Robert Roe finds that AI-software developer DataStories is pioneering the use of augmented analytics to bring data-driven decision making within the reach of laboratories

An augmented future

In a world of increasingly demanding product development cycles, increasing competition and external requirements, AI-software developer DataStories aims to overcome these challenges through the use of self-service AI, or augmented analytics for domain experts. This approach uses analytical tools that provide predictive analytics and machine-learning functionality to help users to automate tasks typically done by data scientists. With the aim of streamlining and

simplifying the use of analytics in the laboratory, DataStories’ approach aims to democratise the use of machine learning and advanced analytical capabilities, cutting the dependence on data science expertise and helping companies better utilise data generated in their organisation. However, it has not been designed as a

tool that relies on big data, users can start a project even if they have data from just 30 to 50 experiments. The platform is designed to make use of information – it is not strictly designed for big data initiatives. The focus is on information and variability, and the team has designed the system to handle small datasets and prevent over-fitting. While traditional approaches to laboratory

software such as laboratory information management systems (LIMS) and electronic laboratory notebook (ELN) have been well established, this new shift aims to provide an extra layer of intelligence beyond recording, aggregation and retrieval of laboratory data. ‘Augmented Intelligence allows the user

to systematically turn all [structured] data into actions. The more data people have or generate, the more critical these tools are to process all that data and turn it into insights and action,’ said Guido Smits, CSO of DataStories. ‘The main potential [of this technology] is

to serve as an amplifier of the value of all the available data and expertise. It will facilitate reuse of historical data and stimulate people to be more complete in the way they record information, such that records are complete, and also contain the entire context needed to reuse that data point later on in a different

10 Scientific Computing World April/May 2019

context. It will help emphasise that data should be treated as an asset that builds and generates more and more value over time,’ added Smits. As research and development faces

increasing pressure for innovation and ROI, entire industries must become smarter and more cost effective. One way to accomplish this is to adopt tools that use both analytic and predictive capabilities, combined with machine learning and AI to turn data into a valuable resource that can be repurposed and reused over time.

An increasingly analytical world As discussed in the white paper from DataStories, one of the stumbling blocks to integrating an end-to-end data-driven culture is in helping users understand the benefits of such an integrated system. While being aware and reactive to the fact that these challenges can go a long way to make the most of the huge amounts of data generated, it is important to properly equip lab users with the tools they require to understand and use data effectively. Smits highlights the differences between

these cultures which characterise the different stages of developing a strategy and approach to make the most of data generated by an organisation. ‘The main difference is on one hand

a culture where a data-point is being generated to answer an immediate question there and then, with no real intention to

reuse that data again (in a different project by somebody else) versus a culture where every data-point is seen as an asset that has long-term value and should be treated as such,’ said Smits. This context implies that extra steps are taken to ensure that data can be reused easily at a later date. This includes the recording of meta-data and setting up policies and tools to maximise data quality and retrieval. DataStories vision is to use a scalable

solution which puts the domain expert at the centre, ready to generate the majority of powerful AI analytics and insights him or herself.

This view is shared by market research firm Gartner, which published a report in 2017 that stated ‘Augmented analytics, an approach that automates insights using machine learning and natural-language generation, marks the next wave of disruption in the data and analytics market.’ This approach turns domain experts into

data scientists that have automated tools which assist them in preparing data, finding

“The more data people have or generate, the more critical these tools are to process all that data and turn it into insights and action”


 Figure 1: degree of analytic maturity @scwmagazine |


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32