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Dataiku – Big Data Startup Aims to Shake up the Analytics Market

Dataiku_logoBig Data analytics is so popular as a concept right now that some Fortune 500 companies are bombarding the airwaves with commercials touting their capabilities. But for most, hiring the services of IBM Watson to bring sanity and strategic value to their data is beyond their means. Other software packages and services come cheaper but are far from easy to implement. Many of those attempting to harness these tools are forced to hire expensive data scientists to make sense of the findings or recruit teams of implementation specialists to collect, cleanse, and standardize the data even before any insight can be gleaned from it. Enter Dataiku with a vision that every company, regardless of industry or size, should have the ability to create its own data-driven strategic advantage by transforming raw data into business impacting predictions.

Those who have dabbled in analytics know that loading, cleaning, and preparing data is a painful and time consuming process. Yet if you fail to accomplish this vital step, no prediction can be made with confidence. Those firms with the time and resources to get this far are faced with the challenge of figuring out the right machine learning algorithms, data models, and testing procedures to arrive at actionable analyses. To make matters worse, multiple disparate software tools are typically involved at each step.

Dataiku’s Data Science Studio (DSS) is a platform that makes the entire process easy, quick, and hassle-free. It enables instantaneous connection to any data store, eliminating integration headaches. DSS detects wrong entries while automatically cleansing, transforming, and enriching data. Even beginners can build precisely targeted models that exactly fit the size, complexity, and shape of the data at hand. Visualisation features make it easy to find correlations, variables, and patterns in order to predict future outcomes and trends with certainty.

Who are these guys? Founded by four data enthusiasts in February 2013 to re-imagine the process of getting from raw data to business impacting predictions, Dataiku’s quest is to empower both experts and beginners in building next generation data-driven business applications and services.  Dataiku has been profitable from the beginning as it sold its software to an array of clients.  It raised $3.7 million this year from two investors to grow its sales and tech team and international development initiatives.

Dataiku DSS has found effective use in a number of medium and large size businesses,” wrote Butler Analytics in a produce review. “One of the more recent applications is to the availability of parking in Paris – using a predictive model to inform motorists where parking is likely to be problematical or more easily found. Many of the more usual applications of predictive models are easily addressed using Dataiku, including customer churn, predictive maintenance, fraud detection, marketing, and logistics.

Dataiku is different. Its founders believe that data analysis projects should consist of a single flow from start to finish. Data Science Studio is the realization of that vision. DSS enables a direct and fast connection to the most common sources and file formats used for data, while streamlining data cleaning time. Instead of people spending 80% of their time accessing, preparing, and engineering data into a useable form, DSS accelerates and automates these processes and combines them on a simple, easy-to-use platform.

Use cases include: smart cities combining public data with machine learning to enhance public services and increase citizen satisfaction; churn prevention that identifies, understands, and engages with customers; content management that predicts the correct content to post, which generates the right leads and ultimately leads to more sales; lifetime value optimization to identify customers and adapt business and marketing strategies; fraud detection to prevent fraud before it happens; logistics optimization to increase operating margins and reduce costs; and predictive maintenance to prevent breakdowns before they happen.

 

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