Data as an Asset: Turn your Data into an Asset Rather than a Liability

Nov 29, 2021

Article by Angel Kanchev

CEO at BRIGHT

Data can be an extremely valuable asset that informs key business decisions and strategies. However, not all businesses realize its full potential.
The successful digital transformation of businesses depends on the way companies leverage the data collected from external and internal sources, and whether they manage to use it to fuel their growth. In short, success depends whether they use their data as an asset, or whether they see it as a liability.

The world is currently generating enormous amounts of data. While the topic is often discussed, most people rarely consider the actual volume of data generated: according to the International Data Corporation (IDC), in 2020 alone, 64.2 zettabytes of data were created or replicated, as an unusually large number of people worked from home. (One zettabyte equals a trillion gigabytes.)

The data itself is subject to many regulations regarding its storage, processing, protection, and accessibility. The growing operational and regulatory requirements have significant costs. But should we look at data only as a liability?

Many companies have transformed the data they own into the foundation of their business model and use every opportunity to extract additional value from it to gain a competitive advantage. It’s nearly impossible to build a loyal customer base or a product that meets customer needs without monitoring and analyzing consumer behavior. And market leaders very well know that.

But are businesses who take advantage of their data and turn it into an asset an exception to the rule?

Is this an area reserved only for the most well-funded and technologically advanced companies?

Is your business ready to make full use of your data?

BRIGHT’s 15+ years of experience has shown us that any company with an above-average digital maturity could turn its data into an asset, rather than a liability. For this, there are five key elements you need to consider.

1. Define your vision and goals

Many data analytics & machine learning (ML) initiatives never develop past their experimental phase and into maturity: PwC’s research shows that only 25% of companies are making full use of data analytics tools and AI-enabled technology, while 50% are currently only using them experimentally.

Leaders who manage such initiatives often fail to set specific goals, and simply try out different strategies to see how each one will work out. However, this lack of clearly defined objectives is one of the main obstacles to transforming your data into an asset. If there’s no specific vision on how to embrace and support key changes in your business processes and how to use your data to grow, these initiatives are left without the necessary energy and attention, once the initial enthusiasm wears off. After all, without a specific goal, any direction is wrong.

Building a vision of how these initiatives will contribute to the success of the business, and communicating it clearly, is critical to their adoption.

2. Know your data

Once the vision begins to take shape, one question inevitably arises:

Do we have the necessary data to make it happen? 

Unlike fixed and financial assets, data mostly remains off the agenda of business leaders, and is often not seen as an asset in and of itself. Leaders see data as the responsibility of IT departments, which are focused on its storage, accessibility, and security. The business value of data often remains unexplored. This is why it’s crucial to:

  • Catalog the available data and its sources
  • Assess its quality and define ways to enhance it
  • Look at the bigger picture and analyze the context of each element
  • Evaluate its potential business value.

A good place to start is the data available from the business systems and applications you’re already using. Usually stored in a structured form, whether in the cloud or on-premises, it is easily accessible and ready to be extracted, processed, and analyzed.

The next step is capturing and analyzing unstructured data related to other company-specific business processes, such as:

  • Data related to the customer and partner lifecycle
  • Customer and partner satisfaction stats and behavior data
  • IT infrastructure data: assets, networks, and systems

This unstructured, siloed data is often neglected, due to the complexity of its processing. Fortunately, technologies that process unstructured data in real time are available today, making solving this challenge easier than ever.

However, the data you own should not be a limitation. The data market is growing fast and is becoming more and more accessible: according to Gartner, by 2022, 35% of large companies will offer or buy data via online data marketplaces.

3. Pave the way for organizational change

Making full use of your data as an asset is not solely a technological project. It’s an initiative that will require changes on multiple levels, and of all business practices and processes. This, naturally, concerns all employees, and requires adoption on all levels.

Like any organizational change, there will be employees who:

  • Support it and are eager to explore the full potential of data
  • Are neutral but are open to change
  • Resist and would prefer to stick to a “business as usual” approach.

Timely communication of the vision, as well as the full commitment of business leaders is key to overcoming these challenges. Large-scale data analytics and machine learning projects have the biggest impact when they’re built by agile cross-functional teams and supported by management and employees on all levels, as McKinsey points out. Building a strong collaborative culture is key, which requires open-mindedness and courage: reframing potential errors as opportunities to learn, rather than as failures, will help overcome resistance.

It’s crucial to attract a critical number of supporters of the vision & its implementation early on: these will be the people at the heart of the transformation of your business.

4. Invest in competence & skills

An old saying goes that to automate the work of more than three people, you need a specialist with knowledge far beyond theirs, to successfully manage the automation process and technologies.

This has proven to be true countless times. Businesses often underestimate the importance of hiring, training and retaining highly skilled employees to transform data into a highly valuable asset, which leads to a failure to realize the expected benefits. Even if you use the services of specialized companies during the initial implementation phase—which is recommended—this should not shift the focus from recruiting and training qualified personnel to carry out long-term change.

Thankfully, the democratization of machine learning technologies has made them much more accessible and easy to use, and not just by mathematicians and statisticians.

5. Use the right technology

We must also touch upon the technological aspect of converting your data into an asset. Fortunately, the development of AI-enabled technologies for unstructured data processing has made them increasingly more accessible and easy to integrate into your environment.

To build a smart data analytics strategy, you need to use a holistic approach which consolidates a few distinct elements:

  • Real-time monitoring and alerting
  • Data storytelling
  • Automated machine learning.

In the not-so-distant past, designing and carrying out such an initiative required a complex amalgam of loosely coupled technologies, platforms, and components. Currently, a fully integrated platform can cover all technological requirements and use cases, and provide you with a high degree of automation.

Data-to-everything and automated machine learning (AutoML) are no longer sci-fi terms that you could read about in Wired, but real solutions at your fingertips.

Turning a disadvantage into an advantage or gaining an edge over the competition has always been challenging. However, it has never been so simple and affordable to transform your data from a liability to an asset. The road is already paved and any company with a clear vision and structured approach can join those who are already taking advantage of their data.

BRIGHT helps you build the right strategy and address the challenges of transforming your data into a valuable business asset. Find out more about how you can use predictive analytics and automated machine learning and AI to inform key business decisions and fuel your growth.

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