Predictions: Key AI Trends in 2020
It’s a fascinating time to be in the data and analytics space. Companies are more aware than ever of the impact data can have on their business. In this article, leading global independent analytics platform Sisense’s head of AI, Inna Tokarev-Sela, shares her predictions on key AI trends that will happen in 2020, as well as what the landscape will look like in 2030.
2020 – AI Will Put the Answers in the Hands of Business Users
As AI becomes more well-known and effective in operational analytics, we will begin to see companies, even data teams, combining different AI components at different times in the analytics lifecycle to come up with better suggestions and insights.
If you look at the AI trends today (graphs, Explainable AI, Continuous AI,) you can see that each one facilitates a specific aspect of analysis, or is used for a specific purpose. Graphs show relationships, Explainable AI helps with transparency behind the analytics, and Continuous AI helps in constantly discovering new insights and revealing a data story within a complete context.
Each one, by itself, brings great insight to the organization. Combine them, and you will get a much more powerful solution.
A typical example would be if a company is using AI to find outliers, then they can combine that insight with AI for key drivers. Now you will be able to find a cause for situations (key drivers) that vary from the norm (outliers) and make decisions about how to change them.
In a retail environment, a company that sells socks may discover a spike in sales (the outlier) from a previous month. The next step would be to analyze the key drivers to find out more details. This type of analysis uncovers details. For example, contributors might be the color red (i.e. product type dimension) or the region of Nebraska (geo-location dimension). Decision-makers can slice and dice these dimensions to find further insights like the demographics of the customers (age groups, etc.), and make decisions to boost sales in other territories with similar demographics.
2030 – Analytic Apps Will Automate Consumption
We’ve been working on analytic apps since the beginning of last year, not because this is a buzzword, but because we believe that analytic apps will further automate the process of AI consumption, making it easier for data teams and business users to use different types of AI.
By 2030 though, quantum computing will be well-established. So for the sake of this trend, let’s say by 2025 every analytic app will be built on a sort of template, or “business question block”. This “block” is the perfect AI guide for the data expert or business user.
Why? In general, analysts and business users know their data. But they don’t know which type of analysis they need on the data. By choosing a template, they will inherit the analysis that needs to be done.
A good example of this is an analytic app for marketing. If you want to show your marketing attribution, your analytics app with analysis components will automatically suggest which data sources you should connect to — Salesforce, Gainsight, Zendesk, or maybe all of them — in order to get the desired analytical results.
Now the focus is on the questions, KPIs, and insights needed instead of the analytics functionality that needs to happen to get there. This will automate the use of AI in the organization and make it more friendly and effective for insights and analysis.
2020 – Knowledge Graphs will Drive More Database Technologies
It’s the perfect time to start using knowledge graphs for analytics. The technology is now standardized, and there is more data than ever before that can be manipulated and cast in a semantic graph format. However, there is more than one way to build a graph, and there are billions of ways to connect the data. Along with companies using knowledge graphs for BI, there will be a need to improve the ways to build the database powering the graphs.
In 2020, companies will begin to store their data in a format that is conducive to graphs and we will start to see more technologies that will help comprise the data in a data lake. These graph database technologies will improve the ways companies can build the database and improve the results that are seen in the knowledge graphs.
2030 – Knowledge Graphs Will Start Using More AI Technologies
Once companies have become comfortable with the knowledge graph concept, they will begin to build their own algorithms so that they can maximize the value of their own semantic relationships in the graph.
From here, we will see companies adding AI to get deeper insights.
A recommendation system, another AI technology, can be based on the graph and be leveraged in applications like autocomplete to make personalized and timely suggestions. By using AI technologies, combining them, and putting them on top of the relationships found in the spider-like knowledge graphs, companies can discover deeper insights that are greater than anything we have today.
For instance, using an AI technology such as a recommendation service on a knowledge graph can help a dashboard designer with sharing suggestions. The dashboard designer will receive recommendations about dashboard visibility based on the relationships found in the knowledge graph from previous dashboards that were created and shared.
This year is going to be the year when everything is going to come up data; data will drive processes and operations through analytics and BI, developers and data teams will emerge into the business and create what every business analyst and end-user have been waiting for — clear and efficient insights that will drive data-decision and lead to making from the top down for more successful businesses decisions.