We live in a complex world that becomes more complex every day. Analytics can help deal with that complexity. “Almost all business processes suffer from excess complexity and variability,” assert Deloitte analysts, David Linich (@dlinich) and Michael Puleo, “but both are difficult to spot — and even harder to eradicate — without fact-based analytical tools.”[1] The good news is that advanced analytics are becoming widely available in platforms like the Enterra® Enterprise Cognitive System™ (ECS). The bad news is that some C-level executives still need convincing that advanced analytics are necessary in the big data era. Simon Owens (@simonowens), a technology and content consultant for the Society for Information Management, explains, “Just because businesses are more open to exploring analytics and executives are dropping data science buzzwords in meetings doesn’t mean you don’t still have to sell your C-suite on investing in such technology. Analytics done right with the best tools and a skilled staff can get extremely expensive, and your C-suite isn’t just going to write you a blank check, especially if you can’t communicate how this investment will positively impact the bottom line.”[2] That’s a fair statement. A business case needs to be made for nearly every investment decision.

Making a Business Case for Advanced Analytics

Simon asked Christopher Dole, co-founder of Soothsayer Analytics, for his advice about implementing new advanced analytics programs. Dole’s first piece of advice was to start small with a project that guarantees a win. “If you’re just starting out in the analytics game,” he told Simon, “it may be tempting to ramp up a state-of-the-art program. But it’s actually more important to get some early wins by capturing the low-hanging fruit.” Dole obviously wasn’t arguing against state-of-the-art analytics systems, he was arguing in favor of proof-of-concept projects and pilot programs to demonstrate effectiveness and ROI before scaling programs. I agree with him. At Enterra Solutions®, we recommend clients use a crawl, walk, run approach. This approach allows a company to assess quickly whether they are asking the right questions, taking the right approach, and getting the right answers. This approach allows necessary tinkering with solutions before they are scaled. Dole’s second piece of advice was not to oversell. “It can be incredibly tempting to hype the potential payoff of analytics,” he told Simon, “but overselling it can result in the C-suite viewing outcomes as failures when they would otherwise be considered wins.”

Sometimes the only thing C-level executives need is a vision about how advanced analytics can help a company become more successful. Martyn Jones (@GoodStratTweet), founder and CEO of goodstrat.com, suggests nine areas in which advanced analytics can benefit a company.[3] They are:

• Customer Value Analytics
• Credit Risk Modelling
• Marketing Campaign Optimization
• Customer Churn Modelling
• Pricing and Yield Management
• Customer Segmentation
• Data Visualization
• Propensity Modelling
• Fraud Detection Analytics

Jones concludes, “Commercial analytics is essentially about the customer, the prospect, the markets, risk and reward. Yes, it can also be about cost reduction, faster and better decision making, and new products and services, but it is really about much more than that. In a sense, the heart of commercial analytics can also be found at the heart of a business’s reason for being.” Tom Cahill, Vice President for Europe, the Middle East, and Africa at Logi Analytics, insists advanced business analytics is past being a nice-to-have capability. “It’s the new status quo for business,” he writes, “if it doesn’t get measured, you will not get budget for it. In other words, everyone — from sales and marketing to product and finance — is expected to leverage analytics to make data-driven decisions.”[4] The challenge, of course, is that most people aren’t data scientists and may not appreciate how analytics can help them in their job. Thanks to embedded analytics packages, they don’t have to be experts. One of the benefits of a cognitive computing platform is that it communicates using natural language. Cahill explains, “Embedding analytics into the applications workers use every day grants users a much more seamless experience, because they don’t have to access multiple tools to find the data they need. By embedding analytics deep within a single application, businesses can empower their employees to create and share dashboards, reports and visual analytics across their organization, with little to no support from IT.”

Mastering Analytics Maturity

Obviously, some businesses have already mastered big data analytics while others are just getting started. Venkat Viswanathan (@venkatlv), founder and chairman of LatentView Analytics, believes, “Most organizations use analytics across their operations, but there are still many that have misconceptions about how well they utilize analytics to inform their business decisions.”[5] His company has developed a five-stage analytics maturity model that allows companies to assess how well they understand and use their data. The five stages are:

Stage 1: Analytical Novice. “Companies in Stage 1 may be lagging behind in adopting an analytics strategy to drive business decisions, potentially eroding their competitive edge. The development of a sound data management strategy has not begun yet or is in its infancy, and data quality and consistency may be poor. Analytics is driven mainly by use of spreadsheets, and business leaders need to gain a better understanding about the value of analytics.”

Stage 2: Analytical Aspirant. “[There is] an interest in adopting analytics to drive business strategy … but these interests still need to be augmented with action. The organization has identified its need for data infrastructure, but the strategy team is not participating in discussions about analytics usage.”

Stage 3: Analytical Implementer. “Receiving a Stage 3 rating means a platform for execution has been built and the business is ready take its first steps toward business transformation. The organization has established an infrastructure for structured data storage and access, with analytics developed by BI tools and shared across the organization.”

Stage 4: Analytical Executor. “Stage 4 indicates the business is doing a great job in formulating and executing business-focused, department-led analytics projects for the organization. Data is managed on consolidated platforms to support advanced analytics along with BI reporting, and analytics centers of excellence are set up at the department level.”

Stage 5: Analytical Master. “Achieving Stage 5 reveals the organization has ensured analytics are aligned to business goals. The organization has a coherent data infrastructure, facilitates big data and real-time streaming capabilities, uses analytics to drive strategic business decisions for all divisions, and customer experience officers are strongly inclined to reference data and analytics in all decision-making.”

Advancing up these maturity stages takes real effort. Thomas H. Davenport (@tdav), a Distinguished Professor at Babson College, explains, “Three things make operational analytics tough, in my opinion. First, to make it work, you have to integrate it with transactional or workflow systems. Second, you often have to pull data from a variety of difficult places. And third, embedding analytics within operational processes means you have to change the behavior of the people who perform that process.”[6]

Summary

Most analysts agree that to succeed in the big data era companies need to leverage advanced analytics to get the most out the data they possess. Davenport concludes, “Organizations embarking on operational analytics are learning that analytics itself is the easy part. There is no shortage of available vendors, both proprietary and open source, of analytical algorithms. But building an operational analytics system means integrating and changing existing architectures and behaviors, and that’s always the hard part. It’s well worth the trouble, however, to build applications in which analytics and smart decision-making are embedded in a company’s systems and processes.”

Footnotes
[1] David Linich and Michael Puleo, “Taming Complexity With Analytics,” The Wall Street Journal, 21 December 2015.
[2] Simon Owens, “How to Sell Your C-Suite on Advanced Analytics,” Information Management, 14 October 2016.
[3] Martyn Jones, “9 Ways How Commercial Analytics Create Business Value,” Datafloq, 7 April 2016.
[4] Tom Cahill, “Using Embedded Analytics to Transform Your Business,” Information Management, 29 June 2016.
[5] Venkat Viswanathan, “Mastering the Five Stages of Analytics Maturity,” Information Management, 12 January 2017.
[6] Thomas H. Davenport, “Cracking the Operational Analytics Nut,” The Wall Street Journal, 31 October 2016.