The Cognitive Business Transformation Playbook

The Bumps in Basics Business Analytics

People talk a lot about Big Data and Analytics. We understand the technologies behind big data analytics quite well, and invest huge resources to constantly upgrade them. By applying statistical Machine Learning, and Artificial Intelligence techniques to large data sets, these analytics tools can evaluate related records of data, and find the secret, hidden patterns, or logics, therein. The discovered logics provide insights that help businesses improve operations and gain value by applying cognitive solutions. But while applying analytics to improve business outcomes may sounds simple and straightforward, the real-life application of these valuable assets can pose some unforeseen challenges…

Accuracy of Data: Defining a business problem is complex. A great number of various parameters — and their inter-dependencies — must be analyzed to determine what their affect or influence is on the problem at hand. Accuracy and relevancy of the dataset is critical. Traditionally and technically, the systems and processes used to collect the data were not originally designed with data analytics in mind. Therefore, significant efforts need to be spent on data transformation and the curing of the dataset.

Capabilities of Analytics Tools: The statistical techniques used to explore a business problem can influence the validity of the outcome. Commercially available tools can deliver excellent results if the data input is formatted correctly and cured of the “noise”. Statistical engineers are very aware that big data analytics is not just a complex mathematical method, but a truly a deep science. Careful analysis of the patterns uncovered using analytics is necessary before applying insights for improving business outcomes. Integrations, typically modifications of software and processes, can easily affect already complex systems so certainty is an imperative.

Business and Domain Expertise: Analytics tools help to work with the data, but solving business problems must start with insights and direction from Subject Matter experts. This type of experience is essential to any analytics program, and allows teams to quickly decide on the relevance of discovered patterns. Many times, the discovered pattern may appear promising based on the available dataset, but its applicability for future state of business may need deeper analysis. Data and analytic tools are fundamental necessities, but business expertise is the silver thread that runs through the entire process of business transformation, from the start to finish.

Changes to Business:

Over the last two decades, businesses integrated software and solutions primarily focused around structured data. These tools were used to automate tasks, and generate business intelligence through the various reporting of key measures. Truly adopting analytics at the core of your business, however will require fundamental changes to business strategies. Therefor a true cognitive, digital transformation will force the re-imagining of an organization’s value proposition, and the re-shaping the operating model. Although this type of change would be difficult for any enterprise, going forward enhanced analytics will give a new transparency to the causes behind key performance metrics, letting professionals run their businesses better.

Think, Innovate, Solve

Studies have shown that 84% of digital transformation initiatives fail, and there is no doubt that the level of change at hand today will test the foundation of many major organizations. Industry today finds itself faced with a curios conundrum. We must change our businesses to incorporate extreme analytics — but those same analytics will in turn, again, change our business. Professionals are faced with having to plan for a future, that in many ways has yet to be discovered. Perhaps Einstein said it best “The significant problems we have cannot be solved at the same level of thinking with which we created them.” Agile, cognitive businesses will have to constantly learn, adapt, and grow just to keep up with emerging models and ideals. For those that want to lead, advanced analytics will have to become part of their DNA.