Health startups are saying that unclear regulations are stifling AI innovation within the sector. In fact, such precautions are vital within the healthcare industry, where it’s literally a case of life or death. But what makes less sense is the sluggish adoption of AI across enterprise SaaS – an area that isn’t being held back by red tape like other sectors are.
So what’s stopping enterprises from adopting AI to streamline and optimize their processes? The first perpetrator is the hoards of messy data that accumulates as corporations grow and add latest tools and products. In this text, I’ll delve into how messy data is a blocker to AI innovation in enterprise, and explore the solutions.
Welcome to the info jungle
Let’s start by taking a look at a typical data challenge that many modern businesses face. Initially, when businesses offer a limited range of products, they typically have clean revenue data that’s all housed inside a single system. Nevertheless, as they expand their offerings and adopt a variety of revenue models, things quickly get messy.
For instance, a business might initially employ a one-time purchase model, but later introduce additional options resembling subscriptions or consumption-based pricing. As they expand, they’ll likely diversify their sales channels, too. An organization that starts with 100% product-led self-serve sales may realize over time that they need the assistance of sales teams to up-sell, cross-sell, and land larger clients.
During rapid growth stages, many businesses simply stack latest sales systems onto existing ones. They’ll procure a distinct SaaS tool to administer each different motion, pricing model, purchasing process, and so forth. It’s not unusual for a corporation’s marketing department alone to have 20 different SaaS tools with 20 different data silos.
So while corporations generally start with clean, integrated data, growth causes data to quickly spiral uncontrolled, often well before businesses recognize it as a problem. Data becomes siloed off between billing, success, customer success, and other systems, meaning corporations lose global visibility into their inner workings. And unfortunately, manually reconciling data is usually so labor-intensive and time-consuming that insights will be outdated by the point they’re able to use.
AI can’t fix your messy data for you
Several prospective clients have asked us – “well if AI’s so great, can’t it just solve this messy data problem for us?” Alas, AI models usually are not the panacea for this data problem.
Current AI models require clean datasets to work properly. Corporations counting on diverse sales motions, SaaS platforms and revenue processes inevitably accumulate disparate and fragmented datasets. When a business’s revenue data is scattered across incompatible systems that may’t communicate with one another, AI can’t make sense of it. For instance, what’s labeled as “Product” in a single system could possibly be very different from “Product” in one other system. This subtle semantic difference is difficult for AI to discover and would inevitably result in inaccuracies.
Data must be properly cleansed, contextualized and integrated before AI comes into the image. There is a longstanding misconception that data warehousing offers a one-size-fits-all solution. In point of fact, even with an information warehouse, data still must be manually refined, labeled, and contextualized, before businesses can use it to supply meaningful analytics. So in this fashion, there are parallels between data warehousing and AI, in that companies must get to the basis of messy data before they will reap the advantages of either of those tools.
Even when data has been contextualized, AI systems are still estimated to hallucinate at the least 3% of the time. But an organization’s financials — where even a decimal point within the flawed place could have a domino effect disrupting multiple processes — require 100% accuracy. This implies human intervention remains to be essential to validate data accuracy and coherence. Integrating AI prematurely may even create more work for human analysts, who need to allocate additional time and resources to correcting these hallucinations.
An information catch-22
Nevertheless, the proliferation of SaaS solutions and resulting messy data does have several solutions.
First, corporations should repeatedly assess their tech stack to make sure that each tool is strictly vital to their business processes, and not only contributing to the info tangle. You could find that there are 10 and even 20+ tools that your teams are using each day. In the event that they’re truly bringing value to departments and the general business, don’t do away with them. But when messy, siloed data is disrupting processes and intelligence gathering, that you must weigh its advantages against switching to a lean, unified solution where all data is housed in the identical tool and language.
At this point, businesses face a dilemma when selecting software: all-in-one tools can offer data coherence, but possibly less precision in specific areas. A middle ground involves businesses looking for out software that gives a universal object model that’s flexible, adaptable, and seamlessly integrated with the final ecosystem. Take Atlassian’s Jira, for instance. This project management tool operates on an easy-to-understand and highly extensible object model, which makes it easy to adapt to several types of project management, including Agile Software Development, IT/Helpdesk, Marketing, Education, and so forth.
To navigate this trade-off, it’s crucial to map out the metrics that matter most to your corporation and work back from there. Identifying your organization’s North Star and aligning your systems towards it ensures that you simply’re architecting your data infrastructure to deliver the insights you wish. As an alternative of focusing solely on operational workflows or user convenience, consider whether a system contributes to non-negotiable metrics, resembling those crucial to strategic decision-making.
Ultimately, it’s the businesses that invest time and resources into unjumbling the info mess they’ve gotten themselves into who shall be the primary to unlock the true potential of AI.
