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Soaring inflation , rising borrowing costs and cuts to public spending are never going to create an easy footing on which to maintain a business let alone grow one . For many organisations , difficult times are predicted and there is a need for urgency with inevitable fears of bankruptcy or insolvency . Understandably , decision-making needs to be quick and accurate . There has to be accountability but there also needs to be a clear direction and future planning . This is where data comes in .
While analysts widely agree on data ’ s intrinsic value and that all enterprises of any size must learn to capitalise on their available data to drive better business decisions , it ’ s not always that easy . Many enterprises face incredible data challenges : a flood of insignificant data , diluted data value , siloed data and inaccessible data . To gain the most from data , enterprises need to start by analysing the data journey across applications and systems . But that ’ s just the first step . Don ’ t just define yourself as datadriven – prove your ability to capitalise on data by becoming insight-driven .
Organisations need quality data and they need that data to be consistent and relevant . Given the scale of data generated currently , this can be a particularly difficult challenge . According to Statista , the global amount of data created , consumed and stored has reached 64.2 zettabytes in 2020 and by 2025 , data creation is projected to reach 180 zettabytes .
It ’ s the sort of setup you get with predictive maintenance , where a model is trained on historical maintenance data that has recorded multiple breakdowns . Once the model is accurate enough , it is used with real-time data generated by the device to monitor equipment . When the model raises a possible anomaly , a notification is generated to alert the maintenance team , or an automated action is taken to remediate the equipment issue .
According to McKinsey , better insights into data can support growth or reduce costs in two key ways ; firstly , through enabling better customer experiences , to increase sales and upsell , while also preventing churn and enabling stock optimisation . The second way is optimised processes , using capabilities , such as predictive maintenance , demand planning and supply chain planning and optimisation to realise real cost benefits .
Alessandro Chimera , Director of Digitalisation Strategy at TIBCO
It ’ s not just virtual meetings , social media , video streaming , wireless and mobile traffic , and broadband pipes driving this increase . It ’ s also the proliferation of much cheaper IoT devices .
Incredibly , just 2 % of the new data created in 2020 was saved and retained into 2021 – the rest was either created or replicated primarily for consumption or temporarily cached and subsequently overwritten with newer data .
To capitalise on data , organisations need to analyse historical data , or ‘ data-at-rest ’. They need to combine this with incoming real-time data , or ‘ data-in-motion ’, when its value is high and close to its creation . Advanced companies that can combine real-time data with historical data and use the latest data science capabilities have a strong competitive advantage . These organisations have fresh data that they can measure , put into context and create comparative analysis , to see trends , for example .
If users cannot read , write and communicate data in context , even the best available data is useless .
Consistently delivering the right product at the right time , with service beyond expectations has become a universal goal , regardless of the industry . A good example of this is global railway manufacturer and management company , Siemens Mobility . The business was looking to use data insights to prevent delays due to equipment failure but also provide accurate insights to customers through its app , Railigent . The challenge was to deliver real-time analytics for both IoT and legacy data and quickly provide insights for relevant stakeholders . That led to data analysis at the Edge , feeding back live
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