TALKING

To gain the most from data , enterprises need to start by analysing the data journey across applications and systems . or loaded and stored in areas before being used – taking multiple paths across a company .

This needs managing , especially as it ’ s important to also maximise data value at every stage of the value chain . And while access to data must be governed for regulatory and compliance needs , it must be accessible to those who need it . A simplified equation is : intelligent data on components and equipment on trains , which has sped up equipment maintenance and minimised delays .

The weakest link

How do we do it ? How do we get the data house in order and achieve the sort of results that Siemens Mobility is now experiencing ? It ’ s important to realise that data value isn ’ t linear across different types . Typically , high-value , real-time data should be instantly analysed and then stored in an aggregated form for historical analysis . The data journey across an enterprise isn ’ t linear either . Different types of data can arrive from multiple sources , such as flat files , IoT devices , process-generated data and user input data – all travelling through the organisation from system to system or application to application . Data can be transformed , copied , aggregated , cleansed , extracted ,

Governed Data x Analytics x Technologies x People x Process = Value Captured

This insight value chain is multiplicative ; its total value is as good as the weakest link in the chain , with typical weak points in organisations being the analytics process and its people . Most organisations have a huge amount of data already available . The same applies to processes , since those are already defined and eventually need to be optimised . Organisations miss capturing value from analytics since most still focus on historical analysis , whereas increased value comes from analysing data in real-time .

The second critical component is people . If users cannot read , write and communicate data in context , even the best available data is useless .

So , what are the three high-level strategies that leaders should know and act on now ?

1 . Increase data quality : Review how data travels throughout the company . Multiple copies , data latency across multiple stages , low quality and uncertain actions influence trust in data and their use .

2 . Support data literacy : Everyone must be able to understand data ; data literacy must be an organisation ’ s top priority to use the value of data in any decision-making .

3 . Use a platform ecosystem : Favour platforms with capabilities able to scale and grow with your business . Make sure that all platform services are well orchestrated and are available as-a-Service , on any cloud , on-premises , or at the Edge . A welldefined ecosystem builds a smarter data journey .

A single , well-defined data value platform is key here . It can manage and maximise value at any stage , as long as it ’ s available everywhere – as-a-Service , on any cloud , on-premises , or on the Edge . The platform ecosystem must scale with business needs and provide multiple horizontal planes , like a data plane or a control plane , and deliver multiple user experiences to developers , DevOps and business technologists . Only then can organisations truly use their data intelligently , speed up high-value decisionmaking and confront the oncoming economic climate with confidence . p

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