#Technology#Data#Digital Transformation

Mixpanel: 2020 Data Predictions

Adam Kinney
|Dec 10|magazine18 min read

Adam Kinney, Head of Machine Learning and Automated Insights at Mixpanel talks to Business Chief about his predictions for the data industry for 2020.

2020 is just around the corner and as we are approaching the New Year, we are looking at some of the most significant data analytics trends that will transform how businesses use data. 

Data has become the mantra for success of innovative businesses a long time ago and next year this trend will continue to accelerate and evolve, bringing in more innovation and more sophisticated approaches to data analytics than ever before. Here are some of these trends:

Augmented Analysis will drive adoption of new approaches to analytics such as causal inference

Augmented analytics uses machine learning and AI to understand complex patterns across data sets and user behaviours to more quickly and accurately answer questions. One key new development in this area will be causal inference. Causal inference is the next big thing in analytics. The idea is to use advanced statistical methods to isolate the most likely causes for particular user behavior. For instance, people who frequently write product reviews buy more online than people who do not write reviews. However, this correlation may be caused by different factors such as review writers being more loyal to the brand, so encouraging brand loyalty may be a better approach to increasing sales and revenue than encouraging people to write more reviews. Traditionally, the only way to isolate these causal relationships is by running a controlled A/B experiment, which is both costly and time-consuming. If businesses are able to isolate the three most likely out of ten possible causes without running an A/B experiment, a lot would be gained. This will simplify decision-making processes and help product teams prioritise. It would also allow companies to better allocate their data analysis resources.

There will be a stronger focus on localised data strategies

Geographic relevance to data privacy is becoming increasingly important. While large, multi-national privacy regulations such as GDPR or major laws like the California Consumer Privacy Act (CCPA) make headlines, there are many smaller, regional laws and customs that are often overlooked. For instance, apart from the European Union’s GDPR regulatory framework on data privacy, each of the 28 member states has their own data privacy rules. Similarly, in the US a growing number of states are considering introducing data privacy rules that all have unique regulatory features. 

This explosion in regional privacy laws has left many companies wondering how to navigate these differences. In 2020 we’ll see a growing number of local and regional data privacy regulations across the world, which is likely to force global businesses to adopt localized data privacy strategies involving regional data residency programs. In this scenario, personal information will be stored within a specific geography where that data is processed in accordance with the local laws, customs, and expectations. 

The rise of intelligence augmentation

Today everyone talks about AI and while we are a long way from true artificial intelligence, machine learning in analytics can help people make smarter decisions today. For example, machine learning algorithms can monitor all of your business metrics and automatically alert you when they change in an interesting way that you’d want to see. Or if you notice yourself that a critical metric has dropped but you don’t know why, machine learning algorithms can sift through the thousands of possible causes to identify the cause for you. 

In we look even further, we can identify a few other key areas where AI can augment human intelligence. In 2020 and beyond, AI will become more widely used for visual recognition and natural language processing, which is the ability to understand human language. One of the most immediate applications is an area called sentiment analysis, in which the AI can judge how someone is feeling by analysing their speech. Companies can apply this type of analytics to customer service to spot when a customer is getting angry and prevent issues that can damage the relationship with the brand. 

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AI will help drive more sophisticated predictions

Then there’s predictions. Predictive analytics tools look at historical data and use machine learning to identify patterns in the data that have a high probability of predicting an outcome. These patterns are used to create a model, and the model is used to predict an outcome from new data that becomes available.

In 2020 predictions like this will become more powerful as companies are able to combine data sources. For instance, they’ll be able to take their user behaviour data and combine it with their analysis from the customer calls, customer support, and use that to make more accurate predictions about customer behavior. Going a step further, AI and ML will be increasingly used to personalise products and service and tailor the user experience to the specific needs of the customer.

The growth of connected devices will drive stronger demand for IoT analytics

The IoT industry is expected to reach $1.29 trn in 2020 and there are already more IoT devices than there are smartphones. This means more complex data sets and in greater volumes of data than ever before. This new environment demands tools and skill sets that most companies don’t yet have and they often have difficulty adapting without IoT analytics.

In the consumer space, companies will use IoT analytics to mine data with the permission of their users. This includes data from mobile apps, fitness trackers, mobile devices, vehicles, and appliances. The same way that companies can track user behavior online, IoT analytics offers insight into how consumers use products in the physical world. This will enable consumer companies can provide increasingly personalised services and users get a better experience.

In the B2B space, IoT analytics will help drive greater productivity, reduce errors, improve understanding of smart city infrastructure and drive efficiency improvements. 

As our world is becoming increasingly connected, data will become ubiquitous and more sophisticated. Understanding how this will impact businesses will be key for making the most of this opportunity and creating better user experiences. 

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