Nurturing a data climate for business progress

And yet, “most data is hidden, polluted, unprocessable and too expensive to extract in a meaningful format,” as an article by PWC’s Strategy & notes while also pointedly stating that “data is not, despite the headlines, the new oil.” Indeed, to command value from data, organizations must have the right climate for it. From there, they can harness the data, translating knowledge into actionable insights. This can lead to gaining a competitive advantage on the market and potentially even disrupting it.

Already, companies across industries have adopted or expanded their application of IoT technology because it expedites connectivity and data collection. By replacing legacy machinery with IoT-capable systems that power smart manufacturing, shop floors around the world are merging their operational technology (OT) with their IT. Fusing these processes and environments yields integrated data insights that let enterprises achieve optimum output from the same, if not fewer, assets. Edge computing solutions, moreover, are boosting smart manufacturing by processing data physically closer to its source, thus reducing latency. As both generators and consumers of data, individual consumers have also grown wiser and more cautious about what happens beyond their own fingertips. This concern has become all the more pressing in the age of 5G, which increases the speed of data transfer, enabling new, more engaging modes of communication and interaction. And unlike ever before, data is being curated with high-level sophistication through the application of AI. As machine learning solves problems with enormous data sets and activates new models for analytics, the demand for high-quality data grows because its consumers seek AI-generated output of similarly high quality.

Data, well-governed and democratized

Nurturing the right data climate for value requires the practical and precise implementation of multiple structures within an organization. At Schuberg Philis, we advise customers to implement a data strategy that connects with their business strategy and sets explicit guidelines for data governance. Next, there should be fitting data architecture, consisting of tools to execute the data strategy in accordance with the business goals. While many organizations can start with simple governance mechanisms, larger enterprises might benefit from a data mesh, which decentralizes and federates the data capability while equipping decentralized teams with their own toolkit, autonomy, and hence power to accelerate. Data security remains nonnegotiable, calling for decisions about how to handle matters, such as personally identifiable information (PII), encryption, and access control. With these fundamentals intact, data governance and management can then be kept up.

Organizations that cultivate a digital landscape with data governance grow trust in their data. Data governance ensures that the data is clean, checked, and verified. That assurance encourages users to make decisions based on facts and figures rather than gut feelings or tribal knowledge. Supporting customers in their data governance means encouraging them to have policies, systems, and guidelines in place for collecting, storing, analyzing, and using data. Clear roles must exist overseeing data ownership and processes for effective data structuring and management. What’s more, the quality of well-governed data should be measured – standardly along the dimensions of accuracy, completeness, consistency, timeliness, uniqueness, and validity. The better the data’s benchmarks, the more trust the data breeds. In what becomes a positive feedback loop, greater trust leads to data that can be used more robustly while imbuing confidence that it is also secure and complies with all relevant regulations.

Another process that we know helps companies reap the value of data is democratization. This means making data available and accessible throughout an enterprise regardless of each employee's skill level. Doing so permits all workers to make data-driven decisions and, in turn, reinvest in a data-driven culture. Like many best business practices, democratizing data encourages a transcendence of siloed working and thinking patterns in favor of a multi-disciplinary, whole-system-in-the-room style of collaboration. This ethos lends itself to greater innovation, business outcomes, and employee satisfaction.

Data solutions, embedded and scaled

Nowadays, platforms can facilitate sharing data across departments and sharing data subsets that can then be aggregated and analyzed according to filters corresponding to domain-specific needs. As an example, for a bank wanting to modernize and centralize its HR analytics, we created a cloud-native platform that automated the entire data analysis process. Its resulting data standardization and replicability benefited the company's entire value chain, from C-level decision-makers to the everyday citizens doing their banking. In the case of a large beverage company, we connected data points across factory machinery to yield robust insights. Production line operators could then read the data via dashboards, allowing them to predict and prevent stoppages as well as tailor metrics to their individual needs.

We have seen how organizations with well-governed, democratized data can smoothly scale their data solutions. These moves can be shepherded by a single data team comprising data engineers, data scientists, and/or data analysts. Or for more elaborate strategies, they may be led by multiple data teams spread across business domains, where domain experts can leverage the right facts and figures at the right moment. To ensure everyone can benefit from these insights, it could also be worthwhile to mobilize a data orchestration team, tasked with supporting all teams and tools within a centralized hub. Via a cloud-enabled data platform, such a hub could offer self-service capabilities that allow teams to scale and configure their own fit-for-purpose data toolkits. The platform could additionally facilitate training workers on how to identify more – and more valuable – opportunities for business acceleration.

Once a data climate is primed for business progress, its entire operational ecosystem can progress too. Enterprises that share the same end goal can pool data to increase their digital resilience. As disruptions still plague value chains, for example, it makes sense for supply partners to share real-time transportation and logistics data. And just as they would within their own company, they can establish an ecosystem-wide data governance framework defining ownership, access, security, and privacy guidelines.

Enabling everyone to use data

For us, it's important to build consumption services that enable all people within an organization to use data at their own level of technological comfort. When given the right capabilities and some training, it is no longer just the specialists, but rather everyone who can work with data. The entire community of users becomes invested in ensuring it stays well governed and democratized. To illustrate, our solutions often involve deploying robust data platforms that enable teams to build their own data products while leveraging cloud-native compute and storage features. Or for mission-critical data processing, we find it wise to adopt the newer role of data reliability engineering since we know that crown-jewel data needs crown-jewel care. Meanwhile, AI-driven engineering is a service currently in development though one that we have already implemented to better seize the power of data.

Regardless of the business case, continuous efforts to make data discoverable, accessible, and trustworthy lead to more valuable insights when and where they are needed. So does having a partner informed by a long history of mission-critical engineering across the world's most vital industries to provide secure, reliable, and highly usable data platforms and products.