IFAC & cloudThing’s PAO Digital Assessment Tool Part 10: Applying Predictive Insights to Your Data
cloudThing | June 8, 2021
IFAC teamed up with cloudThing to offer our members a free Digital Readiness Assessment Tool that would assess an organization’s digital readiness ahead of a digital transformation project. The Digital Readiness Assessment Tool has been designed to measure how digitally ‘mature’ an organization is, or where they already are on their individual digital transformation journey.
The Digital Readiness Assessment Tool is broken down into 11 different pillars. So far, we have blogs covering the following sections:
- Culture & Capability;
- Vision & Strategy;
- Business Systems & Automation;
- Talent Management;
- Product & Service Developing;
- Sales & Marketing;
- Digital Engagement;
- Learning & Qualification; and
- Good Governance
We recommend reading the previous articles before proceeding to Part 10 on Applying Predictive Insights to Your Data.
The penultimate pillar in IFAC and cloudThing’s digital assessment tool will look at how a PAO should handle the large amounts of data generated by organizations these days, as well as how to derive valuable business intelligence using predictive insights.
Appling Predictive Insights to a PAO’s Data
Organizations process large volumes and different types of data, including data that tracks internal operational activities, member activity, and external information. Fortunately, technology provides many ways to generate value from this data by using analytical and predictive techniques to generate insights and provide predictive benefits for the organization.
An effective data strategy grounded in knowledge of data geography and application enables data assets to be capitalized while maintaining high levels of data security. This allows the organization to generate additional value for stakeholders, increase operational efficiency, and improve the experience of clients, members, and customers while ensuring that the organization is making the best use of available data.
Technology and techniques for analyzing and processing data also enable an organization to make data-driven decisions that provide greater value to all stakeholders, proactively reduce costs, and improve service delivery.
Data Democracy Within A PAO
Data democracy aims to create a flat structure of access to data within an organization. This means that the average user can access information in a digital format without the need for specialists to assist them in the collection and/or analysis of data. Democratic data should be of high quality, regulation compliant, and in line with organizational, legal, and ethical standards.
In a data democracy, data is stored in dashboards and self-service systems that provide data in a clear format, removed from data storage complexity, and often in tabular or graphic format for further analysis. Additionally, data democracy ensures that business users can easily define their information requirements, answer their questions, and create data tools, reducing burdens on specific teams and reducing overall wait times for insight.
Ensuring that all staff are equal in their ability to access high-quality data resources encourages a data-driven culture where all are free to examine, draw, and compare conclusions relating to business data.
This democratization of data increases the generation of unique and diverse ideas and viewpoints, resulting in improved outcomes for the organization. Other benefits of a culture of data democracy within a PAO might include:
- Empowered business users that can build solutions to ever-changing problems.
- Improved competitiveness as a blending of domain knowledge and data is enhanced.
- Removed bottlenecks and reduced lead times in responding to data requirements.
- Encouraged individuals that explore relationships between datasets, increasing the likelihood of identifying opportunities.
Centralizing Your Data with a Core-Data Platform
A core data platform centralizes data from all corners of a PAO. It creates a single source of information typically composed of a data warehouse and data lake (see next section for more on these terms). All business processes are natively embedded in a central platform allowing for effective auditing and oversight.
An efficient core data platform will provide a PAO with a centralized view of all aspects of their operations. Relationships between different business areas are clearly defined and represented in the core-data platform through hierarchies and recommended data sources by topic. This ensures that users know the most effective way to interact with the platform while providing a clear data security boundary that, when invested in, can give confidence in high levels of security.
A core data platform:
- delivers a single reliable, comprehensive source of information, removing ambiguity and uncertainty relating to data duplication.
- ensures that data with varying levels of security are protected, and data minimization is actively managed to ensure any non-security or legal compliant data is removed.
- provides a single source of the truth, enabling decision-makers to have additional confidence in complex business-critical scenarios.
A core data platform will integrate with most technologies (such as AI and Machine Learning) to provide additional insights and structure, which is facilitated by the centralized nature of the data.
A Data Warehouse on The Lake…
Business Intelligence (BI) is a key component of any PAO’s data insight strategy, driving the scope and accuracy of key business decisions. Two of the core routes to delivering an effective business intelligence strategy are a Data Warehouse and Data Lake.
A Data Warehouse is a large store of data collected from around the organization that is organized to facilitate analysis and reporting. It is highly structured and is designed to provide data of a specific type and format to an end-user, organized around key business objectives.
A Data Lake is a form of data storage that includes more flexibility in type, structure, and scope, allowing for effective data storage that does not yet have a defined business use.
Traditionally, organizations use Data Warehouses to establish business intelligence strategies, reports, and dashboards. Historically this approach worked well, however, many organizations are finding found that the growth in volume and variety of data collected is outpacing organizational, analytical speed.
That is why forward-facing organizations now use a Data Lake to capture information that is currently outside of the planned business intelligence strategy. The use of multiple data storage technologies for different storage needs reduces the lead time for integrating new data sources into BI environments.
This catalogue of information can be accessed in various ways due to its flexible structure. In turn, this can enhance organizational, analytical capabilities and result in additional uncovered business insight that can be integrated into the formal BI strategy. As the scope of Business Intelligence initiatives grows, the architecture of the underlying Data Lake becomes a key consideration. That is why it is crucial the architecture of the solution strikes the appropriate balance between performance and cost-effectiveness while allowing data teams to react to business requirements in an agile manner for a high level of accessibility to a broad array of data enhancing business decision-making.
Integrating Data Through Data Transformation Processes
Data transformation is the conversion of data and/or its structure from one format to another. Once a data structure is transformed into its most appropriate form, it can be integrated into broader business services effectively.
Data transformation and integration processes allow PAOs to draw on internal and third-party data to support application development and business decision-making. Meanwhile, the automatic collection and integration of data enable stakeholders to spend less time on operational issues and more time on business-critical decisions.
Providing access to a wide range of integrated data at all levels of the organization will increase effectiveness in making key decisions. Having a well-defined system for requesting and incorporating new data sources allows decision-makers to work towards their goals in an agile way.
The core benefit of data transformation and integration is the creation of a standard, usable format that can easily flow through systems without informational errors and violations of data security. In practice, this would mean:
- Manual data integration tasks are shifted to automated pipelines— increasing opportunities for staff creativity and reducing integration errors.
- Data is automatically transformed into usable formats maximizing the insight users can extract.
- Data pipelines collect data from a source on a schedule or in response to changes, ensuring up to date information is provided to users.
- Data flows smoothly from source to user and presents meaningful insight based on context.
Getting to a Single view of the Truth with Master Data Management
Master Data Management is an approach used to manage critical organizational data (e.g., information about clients, suppliers, and employees). It provides a formalized central structure for data integration, ensuring a single point of reference for all in the organization. This process involves the automated examination of data to ensure that it meets pre-defined management criteria.
A proactive Master Data Management program will minimize discrepancies in interactions and reduce conflicts or erroneous decisions based on data quality issues. Maintaining a single master data record also allows the entire business to operate in a more agile and efficient way by reducing time reconciling data sources.
An effective Master Data Management strategy ensures that data warehouses, application data requests, reports, and dashboards comply with management rules. Data is continuously extracted from a single organizational point of reference. It also provides a clear data audit trail and confidence that the organization is adhering to data security and governance laws. Finally, varying levels of data sensitivity can be protected from modification and corruption.
Real-Time Operational Insights
Real-time operational insight is the movement of information from its source to end users that occurs as it is received by the system. During this movement, the data undergoes the appropriate transformation to be viewed by the end-user in dashboards and reports for further visualization and interrogation.
Access to real-time operational data provides PAOs with a keen competitive advantage by allowing them to understand exactly what is happening in the organization at any specific time and thereby, respond to events proactively.
Developing systems that transform this data into actionable insight increases this advantage by providing tools that staff and leadership need to make decisions in a timely manner. Real-time insight provides live updates to underlying data, visualizations, and KPIs. This insight can be viewed in a dashboard and distributed to key stakeholders when triggers are set off, allowing for greater granularity and performance monitoring. This significantly reduces the duration of operational failures as problems are identified as soon as they occur.
Machine-based insight is the exploration of data using algorithms that do not include human assumptions or interpretations. It can work in tandem with human-based insight to provide PAOs with additional business insight compared to purely human-based approaches or run on its own.
Being able to generate machine-based insights for an organization depends on access to large, high-quality cross-sectional datasets. It combines AI, statistics, and data mining to identify relationships that would be difficult for humans to determine alone. Integrating these insights into business intelligence systems and, more broadly, decision-making processes allows organizations to make decisions based on more information than they otherwise would have access to while reducing the time needed to interrogate data manually.
Doing this ensures that a PAO undergoing a digital transformation will quickly move to a more data-driven organizational culture, ensuring business decisions are made due to quantifiable facts.
Commercializing your Data with a Partner Data Service
Partner Data Services are collections of data that are often harvested from multiple sources, then manipulated, stored, and curated by third-party vendors in a way that is beneficial to other organizations and made available as part of a commercial service.
When building a complex business application or analytics platform, augmenting internal data with third-party sources from Partner Data Services allows a PAO to gain a richer picture of their data. It also:
- Improves the reliability and access to data through user friendly interfaces.
- Expands the scope of data available for analysis.
- Increases the array of tools available for analysis.
- Brings in expert advice and guidance on technically heavy decision-making.
- Ensures that all data meets internal logic, and metadata requirements.
- Limits exposure to organizations that could damage PAO reputation.
- Establishes lasting relationships with reliable data partners.
- Builds authority in a PAO’s analysis as data sources are highly respected by others in the field.
Complete Your Assessment!
Locate the email sent on behalf of IFAC Membership, with the subject line, "IFAC PAO Digital Readiness Assessment Tool Launch." Your organization's unique access link will be located within.
Be sure to check out IFAC’s PAO Digital Transformation Series webpage which houses helpful resources, articles and videos on Digital Transformation and is regularly updated!
cloudThing, based in the UK, is a technology company that help organizations such as the British Red Cross, The South African Institute of Accountants, and the Institute of Chartered Accountants (England & Wales) to name but a few, digitally transform by taking advantage of the automation technology available to them on the cloud.