It’s 2020 — “Alexa” can place orders for us online while we’re sitting in our self-driving car on our way to pick up our new virtual reality headset. It’s truly amazing… But why do we still have to rely on Data Analysts to run lengthy and complex queries for us to see simple data outputs? (which may not be what we really needed in the first place!) Why do some solutions rely on query languages that only a minority of people know how to construct? Why aren’t all solutions making data analytics available to the masses?
Amazon does this with Alexa through Natural Language Processing (NLP) — a subset of Machine Learning (ML) that allows computers to understand the languages that humans speak (English, Spanish, Arabic, etc…). While the world of Artificial Intelligence (AI) might seem like science-fiction, we can actually do these things today! Yes, we are saying that computers have the ability to comprehend and learn, but don’t be nervous, this isn’t SkyNet… Yet!
But HOW can we make the job of data analysts simple for a non-technical person?
Data Analytics + NLP = B-I-E (Built-In-Efficiencies)
Yes, we created our own acronym for this blog — but why? Maybe, it’s because of our extensive background in federal work — where the acronym is king, maybe it’s because we are innovators…
B-I-E (in our terms) means anyone in your company can ask the dataset a question (query) in normal speech/language and get the desired output. This works because the AI > ML > NLP converts the natural language into the necessary query to hit the dataset, which eliminates the need for any back and forth with the data team. Once the magic happens, your results are displayed in the desired graphic/table/chart. And if you are really lucky, the solution you are using assists you in selecting the optimized visual for your specific output. (more on this in later blog posts — 😉😉)
These efficiencies can lead to:
Valuable insights produced for anyone with access to the dataset.
Trend Analysis (across the organization) — One department may be focused on sales, but what if the shipping department can look at the data and find a way to process shipments more efficiently — enterprise solutions can open up doors for endless possibilities.
Anomaly Detection — the more diverse the user population, the more unique information that can be ascertained from the dataset
You might be thinking, hmm… What if I am not the person interacting with the data? Or, how does this affect my business?
Bringing the data directly to the business analysts/decision-makers allows users the ability to develop reports, make recommendations, and execute decisions based on their data sets in a more efficient way — saving companies time and money. Less time spent looking for the right data, results in employee efficiencies, and a shorter time to execution.
Now that you know how AI > ML > NLP have changed the querying landscape, how will you adapt and bring data to your employees’ fingertips?
Signed — “A Not So Technical — Technical Person”
P.S. — If you find you’re asking yourself something to the tune of — “But I have all of this data, what do I do with it?” — see our Whiskey Tango Foxtrot (WTF) is Data Analytics Blog.