by Jim Sinur
Data science is in all the headlines today because of the growing amount of big data that needs attention, but the reality is that there is not enough time and energy to sort through all of it in order to look for important and potent signals and patterns to apply to business outcomes.
We would suggest it is as or more important to provide a framework to make great decisions that are likely to affect business outcomes in a positive way. Let’s look at decision management as an equal that interacts with data science.
Emerging Decision Management Leads the Way
In the past, decisions have often been looked at as intuitive or at least based on rich past experience. However, the world has changed significantly in that great decisions are greatly influenced by new decision models, along with emerging and fast data sources.
Decision management creates a realistic context that is guided by desirable organizational outcomes and is aimed at creating a framework for human decision-making. Decision scientists are leveraged to smartly combine design thinking, technology, mathematics, business, and behavioral sciences into a working model for decision-making.
There will be a dependence on data scientists for the details to add the likely analytics, statistics, machine/deep learning and data sources/speeds to support the decision framework. The business acumen drives and behavioral science seeks the answers in converged contexts that are informed by the best insights available from data science. This is an outside-in approach that starts with the decisions and works to the “how” to make them with human consent.
Popular Data Science Dominates Today
Data science seeks to create stories from number/data crunching with various algorithmic or AI approaches off the shelf or helps design new approaches. Because data is so vast and fast, data scientists build software or machines to make these complex, dynamic, and emerging decisions. Because the technology is emerging and rapidly expanding, data science gets a lot of attention. The insights drawn by data science needs to be transformed and fitted into a business context and outcome. Otherwise, it is a waste of time and resources for the moment.
Additionally, data science can certainly add the tests for reliability of algorithms and data, particularly in real time or near real time. There will be discoveries in data science that may impact the business perspective that did not expect an outcome or impact. This is an inside-out approach that looks at data and algorithmic opportunities and undiscovered idiosyncrasies.
Bottom Line: Focus on Balanced Interaction Over Time
While data science helps organizations create and leverage analytics, decision management helps organizations consume analytics in a useful way. This is often done by communicating solutions in simple and sometime linear terms that can be easily understood and socialized in a decision framework, while leveraging decision management and data science platforms that harmoniously work together.
In the epic wars of leveraging big and fast data, organizations need to build a winning strategy of combining decision management and data science.