Organizations Are Getting Too Little Value From Analytics

“Through 2022, only 20% of analytic insights will deliver business outcomes.” -Gartner [2]
Organizations are getting too little value from analytics. Billions are being spent on big data and analytics [1], yet many of the initiatives either fail or fall short of expectations, resulting in wasted resources and a destruction of value. Unfortunately, these failed initiatives result in organizations continuing to make decisions the old way—based on intuition rather than data and analytics. When using data and analytics to make decisions, the decision-making process is slow as business users must wait for each analysis to be produced. This results in organizations not having a complete grasp of their current business state and value from analytics left remaining on the table.

Why are organizations getting too little value from analytics?

Organizations are getting too little value from analytics for a variety of reasons. Primary reasons include 1) a proper data and analytics foundation not in place before making large investments in analytics, 2) staff lacking required analytical skills, and 3) the organization having inadequate analytics processes. These reasons are discussed in greater detail below.

Poor Foundation


A large government agency invested in Tableau licenses, but very few people were using them because no one actually knew how to use Tableau. Investing in a training program and then broadly communicating the opportunity would have resulted in more people knowing how to use the newly available software and would have achieved better results.

A poor data and analytics foundation—that is, data governance may be insufficient, the data may be of poor quality, or the data may be siloed and unready for analytics—inevitably leads to analytics investments that result in wasted resources. The organization’s culture may not be conducive to analytical decision-making, and a lack of collaboration across business units may lead to duplicate efforts and wasted resources. Individual initiatives may not be well-planned, such as when organizations make a large software buy without investing in the training necessary to make the most of the software. Without a strong foundation, the value obtained from analytics initiatives will be, at most, sub-optimal.

Lack of Required Skills

Organizations provide analytics staff with tools and data, but they do not always provide the support and training necessary to use the tools effectively. The result is that staff revert to the less advanced tools they are comfortable using. Additionally, the analytics staff may not know the ins and outs of the business—especially when compared to non-technical staff—while the non-technical staff and executives are usually not analytics-literate, which can result in a breakdown in communication between the various parties. All of this makes it hard—if not impossible—for the organization to fully realize the potential of analytics.

Inadequate Processes

The analytics used to make decisions are typically not located where decisions are made—that is, analytics are not embedded in an organization’s day-to-day workstreams. There can also be a lack of transparency and communication around the data and processes used to generate the analytics results. These together lead to decision-makers who do not understand, trust, and subsequently do not rely on the analytics output, which results in wasted resources and reductions in employee morale. For organizations to get the most out of their analytics investments, insights should be integrated into workstreams, tailored to the business user, and produced by transparent and trustworthy processes.

What will help organizations get more value out of analytics?

There are multiple initiatives that an organization can undertake to ensure they are receiving the greatest value from their analytics. Some of these initiatives include 1) creating an analytics center of excellence (CoE), 2) performing an analytics maturity assessment, 3) implementing a data strategy initiative, and 4) investing in embedded and self-service analytics capabilities. An overview of these initiatives follows.

1)      Analytics CoE

An Analytics CoE can take many forms. The most effective is an internal team that promotes, develops, and evolves analytics to better achieve the organization’s objectives. The CoE should be a permanent team with well-defined roles and responsibilities. CoE responsibilities would include improving technical and non-technical staff’s analytics capabilities, implementing organization-wide standards to improve processes and increase transparency, setting analytics strategy, and promoting analytics across the organization.

2)      Analytics Maturity Assessment

An analytics maturity assessment is an organizational assessment that utilizes an analytics maturity model to evaluate where an organization is at in terms of analytics maturity. The assessment should determine the organization’s current analytics maturity and identify areas that could benefit from analytics. The assessment should also outline the steps required to achieve the desired level of maturity.

3)      Data Strategy Initiative

As defined by Gartner [3], data strategy is a “highly dynamic process employed to support the acquisition, organization, analysis, and delivery of data in support of business objectives.” A data strategy initiative aims to establish standard processes and practices that improve an organization’s ability to manage, transform, and share data in a repeatable manner. Therefore, implementing a data strategy initiative is essential to laying the needed data foundation for advanced analytics.

4)      Embedded and Self-Service Analytics

Embedded analytics is when advanced analytics are directly integrated into enterprise applications. In contrast, self-service analytics is a form of analytics where line-of-business staff can perform queries and generate reports independently. The primary goal of both of these initiatives is to make it easier for business users to use analytics results and make better decisions for the organization. The most efficient way to accomplish this goal is to integrate analytics directly into employee workstreams or give non-technical staff the ability to dig into the organization’s data and generate insights on their own.

What is the best path to getting more value from analytics?

While every organization is different, most organizations should start by creating an analytics CoE. The analytics CoE would be responsible for the entire analytics lifecycle. The CoE would be instrumental in achieving value, given that one of the primary goals of the CoE is to maximize the value that results from investing in analytics.

The next step is to perform an analytics maturity assessment. The analytics maturity assessment will help the organization gain a better understanding of where they are, where they want to be, and what needs to be done to get there. In other words, the assessment will help the organization gain a clearer vision of what needs to be done to get the most value out of their analytics.

The third step is to implement a data strategy initiative. The CoE would design the initiative to give the organization the data foundation needed to be successful. The initiative will also help the organization avoid wasting large sums of money due to poor data quality [4].

The last step is the realization of value through embedded and self-service analytics. This step is when the results of analytics are used to drive organizational decisions that—in turn—result in the value creation.

It is important to note that the work does not end here. Organizations must continue to refine and improve their analytical capabilities to stay ahead of the competition and continue creating value. Once these four initial steps are complete, organizations can engage in even more advanced analytics initiatives to create even greater value.


Organizations often take the wrong strategy when it comes to data and analytics. Many organizations will start by putting out a press release that announces that the organization is becoming data-driven. Then the organization will invest heavily in cloud-based “big data” technologies, purchase licenses for a data visualization tool, and hire a large team of highly paid data scientists with backgrounds in machine learning, AI, and natural language processing. The problem with this approach is that a proper data and analytics foundation has not been laid down. There is no ongoing education program to help staff gain necessary skills, and analytics processes are inefficient and burdensome. The result is a destruction of value, rather than value creation. Laying the proper analytics foundation first will help your organization avoid making costly mistakes and will help you get the most value out of your analytics initiatives. It will also help put your organization on the right track to becoming a top-tier analytics competitor.