Clearly there are some big AI and analytics trends for 2020. Now what? With so many new opportunities in analytics, how do organizations tap into not just trends, but the value those technologies can create for their companies and customers? The greatest value of analytics will come in the form of clear, fast decision-making, predictive trends, and ROI (which will lead to better decisions). These five trends can help grow and improve your patent analytics capabilities.
Recognizing Baseline Data is Not Enough
An Excel document—flat numbers on a page—won’t cut it in 2020. Because of the vast amounts of data, and the ability to make complex connections from it, you will need to learn to augment the data with machine learning and AI. Baseline data is a starting point, but the value comes in making active observations, finding patterns, and developing new queries based on the connections found. And those connections need to be made quickly.
Moving forward, don’t just focus on gathering more data, but also on gathering the right technology tools that will help you crunch it most meaningfully.
Know the Great Value Comes in Prediction, Not Description
Not exactly a new trend, but predictive analytics is becoming a basic. More and more, it’s essential to not just focus on “where we are” but “where are we going?” Where do we want invest time and resources? Do we want to expand into new markets? Finding those opportunities are the way to take the lead in your technology sector. And with advancements in AI and machine learning, those predictions will only become more accurate and more powerful.
Invest in Data Visualization and Graph Analytics
Not everyone is a numbers person. With data paving the way for much of the decision-making occurring across the enterprise, it’s essential to find quick ways to make meaningful connections among the data. One of the ways to do that: Semantic Map in InnovationQ. Data visualizations with the Semantic Map will better help you better understand complex connections between patents, the technology space, and other active participants. These visualizations can be especially helpful with scenario planning and risk management—big issues with lots of moving parts.
In short, it’s often easier when we can visualize what the data is saying. As tools become more advanced that data can be enriched, and predictive models can be developed.
Use Analytics for Lifecycle Management
Lifecycle management is key in terms of product development, and the smartest teams will be using AI and machine learning to optimize their processes, from ideation, conceptualization, and monetization.
Natural Language Processing
Not all of us are not numbers people. Luckily, the lastest technologies make it easier to use natural language syntax. Semantic search in InnovationQ makes it easier and quicker to search for art and make new discoveries. Use concepts rather than keywords to find all the relevant data to feel confident you’ve found the results you need.
Remember: this is no magic bullet in building technologies for the marketplace. Analytics are a great tool, but even the best numbers are meaningless if you don’t have a plan for executing your discoveries or keeping your data up to date. The main point to remember is that being a data-driven organization is no longer an option—it’s a necessity. The right tools can help prioritize your inventions in a first-to-file world.