Data science teams work with stakeholders company-wide to meet an ever increasing need for ad hoc analysis requests. Success here is a two-fold struggle: scaling capabilities to supplement and transform data and metrics on the fly; and, having confidence in the accuracy of the insight. Let’s examine both.
Teams from marketing, competitive intelligence, consumer insights, risk, strategy, corporate communications, corporate development and others rely on data scientists to extract actionable insight. And, according to struggles shared online, they have their work cut out for them.
As we can see below, some companies are still assigning analytics functions to unpaid interns, so the results are, unsurprisingly, less than optimal. And beyond that, those working with various data sources report a myriad of complaints:
How can brands scale their efforts, while maintaining analysis accuracy? Advanced AI is necessary, for starters.
Scaling Data Science with Advanced AI
Advanced AI takes every available bit of intel into consideration and provides multiple avenues for analysis. And it’s an analysis that is rooted in sentiment that goes deeper than sentence, or even for entire documents analysis, which is the industry norm for many of our competitors – and in academia. These analyses simply output a label: ‘positive’, ‘negative’ or perhaps ‘neutral’. Some also present a score alongside or instead of a label, on a range from strongly negative to strongly positive.
But, successful sentiment analysis starts with understanding the context rather than individual words, as well as the ability to interpret sarcasm correctly (when the words used might be positive overall but the general sentiment is negative). And all of this comprises an in-depth, entity-level understanding of what people are saying about your brand or products, your competition, market and or on any other topic that is relevant to your business.
And it also makes the source of these analyses available for further exploration and vetting, but we’ll get to that next!
It’s also important to note that machine learning systems are only as good as the data they are trained on, and unfortunately, these systems are often trained on very questionable datasets – with many containing little to no social media or news data. And, as we all know, these sources are significant consumer and market intelligence data sets.
Many analytics companies claim that they have ‘upgraded’ or ‘retrained’ their sentiment systems based on machine learning models – and those claims should be viewed with deep skepticism – even more so if the language involved is not English.
Here are some questions to consider when evaluating data analytics options:
- How accurate is the data?
- How transparent is the insight?
- Can the solution accommodate large volumes of data?
- Does the solution offer real-time accessibility to the insights and underlying data?
- Can we continuously – and collaboratively – monitor consumer, market, and our own proprietary intelligence?
- Can we export business intelligence into a configurable output (e.g., relational database) – and is it easy?
Let’s talk a bit more about accuracy . .
Having Confidence in Your Data Analytics’ Accuracy
A critical part of instilling confidence in data company-wide involves transparency. Yet many tools provide little detail about the ‘source’ of these positive or negative sentiments, so blind faith is required and that’s not very reassuring. Some products don’t even allow you to see sentiment results for individual posts. Instead, they offer a summary score across an entire group of results.
None of this is very helpful for understanding the details that give rise to the opinions found in qualitative data. And transparency around this intelligence is crucial, as it validates an accurate analysis. Without it, the results are unsubstantiated and its reliability is questionable. And then there’s researcher bias to consider as well!
This is a tough sell, as the first hurdle analysts face is trusting the accuracy of the extracted consumer and market data. They have to question accuracy, in fact, as strategic decisions made using less than accurate intelligence can prove catastrophic.
NetBase Quid offers data that has been cleansed, de-duplicated and enhanced with critical metadata. Data scientists the world over use it to access aggregate metrics processed from billions of indexed resources across all forms of structured and unstructured data. From there, they use it to deliver contextual insight to inform data-driven decisions. And doing this is made easy with an Intelligence Connector!
Revolutionizing Your Data Capture with Intelligence Connector
Having a continuous and automatic stream of metrics and data offers the ability to create analyses that would have taken an FTE months to conduct. With the process now agile and scalable, the entire organization can access insight, rather than relying on reports generated by one overburdened team. Companies can seamlessly transfer cleansed and relational database-ready insight data into propriety BI platforms.
Combining NetBase Quid data with other proprietary datasets lends to specifically tailored understanding of whatever metrics teams in your organization need to measure. It can keep teams apprised of emerging trends, competitive intelligence and monitor a product’s place in the market.
And these continuous intelligence data connections can be built into existing dashboards in your business intelligence platforms (Domo, Power BI, Tableau, Qlik, etc.) or new platforms or dashboards that we create for your specific use case.
With AI-powered continuous intelligence available in real-time, brands are realizing they can scale to meet any use case – and much faster than expected. Reach out for a demo to discuss the potential and see a sample workflow automation in action!