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This outdated modus operandi is a clear indicator that there are maturity gaps in important data management principles such as data access, data quality and data lineage.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.
 

 

Conclusion

 
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?

 

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
 

 

Conclusion

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

 

Conclusion

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.