Digital alchemy: turning data into gold

Digital alchemy: turning data into gold

Digital alchemy: turning data into gold 1498 1001 Muhammad Saad

What’s necessary to perform digital alchemy?

In the rapidly advancing digital era, a common point of discussion for business leaders is the central importance and key value of data, yet few have found effective ways of monetizing such data. The truth is that digital natives and start-ups have built their entire business models around data. It is thus necessary for incumbents to address this issue quickly if they are to compete effectively with digital newcomers.

Which data are we talking about?

Different companies must focus on how best they can categorize their data, as no size fits all. Take one telecommunications as an example, they understand the central importance of data in controlling complex network operations. Telemetry systems on the other hand are well advanced in applying preventive maintenance. IT organizations are using data to help monitor service levels and software testing.

Other companies recognize that data can provide vital insights into customer behavior and is essential to drive decisions relating to new products and customer experiences. Data is also vital at a functional level, especially in sales, HR and finance. What is most key here is that there is a need to carefully segment each data type and apply appropriate processes and controls to each category, no matter your business function.

Taking an end-to-end view of data

Data follows a well-trodden path from ingestion, through analytics and finally into the hands of the end user. Companies must establish a coherent set of processes and a supporting cloud-based platform to ensure smooth passage from one end to the other. There are various modules involved in this end-to-end process, including connections to a multiplicity of sources, mechanisms to clean up and transform incoming data, and tools to help visualize outputs.

One of the biggest challenges I see along this process is authenticating and standardizing data ingestion, given the multiplicity of online channels and devices that connect organizations to the outside world. There’s also the issue of fragmentation of data within organizations, especially due to the extensive use of spreadsheets as a means of accessing and manipulating data from legacy systems. Until workflows are fully automated, we can expect such practices to continue to undermine end-to-end data management.

Converting data into value

Data has become the life blood of emerging ecosystems in finance, healthcare, government and elsewhere. Our ability to share and exploit common data sources accelerates value creation, and this is happening in real time across numerous industries, for example:

  • Operational data has been vital in identifying and repairing network outages during the pandemic for a leading telecommunications provider. Given our universal dependency on telecommunications links for home working and shopping during the last twelve months, preventive maintenance has intercepted multiple hidden faults and enabled near 100% network uptime for the company.
  • A UK-headquartered aerospace company uses sensors to detect potential areas of engine failure such as fine dust that could interfere with flight patterns and safety.
  • Medical device manufacturers are able to exploit the growing ability of healthcare organizations to remotely monitor patients and thus identify health issues at their early stages. Wearables will progress this trend rapidly over the next few years helping to save lives and improve life quality.
  • A leading Portuguese energy company uses advanced data tools to constantly match the supply and demand of electricity. The company has become proficient in developing accurate energy forecasts, using hedging as a way to generate additional sources of profit based on such forecasts.

Democratization of data usage

Even today, many managers and their staff rely heavily on intuition rather than data to make vital decisions. Much of this is supported by local spreadsheets that reflect personal ways of operating. This is in sharp contrast to digital natives, who use data to drive all their commercial decisions.

Traditional organizations are embarking on major change programs to promote data-driven decision making. This can be accelerated with the evolution of visual data tools that improve the user interface and encourage staff engagement. Citizen development and the use of low-code and no-code tooling has helped bridge the gap between traditional and modern data-driven methods of management.

Governance is at the heart of effective data management

The big question for many organizations is ‘who owns our data?’. This can range from the COO and CIO to central data science teams and CMOs, however what is most important here is that data is a business asset and needs oversight from a single corporate function.

IT has a vital but non-exclusive part to play here. IT tools such as data platforms can enable businesses to standardize and integrate data sources as well as providing the essential tools for its manipulation and consumption. However, it must be the businesses who have ultimate responsibility for ownership, especially in a data centric world. Again, much can be learned from digital natives who have organized themselves around their data. In the financial sector, regulation and compliance becomes especially relevant in deciding who controls the data. For example, one of Germany’s multinational investment banks has a central team reporting to the COO, who are responsible for compliance. Organizations need to build data governance into their enterprise architectures and assign appropriate functional responsibilities for operational and commercial data.

To conclude, what actions are necessary to perform digital alchemy?

  • An integrated platform is necessary to consolidate data from multiple sources and promote end-to-end thinking around value extraction
  • Intuitive methods and tools will be necessary to encourage data-driven decision making and overcome cultural obstacles
  • Organizations need to segment their data into appropriate categories to make best use of this vital resource