In this e-guide:
The insights that traditional 131 tools offer are critical for remaining competitive in your market—and forward-thinking businesses will see obvious benefits in getting them even quicker and with minimal manual efforts. This is what the emergence of augmented analytics offers.
Keep reading to discover how Al augmentations like machine learning, NLP, and automation are speeding up the average time-to-insight, delivering larger profit margins and bigger ROI.
Augmented BI Tools Speed Insights with Less Manual Labor
George Lawton, Contributor, SearchMicroservices
Augmented analytics embeds machine learning, natural language processing and other Al functionality into business intelligence software to help citizen data scientists and business users more quickly and accurately locate pertinent data, distill patterns of information, model the data for analysis and interpret BI insights.
Advancements in Al and machine learning technologies beyond traditional BI tools allow businesses to discover nontrivial insights, such as predicting the lifetime value of customers and their preferences to help improve marketing efforts, said Sewook Wee, director of data science and analytics at real estate site Trulia LLC. But there are limitations — in particular, finding the talent, time and resources required to build high-quality analytical models for augmented BI analytics tools to run. The speed of technical advancement and access to data are there,” Wee explained. “The limits to augmented analytics are a matter of prioritization and resource allocation.”
Therefore, companies should familiarize themselves with the ways augmented analytics tools can complement BI before embarking on a major initiative. “It’s important to avoid the temptation to try to build the perfect solution from day one,” Wee cautioned. Starting with a simple model, it’s possible to prove business value, learn and iterate. Those early models become key inputs for future models.
When Trulia was building its first price-range prediction model, it only assigned one price range per user, and that worked for many customers, according to Wee. But the company quickly learned that many users have different target price ranges for different geographical locations. For example, a potential buyer interested in purchasing a home in the San Francisco Bay Area might have different price targets depending on square footage, amenities, commuting distance and neighborhood.
Identify and prioritize insights
This may seem simplistic, but it’s important to know what’s going to be analyzed or discovered by a BI system using augmented analytics tools since the natural language processing (NLP) service needs guidance about the insights it’s analyzing and deriving.
“You can’t just approach it with no guidance and expect the NLP to just know what you’re looking for,” said Stephen Blum, founder and CTO at real-time network-as-a-service provider PubNub Inc. “Augmented analytics are only as smart as your implementation.”
It’s good practice to identify the areas that require deeper insights, then test and validate findings, instead of expecting augmented analytics to replace traditional 131 tools. That’s a good way to get to know the augmented 131 analytics tools, write the correct algorithms and design better models. It may take some time to develop the expertise to design and implement augmented analytics models and algorithms.
“Though the NLP system does the heavy lifting when it comes to processing and analysis, your model plays a massive role in how relevant the augmented analytics are,” Blum advised. Therefore, companies will need a certain level of expertise in NLP, machine learning services and data science. In the long run, Blum expects that BI vendors with augmented analytics features will speed the learning process.
While Al and machine learning are powerful tools, they still need to be focused on a specific problem to produce results. “Start with something that’s hard for people,” suggested Jake Freivald, vice president of product marketing at BI and data management tools provider Information Builders. Detection of patterns, for example, often can be difficult to visualize because there’s a lot of “noise” in the data, but natural language generation can convert the most relevant information into easy-to-understand text.
Business users may struggle to understand the limitations of their models, so Freivald said it’s probably best to let data scientists determine the models to be used and to grant business users access to the models through dashboards and visualizations instead of the more complicated modeling tools.