Understand your data assets and what is required to give you more value

The reliance on digital systems and platforms is prominent as companies strive for efficiency and a deeper understanding of customer needs. To extract the maximum value from data and digital investments, organizations need to align their expectations and leverage their data assets effectively.

The recent emphasis on digitization and cloud-first initiatives has involved migrating systems to the cloud, either by modernizing existing solutions or adopting new cloud-native services. Simultaneously, industries have undergone a shift in their operating models, leaning more towards digital services and reducing reliance on physical connectivity. However, the question arises: what comes next?

To maximize the potential of data, organizations should recognize data as a valuable asset with intrinsic value. Here are some key areas where data assets can bring significant benefits:

• Strategic decision-making: Data provides insights that enable informed and data-driven decision-making. It helps organizations identify patterns, trends, and correlations, leading to better strategic planning and resource allocation.
• Operational efficiency: Data allows organizations to streamline operations by identifying inefficiencies, optimizing processes, and reducing costs. For example, analysing customer data can help improve customer service and resource allocation or improved quality on project investment data.
• Customer understanding and personalization: Data enables organizations to gain a deep understanding of their customers, their preferences, and their behaviour. By analysing customer data, organizations can personalize their marketing efforts, improve customer experiences, and tailor products and services to meet specific customer needs. This leads to increased customer satisfaction, loyalty, and ultimately, business growth.
• Competitive advantage: Data can provide a competitive edge by enabling organizations to understand their customers better and anticipate market trends. It allows companies to personalize their offerings, improve customer experiences, and stay ahead of the competition.
• Innovation and product development: Data fuels innovation and product development by providing insights into customer preferences, market demands, and emerging trends. It helps organizations identify new opportunities, develop innovative solutions, and create products/services that meet customer needs.
• Monetization opportunities: Data can be monetized directly or indirectly. Organizations can sell data to other companies or use it to create new revenue streams. Additionally, data-driven insights can lead to the development of new products or services that generate revenue.
• Risk management and fraud detection: Data analysis can help organizations identify potential risks, detect fraudulent activities, and enhance security measures. By analysing data patterns, organizations can identify anomalies, assess risks, and implement proactive measures to mitigate them, protecting the organization's reputation and financial well-being.

What is Data?

So lets start by identifying what is data? Traditionally the IT department have spoken about IT assets and how we invest in them. This has meant treating each system or technical platform as an asset looking at the investment and operating costs, as we have moved more operational functionality into these systems, typically ERP, CRM, Sales systems, product systems etc. However, with an increase in regulations, together with organisations, seeking to get more value out of their data to be competitive we need to look at the data first and how it is processed in these assets.

With the increase in the types, volumes and distribution of data there is a focus to understand it, describe it (metadata), along with the regulations as to how we can use that data. Data can be used to give us differing insights through information and knowledge, but this requires that we use different ways to get the value out of it.

When trying to explain this I usually compare data to water. If you ask someone to describe water you will get many different answers, H2O, something we drink, we wash with it, somewhere to swim etc. We usually think of things from a personal use and from a personal perspective, we cannot survive without water. So, let’s ask some more details what do we use water for? Again, we need it for drinking, but also for heating, cooling, transportation for example. This opens a whole other box of questions as to how you describe water? Let’s say we describe it as to our usage e.g. a drinks manufacturer may sell bottled water which can be still, fizzy, flavoured or made into different beers, wines or beverages, whereas a hydro-electric plant requires water to drive their turbines or a sewage plant as a means of distributing our waste and a shipping company as a means of transport. All this from an asset, giving us different value through different services for different uses, in this sense data is much the same as water.

When we look at data as an asset, let’s ask the questions can our organization survive without that data?

If we take customers as an example, organisations generally want customers to buy their products or services and to keep their customers. Dependent on your organisation, a customer could be a person, an organization, a citizen, a patient, or an employee. This relationship is usually different in each organization and in ecosystems, for example it can be as a partnership or a pure customer and provider relationship.

Now if your organization is customer focused then customer will be similar to water, in that it is essential for the organizations survival and is used in many different parts of the organization for different purposes. For example:

• in sales for customer interactions,
• in marketing for customer understanding,
• in product or services management to help with development
• and in back-office for customer agreements, financial reporting etc.

The Customer (data) asset is used throughout the organization and flows through the organization. All organisations serve some sort of customer and the better they understand their customer the better the services they provide.

Other examples in some industries are:

• Banking: Data collected for a mortgage application system can be repurposed for other analyses, such as risk assessment or customer segmentation.
• Healthcare: Combining patient records, medical history, and real-time monitoring data can potentially improve patient outcomes.
• Retail: Analysing customer behaviour data helps personalize marketing and optimize inventory
• Energy Sector: Managing data assets in the energy sector involves complex ecosystems with focus required on data quality, integration and security. To aide in improving transparency for customers, to help modernize with new services and efficiency to conform to regulatory requirements.

Practices that help you extract the value from your data

To fully leverage the value of data assets, organizations need to prioritize data management practices like data governance, security, privacy, and ethical considerations. This involves establishing clear guidelines and processes for data usage, ensuring data protection, and complying with relevant regulations.

Data quality is also crucial for extracting value from data assets. Organizations need to ensure that data is accurate, clean, and reliable. This may involve data cleansing, normalization, and verification processes to eliminate errors and inconsistencies in the data.

Accessibility is another important factor in deriving value from data assets. Organizations should ensure that data is easily accessible and available to relevant stakeholders. This may involve implementing data integration and sharing mechanisms, as well as data visualization tools to make it easier for users to understand and derive insights from the data.

Additionally, contextualizing the raw data is essential. Raw data may not meet everyone's specific needs, and it may require additional processing or enrichment to make it useful for a particular purpose. This may involve applying machine learning algorithms, statistical analysis, or domain expertise to transform the data into meaningful insights.

By effectively managing data assets and addressing these considerations, organizations can maximize the value they obtain from their data and drive their digital and data-driven initiatives forward.

It is important to remember when managing and communicating about data assets, the difference between strategic data assets and operational data assets. Let's look at each one:

1. Strategic Data Asset: A strategic data asset refers to data that is critical for supporting the long-term strategic goals and objectives of an organization. It is typically high-level, aggregated, and provides a big-picture view of the organization's operations. Strategic data assets are used by top-level executives and decision-makers to understand trends, patterns, and insights that shape the organization's overall strategy. Examples of strategic data assets include market research data, competitive analysis data, customer data, and financial performance data, ESG Data.
2. Operational Data Asset: An operational data asset refers to data that is necessary for day-to-day operations and immediate decision-making within an organization. It is typically detailed, transactional, and focused on supporting specific business processes or operational activities. Operational data assets are used by operational teams, middle managers, and front-line staff to carry out their daily tasks effectively. Examples of operational data assets include sales transaction data, inventory data, customer support tickets, production logs, and employee performance data. Operational data asset can also be a form of data product.

While both strategic and operational data assets are valuable, they serve different purposes and support different levels of decision-making within the organization. Strategic data assets provide insights into the bigger picture, helping shape long-term strategies and guide the organization's direction. Operational data assets, on the other hand, help optimize day-to-day operations, improve efficiency, and address immediate operational challenges.

Both strategic and operational data assets are essential for the overall functioning and success of an organization. Proper management and utilization of both types of data assets can improve decision-making, efficiency, and competitive advantage.

I have used customer as an example data asset, but you may have other important assets, the point being to understand that to get the value out of your data. You need to manage and invest in the data management practices crossing organisation boundaries, to improve and ensure trust in your data, for better decision making and to drive business growth.