It should be the least controversial idea in the world: when people have easy access to quality information, they’ll make better decisions.
But for so many of the world’s businesses, this quality information lives behind a veil that can only be pierced by trained professionals who specialize in data science or other niche topics. The user experience of business data generally suffers for companies seeking to make an impact ” employees must be able to interpret a complicated graph in order to arrive at actionable information, or be able to understand a number of complex acronyms or abbreviations. That’s why it’s likely that employees are leaning on domain experts in order to explain the nitty-gritty and provide the underlying analytics. This approach is less than totally productive because it doesn’t promote the worker autonomy that actualized businesses depend on in pursuit of a goal.
What’s worse is that employees may also be dismissing the data altogether. This weakens an organization’s decision-making power and increases the overall risk of arriving at a suboptimal course of action.
It’s easy to suggest data visualization tools as the catch-all solution for making it easier to interact with business data. These software products serve their purpose, but they commonly remain in the domain of specialized technical staff. They represent a stepping stone, not the destination, for companies seeking higher employee engagement with data.
In order to create new opportunities for workers to leverage the data that’s already at a business organization’s fingertips, we need a step-change in how people engage with that data. Specific implementations will vary from company to company, but it shouldn’t be terribly difficult for any employee to recontextualize himself or herself as a data scientist that makes informed decisions backed by historical business information. These employees don’t need expensive and time-consuming data science training, they only need to tag the skills they’ve been developing their entire lives: communication.
Accessing the right piece of data should only be as difficult as forming the question necessary to yield it. Existing data visualization tools may be useful when people are used to seeing an overview-style presentation of data with charts and graphs in a big report, but there are more and more tools that can be used on top of existing platforms that allow users to get data in real-time. Processing inquires, whether straightforward (Who were our three biggest customers in Q3 2018?) or complex (What are the three highest revenue cities of Product A the past five years in North America?), deserves rapid-fire, accurate responses. And technology is finally at a place to fill this gap.
It’s called natural language processing, often shortened to NLP. This is a branch of artificial intelligence methodology that lets computers interact with common human language in order to run calculations and fetch data. Now human workers without any training can make queries as if they were speaking to a human data scientist dedicated to their requests alone. It’s only a matter of having the data on hand and having an AI tool capable of scouring it.
This type of user interface nearly shrinks away to nothing, leaving organizations all the better for it. Instead of employees messaging a specialist with their query (who is likely working on a different task, by the way), employees can send that same message a piece of software that understands what they seek and returns relevant results immediately.
This intuitive user interface coupled with rapid response time leads organizations to a meaningful ROI in three arenas. They go as follows.
Productivity: employees spend less time in pursuit of data.
When workers are used to seamless consumer-grade apps, they have a hard time working with clunky and inefficient platforms. But people find greater productivity when they can use the tools they already prefer or feel confident in.
Yet mainstream data tools nowadays were designed with the specialist in mind. They exist to serve employees with niche knowledge who are specifically trained to use them. This presents a major hurdle toward getting more workers to use or otherwise engage with data, and the specialist is the bottleneck that the data must go through. This person is saddled with the burden of interpreting and making available data to the rest of the team.
But the natural language paradigm changes this forever: as easily as an employee could ask the human data scientist a specific question, automated software can now answer it. Whether it’s identifying an arduous business trend or a single data point, business intelligence platforms that can process natural language requests increase productivity and efficiency.
Data-driven decisions trigger untapped business results.
When it’s easy and ordinary for employees across the board to query data, with or without any special data science certifications, they’re simply going to make better decisions as a result. Big data methodologies are overly complex, calling not only for the gathering of massive amounts of information but cleaning and sorting it across different sources before integrating it with various systems.
This process is too easily overwhelming for an uninitiated worker. Even if a company has a requisite piece of data on hand, it’s less likely that workers will seek it out and use it to inform their decisions unless it is very easily accessible.
Natural language is a user interface as old as writing itself, so it comes quite naturally without any training necessary. And we’re already using this in our daily lives through browser searches. When we need information about something, it’s a quick Google search away. Having such quick and easy access to information comes intuitively in our daily lives, yet we haven’t managed to create this in our workplaces. Yet by 2025, 75% of the workforce will be digitally native. They will demand that their enterprise software be as intuitive and functional as the platforms they use in their personal lives, not settling for information gatekeepers.
Reduced risks: organizations can take better-calculated bets.
Businesses might not always have the full picture, but easy access to quality data can help them paint it. When you don’t know everything, the simple truth is that you want to know as much as possible, and by making it easier for workers across departments to access higher-quality data, they stand an improved chance of making calls that will pay off in the future.
Historical data can inform what’s likely to happen in the future. While every company wrestles against the unknown future, it’s clearly in their interest to democratize access to the data that helps them come to grips with the unknown.
It’s simple: when people have access to higher-quality information, they’re going to make higher-quality decisions. And natural language probably the most desirable interface for interacting with and accessing the piles of data that companies already collect by default. That data serves less use if it’s left to the data specialists.
Businesses can instead reimagine each of their workers as a kind of data specialist. They only need a proper user interface in order to see a meaningful return on their investment in how they wrangle their data.
