Why Data Management is Key to Digital Transformation

The Value of Data

Historically, collecting and applying data to support business activities was a time-consuming manual process. Accordingly, data usage was often limited to essential activities. Nevertheless, organizations have always seen data as an enabler of business operations and better decision making.

Eventually, digital computing became a staple technology that initiated a major data revolution. It allowed a smarter data chain by utilizing new capabilities to store and retrieve data on a larger scale. Likewise, the rise of the internet and improved IT infrastructures allowed data access and sharing throughout businesses and geographies.

This pattern of technology advancement still continues today. But now, data management is evolving into a sophisticated domain in conjunction with digital transformation (DX). Moreover, technology is carving out new opportunities for organizations to collect, process, and apply data.

Specifically, the prospect of artificial intelligence (AI) and machine learning (ML) is giving us a taste of smarter data management. Furthermore, the growing amount of data—both proprietary and public—requires better ways to connect and use it in a multi-disciplinary way.

On the one hand, there is the untapped potential of already existing data internal to an organization. On the other hand, big data offers unprecedented opportunities for organizations to gain insights into their business and target markets, discover behavioral patterns, and identify trends. These developments elevate the value of data and make it a strategic asset.

Nonetheless, the question remains: Is smarter data management a must-have capability in today’s digital world? Moreover, can companies afford not to establish better data management practices as they transform their organizations?

The State of Digital Transformation

One would think that by now most organizations should have achieved meaningful levels of digital transformation. Surprisingly, the progress is not as rapid and as broad as we might believe. This is especially remarkable because basic technology that enables DX has been around for more than two decades.

A 2016 report from the McKinsey Global Institute highlighted the underperforming DX levels across Europe at a combined 12%. Other regions such as the United States showed better progress at 18%, but even this progress was not distributed equally across sectors and organizations (“Digital America: A tale of the haves and have-mores”).

Since then, the hype of DX has further raised awareness among key decision makers. Peer pressure continues to trigger a flurry of DX initiatives. For example, a white paper prepared by PTC (Parametric Technology Corporation) in 2020 summarizes results from a survey of global industrial companies about their DX progress.

The white paper reinforces the general trend of business leaders to urge their organizations to invest in DX initiatives. Although the results do not quantify actual transformation levels, the data captures the breakdown of companies based on their attained DX maturity stage (initial evaluation/planning, experimenting/piloting, company-wide rollout).

Also, the survey only includes companies that generate at least $250 million in annual revenues. In other words, the feedback reflects the progress of sizeable organizations. Of these organizations, 92% were engaged in various stages of DX activity. Additionally, the results underline that an organization’s executive leadership is instrumental in enabling DX.

All in all, trends indicate that there is both desire and anxiety for organizations to pursue DX in some form. However, data also suggests that regions, industry sectors, and organizations are at differing levels of progress. The reasons are complex because committing to a DX strategy depends on several conditions besides strong leadership. Among these are an organization’s existing abilities to implement DX initiatives and past track record with organizational change. This is where third parties have an opportunity to offer expertise and facilitate the necessary change to allow tangible progress.

Why Proper Data Management is Key

Regardless of an organization’s commitment, without meaningful and quality data, DX initiatives can run dry quickly. Furthermore, as organizations increase their investments in AI-enabled solutions, developing a data management strategy becomes a must-have capability.

Most organizations would probably not consider formal data management a core competency of their business. Still, all business activities involve data to support operations and decision making. While conventional data usage enables transactional activities, it does not take advantage of sophisticated data management practices.

More importantly, managed data is visible and accessible. This means that the same data can support multiple activities and business areas, which allows for cleaner and leaner data sets. This is an issue for traditional business operations where data redundancy and contamination can be widespread.

Yet, in the foreseeable future many business activities will likely rely on various forms of analytics  (e.g., predictive, prescriptive, descriptive, augmented, etc.). This type of data processing involves AI, machine learning, natural language generation (NLG), and massive amounts of internal and external data. Equally important, future business operations will impact all organizational areas—even those areas for which data management is not a focus today.

In general, DX initiatives aim to provide net gains in several areas:

  • Operational efficiency
    (e.g., cost reduction/avoidance, product/service quality, process optimization)
  • Customer engagement
    (e.g., customer experience, marketing, sales, support)
  • Innovation/creativity
    (e.g., idea generation, IP management, R&D)
  • Workforce productivity/output
    (e.g., better trained/skilled staff, cross-functional collaboration, knowledge sharing, targeted resource utilization)

Examples of Data Management Applications

Content Management

Content management is a universal need and applies to all organizations. If we would sample content across an organization, we would be surprised how much content an organization creates and consumes. Now add languages, geographies, and internal/external audiences and we quickly realize how complex and critical intelligent content management is. Furthermore, data is the foundation of knowledge and insights communicated through content.

However, content by itself has only limited potential. By classifying content through metadata, we can easily organize, retrieve, manipulate, and distribute content. Moreover, tagged content is discoverable and leverageable across more channels, document types, and content users.

Additionally, metadata tagging can be applied to various content types such as text, audio, video, and imaging. Also, metadata is common for language assets, which includes translated content stored in translation memories and glossaries. Because AI-enabled content management relies on machine learning, it requires metadata and keywords to optimize content output (relevance and quality).

Healthcare

Healthcare is an obvious application for advanced data management. Improving the delivery of healthcare services is a growing concern as humanitarian crises, disasters, climate change, and pandemics highlight. Furthermore, aging populations and globalization continue to challenge existing healthcare systems and established institutions.

For instance, targeted and personalized healthcare requires smart data management to improve quality, cost, and delivery speed. Combined with predictive models, healthcare providers can make better decisions by anticipating the future health of patients and their treatment.

Similar opportunities exist to address global health. However, these come with their own challenges as the World Health Organization (WHO) highlights in their outlook for the next decade. While the impact of technology on health is easier to promote, the impact of good data on delivering healthcare is not always recognized.

Manufacturing

The manufacturing sector has been an early adopter of intelligent data usage, which does not come as a surprise. For manufacturers, tracking and monitoring critical process data enables quality control and ensures equipment uptime.

Moreover, with the help of AI and machine learning, manufacturers can create intelligent production environments that can further optimize many business activities such as:

  • Supply chain management
  • Value chain management
  • Resource management
  • Demand planning/forecasting
  • Predictive maintenance
  • Product design
  • Process control
  • Quality control
  • Cost control
  • Safety management

Marketing

Like the manufacturing sector, marketing, too, has been an early adopter of smart data management. By tracking purchasing behaviors, companies can accurately capture the customer journey and offer unique experiences. In addition, smart data management allows improved profiling of ideal customers and content marketing.

Furthermore, while conventional business analytics offers general insights, AI and machine learning significantly elevate the effectiveness and speed of marketing. Some of the gains include scalability, individualization, and real-time interaction with prospective and existing customers. Moreover, with AI and predictive analytics, companies can optimize marketing campaigns across more channels to reach their target audiences.

Brand Protection

One of the negative side effects of globalization is an increase in counterfeiting and copyright infringement. These threats not only impact the bottom line of businesses, but also the safe and proper use of products.

Counterfeit products are plentiful and can include consumer goods, medical products, foods, equipment, materials, and more. Moreover, many online retailers such as eBay, Amazon, and AliExpress are experiencing pressure from product owners and buyers to address the rise of fraudulent products.

To counter these threats, many businesses utilize technology and monitoring services. Both technology and service providers increasingly deploy AI-enabled solutions to scan global markets to identify potential infringement. The success of detecting issues quickly requires optimization of keywords and ongoing machine learning.

Innovation Management

Innovation and IP management are critical for sustaining growth and maintaining competitiveness. For many companies, innovation usually involves multiple departments that can be geographically and organizationally dispersed.

This imposes collaboration challenges that can make innovation sluggish and ineffective. Therefore, a good innovation management strategy combined with a data management strategy can help establish transparency and facilitate successful collaboration. Moreover, companies can utilize IP intelligence and prescriptive analytics to prioritize investments and improve their IP management.

Conclusion

Data is more than just raw information. It is usually a source for finding answers to our questions—any questions. In fact, we can trace back all data usage to a question, or a set of questions. Essentially, our quest for answers is the driving force for collecting and using data.

And with ongoing advancements in AI-enabled solutions, the future of business operations will rely heavily on data, particularly big data. Fortunately, data management is evolving as a core competency across industries and with the help of specialized service providers.

While the data-driven organization is a well-accepted concept, achieving high DX maturity is still lagging behind for many organizations. But even businesses that have made progress might not have established smart data management to support it.

Therefore, recognizing data as a strategic asset is instrumental for developing impactful data management strategies. However, this also assumes appropriate data and quality data. Hence, the success and effectiveness of DX is intricately linked to the underlying data and its management. Another way of looking at it: If DX is the engine, then data is the fuel that runs it. For a truly data-driven organization, both ideas go hand in hand.

Likewise, an organization’s executive leadership is essential for promoting DX and necessary organizational change. Without it, the claim to be a data-driven organization remains a paper tiger.