Businesses that can adapt and innovate will be able to stay competitive and grow as the Power & Utilities industry undergoes transformational change.

At the heart of this change is the ability to maximise the immense potential of data to unlock new opportunities and create value along the Power & Utilities value chain from supply to the customer. Data is at the core of every business. How organisations maintain, govern, and utilise their data will either hinder or fast-track their efforts to reshape their business models and capture new market opportunities.


Data availability allows faster and more insightful decision making. Embracing new technology and innovative solutions will increase the effective use of data, provide user-friendly solutions, and create new services.
Enterprise Data Transformation

Enterprise Data Management focuses on the processes and activities around the quality, accuracy, security, availability, and governance of data within an organisation. It provides a view of the data produced and consumed across an organisation.

An assessment of an organisation’s Data Management Maturity level is an important step in understanding the current maturity state. It will identify issues such as siloed data and poor data quality and lack of data management standards.  The data management assessment should also include a review of current working practices that will shine a light on deficiencies that need to be addressed.

Actionable improvements can then be planned to improve capability and impact performance. This enables organisations to implement new ways of working to be more productive, reduce costs and streamline inefficient processes. These changes will be an integral part of a strategic roadmap that fully considers the people, processes, data, and technology elements required in any successful transformation.

The roadmap will include addressing urgent remedial actions as well as the delivery of value creating solutions and processes. Remedial actions will include addressing non-compliance, increasing business resilience, and improving safety. This will improve data quality, promote better data governance standards, and accelerate the journey to becoming a high performing data-driven business.
Value Creation opportunities
Improvements in how enterprise data is collected, secured, and accessed provides the foundation for better data integration allowing value creation opportunities that include:
  • Digitising work processes
  • Mobile and Industrial Applications
  • Advanced Analytics
  • Machine Learning/Artificial Intelligence
  • Geospatial Analytics

Front line teams can work more effectively by digitising work processes and utilising integrated digital platforms. Implementing mobile solutions for field operations will replace manual and paper-based processes with standardised digital and automated processes.

The creation of a connected workforce brings end-to-end visibility of operational workflows and allow better data-driven decisions. Mobile and connected worker solutions can also enable remote assistance from an expert or enable wider collaboration both of which can increase productivity and enhance safety.

Data or Industrial historians are typically used to collect and store operational data that are used by other systems for analysis and reporting. Industrial applications use operational data for monitoring and asset performance management.


Intelligent Plant have an open and secure Industrial Internet of Things (IIoT) portal known as the Industrial App Store. The app store provides cloud-based solutions that can be used for the remote performance monitoring of equipment through the analysis and visualisation of real-time data. Intelligent Plant and ORE Catapult collaborated on a project to demonstrate the ability to safely collect and share offshore wind real-time turbine SCADA data whilst adhering to cyber security best practice. The success of this collaboration provided clear evidence that better data management and analytics is key to improving both operations and maintenance.

Condition-based monitoring and predictive analytics are used to enhance functionality, improve asset reliability, and reduce maintenance costs. AI-driven solutions can further assist engineers and technicians in detecting emerging issues early and providing analysis to optimise operations. AI and Machine Learning also allow further improvements such as the reduction in Outage Management & Equivalent Forced Outage Rate (EFOR) and enable enhanced Distributed Generation Management.

Geospatial applications can be used across the value chain for both operational teams and customers in providing location-based services and analytics. Mapping and real-time spatial analytics provide users with key up to date information for real-time decision making and positive customer experiences. The use of edge computing technology allows the processing of data at or closer to a location increasing response times and business agility.

Harnessing the power of data gives companies competitive advantages by improving operational efficiency, improving asset reliability, and increasing the speed of decision making. However, to fully maximise the potential of this opportunity, better data management and data utilisation needs to be key factors of this transformational change.

An assessment of current working practices, a review of data management and the implementation of value creating solutions will enable better operational decision-making, improved asset reliability and increased profit margins.
Contact us to book a consultation to discuss how we can help you with your business transformation plans.
Many organisations are looking to transform their enterprises with digital solutions. A question to senior leadership and executives in each organisation is how much are you using applications such as Microsoft Excel and Access to run your business?


The reality is that a lot of businesses still rely heavily on applications like these to manipulate data, create reports and make business decisions. These poor practices such as the heavy reliance on standalone applications also highlight poor data management practices where the manual processing of data is still commonplace in businesses.

“It’s not optimal, but we’re getting by”

This outdated modus operandi is a clear indicator that there are maturity gaps in important data management principles such as data access, data quality and data lineage. Data stored in spreadsheets and access databases limits the audience that can access this data to make business decisions. The wider availability of this data would allow for a greater level of self-service provision so that the right data is available to other users in the organisation.

Using siloed applications makes the job of ensuring the data is accurate and complete very difficult and leads to the creation of multiple versions of the same data. This makes the task of maintaining master data very challenging. Having a data governance model in place is essential to improving data quality and ensuring data compliance standards are met and adhered to. A high reliance on standalone applications also reveals the lack of an enterprise data model which is a type of data model that presents a view of all data consumed across the organisation.

Data lineage is important as it allows for the ability to track data from source through to how it is used. It also helps to build trust in the data being used and in investigative activities such as Root Cause Analysis, where it becomes easier to trace errors.

Data protection and having controls on how that data is accessed is of growing importance in both adhering to regulatory compliance and in cyber security. Lack of these controls increases the likelihood of data breaches. Cyber security at its core involves protecting data from cyber threats and so data governance is essential to cyber security.

“Out of interest, what am I missing out on?”

The manual processing of data highlights the lack of readiness for harnessing the large potential of an integrated system landscape. The lack of a joined-up approach to data management and having poor integration between your systems will hamper efforts to make effective business improvements. The benefits of an improved system landscape are discussed in another blog called ‘Are poor System Integrations slowing down your transformation efforts?’.

McKinsey’s report on digital transformation in 2018 reported that more than 70% of transformation projects fail.

Some of the greatest challenges in improving data quality highlighted in the report were:
  • The poor quality of data entry at the system of origin
  • Inefficient data architecture
  • Ineffective governance model

In the same report, the key factors for successful transformations were:
  • Tools that allow information to be more accessible throughout the organisation
  • The ability for self-service for employees and business partners

Improved data management is therefore a tangible incentive to improve current practices that will vastly improve operational efficiency, provide greater adherence to regulatory compliance standards, and support your cyber security measures.



Minimising the reliance on standalone applications to make business decisions and providing greater access from integrated systems, will allow for improved data management practices. These changes are in turn a good indicator that data is being stored systematically. The automation of what were manual business processes workflows will bring greater efficiency, consistency, and data quality.

The use of data lakes where structured and unstructured data from a vast range of sources can be captured, stored, and updated instantly will greatly improve data management. These initiatives coupled with other enabling solutions such as the adoption of cloud technology, allows organisations to be more innovative, allow for improved cyber security measures, and be more adaptive to change.

Organisations that have in place strong data management practices will be in a better position take advantage of new business opportunities at a much faster rate than their competitors.

What steps are you or your organisation taking towards improved ways of working?
In many organisations digital solutions are deployed to improve operational effectiveness and to promote better ways of working. Eliminating outdated and inefficient processes and utilising new technologies allowing greater collaboration, automated workflows, and faster access to business data are some of the benefits that can be achieved.

System Integration

What is often over-looked until it becomes an issue are the challenges of integrating solutions into old technology and old processes. The 2019 State of Ecosystem and Application Integration Report says that poor integration could cost organisations up to $500K a year. These losses result from operational inefficiencies through lost orders, missed SLAs and lost revenue opportunities.

Challenges can include mismatched data types, systems not supporting modern protocols, security inadequacies or simply cost. For example, SAP use proprietary BAPI interfaces which work well if you are integrating other SAP systems, but not well if you are attempting to use anything else. Often the challenges lead to integration being shelved in favour of dual keying data into more than one system. Once dual keying is accepted it can often be left in place for a considerable time which in turn leads to human generated errors.

Longer term strategic planning vs short term solution benefit

The system integration conundrum for many organisations lies in weighing up the cost and time required to undertake the necessary landscape/technology stack review and to develop a simplified integration strategy versus the cheaper and quicker system integration with workarounds/patches approach.


In many cases the proposed digital solution satisfies an immediate business pain-point or need which can make for a compelling case for the fastest deployment option. Workarounds and patches serve a purpose in the short-term to alleviate limitations, however inadequate process and systems integration also can increase complexity and costs over time.
The seamless connectivity of enterprise data is a key enabler for digital transformation success and requires careful planning and a staged approach.
System Integration planning should consider the following two-stage process:

Stage 1 – Assess the current operational landscape (AS-IS)
  • System landscape review - review and identify all the systems, including legacy and middleware, identifying each systems purpose and its connectivity (or lack of) to other systems. This should only be a high-level preliminary review based as for a large organisation this type of analysis could takes many months if not years just by itself.
  • Business Process Mapping – having up-to-date business process maps will identify manual processes and workarounds that are currently in place and will also highlight where operational improvements can be made.

Stage 2 – System Integration planning (TO-BE)
  • Operational landscape simplification – identify improvements and eliminate the manual processes
  • Data integration review – review and understand your data integration needs. This will identify the interface types required for system integration. An up-to-date data model will be a key output
  • Middleware consideration- identify the appropriate middleware to enable the planned architectural changes, where possible using existing middleware if the capabilities are sufficient.
  • Future vision – the output of the System Integration planning is an input to a digital transformation roadmap.


The benefits of a simplified systems landscape

The benefits of a better integration ecosystem include:
  • Elimination of costly manual processes
  • Automation of critical transaction systems
  • Greater visibility of end-to-end data flows
  • The potential replacement of costly legacy systems where integration may make systems redundant (such as when dual keying)
  • Increased revenue thorough improved business processes
  • Better decision making
  • Adoption of new business models
  • Easier integration with external systems allowing greater supply chain integration
  • Ability to scale digital transformation efforts
  • Maintain lower ongoing costs


System Integration planning is an important element in enabling digital transformations and the realisation of an integrated platform that allow for a faster realisation of your transformational goals.

An integrated system will streamline your processes, reduce costs, and ensure efficiency.

Not reviewing and simplifying your system integration landscape may lead to poor decisions being made and not being able to harness the full benefits and growth opportunities of digital solutions when deployed.

Please share your thoughts on this discussion topic which is a key enabler to successful business transformations.


The Data Historian is a time-stamped database that stores Real-Time operational data that can be accessed and used for a wide range of purposes such as visualisation, event tracking, production reporting, and consumption by other systems for Advanced Analytics and Asset Performance Management. Market Research Future ® report that The Global Data Historian market is estimated to reach USD 1.4 Billion by 2024.

With the onset of new and disruptive technologies, data lies at the heart of these transformation initiatives. With improved computing and network infrastructure the demand for data has grown increasing the importance and value of a data/industrial historian in Data Management.


Operational Excellence opportunities

Operational Excellence is the realisation of executing your business strategy effectively and reaping the benefits of Continuous Improvement initiatives. There are many focus areas that contribute to this objective such as a leadership engagement, commitment to quality, and a strategic focus.

The Data/Industrial Historian enables the capture, storage, and usage of plant data for a variety of things. The list below highlights just some of the operational focus areas that data stored in historians can be used for:
  • Process Automation
    • Advanced Process Control
    • Real-Time Optimisation
    • Calculations (e.g. detailed operational calculations or composition estimators for inferential control of distillation columns)
  • Visualisation of Operational and Production Data for analysis and troubleshooting
  • Operational Dashboards and Reports
  • Production and Event Tracking
  • Environmental Auditing
  • Asset Performance Management
  • Predictive Maintenance
  • Big Data and Advanced Analytics
  • Digital Twin technologies
  • Machine Learning and Artificial Intelligence


Digital Transformation Enablers

Initiatives such as Industry 4.0 and the Internet of Things (IoT) have great value potential for the process industries and manufacturers. At the core of these technology solutions are data platforms and data management. With the right strategy, technology selection, and delivery approach, lie the promise of scalable and tangible cost benefits and the unlocking of new business opportunities.

From a data architecture infrastructure perspective, we now have more options available to us to store operational data that includes, on-premise, cloud, edge, and the data lake. Each option has its own technical merit and so there needs to be careful consideration and a enterprise strategy developed that considers the data, the operational processes, business requirements and future growth plans.

This will lead to determining an optimal data architecture infrastructure potentially utilising newer technologies such as cloud and edge infrastructure. This improved architecture can allow the integration of unstructured data and IoT data opening further opportunities to improve operational performance and streamline operations.


Having a clear plan and a targeted approach

So, what are the steps we need to take on this journey?

1.    Carry out an audit of your Data Historian as this will highlight how it is being used currently.
2.    Undertake an internal assessment of your operational activities as this will highlight opportunities for improvement and identify opportunities to improve or leverage the use of the process historian
3.    Establish a roadmap for making improvements that consider remedial actions, quick wins, and longer strategic objectives
4.    Undertake a pilot project and adopt an agile and scalable approach



With the substantial operational and productivity improvements that other newer technologies such as IoT, Advanced Analytics and AI can provide, at the core of each is data.

By ensuring your data is collected, secure, accessible, and of high quality, it provides the foundations to making tangible and scalable digital transformation improvements to your operations.

The benefits realisation will be reduced costs, better data-driven decisions, improved cybersecurity, and the creation of new business opportunities.

Contact us to see how we can help you with improving your Data Historian usage and increase your competitive edge.


Data is at the core of every business and how organisations maintain, govern, and store their data will either hinder or fast-track their efforts to reshape their business models by harnessing the immense potential of data.

With the many changes taking place to respond to our changing current working practices lieu of the worldwide pandemic, there also lies the opportunity for a Data Management review to fully harness the benefits that digital solutions can provide to improve your operations and business performance.

Taking a time out to rethink your current strategy

Much like in several sports like basketball, a time out allows us to take stock and modify our current strategy. A review of your current data management strategy and practices in a time when we are having to make other strategic changes could provide opportunities for a co-ordinated effort.

Cross-functional collaboration will help to reduce siloed actions that may address specific needs but do not take account of the wider organisational picture. The strategy review can also help to provide clarity on feasible options for the future.
A review of current practices using a framework such as a Data Management Maturity (DMM) model for example, will allow organisations to evaluate their capabilities and build a roadmap to accelerate progress in delivering value to the business.

A data management review will allow for the identification of:
  • Data quality issues and the need for improved quality rules and data governance
  • Regulatory Compliance issues
  • Operational inefficiencies (e.g. workarounds, lack of system and data integration) that if addressed would provide considerable benefit in reducing operational costs and improved efficiency
  • Legacy systems integrated with old interfaces that are costly to maintain
  • The level of readiness for strategic digital transformation initiatives

Now for the team talk

A team talk provides the opportunity for a review and active discussion. The data management review should focus on a current state assessment and a capability gap analysis. Key dependencies should also be factored as they will need to be part of the corresponding implementation plan.

Key outputs of a data management review will include:
  • A vision statement
  • Programme scope
  • Major gaps identified in the current state resulting from a Data Management assessment
  • Identification of high-level roles and responsibilities
  • Success measures and metrics
  • Business benefits

Drafting the new strategy

Keeping with the sporting theme, we are ready to draw up the new plan of attack. Having completed the review, an implementation plan can be drafted. This will typically contain a mixture of remedial changes and strategic implementations that all serve to improve operational performance and achieve strategic objectives.

The ensuing activities need to align to a strategic roadmap. By doing this, goals will emerge that are more specific as they represent desired achievements in quantifiable terms.

Examples of key activities in an implementation plan are:
  • Establishing a new governance model
  • Implementing remedial improvements in a phased and prioritised manner that eliminate manual processes and workarounds (AS-IS) to better defined business and process workflows (TO-BE)
  • Carry out a pilot Master Data Management (MDM) or Data Historian implementation ahead of further phased rollouts
  • Implement cloud-based data management solutions for improved data access, scalability, and cost savings
  • Deploy solutions that create new market opportunities

Putting words into action

Having had the time out, the team talk and made the required adjustments, we can put to action the cumulative effort of taking stock and producing an improved strategy. The approach should be an agile one that allows benefits to be realised early and incrementally.

The cost benefits of this approach will be both immediate and long term. The immediate benefits will be realised from short term improvements and quick wins. Medium-term and long-term improvements would then follow, and having a strategic approach will have the added benefit of:
  • Greater Integrated Operations with users sourcing data from trusted sources
  • Improved data availability for business processes that require consolidation and aggregation such as data analytics, business intelligence and KPI reporting
  • Reduced project delivery costs through simpler and optimised data and system integration
  • An integrated architecture platform
  • Incorporating related cyber resilience initiatives
  • Improved readiness for unlocking further value by implementing Machine Learning and other Artificial Intelligence solutions


Given timely and accurate data is key for making the right business decisions, a review of your data management strategy will allow you to address operational inefficiencies and risks. Addressing the issues identified in the assessment will bring about performance improvements, increased profitability, and better operational practices.
It will also allow you to improve your readiness to implementing strategic solutions and potentially reducing the cost of these implementations and their ongoing maintenance costs by way of improved Data Architecture and System Integration capabilities.
Contact us to see how we can help you with your Data Management Strategy.

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