You Do the Math

Algorithms are the key to creating more business value from data, so everybody needs to become a bit of a data scientist to raise the corporate IQ

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.


  • An innovative push from the open source world has accelerated the development of advanced analytics, algorithms, and AI, shifting from insights that describe or (at best) diagnose, to predictive and even prescriptive insights.
  • 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 and AI-based insights, if made available to the business, can make a decisive difference in business performance and competitiveness.
  • Off-the-shelf and AI-enabled Do-It-Yourself analytics are a quick, viable alternative to building algorithms from scratch “by hand;” this is crucial in a time of scarce resources and the need for quick results


  • 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.


  • 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.
  • Augmenting existing products and services with (built-in) insights and AI, adding value to the customer and potentially increasing revenue, potentially even through new business models based on monetizing insights and algorithms.


  • Open Source ecosystem

Hadoop, Spark, R project, Cloudera, Hortonworks

  • Advanced Analytics platforms

– SAS Viya, Microsoft Big Data and Analytics, IBM Analytics, Knime, RiverLogic prescriptive analytics, GE Predix platform, C3 Digital Enterprise platform for AI and IoT, Dataiku Collaborative Data Science platform, H2O automated machine learning platform, DataRobot automated machine learning, Alteryx data science and analytics platform

  • Analytics solutions, marketplaces, and communities

–Kaggle data science crowdsourcing, Microsoft Team Data Science Process, Alteryx analytics marketplace, Data Ventures, BlueYonder

Featured Expert

Marc Chemin

Expert in Big Data and Data Science