What is Decision Science? If you’re like most of the business decision-makers we talk to, you are constantly searching for ways to improve your decision-making process. When it comes to gaining insight on how to get ahead and stay ahead in the market, the pressure on organizations to be data-driven and operate in real-time is real.
Gone are the days when data analytics conversations revolved around how to aggregate and store massive volumes of data.
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The old paradigm saw a data provider (usually IT) spending weeks or months creating accessible datasets for downstream technical and business users, and then unleashing data scientists and business analysts to find the value buried in those datasets.
With time, modern, agile, cloud-based analytics platforms and self-service tools eliminated much of the technical heavy-lifting to make it easier for a broader range of people across the enterprise to benefit from insights, but that approach has only taken businesses so far.
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Today, despite the information deluge, enterprise decision-makers are often unable to access their business data in a useful manner. In our conversations with clients, we’ve found two main reasons for this:
- BI adoption levels are patchy and deliver surface-level KPIs, while the goal of achieving tangible analytics ROI is limited by achievable time to insight, the amount of human intervention required, and the range of insights available.
- Analysis tools are still designed for those who speak the language of statistics, making it too hard for the everyday user to actually “ask” the questions they want answers to – from the routine (“what happened to x?” and “where did it happen?”) to the insightful (“Why?” and “What if?”).
The end result?
Decision-making moves at a slower pace—and the true power is locked in the hands of a few.
As business decisions become more connected and contextual, the decision-making process also becomes increasingly cumbersome. Today’s data ecosystem consists of complex technology, siloed and noisy data, and sparse talent. To address this, organizations are turning towards more sophisticated Automated Machine Learning (AutoML) platforms to offload the burden of developing bespoke AI and ML models from data scientists—and putting the power of these models into the hands of non-technical users, allowing them direct, on-demand access to actionable insights.
But easier data access doesn’t necessarily translate into more accessible insights. While acquiring the data is often the single largest limiting factor in getting to an insight program, it is still the visible part of the time-to-insight iceberg. The big looming part underneath is the time needed to translate the data to insights at the lowest level of individuals managing various business functions. Insights platforms still need to be able to truly “democratize” decision insights.
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To break this barrier and deliver more accessible and autonomous forms of intelligence, modern decision insights platforms present engaging data stories with personalized narratives. Such platforms are powered by automated insight generation, advanced analytics, ML algorithms and a natural language search interface, to make it easier for individuals across the enterprise to ask questions of—and interact with—the data. In reality, this boils down to decomposing everyday business questions to determine the right context, trigger the right analysis, select the most relevant models, methodologies and algorithms, and then validate, summarize and communicate that analysis in a way that is compelling and easy to understand for decision-makers.
Using AI, advanced data analytics, and decision science to allow business users to make better decisions, is accelerating innovation across industries and functions. Many organizations are already tapping into the extraordinary potential of such platforms. Here are a few examples from the clients we’ve worked with:
A leading global pharmaceutical company, struggling to monitor multiple clinical trials in a disruptive environment, can now leverage sophisticated machine learning models (all working in the background without human intervention) to identify project risks and simulate real-time scenarios to plan timely interventions.
A global CPG company looking to optimize their e-commerce brand portfolio can spot changing consumer trends and identify untapped market opportunities with autonomous recommendations.
At a global manufacturing company that was facing ever-increasing backlogs in processing w******* claims due to heavy dependence on data teams; decision-makers are now empowered to conduct comprehensive data exploration, enabling quick iteration and rapid diagnostic drill-downs.
Not only has the platform helped accelerate insight generation, but it has also freed up data teams and business leaders to move beyond analysis and focus on actions, recommendation, and creative thinking – and most importantly, to do it intuitively without the burden of learning new non-core skills, memorizing filters/dials/button selections, and generally spending time on process rather than the outcome.
The writing is on the wall: organizations that will leverage data for improved decision-making will emerge as winners in today’s digital world.
Business decision-makers face a choice- they can either adopt a modern decision platform that enables them to uncover insights and take actions that will drive future business success—or ignore this opportunity and risk falling behind in an economy where unpredictability and disruption are becoming the norm.
[To share your insights with us, please write to sghosh@martechseries.com]
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