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In today's rapidly evolving digital era, business leaders frequently discuss the critical importance and critical value of data, but few have discovered effective methods of monetizing such data. The reality is that data is at the heart of digital natives and start-ups' entire business models. Thus, incumbents must address this issue expeditiously if they wish to compete effectively with digital newcomers.

Which data are we discussing?

Different businesses must prioritize the best way to categorize their data, as there is no one-size-fits-all solution. Consider the telecommunications industry as an example; they recognize the critical role of data in managing complex network operations. On the other hand, telemetry systems are highly advanced in terms of preventive maintenance. Data is used by IT organizations to monitor service levels and software testing.

Other businesses recognize that data can provide critical insights into customer behavior and is critical for making product and customer experience decisions. Additionally, data is critical at the functional level, particularly in sales, human resources, and finance. What is critical here is that, regardless of your business function, you must carefully segment each data type and apply appropriate processes and controls to each category.

Taking an end-to-end view of data

From ingestion to analytics and finally into the hands of the end user, data follows a well-trodden path. Businesses must establish a logical set of processes and an accompanying cloud-based platform to ensure that information flows smoothly from one end to the other. This end-to-end process is composed of numerous modules, including connections to a variety of sources, mechanisms for cleaning and transforming incoming data, and tools for visualizing outputs.

One of the most significant challenges I see along this path is authenticating and standardizing data ingestion, given the proliferation of online channels and devices used by organizations to connect to the outside world. Additionally, there is the issue of data fragmentation within organizations, exacerbated by the widespread use of spreadsheets to access and manipulate data from legacy systems. Until workflows become completely automated, such practices will continue to jeopardize end-to-end data management.

Converting data into value

Data has become critical to the development of emerging ecosystems in finance, healthcare, and government, among other sectors. Our ability to share and exploit common data sources accelerates value creation across numerous industries, including the following:

For a leading telecommunications provider, operational data was critical in identifying and repairing network outages during the pandemic. Given our near-universal reliance on telecommunications links for home working and shopping over the last twelve months, preventive maintenance has uncovered numerous hidden faults and enabled the company to maintain near-100 percent network uptime.
An aerospace company based in the United Kingdom employs sensors to identify potential areas of engine failure, such as fine dust that can obstruct flight patterns and safety.
Medical device manufacturers can leverage healthcare organizations' growing ability to remotely monitor patients and thus identify health problems in their earliest stages. Wearables will accelerate this trend over the next few years, assisting in the saving of lives and the enhancement of life quality.
A leading Portuguese energy company employs advanced data analytics to maintain a constant balance between electricity supply and demand. The company has developed expertise in developing accurate energy forecasts and in utilizing hedging to generate additional profit streams based on these forecasts.

Democratization of data usage

Even today, many managers and their staff make critical decisions based heavily on intuition rather than data. Much of this is facilitated by local spreadsheets that reflect individual operating styles. This is in stark contrast to digital natives, who make all commercial decisions based on data.

Traditional organizations are implementing significant change initiatives to facilitate data-driven decision making. This process can be accelerated through the development of visual data tools that enhance the user interface and boost employee engagement. Citizen development and the use of low- and no-code tooling have aided in the bridge-building process between traditional and modern data-driven management methods.

Governance is at the heart of effective data management

For many organizations, the critical question is 'who owns our data?' This can include the COO and CIO, as well as central data science teams and CMOs; however, what is critical here is that data is a business asset that requires oversight by a single corporate function.

IT plays a critical but not exclusive role in this. IT tools such as data platforms can help businesses standardize and integrate their data sources while also providing the necessary tools for manipulating and consuming the data. However, businesses must bear ultimate responsibility for ownership, especially in today's data-driven world. Again, there is much to be learned from digital natives who have centered their lives on their data. Regulation and compliance become particularly important in the financial sector when determining who controls the data. For instance, one of Germany's multinational investment banks has a central compliance team reporting to the COO. Data governance must be incorporated into enterprise architectures and appropriate functional responsibilities for operational and commercial data must be assigned.