Data transformation is a critical requirement for marketers today, particularly as more companies are pursuing significant direct-to consumer efforts. With these efforts, brands are increasing their focus on first-party customer data, and they need platforms that enable the organization to work efficiently, effectively, and unbounded. These platforms must empower every part of the organization to quickly glean insights from the data. They must leverage artificial intelligence and machine learning, while minimizing the limitations of IT’s governance practices and overcoming the competitive dominance of certain brands. And while these platforms still adhere to proper data governance and compliance, they don’t allow it to limit their power.
As organizations drive toward data transformation to achieve customer experience transformation, technology (especially data platforms) is a fulcrum aspect of the transformation. For organizations to succeed, they must find a balance between managing data and operationalizing data, and data comprises everything from the customer identity to all descriptions and behaviors of the customer. Traditionally, organizations can easily acquire and implement technologies or hire partners to accomplish this. Where organizations struggle is in creating ways to work with these technologies that don’t hinder efficiencies or effectiveness in other business areas.
One key example is that IT organizations implement governance and development processes that slow down the ability to adapt to market conditions. For IT leaders, it is fundamental that they understand that all processes, IT and Business, are critical to success and must be rooted in agility. If any process limits the speed at which change is applied, we have erred in creating and implementing our processes.
A Data Platform to Empower the Entire Organization
Once an “allegiance” of sorts has been made between the business side and IT, the entire organization can unlock incredible value as data is available to each team. When building platforms, you must consider both sides of the coin: what technologies you are going to use and how you will use them. There is a clear line of delineation that separates the IT world and the business world. This line lies between the creation of a useable data asset and the application of that asset to achieve a business outcome. It becomes muddled when the appropriate technical skills are missing on the business side. IT is required to bring technical skills to bear for the business and implement processes that ensure consistency and efficiency. This is where the business loses agility.
This ideal data platform has two specific realms:
- an enterprise data lake, which is used to collect, collate, and construct a data asset
- a business layer that enables individual business areas or brands to consume this asset, unburdened by the influences of other brands or IT.
The lake is where data is collected and transformed into a valuable data asset, considering a common taxonomy, governance, and compliance. Business- or brand-specific rules should not be applied in this realm. If these rules are applied here, businesses lose the ability to control their destiny and adapt to changes quickly. The consumption layer should allow for many smaller data assets to be created for specific use cases. It should not rely on a monolithic structure, such as a data warehouse. This helps minimize the effect of technical debt. When change occurs, you only have to consider the use cases for the specific data assets in the consumption layer, and the number of use cases should be few.
The principle at play is called data fabric, an interconnected set of data assets that enable each part of the business to achieve its goals and react to changes quickly. One group does not hinder other areas of the business, but all aspects of governance and compliance are maintained centrally. The success of this technology also lies in the spread of technologists throughout the organization. You must look at data transformation holistically. Organizations should adopt a data fabric architecture, limit investment in organizational silos, and invest in business technologists.
This move does not happen unless IT and the business are aligned and ready to take on this challenging task together. It does not happen overnight, but the end result is critical for making data transformation a reality. And in today’s environment, this transformation is a must-have for success.
