9 Smart Data Trends you need to know for 2017

The pace of technology

Technology evolves very quickly, and the world of business needs to keep up.  So much has happened in the past 12 months and what was considered new last year, is simply not good enough anymore.  This pattern will repeat itself next year, so it pays to stay at the front of the pack. Here’s our pick of the 9 Smart Data trends that are going to be hot in 2017.

9 Smart Data Trends

1.     Machine Learning

Machine Learning is a form of AI and a branch of computer science.  It is focussed on searching through data for patterns and then using these patterns to adjust the model’s actions. It’s ideally suited to anomaly detection, association rules, dynamic grouping of customers, and general predictions.  Machine Learning has known limitations, yet significant advances have been made in recent years, and the strength and reliability of these models are ever increasing.

2.      Predictive analytics

Last year saw a huge surge in BI tools and a proliferation of data visualisation experts.  Many organisations have adopted data visualisation programs and are starting to grasp the value inherent in their data.  The limitation of data visualisation is that it’s historic.  Predictive analytics will take this from a “what happened” to a “what will happen” as firms embark on the path to deeper analytics.

3.      Neural Networks

Neural Networks are another branch of AI that has gained momentum in recent years.  The science of Neural Networks is classified as Deep Learning and is one of the techniques for running analytical methods like clustering and classification.  Its concept is based on the learning process of the brain, and it is used extensively in the finance industry, mainly because it can very quickly process large sets of data and produce valuable outcomes. You’ll also find it in image recognition, weather data, and handwriting analysis.

4.      Data governance

The breadth and depth of data repositories have grown dramatically.  This growth pattern will continue with a vengeance, and the job of making the data safe will become ever more important.  Effective data governance focusses on the integrity, security, availability, and management of data.  While it helps organisations realise the value of their data, it also protects them against potential vulnerabilities.

5.      Blockchain

Blockchain technology was introduced to the world via bitcoin.  The power of blockchain is its ability to record any form of transaction with a very high degree of security, coupled with the inability of anyone to modify data retrospectively.  Another appeal is that blockchain is a decentralised technology and uses a peer-to-peer network.  Blockchain is still gaining trust in other industries, but the technology has sparked the interest of so many organisations around the globe that it won’t be long before it is everywhere.

6.      Dashboard maturity

It’s no surprise to hear that Excel simply doesn’t cut it anymore.  If you haven’t already tried out one of the many BI tools, then do so right now.  The most cutting edge applications are quick to get started with, connect to virtually any data source, and come packed with examples or are attached to a hugely excited community.  Once connected to your data, you can slice and dice it any way you like and present it as an interactive live dashboard to share with the world or just those special people in your life.

7.      Digital assistants

Apple has ‘Siri’, Amazon has ‘Alexa’, Microsoft has ‘Cortana,’ and Google has ‘Google Voice Search’.  These voice-activated search tools interpret your voice commands to provide you with the answers you need in an instant.  It won’t be long, though, before everyone has access to digital assistants for more than just personal devices.  Industry developments have seen startups like HyperAnna create voice-activated data analytics tools that provide actionable insights.

8.      Integrated historical and real-time data

Analytical algorithms either analyse real-time data, or they use swathes of historic data. It’s rare for predictive models to use both.  However, with modern models like deep learning and recursive neural networks, plus the abundance of computing power, this is made all the more possible.  E-commerce has embraced this approach already and use it heavily in augmenting the experience of online shoppers in real-time.

9.      Data science skills shortage

If you’ve tried to hire a Data Scientist in recent months, you’ll know that it’s not an easy task.  Last year the job was called the “sexiest job in the world.”  Finding a good data analyst is challenging, mainly because the role is new, and the combination of skills required (both people skills and mathematical skills) is rare to come by.  So companies will need to step up and be more strategic with recruitment and outsourcing.

You may already be embracing some of these, which is fantastic.  If not, then we hope we’ve inspired you by these 9 smart data trends to take a deeper look and discover how each of them can help turn your Big Data into Smart Data.

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