There’s no industry that couldn’t be improved by actionable insights, and over time, every industry will be. For example, one fintech problem requiring actionable insights today is credit risk assessment. A well-structured fintech algorithm could easily use machine learning (ML) to advise a human agent to approve or reject a business loan application.
That advice is a quintessential actionable insight: The algorithm models the applicant’s probability of success by training on a mountain of historical data and market factors. The resulting insight is the algorithm’s classification of the applicant as an acceptable or unacceptable risk. The action, in this case, is the human agent’s decision to grant or deny the loan. Businesses of all kinds today are dependent on such actionable insights, ideally infused into workflows, driving better business outcomes without users having to leave their primary tasks to look for answers in the data.
Actionable insights: The core of business intelligence
In every industry, actionable insights arising from analytics and AI are no longer a luxury, but a necessity for achieving competitiveness. E-commerce platforms require high-level fraud detection systems to advise their human reps on courses of action. Actionable insights have a special connotation in the field of data analytics: An algorithm evaluates a vast data store and effectively advises a human agent on the best action to take. Implicit among assumptions about machine learning is that algorithms will identify a pattern or outcome in time-series data, for example, which human beings could not do in practical time. There are countless opportunities for turning data into actionable insights in industries like:
- Finance: Optimal portfolio trading
- Healthcare: Diagnosis, intervention, administration
- Advertising: Market analytics and placement
- Pharma: Protein folding and drug synthesis
- Retail: Demographics and style trending
- Education: Assessment and teaching methods
The race to put actionable insights into the hands of users is on. When one player in a field turns data into actionable insights, all competitors must follow suit to keep pace. Innovative use of AI and ML methods in all fields will only accelerate this arms race, putting more powerful actionable insights in the hands of workers of all kinds.
Actionable insights example: Broadridge
Undoubtedly money is the great motivator, and AI solutions must improve profitability. Therefore, let’s see how ML models in asset management and optimal portfolio trading produce actionable insights. Broadridge Broadridge Asset Management Solutions is a $4.5 billion revenue assets management company with clients generating unique data stores in diverse containers. An analytics challenge here is the integration of multiple data streams to feed an analytics platform and subsequently generate actionable insights that are valuable across a portfolio spectrum.
On the fundamental level, preparing data for analysis is often called wrangling. To business intelligence execs, the top-level view is the integration of everything from containers to ML technologies and visualizations. Analytics must cope with both structured and unstructured data to achieve optimal results. As we’ll see, very few ML and AI analytics platforms today embody the data science and engineering sophistication to accomplish such a feat with an outcome of actionable insights whose profit justify their cost.
A particularly profitable outcome achieved by Broadridge is the use by its portfolio managers of ML analytics to forecast the best portfolio positions. Broadridge accountants are also leveraging analytics to generate actionable insights that include forecasting accounts receivable and payable outcomes leading to sharper awareness of overall assets.
Infusing actionable insights into workflows — the future of business
Turning data into actionable insights is where BI and data analytics platforms like Sisense shine. Modeling complex and multidimensional data parameters in innovative ways and presenting those insights within workflows will change the future of every industry. For example, these systems will be able to evaluate millions of model configurations and dynamically make corrections to them based on live event streaming data in order to continuously update business-facing users with the best possible insights. The result will be more businesses where all or most decisions are made on underlying data, not gut or guessing, leading to increased conversions, lower churn, and high revenues for those who embrace actionable insights. Those who don’t won’t have much of a future to speak of.
Eitan Sofer is a seasoned Sisenser, having spent the last 13 years building and shaping our core analytics product, focusing on user experience and platform engineering. Today, he runs the Embedded Analytics product line which powers thousands of customers and businesses, making them insights-driven. Eitan is also an avid music fan and surfer.