Bottlenecked business processes can spell slow-motion disaster for companies in search of a competitive edge. So why do so many of these businesses rely on a few trained Data Specialists to not only provide the rest of the team with valuable information to help them execute and achieve but furthermore conduct their own higher-level analyses that help forecast the future?
Companies around the world face data accessibility challenges, and they’re distinctly harmful to IT departments and prevent them from making all the difference they could. Business teams bombard Data teams with requests on a daily basis in order to more fully fill their job descriptions. It’s understandable why this is the case: even though companies can have a number of analytics options available at any given time, they are predominantly too complex for non-technical employees to use without special training or direct help from the Data team.
That’s why we’re seeing an emergence of Business Intelligence platforms that more readily bridge the gap between natural human language and high-level data queries. If employees can learn information from the data team by asking literate, sensible questions, then a technological tool that can process those queries in the same natural language represents the next level of efficiency in data user experience.
We’re aware of a bank that successfully reduced the number of analysts needed to handle business requests, which freed up the budget while restoring momentum toward more complex initiatives, like predictive analytics. We’re aware of a Manufacturing company that was able to get by with fewer expensive software licenses for a particular Data Visualization tool to serve more than 50 users. An energy utility company used these enhanced tools to identify network anomalies, and a sports company managed to improve its internal decision-making process for managing its players’ performance incentives.
Even business newbies know that new efficiencies like these directly equate to increased revenue, improved employee morale, enhanced decision-making abilities, and a more robust company overall. There are three big ways that this happens.
Increased Agility Means Fewer External Requests for the Data Team
Companies across the board depend on collecting and interpreting large swaths of data to understand their existing positions within a business environment and form a plan to improve it. But this data is too consistently siloed away from teams at large — it is left for the specially trained data wonks to work with, and this is a clear liability.
What if every employee was a Data Scientist by virtue of being able to pose a question to software? Instead of occupying the Data team’s time and resources to gain access to specific information — last year’s revenues or the top-selling products of the quarter, for example — they could type this question into a computer and get instant relevant feedback?
This not only raises the average competency of every employee by endowing them with the ability to help themselves to the data points they seek, but it eases the burden on a company’s specially trained data team. Instead of catering to a number of pings and requests that come from across departments, they are instead free to do the heavy lifting and analysis that their job description more immediately calls for!
Fewer Dashboards Means Increased Efficiency
Data Science Software Products tend to be exclusive to those who have specific training. Whether you’re seeking a single data point or looking to identify a complicated business trend, you’re bound to need to drill down through a complicated interface of dashboards and charts until you arrive at exactly the right one.
But there is no such complex interface when we interact with our human coworkers. We simply ask them a question, and they either have the answer or they don’t. You might even say that Business Intelligence platforms with this uniquely “human” layer to their interface — they can understand natural language requests — have the easiest user interface of all. It’s just a conversation!
Increased Savings Means Your Existing Budget Goes Farther
“Data Scientist” can be a pricey job description to hire for, and large enterprises might have entire teams of these people on staff. They work to handle requests from other employees while also running their own complex analysis to inform intricate, strategic business decisions.
But Business Intelligence software that can process natural language queries empowers employees by providing easy access to data, providing intelligence that everyone from a Data Scientist to an events marketer will understand. If a worker can form a question, then the platform can instantaneously provide a worthwhile answer that would have otherwise occupied the time and resources of a dedicated Data Science team.
When every member of a business organization has easy access to that company’s store of data, it democratizes the relatively elite school of Data Science. Data from the past and present is an invaluable tool in helping companies navigate the uncertain future. If access to that data is bottlenecked by an elite team that only represents a fraction of the whole, then it’s a self-evident liability on the road to business success. Business Intelligence platforms that can process natural language will smash these bottlenecks.