Top Trends in Data for 2018 - Part 2
PERSPECTIVES

Top Data Trends for 2018 – Part 2

In the first half of Top Trends in Data for 2018, Dr. Kirk Borne, Principal Data Scientist and Executive Advisor for Booz Allen Hamilton, outlined five trends in Data Science and AI from 2017 that are carrying over into 2018, including Machine Intelligence, Hyper-personalization, and more. In future posts in this series, he will explain the real power of AI and Data Science to augment and assist data-driven decisions and actions in all organizations, regardless of size, industry, or application domain. Here are five more trends to keep your eye on this year!


 6. Behavioral Analytics – Modeling our Hierarchy of Needs

Behavioral Analytics

Human behavioral science is older than all of our data and analytics capabilities. We now have data and analytics to build predictive (what will happen?) and prescriptive (how can we optimize what will happen?) models of human behaviors. Since most – or maybe all – of our digital applications involve humans, then behavioral analytics should be and is becoming a central component of many application domains: marketing, cybersecurity, healthcare, and more.

Modeling of human needs, interests, intents, motivations, and actions will reap a rich harvest of insights and actionable intelligence in many industries and organizations. Thank you Abraham Maslow for enunciating our hierarchy of needs – we really needed that!

7. Graph Analytics – Taking Center Stage

Graph Analytics

I believe that it was the great playwright William Shakespeare who said, “All the world is a graph.” Okay, he actually said “stage”, not “graph.” But, he could have said “graph” if he had realized that the stage upon which humans live and act is best represented by entities (the nodes of a graph) and the relationships between those entities (the edges in the graph). This includes social graphs, product graphs, interest graphs, influence graphs, and more. The graph connects the dots that aren’t connected! In other words, graph analytics discovers, explores, and exploits those entities and events that are transitively connected, even if those have no transactional connection.

Graph databases and linked data (such as semantic databases) represent the true data structure of our world. The natural data structure of the world is not rows and columns (spreadsheets), but a graph. Graph analytics exploits this true structure of things (including IoT contextual data) in more powerful and insightful ways than other instantiations of database technologies could ever achieve. Applications include anti-money laundering, fraud network detection, root cause analysis, and marketing attribution.

8. Journey Sciences – Telling Your Data’s Story

Customer Journey

Journey sciences correspond to the application of journey analytics (including graph analytics) on spatio-temporal data from people, processes, and products. The journey is powerful for both predictive and prescriptive modeling.

Journey sciences represent another natural methodology for data analytics (like graphs) that are important for industries from telecom to healthcare and more, since journeys are essential to understanding, decision-making, and storytelling.

9. The eXperience Economy – X Marks the Spot

eXperience Economy

Design Thinking is a core component of any creation and innovation process. That includes digital designs (including data science models) that are built for human consumption. The design must take into account the experience of the user (UX), customer (CX), employee (EX), or other stakeholder (e.g., healthcare patient or student).

Success or failure of digital applications now hinges on the UX. Success or failure of marketing and sales applications now hinges on the CX. Success or failure of employee retention now hinges on the EX. Empathy humanizes the design, and X marks the sweet spot in the digital economy!

10. DataOps – DevOps for Data

DataOps

The fast-paced data analytics world demands an agile mindset and process. We can call that “DevOps for Data”, or simply “DataOps”. The characteristics of Agile include incremental, iterative, flexible, fail-fast, and MVP (Minimum Viable Product) development cycles.

DataOps delivers fast requirements analysis, V&V (verification & validation) of models, lessons learned, insights, return on investment, innovation, and big value from your big data assets. Before you take your product on the road, take DataOps to your product – don’t leave home without it!

Beyond the Buzz (and the Hype)

The raison d’être of big data and its applications (analytics, data science, machine learning, and AI) are to generate new value, to enable new discoveries, to empower better decisions, and to inspire innovation. We are already seeing this in diverse domains and industries worldwide (such as healthcare, sustainability, cybersecurity, science, engineering, logistics, education, manufacturing, customer care, consumer goods, financial services, energy, retail, marketing, sports, entertainment, telecommunications, automotive, government, nonprofits, legal, etc.). We will discuss some of these applications in future posts.


Now, take the conversation to Twitter! Agree or disagree with this list of what to keep an eye on in the new year? Want to add something Kirk left out? Tweet @KirkDBorne using the hashtag #datamakespossible right now!

Which of these data trends is the most intriguing to you?

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