Based in Ottawa, canada, Tuesday Standard is a blog about consumer insights, digital measurement, online engagement and marketing.

The Tuesday Standard - Top Analytics Trends and Technologies for 2018

The Tuesday Standard - Top Analytics Trends and Technologies for 2018

2017 turned out to be quite the year for advances in analytics technology, practices, and overall integration into organizations of various types. In the work that I do, we have doubled down on measuring consumer and member behaviour to build better products, and added tracking of member insights to our CRM to help us with segmentation, etc. Exciting stuff in the year ahead to enable my team to add even more value to the organization and facilitate better decision making.

To that end, I have put together two lists related to analytics that our field is looking out for in 2018. These lists come from topics I have seen in articles and at events focused on analytics, such as the eMetrics Summit, and other events where analytics’ related topics are becoming increasingly common, such as MarCom in Ottawa, and the ForeSee® Summit.

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Top 13 Analytics Trends to watch in 2018

  1. Self-service business intelligence tools that draw data from a broad spectrum of sources. Examples of tools: Tableau, Salesforce Einstein, Sisense, IBM Watson, Domo, etc.
  2. Artificial Intelligence and Machine Learning
  3. Predictive and Prescriptive Analytics
  4. Natural Language Processing
  5. Data Quality Management
  6. Multi-Cloud Strategy / Cloud Storage and Analytics
  7. Data Governance – (This was partially addressed back to this governance post
  8. Security – growing awareness of the data accessibility, storage, and scalability requirement differences with analytics and big data in particular are leading to advances in both approaches and technology for security and data integrity.
  9. Real-time Business Intelligence Dashboards – operational, mobile, situational
  10. Growing importance of the Chief Data Officer
  11. Embedded Business Intelligence - integration of a BI tool or selected features, into another business application to fill the gaps in the application’s analytics or reporting functionality.
  12. Collaborative Business Intelligence
  13. Voice of customer Intelligence

Tools and technologies trend up and down based on the buzzwords that are floating around at the time. Hype drives much of this conversation, but underneath, you can see several technologies shifting to more mainstream use. This has certainly been the case for Artificial Intelligence, which is increasingly being built into tools such as Salesforce.com, to bring this exciting new technology into use that is more pervasive.

The trends related to these tools remind me of one of the mantras of efficient analysis which is to “do the computing where the data resides” – instead of extracting, processing and analyzing. This fits in quite well with the increasing use of real-time dashboards and AI decision making and predictive analytics.

As an added bonus, I thought it would be also interesting to include 8 technologies to watch in the year ahead – IMHO:

8 Analytics Tools/Technologies to watch in 2018

  1. Artificial Intelligence including tools such as IBM Watson
  2. R Language – a free programming language and software environment for statistical computing and graphics. R is not new, but is becoming more mainstream in the world of data analytics and data analysis.
  3. Deep neural networks – computing systems (a type of artificial neural network) inspired by biological neural networks that can model complex, non-linear relationships
  4. TensorFlow (Google’s open source machine learning and neural network library – used by Translate, Maps, Google Apps, etc.)
  5. MXNet (a deep learning framework similar to TensorFlow – lacks visual debugging that TensorFlow has – but offers imperative language calculations)
  6. Microsoft Cognitive Toolkit – a deep learning framework developed by Microsoft Research which describes neural networks as a series of computational steps via a directed graph;.
  7. Scikit-learn – open source project based on python toolboxes focused on machine learning
  8. Jupyter Notbook (originally called iPython Notebook) is an open-source web application that allows data scientists to create and share documents that contain live code, equations, visualizations and explanatory text.

In the coming weeks, look for more in depth articles on the trends to help provide background, tools, and stellar examples for those at various stages of exploration of these trends and technologies. Please let me know if I have missed anything you believe should be there.

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