Platform (CDP) can range from $100,000 to $300,000 annually, based on Gartner client and vendor conversations, while the labor costs to build and maintain a CDP in-house can be significantly more. In addition to purchasing a CDP,  organizations must also hire technical experts to run these platforms, manage the data that inputs into them, and extract insights using data visualization tools like Looker, Tableau, or Domo.

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Particularly for small-to-midsize businesses, these CDP costs are a nonstarter. 

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As a result, small to mid-sized saas businesses have been forced to make incredibly difficult decisions when it comes to customer data and business intelligence - rely on siloed data that is often incomplete and inconsistent, or invest significant amounts of time and money into building a traditional BI infrastructure.

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This challenge, in parallel with the desire for more immediate access to customer data and insights, is the primary driver of the low code/no code movement.

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What does traditional business intelligence cost you in time?

In addition to a major annual budget expenditure, BI initiatives also require significant ongoing time investments from both the technical experts you hired to run your CDP as well as other resources from your business.

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Data Team Backlogs

Data analysts need to process data requests from all teams across an organization. When we consider the push towards data-driven decision-making and the speed at which go-to-market teams need to operate these days, it quickly becomes apparent that your data team will not be able to keep pace with the urgency of the requests form from your go-to-market organization. This problem compounds as the size of the organization increases. The number of requests a data team receives quickly begins to backlog resulting in a governing effect across the organization.

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ETL - Extract, Transform, and Load

One of the biggest challenges in consuming customer data is structuring it in a way that enables it to be joined, organized, and made available for consumption. With this in mind, data architects need to spend time cleaning, normalizing, and connecting data – not just extracting insights from it. Not to mention, folding in new data sources and removing old ones as GTM teams change their tooling.

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These time investments result in front-line teams being slow to react to opportunities and risks that are captured through customer data. Often, GTM  teams are waiting for data to be processed, queries to be run, reports to be generated, or going through an internal back-and-forth to ensure queries are built correctly and insights are properly positioned.

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These time delays are difficult to quantify because they impact each business differently, but when you consider the speed at which software and information move these days, this delay in customer intelligence is putting your team at a disadvantage, and negatively impacting your customers' experience.

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What does Traditional Data Management cost you in customer experience?

One of the larger threats businesses face in not having a robust business intelligence system is that they can become misaligned with customers in their offerings due to incomplete data. The time and budget crunches of traditional BI can delay response time to new opportunities because businesses cannot adequately prioritize their overwhelming workloads. Without insights extracted through analyzed customer data, businesses lack the context they need to understand how customers feel, which leads to misalignment between what customers want and what the business is offering them.

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Today, businesses tend to over-emphasize product usage data when triggering retention, churn mitigation, expansion, and new business opportunities.

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This data in isolation can be misleading. Consider this example:

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One of your customers is a very active product user. They are hitting all your KPIs and are using all of your key features. They spend a great deal of time in your product. From a product usage standpoint, they look like a very happy customer …

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But, what you can’t see in this data is that a lot of the time they are spending is dealing with confusion in your UI and that they’ve been active in online communities trying to find help from their peers, they’ve even asked about alternatives to your product.

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In this example, you can see how looking at these different data sources can paint dramatically different stories. Reacting to either data in isolation will result in an out-of-context customer experience.

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The importance of joining disparate data sources has never been more urgent. SaaS has pushed the conversation away from owned properties, to many different online sources (social, community, chat, reviews, etc), and customers have access to more information than ever.

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All of these forces combined put an immense amount of pressure on saas businesses to deliver a highly relevant, and timely experience. Do this well, and your customer will love you. But, screw it up and they will quickly start their search for a tool that will meet their demands.

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What does Traditional Data Management cost you in go-to-market alignment?

Even after significant investment in BI, teams continue to operate in their siloed data tools. Sales teams work in Salesforce, marketing works in Hubspot, customer Success uses a combination of SaaS tools like Gainsight, Totango, ChurnZero, and HubSpot, while growth teams and executives look to BI to inform how their day-to-day operations are influencing business success. This produces an ecosystem of employees in your business focused on different data sources to determine how the business is functioning. With everyone chasing different data, it’s easy for the day-to-day business operation to get lost.

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Teams that rely on data from their siloed tools struggle to stay in alignment and also spend significant amounts of time auditing and reconciling their siloed data. This slows them down and can result in misalignment of goals, confusion, inconsistent customer experiences, and ultimately, missed opportunities.

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Conversely, with a low code/no code business intelligence solution that aggregates and structures customer data in a way that optimizes insights, businesses can mitigate these costs, confusion, and poor customer experiences. Additionally, they can accelerate the time it takes to extract insights from data and better understand their customers.

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To learn more about how to leverage a modern business intelligence tool that speeds data collection and insights, and enables business professionals to unify customer data and generate meaningful insights, with no code and no technical expertise visit: https://www.loudnclear.ai/.

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