The growth in the number of connected devices, new regulatory compliances, the promise of 5G, and evolving methods of using and analyzing data have ushered in a new age driven by insights gained from connecting customer, strategic, and operational data. Businesses are transitioning into becoming more data-centric now, calling for more advanced data management and analytic systems. Data underpins the success of organizations in mining both physical assets and digital business opportunities—improving accuracy, increasing efficiency, and augmenting the ability of the workforce to deliver excellent value. Data, essentially, is the lifeblood of any organization, and hence, its accessibility, monitoring, and availability become an essential factor for business agility

According to Gartner, Data and analytics are the key accelerants of an organization’s digitization and transformation efforts” and that “Leading organizations in every industry are wielding data and analytics as competitive weapons.” In its market forecast for 2019-2023, IDC predicted that there would be a growing increase in “the importance of data in the modern enterprise.” 

Companies realize the potency of information and data in today’s technological landscape. But very few organizations have learned how to manage and value their data. 

WHAT DOES DATA AS AN ASSET MEAN?

Many companies have realized the potency of utilizing data in their strategic operations. Data, additionally, provides actionable insights, as we saw in the above use case, which can effectively help run the business and achieve business objectives. To capitalize on data’s potential, organizations must fundamentally change how they view and use data. Businesses worldwide are investing billions in unlocking its secrets and enormous disruptive potential. Data is at the heart of new business models, technologies, and an ecosystem of companies providing almost anything as a service. Owing to Data being this critical, many large-scale organizations have appointed a Chief Data Officer (CDO) to oversee and handle Data. This understanding and perception of Data are what has come to be known as ‘Data Asset.’ Just as companies value and manage tangible assets, similarly, these perspectives view data as an asset for its strategic value. 

Traditionally, enterprises have considered data as a cost (overhead) that needs to be captured and managed. But in a digital world, Data is now an essential source of competitive advantage. Recently Microsoft CEO – Satya Nadella mentioned that Data requires its individual row in the Financial books. Data is now its own Asset Class, alongside traditional business assets (such as physical or human assets). 

Data becomes an economic asset only when we can derive information & actionable insights from it

WHY IS DATA AS AN ASSET RELEVANT MORE THAN EVER? 

  • “Data and analytics will become the centerpiece of enterprise strategy, focus and investment.” (Gartner)
  • Data, analytics, and AI are opening the door to new possibilities for how organizations can grow and differentiate themselves against competition at an accelerated pace. (Accenture)
  • The full potential of digital technology will only be realized if Data is appropriately processed. High-quality Data is the one utterly essential ingredient to the successful digital transformation of the world around us. (Accenture)

EIGHT CHALLENGES IN IMPLEMENTING DATA AS AN ASSET

The rule of thumb here is to view data as a journey and not a destination. 

To solve any data problem, there needs to be a clear-cut problem to be addressed. Businesses need to ask themselves if their current Data is performing as a trusted asset and whether it contains valuable information or not. Based on my experience, these are the top 8 challenges in implementing “Data as an Asset.” 

  1. Unclear Business objective: 
  2. Mismanagement of data assets 
  3. No standard Taxonomy 
  4. Lack of skilled Data Science and AI Engineers 
  5. Not developing a sound plan
  6. Quality of Data
  7. Data Literacy Gap
  8. Data Security and Compliance

Data platform technology is as essential as Data itself, and hence, organizations are turning towards tools that help them manage data better. The efficacy of data analytics and Data is undisputed, and if you as a company are transitioning into becoming more data-powered, here are my ten mantras to kickstart your data journey for implementing Data as an Asset.

 

15 MANTRAS FOR IMPLEMENTING DATA AS AN ASSET

1. Define your Data Strategy

Every organization is different; there is no definitive checklist for a data strategy. Successful data strategies come in many shapes and sizes, tailored to each organization’s strengths and weaknesses. 

According to Accenture research, Data-driven organizations with an enterprise strategy are growing at an average of more than 30% annually. 

Effective data strategy should be human-centric and consider different personas: owners, stakeholders, customers, front-line workers, analysts, sales, marketing, Support, and other users. Organizations that encourage the workforce to think about information and data as a strategic asset can extract more value from their systems.

Consider whether the organization sees data as an asset, a liability, or both. It comes down to having a business strategy for your data (not just a data strategy for your business).

Digital leaders need to:

  • Implement tangible, measurable metrics linked to business outcomes.
  • Plan their data future with a strategy to win in the age of intelligence, and that starts by developing a data architecture blueprint and executable roadmap.

2. Disrupt business models with AI

Data and AI are—unsurprisingly—vital to establishing a data-centric business and creating productivity gains in the current model to invest in the new one. When Data is fuel, you can use it to drive entirely new business models. A typical example is the platform business model (which may also be a platform business). While traditional businesses focus on delivering a specific product or service, platform businesses focus on connecting customers with what they need. They create value as networks, bringing an ecosystem of players together. And to do that, they must also treat data very differently. It’s their critical asset—and they organize their business to exploit it to the max.

The organization should also look at augmenting their product/service business towards one enhanced by network economics.

Disrupting business models with AI will create opportunities for innovation, analytics, and optimization.  By utilizing pervasive data intelligence, the company can enable digital decision-making in all areas of its organization. It also will help us to understand our organizations and customers deeply. Additionally, when AI and machine learning disrupt business models, they control manual tasks. This helps our employees to drive innovation as part of their everyday practice. 

3. Establish the right Data Culture and Architecture

How do you get your organization to value data? Technology isn’t enough to transform your company into a data-driven organization. Without a robust data-driven decision culture, organizations end up missing opportunities to use their collected Data effectively. This sometimes creates issues with data consistency or internal processes.  Right data culture makes your company more efficient. It ensures that your data helps in modernizing business processes and improving the customer experience. The value of Data must permeate across the entire organization, from critical initiatives to the business processes and culture. At a minimum, the enterprise needs to: 

a. Clarify data accountabilities across the enterprise

b. Consider Data Curation and Data Quality as core business competency

c. Re-imagine a frictionless trusted data supply chain and embed Data-Fluency as a cross-business group strategic imperative

d. Develop a standard Data Taxonomy and Dictionary

4. Implement DataOps

Implementing a data strategy is a daunting task if an organization does not change the traditional IT operations in the context of Data Acquisition and Management. DataOps or Data Operations will allow you to connect data creators with data consumers to find the value in all collected Data. It is pivotal for enhanced data protection, crunched cycle time for insights deliveries, and efficient data management. 

When you move to DataOps, you can easily focus on enhancing the communication, integration, and automation of data flows between data creators and consumers. DataOps uses metadata to improve the usability and value of data in a dynamic environment to deliver value faster.

DataOps can help infuse life into your data.

5. Establish Tech Intensity initiatives for Data-Fluency enablement

To capitalize on data as an asset, organizations will need to become more data-driven and fluent. Organizations should invest in rolling out Tech Intensity initiatives for Data-Fluency enablement. 

The Accenture research “The Human Impact of Data Literacy” identified how the data literacy gap impacts organizations’ ability to thrive in the data-driven economy. First, despite 87% of employees recognizing data as an asset, few use it for informed decision-making. Only 25% of surveyed employees believe they’re fully prepared to use data effectively. Just 21% report being confident in their data literacy skills (i.e., their ability to read, understand, question, and work with data). Only 37% of employees trust their decisions more when based on data, and 48% frequently defer to a “gut feeling” rather than data-driven insights when making decisions. The research identified three ways that show how a lack of self-sufficiency to work with data affects employees’ ability to assume their roles in a data-driven workplace:

a. Data appreciation isn’t fully translating into employee adoption.

b. Lack of data skills is limiting workplace productivity.

c. Changing technology practices are adding to modern workplace pressure.

Data literacy is an essential future skill set, but CXO will not be successful by simply mandating adoption. To realize the full potential of data, it’s imperative to invest in programs to upscale the data and analytics skills of staff across the business and set baseline expectations for Data Literacy (resulting in Data Fluency) and data-driven decision-making throughout the organization. Organizations should develop a foundational capability framework, supported by appropriate Tech Intensity enablement programs and a mix of agile, innovative ways of hackathons and gamification, to meet their unique requirements. 

In addition to the Data-Fluency enablement, I would suggest leveraging the Low Code / No Code powered personas of “Citizen Developers.” Citizen Developers are business users with a functional context and little technical acumen, rather than a traditional developer who can create business value applications. Citizen developers can take advantage of advances in SaaS, PaaS platforms like Microsoft Power Platform to rapidly build and deploy technologies that both harness existing data and create new sources for others to use. Citizen developers have the required business and functional context that puts them in a unique position to innovate and develop new solutions to consume, integrate, and share data across a wide range of products and services.

6. Establish Data Signals and Patterns Repository 

It is challenging to comprehend and capitalize on the ever-expanding wealth of data available without the proper functioning of traditional operating principles, indicators, and decision drivers. That’s why enterprises need a new approach – which “listen” to your data and convert it into “signals.” 

Signals are analytics building blocks created by converting a combination of internal and third-party structured and unstructured data into complex algorithms that AI systems can use to enhance prediction accuracy significantly. These signals will allow organizations to be available through the data marketplace as a signals repository. 

With the help of these signals, organizations can respond to changing business needs quickly, add economic value, and reduce the demand for data modelers and data engineers. 

The use of signals repositories will also impact the product and service development lifecycle.

Organizations will quickly drive product or service design, development, and delivery, by integrating real-time signals and other data to create brand new modified products and services.

7. Establish Data Marketplace – for Data sharing and sourcing across ecosystems

Data is considered a foundational layer in traditional IT architecture and more as a byproduct of business operations and applications developed for Systems of Records. Traditional Data Supply Chain was sequential: data was created/generated, acquired, extracted, and occasionally stored for later consumption or distribution or regulatory compliance, often resulting in Stale Data or inaccessible in the process. 

Today’s organizations fully recognize that Data resides at the core of the business and needs to be accessed and analyzed to unlock the data value. Which effectively requires the entire Data supply chain to be re-imagined.

Creating data ecosystems is imperative for all companies to reap the benefits from sharing and managing each other’s data. For example, during COVID-19, public and private companies collaborated to effectively share data that helped them gain insights into the spread and tracing of people affected by the virus. Frictionless data sourcing and sharing across your partner ecosystems across your supply chain and partner ecosystems are the way to go.

Data Marketplaces will serve as a one-stop shop for connecting data producers with data consumers. These data marketplaces will offer cataloged and categorized data from internal and external third-party sources; advanced algorithms and machine learning will be used to carry out data curation activities. Data consumers will be able to “shop” for the best available Data for their business needs, much as they would shop for items on any e-commerce site. This Data can be curated further by marketplace participants themselves, while crowdsourcing can suggest similar or relevant data. In time, internal data marketplaces could be opened up to external parties such as customers and suppliers.

Pro Tip: 

Enterprises should take a new pivot and establish a Data Monetization Strategy.

8. Use AI and ML Algorithms

Artificial Intelligence, Machine Learning algorithms, and automation are revolutionizing how organizations communicate, manage and interact with data. AI in the Business Intelligence (BI) realm helps to analyze data swiftly and efficiently without much effort on the company’s part, saving everyone significant time and money.

Similar to Signals and Patterns Repository, Enterprises would need to differentiate their products and services based on the accuracy and dynamic behavior of AI/ML Algorithms. 

Future businesses would be differentiated based on how sustainable, resilient, and responsibly they participate in Circular Economy and Algorithmic Economy. 

9. Democratize Data – Secure Data Access and the correct type of BI and BI tools

To become data-driven, organizations must shift from the dominant enterprise approach to Data. Ownership of data and its analysis has been in the hands of a few specialists. 

Democratization of data starts with secure Data Access, Data Fluency, and Empowering Data-Driven Culture. But the journey for each of these three elements can be accelerated, enabling and providing the correct BI capabilities and BI tools to different user persons. 

Leading data-driven organizations do not just capture and curate data at speed; they close the loop by embedding the right insights from data into business processes and the hands of business users. Data visualization is a more transparent, intuitive, and contextual way to view data—beyond just the numbers. Through data visualization, companies harness the actual value of data by accelerating comprehension, accelerating insights generation, and enabling organizations to make smarter and quicker decisions. 

Just implementing “data as an asset” is not sufficient. You need to deploy the right BI tools for different user personas to analyze, manage data, and make data-driven decisions. Choose the correct type of BI for different personas in the organization with well-defined data semantics and taxonomy.  

10. Data Governance 

To unlock the value of data, it needs to be rightly governed to keep the features that make it accurate, intelligent, trustworthy, and connected. Data Lineage, Metadata, data source, and other attributes for high-quality data assets should be interpreted and managed constantly. 

These features create the foundations for the trusted use of data throughout the enterprise and create a strategic data governance framework. It is essential to evaluate and manage data quality by establishing a robust data governance framework. Good governance secures high-standard business metadata and data profiling. 

  • A robust data governance program supports and amplifies the firm-wide commitment to establishing and maintaining a data quality framework.

  • Data governance is the common factor that makes risk and privacy policies more manageable and more effective. 

As the data supply chain changes, data management and governance will need to be re-imagined as well. Current approaches to managing data and ensuring its quality tend to be human labor-intensive, simplistic, and hard-wired rules-driven (relying on subject-matter experts to define rules) and handling data profiling and monitoring. While marginal improvements can be achieved in certain areas, these traditional approaches can’t efficiently scale to address today’s big data sets, nor are they nimble enough to adapt to the needs of a rapidly changing business landscape. Organizations will need to use new approaches that apply AI, machine learning, and even deep learning to core data management and governance matters to overcome this challenge—particularly data quality. Using ML and Deep Learning can significantly reduce, if not outright eliminate, an organization’s need to manually profile data, develop rules, prepare reports, and monitor results.

11. Establish Data Ethics principles 

Data ethics is a branch of ethics that evaluates data practices—collecting, generating, analyzing, and disseminating data, both structured and unstructured—that can adversely impact people and society. 

Data Ethics includes addressing and recommending right and wrong conduct concepts, with transparency, traceability, explainability/defensibility of actions, and decisions driven by automated/artificial intelligence (AI); in relation to Data in general and personal data in particular.

With AI algorithms being used to differentiate the business, and without regulated codes of ethics, an unconscious bias of algorithm creator, businesses need to create and maintain a structured and transparent data ethics strategy, which can help business:

a. to build trust

b. use fair practices 

c. with Data Privacy Compliance 

When your customers believe you work ethically, honestly, and transparently with them, you build fierce loyalty, which translates to continued interactions and profits. You literally cannot buy such clients with money, but you can endear them for life with proper data ethics.

12. Data Observability 

Data volumes, the complexity of systems, and business expectations are increasing, calling for better data systems observability. 

In simplistic terms, Data observability is a concept pulled from best practices in DevOps and Software Engineering, refers to an organization’s ability to fully understand the health of the data in their system. By applying the same principles of software application observability and reliability to data, these issues can be identified, resolved, and even prevented, giving data teams confidence in their data to deliver valuable insights.

Here, the tip is to answer critical data problems with complex data systems and ensure they remain resilient. Data Observability, essentially, provides visibility into AI applications and analytics, providing helpful insight for businesses. It also allows data professionals to identify, predict, and solve problems to increase data scalability and optimize their data pipelines to meet business requirements. 

13. Define Data security and compliance controls

Data security has taken center stage in the past couple of years. The success of “Data as an Asset” implementation is entirely dependent on ensuring Data is secure both from a perspective of integrity and access rights

With data security regulations like Personal Identifiable Information (PII), Payment Card Industry (PCI), Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR) going stricter on compliance (audit and penalties); the organization must define the required security controls and data architecture to meet all the regulatory compliances 

For Data Security, an organization needs to adopt technologies like Data Loss Prevention (DLP), encryption, tokenization, secure computing, and the broad-based implementation of cybersecurity, application security. 

14. Hire the right Data Engineering and AI talent

Companies that want to implement data as an asset should also focus on hiring the right talent. These engineers can create business ideas, identify beneficial approaches, test and improve them and use data more efficiently. 

15. Establish a Chief Data Officer and office of CDO

Data isn’t just enabling business but driving it. To be successful in this journey, organizations need to create an empowered role of Chief Data Officer (CDO) and office of CDO. In the short, term business leaders and the board would look to CDOs to help develop new data-based agile business strategies so their company could gain a competitive edge. CDOs are expected to serve as change agents, straddling effortlessly between technology and business agenda. 

To accelerate the Data as an Asset journey, CDOs would need to develop Modern Data leadership traits and ambition to lay down the foundation now to prepare their organization for the era of the digital ecosystems, the world of the Internet of Behaviours (IoB), and multi-party products & solutions.

SUMMARY 

Data is going to power the future of Enterprise. It is high time for organizations to realize that the key to business agility lies in managing and valuing your data from an asset perspective.

In the new age of the customer, refining data into an asset will unlock new business models and ways to thrive in a highly volatile market. Data Fluency and empowerment will be the actual determining success factors in a data-literate world. Successfully adapting to this ‘Data as an Asset’ model indicates the quality of your data and the enterprise’s maturity to handle DataOps and Data Governance. Organizations must turn data into actionable information, which will be a critical operation for businesses soon

The organization that will transform data into a business asset would provide insights and clues into the business continuously to answer – Where it is? What happened, Why it happened, and most importantly, Where it could go?. 

Data is no longer an enabler of the business, but Data is steering the business. Organizations that put culture, customers, and people at the center of the data-driven transformation will emerge as leaders – beyond incremental gains and scale the value of their data assets. So, why the wait? The time to change is now!