Combating Tool Sprawl in Enterprises through No Code solutions
Tool sprawl in enterprise teams results in loss of productivity, siloed data, threats to data security, and data sprawl. No code workflows and tool centralization are key to countering its effects.
Tool Sprawl: An overview
The time-tested adage that too many cooks spoil the broth is entirely appropriate to summarize tool sprawl within enterprises in the current digital landscape. It occurs when enterprise teams and organizations add so many tools to their stack that they begin to experience collateral damage that outweighs the anticipated benefits.
Tool sprawl occurs when the toolchain loses overall efficiency due to unchecked additions of new tools. Under such a scenario, enterprises find it increasingly challenging to navigate the extensive toolchain. As a result, time, resources, and productivity are lost, adversely impacting business outcomes.
Having multiple single-use case tools leads to tool sprawl.
For example, critical functions such as cybersecurity account for multiple single-use case tools. According to one estimate, globally, organizations have 29 security monitoring tools in place on average. Such point solutions only capture siloed data, necessitating more comprehensive visualization or analysis tools. Disparities between single-use case tools hamper integration, leading to disjointed operations and greater resource utilization.
The effect of tool sprawl is also being seen in the AI lifecycle, where vast quantities of data are involved. Within this lifecycle, data labeling, where labels are added to raw data to train ML models, the negative impact of tool sprawl is quite pronounced, as organizations tend to have multiple single-use case tools and point solutions to label different data types such as images, texts, audio, video, etc.
With increased digitalization and data proliferation, tool sprawl has become a recurrent phenomenon. More significant innovation is inundating the market with more tools and upgrades, which only complicates the efforts required to alleviate tool sprawl. As each organizational department proceeds with the unchecked addition of more tools, data silos are bound to widen and adversely impact overall productivity.
According to HubSpot, about 82% of employees lose up to an hour every day just managing all of their tools. The lost productivity is aside from more unintended consequences such as data sprawl, siloed data, poor integration, and data security threat.
The consequences of Tool Sprawl
Efficiency and productivity plummet
If the HubSpot study is anything to go by, productivity loss is the most significant consequence of tool sprawl. As enterprise teams juggle between multiple tools, they become prone to burnout and spend disproportionate time on trivial functions. The time, which could be spent on more productive and value-oriented activities, is now lost.
Lost productivity and impact on collaboration
When tool sprawl is an organization-wide issue, it creates a culture of disjointed operations and a lack of inter-departmental collaboration. Today, it is also essential to view this problem through the lens of remote working, where the need for collaboration and productivity is considerably high.
Siloed data and the domino effect
Tool sprawl often leads to data sprawl, which is counterproductive to AI-driven enterprises, where the process efficiency has a direct bearing on AI analysis and the derived insights. By making it hard to collate vital data, tool sprawl makes it difficult to derive insights due to the vast amount of data spread across multiple tools.
If each enterprise team uses multiple single-use case tools, it becomes increasingly challenging to harmonize different data sets, leading to insights whose credibility can be questionable. Therefore, siloed data is essentially the end of optimal outcomes and the start of many issues resulting in a domino effect.
Lack of integration between tools
In the survey mentioned earlier, over half of the respondents said they no longer use multiple tools for a few reasons, chiefly the lack of integration. The potential financial loss due to the lack of integration, obsolete technology, and skill gap could grow to $235,000 if the outcomes are at odds with the GDPR. Without integration, productivity leaks across the value chain, hindering the organization from scaling, making valuable upgrades, and growing.
The immediate threat to data safety and security
While the detriments of tool sprawl tend to take their toll on an organization gradually, they can pose immediate cybersecurity threats. Multiple single-use case tools often translate to poor operational visibility and insights, compromising the security teams’ ability to fend off cyberattacks successfully.
Increased Mean Time to Detection and Mean Time to Repair
Lack of integration and collaboration hinders proactive tackling of anomalies and vectors. And the resulting increase in Mean Time to Detection (MTTD) and Mean Time to Repair (MTTR) can cost a company dearly.
No code solutions
Since tool sprawl is the consequence of the unchecked addition of new tools, the first step in addressing the issue is to develop foresight. Before a new tool is implemented, enterprise teams must check toolchain feasibility in terms of integration and compatibility.
No code solutions also offer the means to combat tool sprawl as they do not require highly technical abilities to operate digital businesses. Platforms such as Coda and Notion are notable examples of no code solutions, finding applications in website building, product management, monitoring, word processing, spreadsheets, and database functions.
Build no code workflows using drag and drop that address multiple-use cases
Through no code workflows, any organization, without expensive technical recruitment, can streamline functions related to digital working. While modern no code solutions retain their simplistic, drag-and-drop features and user-friendly interfaces, they are becoming more advanced and bridging gaps with ML Ops-heavy models. Most importantly, they are significantly more affordable and agile. As a result, practitioners can stay nimble with evolving market conditions by leveraging rapid deployment capabilities.
Labeling tool sprawl in enterprises
Typically, AI-driven enterprises that require labeled data use single-use case tools for each data type, such as Labelbox for image annotation, Datasaur for NLP annotation, Scale for computer vision annotation, and Superannotate for video annotation. This use of multiple platforms and tools for each data type invariably leads to tool sprawl within the labeling ecosystem.
If a tool is central to the functioning of a team and accompanies integration challenges, it is advisable to explore implementing labeling solutions like Taskmonk, which is a no code application for enterprises that allows the handling of multiple data types and labeling teams on a single unified platform. Such unified platforms come inherently without silos and characterize high interoperability, which minimizes the possibility of tool sprawl.
The Taskmonk Advantage
At Taskmonk, we power data labeling operations for enterprises enabling them to create differentiated data-centric AI at scale.
Create data-centric AI at scale
Using Taskmonk, AI teams can manage multiple labeling partners on a single platform and optimize labeling costs using our proprietary allocation algorithms and AI models. Taskmonk’s purpose-built no code framework also allows AI teams to create customized labeling solutions and QC workflows across data types and labeling processes.