Envitia’s CEO Nabil Lodey looks at how companies with an extensive asset base can utilise data to meet their business targets in this
Organisations with an extensive asset base should challenge their approach to asset lifecycle planning to meet the latest cost efficiencies and carbon targets.
At the heart of this challenge is data. Yet generally company data is poorly managed and maintained. It is not unheard of to have 70%+ of asset data missing or of poor quality thereby placing an organisation’s decision-making in jeopardy.
Declaring your data as “unfit for purpose” won’t come as a surprise to many due to the abundance of legacy systems and a lack of a data management process.
Our experience at delivering asset data transformation projects has uncovered five major lessons you can learn to meet this challenge head on:
1. Use recognised data experts
The market is congested with digital transformation and data experts and it’s hard to differentiate between real expertise and a slick marketing and sales pitch. Our advice would be to look for deep specialist skills from organisations that truly understand data rather than a data technology.
This means looking beyond expertise in Business Intelligence tools such as Power BI & Tableau (they have their use of course – see later), even looking beyond using specialist data tools like Alteryx. The reason is that these expert users will always revert to their software for your project when that might not be the best course of action. We would also be wary of data consultants whose aim is to sell more consultancy rather than maximise value to you with the best solution.
So our recommendation is to look for suppliers that are agnostic to the technology used and then look at their data quality and data modelling credentials to really expose their depth of true data expertise. Also ask how they would measure the value delivered to your organisation to ascertain whether they’re focused on your business and not theirs!
There is absolutely no point fixing data in line with some theoretical best practice if it’s not going to be used and exploited to make better decisions.
2. Align asset data to your organisation’s business objectives
Understanding the strategic business objectives of the organisation should shape any decision related to the asset planning life cycle. The next step is identifying what data is required to optimise those objectives. This will help prioritise activity such as:
• Delivering cost efficiencies through optimising or reducing the asset base,
• Maintenance of assets to ensure operational effectiveness,
• Planning for asset replacement and investments, or
• Meeting carbon targets.
Data that is related to the prioritised activities will be more valuable to the organisation than other data and should be the focus of your attention.
3. Get the Data Foundations right before migrating to specialist Software
Data Foundations is about understanding how the data aligns with the business objective and then going through a series of specialist data processes to manipulate the right data, into the right place, in the right format, at the right quality and therefore ready to be turned into insight.
We call this being “data ready”…
We recommend the following process:
a. Data Profiling & Analytics of Current State of Data
This is about understanding the current value of your data and the potential value of data once work is complete. This stage will provide in-depth data analysis reports on how much data is available or missing, and how data is being used and stored. It won’t impact operational systems and will provide an organisation with a roadmap for business decisions to be prioritised based on value delivered. There’s always plenty of quick wins available that won’t cost a fortune and if you’ve prioritised the key datasets that are aligned to your business objectives (as per above), there’s only a fraction of data that requires transformation.
b. Data Validation:
This will align data quality issues against business rules and the data standards adopted within your organisation. This means that the data will follow the agreed rules of how it should be stored and maintained. Without this stage there is no way of understanding the effort to cleanse poor quality data and no way of measuring the benefit of cleansing the data.
c. Data Cleansing
Sometimes there can be millions of data records that require cleaning and this is most efficiently achieved in an automated and repeatable manner. Data experts will design a consistent and robust method of improving quality of data which can then be audited. Data enhancements will sometimes be required within this stage.
d. Data Modelling:
This is at the heart of any data foundations project and it’s the “brain” that pulls everything together. A data model will go through a process of modeling to define all the structures and relationships between datasets. Data modelling is a fundamental necessity for any data migration project which will undoubtedly have fragmented data sitting in multiple databases as silos of information without any agreed strategy to migrate to a specialist asset management software.
For data migration ambitions, it is highly recommended to assess data quality first, then merge into a clean reconciled database before the migration. As per above, it is also highly recommended to bring in specialist data experts first to get your data fit for purpose before migration to a specialist software platform.
4. Dashboard Reporting
There is no point completing the above steps unless you can demonstrate, with evidence, the value of the project. A dashboard not only provides an aggregated view but allows a deep dive into priority areas for a more in depth view. As mentioned before, the dashboard is a powerful visualisation capability – it won’t offer the actual data expertise that’s needed.
5. Don’t Stop
Data foundation projects are not a one-off exercise so they need a data governance process in place to not only maintain asset data but to enhance with additional data from internal and external sources. The more enriched the data, the better the decision making.
Without this focus on data foundations, organisations are wasting money unnecessarily. Some of our customers have saved millions by identifying how many assets they actually have, breaking them into components that need to be maintained separately, and thereby saving on replacement costs and operational failures by revising their maintenance budget to one that now accurately reflects their asset base.
This is now an ongoing activity to maintain accuracy and senior executives finally have confidence in key decisions around the asset planning lifecycle.
By Nabil Lodey
CEO of Envitia