[As community content, this post reflects the views and opinions of the particular author and does not necessarily reflect the official stance of Neo4j. This post originally appeared on the GrapheneDB blog and is used with permission.]In a recent article on Forbes, Neo Technology CEO Emil Eifrem shared his predictions for the graph database space in 2016. If you haven’t read them already, the article is definitely worth a read.
We were inspired by Emil’s predictions and gathered our thoughts on trends we see will rise in 2016.
Trend #1: The Rise of Data-Driven Decisions
Startups are already known for being extremely data-driven in their decision-making process, so this may not be something new. However, we are seeing more and more disciplines that were not data-driven in the past that are now adopting this way of thinking.
Take digital marketing, for example. Even looking back two years ago, the success of a digital marketing campaign was still hard to measure. Looking back further than that, things become even more cloudy.
Do you measure the success of a digital campaign through just Facebook Likes or Retweets on Twitter? That seems simplistic and not truly reflective of user activity (e.g., how many retweets result in sales?). Graph technology can help marketers make sense of data relationships and connections throughout the course of a campaign in order to get a more holistic picture of a user’s journey.
While there was a reluctance in the past from some practitioners in the digital marketing space to adopt data-driven decision-making (due to fear that it would expose their marketing efforts as being ineffective), leveraging data to make decisions as it relates to marketing campaigns has become the only way to stay competitive. If you’re making decisions based on assumptions, you’re not going to be very successful.
It’s not just marketing that is adopting data-driven decision-making at a rapid pace. Everyone is simply becoming more data-driven. Companies can use data to shape upcoming products, see how well a feature is working or even streamline internal processes.
Trend #2: Data Becoming Increasingly Interconnected
This one again is nothing new.
Data has always been interconnected, but we have always been storing it in a “flat” way. We have been oversimplifying data models because the technology to look at data in its natural “interconnected” way was simply not there. Thanks to graph database technologies, we are now discovering new opportunities and new ways to look at how data is interrelated.
User behaviour is very complex, and looking at things in isolation as we have been typically doing so far, is really an oversimplification. We have been segmenting data far too long simply because we didn’t have the technology. Graph databases came along because data has always been interconnected.
For example, take a user’s purchasing decision. If you only look at when a user made a purchase and what they purchased, you’re missing a lot of the important factors that led to the purchase. That’s the data that will help you make better decisions about how to engage your users!
More and more companies, such as adidas and Walmart, are adopting graphs because they are a superior option in understanding how users make purchasing decisions. This enables companies to target actions and campaigns that work. Being smarter about the user is where the market is headed, and graph technology helps with that.
Perhaps you had suspicions that you could make sense of data in this highly interconnected way, but you never really had the tools. Now, with graph technology, we’re seeing a new way of thinking about data. It’s a paradigm shift and a whole new world of opportunities!
Trend #3: Polyglot Persistence
Companies are now managing an increasing complexity in their system. For some time, there was a trend to implement systems in one technology stack. Maybe you did everything in Java because it was company policy.
Looking at highly complex apps, like Uber or Airbnb, you cannot run such a complex operation with just one tech stack. You have to combine different technologies. There are now many different tools for any problem you need to solve. Everything is distributed, so companies are developing in an increasingly polyglot way.
Polyglot persistence means storing data in different databases, depending on what you need. You may have a MongoDB, Redis and Neo4j database for different requirements, as they all excel at different things. This set-up is becoming increasingly normal. You can no longer just pick one database or stack and stick with it, you need to pick the best tool for the job.
For example, if you wanted to build a video streaming and recommendations service, you could store videos on one central database, but have the recommendations engine on a separate database, such a graph database, that is better suited for making sense of connected data.
This polyglot way of developing systems does not require you to know every system or stack out there, but it does require deep collaboration between team members with different expertise in order to create complex applications. While polyglot development is not new, it is fairly new at the database level.
A couple of big trends we see in this space are the use of Apache Kafka to keep databases in sync, as well as more and more mature tools will be entering the market to facilitate connecting popular databases to each other, lowering the bar for polyglot persistence.
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