Gartner reports that poor data quality costs businesses $12.9 million annually. Often, data initiatives are developed and iterated to suit the needs of a business, but they fail to deliver the requisite value. In this article, we explore the causes of these failures and approaches to alleviate the challenges.


Why Data Initiatives Don’t Deliver the Value?

Poor Data Foundation

As per Gartner, most organizations believe data is vital, with nearly 80% of CEOs outlining that their companies will lose the competitive edge if they do not efficiently use data.

However, organizations cannot expect to have effective data hygiene practices in place unless they have a fully established data governance program. They cannot access or combine the data they have because it is locked away in departmental silos. They may not even be aware of the information required to nurture effective operations.

Furthermore, many organizations lack the necessary core technology to achieve their data-centric goals, as they pursue solutions that promise great profits but aren’t exactly a good fit for their requirements.

The Ambiguity of Roles and Responsibilities

One common issue that businesses encounter is determining how to organize the team (or teams) of data analysts. Data initiatives may struggle to deliver substantial value if they lack clearly defined objectives and a strong link with the organization’s overarching business goals.

Confusing or contradictory goals can lead to consequences and impede decision-making processes. Besides, businesses frequently hire techies but lack the infrastructure to support them, which generally results in one of two outcomes:

  • Because the analysts are dispersed across multiple business divisions, they are unable to learn from or interact effectively with one another, resulting in work being duplicated owing to a lack of visibility.
  • They are all gathered together in one siloed team, which causes them to lose track of what is going on. As such, they find it difficult to interact with the numerous teams they should be assisting.

Insufficient Data Structure 

High-quality data is essential for data efforts to deliver trustworthy insights and practical suggestions. Poor data quality, missing datasets, or data distributed across several systems can stymie data initiatives and introduce biases and mistakes into analytical processes. Decisions based on incorrect analysis might lead to erroneous strategies and unsatisfactory results.

Furthermore, poor data quality frequently necessitates more time and effort to clean, validate, and reconcile data before it can be used for analysis. Manual data cleansing can be time-consuming and resource-intensive, causing decision-making processes to be delayed and operational efficiency to suffer.

Siloed Data and Lack of Communication 

Data silos and a lack of communication within organizations can harm data initiatives, resulting in fragmented insights and missed opportunities to leverage varied skills. When data is housed in several systems or departments, it becomes difficult to integrate and analyze the information holistically, resulting in a limited grasp of the whole picture.

Because multiple teams may obtain and analyze similar data independently, siloed data frequently results in repeated work. This duplication costs resources and reduces efficiency. Furthermore, it impedes the capacity to obtain comprehensive insights by merging and correlating data from many sources.

It is also, unfortunately, common for the insights available from data to remain inaccessible to the business users who are best placed to act on them. Far too often, information is buried inside arcane schemas and overwhelming reports that business users can’t access or understand. While business users expect actionable insights, what they end up getting is complex reports that take long to decipher and call for too much specific technological knowledge to parse.

Furthermore, organizations miss out on the ability to tap into varied skills and viewpoints in the absence of cross-functional collaboration.


How to Make Data Initiative Deliver Value?

Define Goals and Responsibilities 

Businesses should clearly articulate the goals and objectives of the data initiative. They should focus on identifying specific outcomes and measurable metrics that will determine success. The goals should be realistic but challenging enough to ensure that the initiative will succeed.

Besides, the business should formulate and document a data vision that defines the desired outcomes. This is a way to communicate with stakeholders and other decision-makers how the business could benefit from data-driven decisions, processes, and technologies.

Furthermore, organizations should establish clear leadership for the data initiative. If there are multiple people involved in a project, these individuals need to have one person in charge of shaping interdepartmental collaboration and guiding team activity.

Nurture a Data-Driven Culture

To nurture a data-driven culture, it’s essential that organizations invest in a culture that values and embraces data-driven decision-making, encourages employees to use data to support their ideas and initiatives, and recognizes and rewards data-driven successes.

How to do that?

  • By democratizing systems that make it easy for anyone to contribute their insights or ask questions about the business
  • By creating incentives for employees at all levels to experiment with new ways of doing things, even when there’s no direct value delivery

Ensure Data Quality and Accessibility

Data cleansing, validation, and enrichment processes can help businesses improve data quality. With these, businesses can assign a project an appropriate amount of risk. They can also put in place strong data collection systems and invest in technology that allows for effective data integration and accessibility.

A key part of this paradigm is demystifying the data to make it easier for business users to understand. Effective data visualization solutions are a must to enable the creation of visually-appealing, yet meaningful and relevant reports that business users can act upon.

High-quality data can relay information on how efficiently an organization is working and illustrate ways to enhance efficiency and productivity through additional analysis and reporting.

Emphasize Continuous Iterations and Improvements 

Treating data efforts as iterative processes necessitates a continual improvement mindset. It requires routinely monitoring and analyzing the outcomes and performance of data projects in relation to predefined objectives and indicators. Organizations can acquire useful insights into what performed well and what needs to be improved by analyzing the results. This feedback loop enables the discovery of strengths, flaws, and opportunities.

Based on these findings, further activities, such as refining data collection methods, upgrading algorithms, or optimizing analytical models, can be carried out. This iterative approach promotes a culture of learning, flexibility, and creativity, ensuring that data efforts improve and add value over time.

At the end of the day, realizing the full potential of data initiatives requires expertise in integrating data, ensuring data quality and consistency, establishing a comprehensive data governance strategy, and more — precisely where Trinus can help. Explore our data management capabilities here.