Karen Passey, Enaxis Consulting

Advanced analytics promises to unleash previously untapped insights to ignite revenue growth and significantly lower costs. Artificial intelligence (AI) is now being represented as a logical next step along the advanced analytics continuum.

A panel addressed the status of these initiatives and what businesses need to understand prior to progressing both of these often-misunderstood technologies during the Enaxis Leadership Forum earlier this year.

Panelists included Richard Baraniuk, professor of electrical and computer engineering at Rice University and founder of OpenStax; Case Carstensen, director of Data and analytics, for Baker Hughes, a GE Company; John Rowe, a partner for Enaxis Consulting; and Hardeep Singh, chief of health policy, quality and informatics for Baylor College of Medicine with Jeremy Graybill, data science and advanced analytics leader for Anadarko as moderator.

Here are some of the key takeaways from this panel discussion:

1. AI is at peak hype, but there’s much to be gained from machine learning.

AI is a term that is often misused and overhyped. Many organizations tend to confuse AI and machine learning. According to Baraniuk, we are a long way away from true AI. Gartner, too, seems to think so. According to the 2017 Gartner hype cycle for emerging technologies report, true artificial (general) intelligence is more than 10 years away. That being said, significant progress has been made in the main building block of AI, machine learning. Machine learning algorithms detect patterns and from those patterns, predict or optimize performance. The value of selecting and using such algorithms is that they rely on historical data, rather than on explicit programming instructions. As new data is fed into the model, the algorithms adapt to improve efficiency over time. Figure 1 shows a high-level explanation of the different types of machine-learning.

2. Embrace a holistic view.

Far too often, executives spend hundreds of thousands of dollars (and in some cases, millions) on the latest advanced analytics technologies, only to end up disappointed—and in some cases looking for another job—because the expected ROI is not realized. This is typically due to poor strategic alignment, poor implementation and execution, or both. To be successful, a holistic approach to analytics is a must; an approach that includes a vision and strategy, governance, people, process, technology and data. Figure 2 shows these seven key aspects of the Enaxis Analytics Operating Model.

3. Keeping Pace with Emerging Technologies
Machine learning is growing faster than it can be implemented, primarily due to a lack of talent and organizational challenges on how to adopt these technologies. Many companies have a dedicated team that focuses on finding and evaluating emerging technologies that can be game-changers in their industries. There are many different sources of emerging technology collaborations:

  • Vendors—Conduct POCs with startups and other organizations that are developing cutting-edge technologies and looking for industry partners to collaborate with;
  • Academia—Join academic consortia or bring in interns (typically Ph.D candidates) that are conducting research in your function or industry and
  • Crowdsourcing—Collaborate with crowdsourced solution providers that harness the power of the crowd to unleash possibilities that come from a variety of different industries. This also helps increase the bandwidth and throughput of internal analytics professionals.

4. Agile is the new delivery model.
Over the last decade and a half, Agile methodologies have gained enormous traction in the world of software development and, more recently, in other functions and industries. But how does one apply Agile principles in an analytics setting? This is typically done by taking a customer-centric approach and defining customer “user stories” that reflect the needs of the customer. Taking an iterative and incremental approach with fast feedback from customers and stakeholders at the end of each iteration is critical to success, especially in an environment where market dynamics and customer requirements change frequently. Through this approach, the primary focus of all efforts is delivering value to customers.

Being customer-centric involves having a robust stakeholder engagement and adoption model. Such a model identifies and includes key stakeholders early in the analytics delivery lifecycle to create analytics solution champions and avoid “black-box” rejection of those solutions.

This article was first posted by Enaxis Consulting.