We know, we know. Data science isn’t only about math. However, the future of your business lies in algorithms. It will rely on these – leveraging internal and external data – to make better-informed decisions, predict the future, and even prescribe what should be done to achieve objectives. An eclectic catalogue of algorithms can be the most differentiating business asset, whether pertaining to the customer experience, internal operations, human resources, risk, fraud, or “things.” And there is a quickly growing market of sector and domain algorithms out there as well, algorithms that are ready to be used right out of the box. So you don’t need to science your way out of this all on your own.

What

· An innovative push from the open source world has accelerated the development of advanced analytics and algorithms, shifting from insights that describe or (at best) diagnose, to predictive and even prescriptive algorithms.
· With more – diverse – data available from internal and especially external sources, findings are corroborated, rather than depend on guesswork, and thus become much more accurate.
· A catalog of these algorithms, if made available to the business, can make a decisive difference in business performance and competiveness.
· Off-the-shelf analytics are a quick, viable alternative to building algorithms yourself; this market is rapidly growing.

Use

· A life science company uses weather and social data to refine forecasts, streamlining their supply chain.
· Unilever actively analyzes social media to refine campaigns, decide on marketing strategies and protect brands.
· A global insurance company develops analytical models to analyze external media for events that could affect their customers, and hence their exposures.
· Daimler China analyzes internal and external data to accurately predict arrival of a car at the dealer, right from its arrival at the port.

Impact

· Getting more new value from data from various – often external – sources, beyond the traditional business intelligence benefits.
· A better understanding of future customer behavior, optimizing the supply chain, shortening delivery routes, saving energy, identifying the right personnel for the job, predicting health issues, tax fraud and machine defects.
· Modeling, simulating, and deciding around alternative business scenarios and key outcomes to decide the next best action.

Tech

Open Source ecosystem
Hadoop, Spark, R project, Cloudera, Hortonworks

Advanced Analytics platforms
SAS Viya, Microsoft Cortana Intelligence Suite, IBM Analytics, Knime, RiverLogic prescriptive analytics, GE Predix, C3 analytics

Analytics solutions, marketplaces, and communities
Kaggle data science crowdsourcing, Microsoft TDSPAlteryx analytics marketplace, Data Ventures, BlueYonder

Contributing expert: Mamatha Upadhyaya
Here’s the full overview