Data rules. Companies are beginning to see the value in collating data for actionable insights. However, the challenge is not acquiring data (companies are drowning in it) but getting them analytics-ready.
It is one reason why companies are now looking hard at data transformation. The promise is that making data available can drive digitalization and make an entire organization data-driven. It offers opportunities not only in driving business agility but allowing companies to adopt new data-driven innovations. Essentially, it makes data a competitive advantage.
That’s the dream, but what’s the reality? To find out, CDOTrends partnered with Microsoft to organize a virtual roundtable. The invited data, digital, and transformation leaders from Singapore, Thailand, and Indonesia shared the practical challenges and data journeys.
Small steps needed for data transformation
One of the key challenges was how companies could roll out a data transformation strategy.
It was a challenge that Microsoft’s Karthikeyan Rajasekharan, sales director for data analytics and AI in APAC for Microsoft, understood well. He advised companies to choose the right departmental systems to transform and deliver results before moving to other departments.
“I'll give you one example. Today, in Microsoft, forecasting is a huge part of what finance does. It occupies a huge amount of time for our financial analysts. Now, because of our work [in data transformation], a significant portion of the forecasting is AI-driven. So that the human agents, the chief financial officers, and the CFO department can focus on other things,” said Rajasekharan.
Participants noted that selecting the right systems is always a challenge. And there is a strong temptation to take a companywide transformation project. Some even suggested the tried-and-tested approach of focusing on systems that are easier to transform before moving to the more difficult ones.
Rajasekharan cautioned on all these approaches. He noted that the easy transformation of a system might not reflect the true ROI of the effort. “As a result, people might not appreciate the actual value of data transformation,” he said. Some of the participants agreed, suggesting that data leaders should transform core systems and processes.
Get the culture club right
One major transformation challenge is getting adoption and addressing job security fears. At the same time, employees need to be ready to use AI and data-driven tools, which requires significant investment in education. It prompted one participant to characterize data transformation as actually people transformation, while another participant noted that transformation success “comes right down to the people on the ground.”
Rajasekharan agreed. In his presentation that highlighted Microsoft’s internal transformation efforts, he highlighted the strong emphasis on developing the right data culture. Retraining and upskilling need to be done in parallel or even before the data transformation effort. Some participants noted that today’s advances in dashboards and data visualization could help those unfamiliar with data analytics.
But it is never going to be easy. One participant from the healthcare industry noted that it is one reason why he created multidisciplinary teams to drive these transformation projects.
One area where all participants agreed was the rising importance of data literacy in transformation. Without a strong appreciation of what data can do for their jobs, employees may not necessarily see the value. To drive data literacy, companies need a companywide program.
The challenge is that there are no blueprints for successful data literacy. “Every company is unique and has a different culture. Therefore, companies need to tailor their data literacy programs to the employee base,” advised Rajasekharan.
Data trust should be the first goal
It was evident that data transformation cannot succeed if the users do not trust the outcomes. Even if a company has the right data culture, argued some participants, data trust is still essential for adoption.
One way to build data trust is to make data transformation initiatives less of an IT project. During the discussion, participants agreed that transformation needs a multi-disciplinary approach to drive it that collates different viewpoints and aligns the project outcomes to actual business ROI. Creating a center of excellence can build such data trust.
Another related point was data democratization. Rajasekharan noted that it could drive data trust by getting different parts of the organization involved. For example, he shared that data democratization helped drive collaboration between different data owners or departments via a data lake and a robust data catalog in Microsoft.
Having a solid data governance framework helped Microsoft to drive data democratization, said Rajasekharan. But participants shared that this should not be just top-down driven. While senior management support is essential (with some participants suggesting chief executive officers should drive this project), there should be a second grassroots initiative to shift mindsets about data governance.
However, data governance will take time. A few participants noted that companies need to be patient, whatever the approach, as it takes time for data governance to become part of the culture.
Data lineage is becoming the next challenge
Participants noted that data lineage and metadata management were becoming the new focuses for many companies, especially in highly regulated industries.
“The emerging trend that we see at the moment is around ingesting metadata information on a periodic level and creating data catalog out of them,” said Rajasekharan.
The ability to drill down to data relationships was vital, and many participants saw it as a critical component of data governance. The call for data lineage across the organization also showed that data governance was no longer a compliance matter, and companies were looking to monetize data by understanding data lineages.
“The notion of ingesting the metadata and creating a catalog is the first step that most of our customers make,” observed Rajasekharan.
Yet, it is still early days. Many participants noted that their organizations are still searching for golden records and master data management projects. They see data lineage as the next step. However, one participant pointed out that data lineage is already shaping the healthcare industry allowing medical practitioners to correlate different symptoms and identify diseases faster.
The expanding of the chief data officer
Everyone agreed that data transformation had altered the role of the chief data officer (CDO). They are no longer just a custodian of all data matters; CDOs work much closer with businesses to create a data-driven culture.
“It's the use case and the end-user feedback that actually drives much of the innovation. So, for example, recently, we just went to the customer, and we ran a data hackathon on their behalf, where the data is there. It is, after all, the end-users who are the ones who are coming in and figuring out different ways of making use of that. So that feedback loop is absolutely critical,” said Rajasekharan.
CDOs are also working closely with solution providers like Microsoft as their goals and scope expand. The close relationship helps them figure out how technology can address a new challenge, while solution providers understand what features and solutions they should be developing.
“We're getting chief data officers to put on so many different hats these days. For example, one emerging role is a CDO becoming the data ethicist to ensure the data is being used correctly. So it's a growing space,” added Rajasekharan.
Winston Thomas is the editor-in-chief of CDOTrends, DigitalWorkforceTrends, and DataOpsTrends. He is always curious about all things digital, including new digital business models, the widening impact of AI/ML, unproven singularity theories, proven data science success stories, lurking cybersecurity dangers, and reimagining the digital experience. You can reach him at [email protected].
Image credit: iStockphoto/eric1513