EDITOR ’ S QUESTION
BILL SCUDDER , SVP , AIOT GENERAL MANAGER ,
ASPEN TECHNOLOGY
The COVID-19 pandemic accelerated the digitalisation of organisations , down to the way they store and access their data . This transformation revealed the limitations of the traditional data management model , where data is siloed by teams , sources and locations . This kind of data gatekeeping significantly hinders visibility , as only certain people with unique access or domain expertise are able to understand or even access data sets that may be relevant to others across the enterprise . To help facilitate this change , enterprise IT teams should put a clear strategy in place to ensure that they ’ re implementing all of the proper tools they need to get adequate information from all sources . A big part of this strategy should be to implement a data historian . Data historians have evolved , moving beyond standardised aggregations of process data to become the anchor technology for industrial data management strategies .
With IIoT and capital-intensive industries amassing more and more data , they ’ re running into the problem of being unable to manage it or know what to properly use it for . However , Big Data has an important role to play in arming organisations with the resources and information they need to enable data-driven decisions that can improve business-related outcomes . When analysed properly , the benefits of Big Data can include optimising production , real-time visibility and enhanced decision-making , allowing teams to be more productive , effective and innovative .
For capital-intensive industries such as manufacturing and industrial facilities , Big Data is essential to operations . Big Data can help with predictive maintenance so supervisors can schedule plant downtime to repair assets before unexpected costly breakdowns occur , provide anomaly detection to alert workers to small deviations from the norms of quality and predict with greater certainty around supply chain management challenges .
That said , the biggest challenges we ’ ve seen organisations face is operating under the assumption that there is a one-size-fits-all solution . This is not true – organisations must continuously re-evaluate their workflows and processes for collecting , storing , optimising and presenting data to ensure they ’ re reaping the greatest business value from it . This shows up in practice when thinking about auto discovery . Many IT leaders believe that there are tools that will auto-discover relevant information across all of your data , whereas there ’ s an age limit on what these tools can work with – data that is past a certain time frame is usually undetectable , for example . Another challenge comes from a generational , operational expertise gap . Many organisations are having difficulty finding the right people who have the means and knowledge of where data is stored and what format it ’ s in . This all circles back to making sure you have the correct data integration strategy in place – it makes it infinitely easier on the employees when a set plan has been made and executed on .
CIOs need to be aware that more data isn ’ t always better data . With the influx of data that organisations have received through digitalisation efforts , many have found themselves in the middle of a “ data swamp ” with every piece of possible data included . CIOs and IT leaders should begin by identifying data related to the problem they ’ re trying to solve before moving on to the next step . From there , a data historian can solve everything they need to ensure that relevant datasets are being continuously pulled .
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