- 1 What is data governance?
- 2 Data governance framework for executives
- 3 Data governance & standardisation
- 4 Data governance tools for engineering, construction, and operations
- 5 How to choose a data governance tool
- 6 Data governance for the industrial sector
- 7 Common challenges
- 8 Business benefits – practical examples
- 9 Conclusion
What is data governance?
Data governance is the systems and philosophies related to the monitoring and decision-making of an organisation’s data. It is common for different business functions to create and defend their data repositories, which may come into conflict with overall strategic goals. This can lead to operational inefficiencies that require executive intervention. In simple terms, data governance is about what to do with your company’s data and following up to make sure it is done. Good data governance involves a framework that creates and manages a strategic vision for data repositories across the business and enforces the organisation’s data management processes and policies when conflict arises.
Data Governance vs Data Management
Data management, a concept often confused with data governance, takes the data governance framework and implements governance decisions with processes, technologies, controls, and other logistical elements that allow for smooth and easy access to data-enabled resources. The goal of data management is to ensure that these resources are accessible when needed by users. Security, architecture, analysis, warehousing, and quality assurance all fall under the umbrella of data management.
Data governance and management are best seen as two sides of the same coin. Data governance involves large-scale strategic decisions about how an organisation aligns its data to business goals while minimising operational risk. Data management is concerned with how to organise, protect, and access an organisation’s data. It controls the data lifecycle, while governance polices data management practices.
Data governance framework for executives
A data governance framework helps shape an organisation’s data as a business asset, moving a company away from unstructured, siloed data created for the bespoke purposes of each business unit and toward a unified vision for all data to help provide competitive insight across the entire enterprise. Strict data governance helps take data from an abundant but scattered raw material and refines it into crucial information that allows the business to capitalise on new business and revenue opportunities. Business benefits include:
· Accurate, complete, and real-time data leads to better decision-making and operational risk mitigation
· Improving business planning and forecasting
· Identifying productivity issues or redundant processes, boosting efficiency
· Enhanced financial reporting allowing a business to target and eliminate unnecessary costs
Often, establishing a data governance framework involves moving from a set of informal protocols to a formalised set of established guidelines to aid in strategic, tactical, and operational decision-making. As such, executives are a crucial component of the process.
Overall objectives: The big picture objectives of data governance involve several factors:
· Creation of data strategies, architecture, policies, and procedures
· Tracking and enforcing compliance with policies and procedures
· Manage and oversee data projects and services
· Resolve data-related issues and ensure data quality
· Recognise the value of data as an asset
While each company may have additional needs, at its core these factors are required for a successful data governance program.
Vision and principles: Technical elements are necessary for data governance, but to meet the needs of a company, any framework must project a set of principles by which it will operate. These serve not only as a type of roadmap for decision-making but should also marry the company’s culture and business goals with their data governance procedures. The Data Governance Institute (www.datagovernance.com) has created a set of eight guiding principles to which every data governance plan should adhere. These principles are:
· Change Management
Each of these principles has importance, but a business leader should focus on transparency, checks-and-balances, and change management. Transparency and checks-and-balances involve high-level decisions to meet regulatory and compliance standards. Change management requires top-down oversight to ensure that individuals involved in the data governance structure have the tools to adapt to changes in data outcomes.
Decision-making: A data governance framework allows a business to decide which data is relevant to achieving its strategic and tactical objectives. This requires collaboration between business functions, such as IT, operations and sales and marketing, and paves the way for the integration of data silos leading to better business insight from data sets. These insights not only improve strategic decisions but also allow for better day-to-day decision-making from the field. Further, an organisation can purge unnecessary data creating a cleaner and more powerful analytics function for each stakeholder.
Rights: After a company has determined who will be involved in the decision-making process, the next step defines who has the rights to decide what. A common tool for this involves the RACI method.
Responsible: The person(s) who does the work.
Accountable: The person(s) responsible for ensuring work is done.
Consulted: The person(s) who provide input.
Informed: The person(s) who are notified, but not actively engaged in the work.
By finding out where a person’s role puts them on the RACI continuum, their rights can be established. For example, in developing data governance policies, certain members of the Governance Council will be responsible, while the head of the Council will be accountable. The other members of the Council will likely be consulted or informed, depending on their role.
Data stewards are the points of contact for all things data related. They are the link between the policymaking bodies (e.g., data governance council) and those who enact those policies (e.g., data management team). Data stewards are responsible for reconciling conflict, defining data values – such as type and length – reporting on data quality metrics, and determining usage details for third-party contractors. This function works best with complementary skills between achieving business objectives and understanding technical IT language. Steward can be organised in a variety of ways, including for each business unit, process or project, but the key to making them successful is granting them authority to oversee data in their scope.
Data governance & standardisation
The first step towards data governance is to standardise procedures for people, processes, and technology. This involves ensuring people have the skills necessary to meet data governance policies, processes are streamlined across an organisation, and technology can be used to enhance productivity.
It is imperative to provide people with the necessary training and education to adapt to new data governance procedures. Programs geared toward upskilling employees must be designed to engage and teach employees using personalised learning systems and provide them with the opportunity to practice their skills to maximise retention.
The main challenge when upskilling workers is change resistance. Resistance to change is common, but effective business leaders know how to counter this resistance by engaging employees in the process. Most resistance to change doesn’t come from the technical elements, but the social aspect of change. Employees may feel uncomfortable as their role within the organisation is changing, or their day-to-day tasks are being altered. The best counter to this is to ensure employees who are involved in the change get to participate in making it. This will help them to have a personal stake in new procedures and create more effective and meaningful change.
Standardisation helps to unify processes across individual siloed functions. Each location, team, or even person, may have its process which collects its own data. Establishing governance over these processes can enable an organisation to regulate how tasks, and the data they produce, are handled. Standardising processes through governance can help to identify what repetitive tasks may benefit from automation and where bottlenecks are occurring. A lack of standardisation can inhibit planning, forecasting, and reporting on these processes.
Once employees have been upskilled and processes have been standardised, the next step is to enhance them with technology. A properly implemented data governance solution can lead to major productivity gains. Adding data governance can help to:
· Ensure data from multiple sources has a single repository
· Meet regulatory requirements
· Enhance the ability for decision-makers to access relevant data
· Filter irrelevant or out-of-date data
· Identify opportunities for automation
· Guarantee reliability of gathered data through proper operational procedures
Data governance tools for engineering, construction, and operations
Data governance tools have been a boon for helping businesses control their data strategies and management. An organisation must determine if they have the need for a data governance solution. However, data governance software isn’t a one-size-fits-all tool. If a company has a specialised field – such as engineering – it makes sense that they would require a specialised data governance tool, such as an ability to integrate with CAD software. Understanding when and how to select a data governance tool is crucial knowledge for executives in the engineering, construction, and operations fields.
When you’re ready for data governance software
How does a company know when they are ready to start using governance software? There is no simple trigger, like crossing a threshold in revenue or having a certain number of employees. Instead, if the management of an organisation can say yes to any of these questions, it might be time for a data governance solution.
Am I in a highly-regulated industry?
If your industry is subject to strict regulatory oversight, there is a strong need to establish formal procedures for your company’s data. Regulatory bodies often have compliance requirements for data lineage and the care and control of sensitive data.
Do I need to create a master data copy?
Companies frequently need to consolidate all their data about a particular process, product, or procedure into a “golden copy” or master data copy. Reasons range from business goals to implementing enterprise resource planning.
Does my company use analytics or big data?
Any company trying to take advantage of analytics requires a robust data governance tool. From streamlining workflow to understanding market segmentation, to properly utilise big data it first needs to be appropriately managed and guided by governance.
How to choose a data governance tool
Selecting a tool involves knowing what you truly need out of your governance solution and finding the right fit. The following list is comprised of key features to examine:
While every data governance solution will manage data artifacts, the extent of the management tools may differ. Basic elements, such as the ability to track, create, read, update, and delete data elements are mandatory. Quality data governance software will also allow you to scan and identify artifacts, manipulate metadata values, manage relationships between data elements, and classify data based on its uses.
Enhanced management elements:
A data governance tool must track your data management and ensure it meets standards and protocols. It is essential that the tool allows you to engage in quality and lifecycle management, data lineage, process tracking and reporting, and control access to the data glossary.
Even the most cutting-edge company still produces some “legacy” or unstructured data that doesn’t fit easily into a database. For engineering and construction firms, the ability to store manuals, product specifications, blueprints, and digital media is critical in a data governance tool. Ensure the tool you select can store this legacy data and label, sort, and update it with metadata.
What good is a data governance tool if it cannot integrate your company’s governance structure? Make sure the tool can create and manage governance roles and responsibilities as well as tracking the change approval process.
Most organisations will want a data governance tool that is capable of handling collaboration between different users, so the ability to manage permission rights among users and to track work progress is vital.
Even though it deals with an inordinate amount of technology, data governance is fundamentally a business endeavour. That’s why it is so important that data governance software also is aligned with the business side of a company. The ability to document, monitor, and track the success of strategic plans or business processes will help in demonstrating value to those who might be less enthused about adopting a tool.
Data governance for the industrial sector
Not all sectors of business have the same needs for their data governance tools, and the engineering, construction, and operations industry is no different. The data needs of a natural resources engineering firm differ greatly from those of software manufacturers. As such, it is necessary to examine specific elements of a solution to ensure it meets an organisation’s needs.
Data preparation and quality:
Data is only as good as its usability. Data preparation is the key to ensuring that your data is capable of being used for discovery, mining, and advanced analytics. Preparing data and checking for quality allows an organisation’s business analysts and data scientists to produce tangible results out of the heaps of data gathered by modern systems.
One of the special concerns of the industrial sector is the ability for data to be moved between data warehouses and existing tools. If there is a weak link in the chain of data interoperability, projects will suffer. The ability to exchange information is an absolute necessity in large-scale industrial projects and the ability to easily handover data can be a driving factor for the success of a project.
Capital Facilities Information Handover Specification is a new standard for delivering information across the information supply chain in the industrial sector. This standardisation is vital to reduce costs, improve safety, and minimise risks. With a potential impact of two to four percent savings on capital costs, CFIHOS is an absolute must-have for an industrial organisation’s data governance tool. With a breadth of solutions available, understanding and choosing a provider with industry expertise (and CFIHOS certification) is vital.
Data governance faces several issues, from executive buy-in to scope creep, but some of the most common problems emerge not from the modern day, but from the past.
Legacy data integration is the most prominent issue encountered with data governance systems. A company that has been around for decades before the widespread adoption of digital transformation will likely have an extraordinary amount of legacy (or “unstructured” data) that needs to be incorporated. This data shouldn’t be shunted off and ignored as proper analytics often requires integrating this legacy data for analysis and projections. A data governance solution that doesn’t incorporate plans to integrate this data is missing a key element.
Along with legacy data integration is the challenge posed by pen-and-paper data. It’s not uncommon for project managers and their employees to jot down information while working on a job site. For several reasons (e.g., safety, regulations) this data can be imperative to record. Proper data governance procedures should take this pen-and-paper data into mind when formulating policies.
One of the most common issues with data governance comes from homebrew solutions. These are often found when a company feels that it doesn’t require a complex system for their data needs, and they attempt to manufacture their own solution from the ground up. These solutions often end up more complex and challenging in the long-run, as every new problem requires a unique fix and retrofitting of the system.
A final common challenge presented to data governance is the danger of data warehousing or data lakes. These occur when an organisation (without data policies) store every piece of data in a semi-structured database. Without proper quality controls, identification, and metadata labelling, these data warehouses can quickly get out of control. This data is virtually unusable in this state, and it can’t be utilised for any serious analysis.
Business benefits – practical examples
Organising data as an abstract concept can be a hard sell. The business world often comes down to how something can help increase revenue. Luckily, leveraging data can do precisely that.
Engineering and construction:
The engineering and construction industries are replete with tangible business benefits from implementing quality data governance. For example, analysing past projects to see what elements met budget expectations and what contributed to overrun can help to control costs. Likewise, tracking active and idle times utilising sensors on equipment can help determine which machinery should be purchased or leased and whether upgrades are worth it to improve fuel efficiency. That same data can also be used to track the ecological impact of a project for environmental oversight.
A recent project in the oil and gas sector has demonstrated the value of data analytics. Using historical modelling coupled with geographical tools, an analytics firm has helped one drilling company to reduce the number of wells drilled by 20%. This alone would be a significant saving, but the project also managed to have no impact on the estimated ultimate recovery (EUR). By leveraging the company’s data, they were able to quantify the impact each well was having on the EUR, thereby maximising the yield and minimising waste.
Operations and maintenance:
Operations and maintenance can often be overlooked, but data governance can have a real impact on these industries. Ongoing analysis of sensors from sites can help to determine energy conservation, power expenses, utilisation, and performance on any number of metrics.
An example comes from bridge maintenance. Bridges undergo an immense amount of wear and tear. Aside from the stress of having tens of thousands of vehicles crossing them daily, they are exposed to the elements and are expected to stand for decades with minimal maintenance. By utilising sensor data embedded at multiple points in the structure, analysts can determine the rate of impact and plan for better preventative maintenance. This data can also be potentially leveraged to motivate other projects, such as further bridges or roads.
While many long-term benefits of data governance are obvious – reduced liability, enhanced decision-making – some are less so. A report from the New York Times found that data scientists are spending most of their time simply organising and cleaning up data sets to make them usable. Dedicating much of a highly-skilled worker’s time to simply making an organisation’s data useable is a tremendous waste and prevents them from doing what they do best: finding insights to run a business more efficiently. Data governance tools help those tasked with data analysis to avoid spending their time as data janitors and instead work to utilise data for the many benefits it can bring to a company.
A universal truth of modern industry is that everyone produces data, and the construction, engineering, and operations industries are no different. From blueprints to costs and sensor readings to invoices, data is constant. An effective data governance solution will turn what could be a headache into a valuable tool that can aid business on many fronts. Data governance makes sure that all this data becomes actionable while putting into place the systems necessary to react to changing IT needs, regulatory policies, and business dynamics.
Governance can help to not only streamline existing processes and projects but to uncover new avenues and industries to explore. It can discover historical trends to avoid pitfalls or analysis can reveal the ideal time to engage in a new construction project. Collecting and dissecting data from sensors and probes can help to pioneer more energy efficient facilities. Geotagging and tracking equipment can help to reduce shipping costs and minimise downtime. While the business benefits of data governance might appear to be minimal with a cursory look, a more in-depth analysis reveals they are vast and deep. The use of data can head off many unnecessary costs and reveal countless business insights. When it comes to data, an ounce of prevention is truly worth a pound of cure.