What Analytics Trends Will Matter in the Coming Year?

For those who are passionate about staying on top of the latest trends in analytics and analytic technology, the year ahead is likely to be full of interesting changes. After being in the data and analytics business for more than 25 years, I have seen a lot of trends come and go. The key trends that follow are the ones that will make an impact on your organization.

If you have any questions about the increase in the criticality of data and analytics in business, take note of the dramatic increase in the number of companies that have created positions for Chief Analytics Officers (CAOs) and Chief Data Officers (CDOs). As of the middle of 2018, 57% of enterprise organizations now have a Chief Data Officer, and 24% are considering creating a Chief Data Officer position. If creating a solid data and analytics strategy did not matter, the trend in the creation of these positions would be vastly different.

Actionable Analytics – Are We There Yet?

One of the most promising things about machine learning (ML) and artificial intelligence (AI) is that when employed well, they provide highly productive business value for the enterprise. The question to ask as we traverse the analytic maturity curve is whether the real-world business value is measuring up to what is promised by the articles and marketing papers.

In 2019, you will continue to see further production deployment case studies of analytic solutions at companies and the realization of business value from the deployment of those solutions. Coca-Cola, Netflix, Amazon, and Pepsi are some of the most notable organizations who have told their stories about putting analytics into action to achieve competitive advantage and increased brand awareness.

As more organizations put analytic strategies into action, companies will look for ways to tune and refine the models that have been employed. You will start to see an increase in those same companies asking questions regarding the algorithms used behind their solutions. One of the hardest things to explain is the dichotomy between the effectiveness versus the understandability of analytic models. Often, when a model is highly effective, it is difficult to understand. Conversely, when a model is easier to understand, it may not be as effective as some of the more complex models.

These questions come from the fear of the unknown. People aren’t ready to turn over complete control of their decision making to machines just yet. However, use cases such as self-driving cars are rapidly changing people’s minds due to the “wow” factor. As with any technology adoption, the further into the maturity of that technology that we go and the better that we understand the decision-making algorithms behind the models, the more comfortable that people, and businesses, will become.

Analytic Platforms – Which Ones Should I Employ?

As more companies line up to participate in the competitive advantage that ML and AI can offer, you will need to know the best ways to architect and deploy the technology. Cloud investment continues to grow with 70% reporting significant or moderate spend. Investment in newer digital technologies is increasing but still relatively small in comparison. Although analytics is only part of the investment in the cloud, it is an essential component of that investment. Questions that need to be addressed by key stakeholders in the organization before deploying analytics to the cloud include:

• Should you deploy on-premise or in the cloud?
• If you deploy to the cloud, should you go 100% cloud or use a hybrid model?
• How much will it cost to migrate?
• Should containers be a part of your strategy?
• How do you orchestrate the management across all of your hardware and software resources?

In an upcoming blog, we review the factors that need to be considered when deploying analytics to the cloud. Along with the research of where the software and hardware should reside is the lifespan of the computing power that you employ.

One of the disruptive technologies in the conversation is that of containers. A container consists of an entire runtime environment: an application, plus all its dependencies, libraries and other binaries, and configuration files needed to run it, bundled into one package. By containerizing an application platform and its dependencies, differences in OS distributions and underlying infrastructure are abstracted away. This strategy helps to get more out of the hardware resources that you employ.

Gartner reported some compelling container software usage trends in an August 2018 article:

• 59% of the respondents were planning to deploy containers within the next 2 years and 19% of respondents are already using container software.
• Of those running container technologies, 83% are running in production as compared to just 67% last year.

As more and more customers move their analytic strategies to the cloud, you need to ask yourself if you are taking advantage of the cost, efficiency and productivity gains that are offered by doing the same.

Machine Learning and Artificial Intelligence in Fintech – Is Reality Matching the Hype?

Fintech is the area of technology that focuses on computer programs and other technology used to support or enable banking and financial services. An example of fintech in action is an anti-money laundering (AML) detection and investigation solution. AML solutions, such as SAS AML and Actimize, are deployed by various financial enterprises for the primary purpose of detecting suspicious transactions and analyzing customer data. One such enterprise is Bank of America, who has been a SAS AML customer for more than 25 years.

In July 2018, our company consulted with a mid-sized bank in the northeast U.S. They engaged us to perform an assessment that provided a review of their regulatory compliance process against other similar sized banks doing the same thing. In addition, they asked us to document benchmarks in the industry for the same processes at peer banks. Among the research that we performed, we spoke to industry practitioners, data service provider firms, consulting companies and tier-one banks about the adoption of ML and AI across the industry. What we found out, among other things, is that as big as the hype is around ML and AI in financial services, there are very few banks who are using AI and ML in their BSA, AML and regulatory compliance processes.

To quote a domain expert from one of the largest financial services data service providers with solutions in the fintech and regtech space, “…over 90% of all of the banks that we talk to are still using Excel and SharePoint solutions to manage the regulatory compliance process.” Financial institutions acknowledge that technology needs to be a part of the process, but they have no incentive to invest in technology. On a technology adoption curve, the banking community is estimated to be at 1 (1 being no adoption and 5 being mature adoption). Further, the expert acknowledges that banks are high in terms of realization on the need for technology in the regulatory compliance process, but they are very low in terms of adoption of technology in the process.

2019 will be an exciting, evolving time in terms of what to watch for in analytics. Keep an eye out for the continued progression of companies along the analytic maturity curve, cloud migration and container usage patterns, and Fintech usage of ML and AI.

 

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