Why Your Data Strategy Might Fail Without This Crucial Element

The importance of data is accelerating in the post-ChatGPT world. Companies are awakening to the reality that their generation, processing and understanding of that data will be the key to future competitive advantage. In a company, knowledge is produced inside the minds of its employees. Institutionalizing that knowledge depends on aggregating it and building a process of storing and disseminating those learning more broadly within the organization. Unfortunately, most organizations have underinvested in this important area.

When companies don’t institutionalize knowledge, learnings are closely guarded, resulting in only the simple employee or group becoming more valuable rather than the company as a whole. As employees turn over, this hard-earned knowledge is lost to the competitors who pick up this talent. This leaking knowledge makes it extremely challenging for a company to become more intelligent.

To prepare for this new data and AI driven world, companies need to start putting in place the systems, procedures and culture to ensure this valuable knowledge will become a permanent part of a company’s capabilities.

Becoming data driven today is akin to the decades-long struggle to implement CRM systems

The challenges faced by corporations to become more data driven in many ways reflect the challenges in the early days of Customer Relationship Management Systems (CRM). Companies had to push sales employees hard to get them to share information on prospects and meetings. One of the biggest friction points around institutionalizing knowledge is that often times what is best for the organization as a whole and what is best for the individual are not always completely aligned, especially in the early days of a large transition.

It’s hard for any modern sales organization to imagine managing a sales team without the use of a CRM. Among other things, working without a CRM led to issues such as:

• ‍Data Fragmentation: Without a centralized CRM system, customer data can become fragmented across different departments or individual employees. This leads to inconsistencies in data handling and makes it difficult to get a comprehensive view of customer interactions and sales history.
• ‍Inefficient Communication: CRMs facilitate better communication within sales teams and across different departments by providing a shared platform for accessing customer information. Without a CRM, teams might struggle with miscommunication and delays as they rely on disparate systems or manual methods to share important customer details.
• ‍Loss of Critical Information: Sales teams without a CRM are more susceptible to losing critical information, especially when sales representatives leave the company. This loss can result in missed opportunities and a lack of continuity in customer relationships, impacting long-term sales strategies.
• ‍Decreased Accountability: CRMs help in tracking individual performance and ensuring accountability by recording interactions and activities related to each customer. Without such systems, it's challenging to pinpoint responsibility for lapses or to recognize individual contributions accurately, potentially leading to decreased motivation and engagement among team members.

These issues closely mirror what has developed across organizations with regard to data:

• Data Redundancy and Inefficiency: Different teams or departments might end up evaluating, purchasing or collecting the same data, leading to unnecessary duplication of efforts and expenses. This redundancy not only wastes resources but also complicates data management tasks.
• Inconsistent Data Practices: Without a centralized system, different teams may follow varied protocols for data collection, processing, and storage. This lack of standardization can lead to inconsistent data quality across the organization, making it difficult to trust the data for making critical business decisions.
• Siloed learning: When data is managed in silos, knowledge around best practices only accrues locally, not enterprise wide. Different teams across the organization continually duplicate work building and rebuilding knowledge that the firm has elsewhere or has lost through attrition.
• Increased Security Risks: Decentralized data management often leads to inconsistent enforcement of security measures, increasing the vulnerability to data breaches or unauthorized access.
  

Why building a data warehouse and loading a data catalog isn’t enough

In most data organizations we’ve interacted with at Nomad Data, people have much more closely followed the school of “if we build it, they will come”, spending most of their time building out data warehouses and amassing catalogs of internal and external before having any process in place around knowledge management. Without a scalable process they run into friction in the many areas listed above. Once this shiny new infrastructure is in place, friction accumulates around taking advantage of it because the people side of the equation has been ignored.

Data leaders need to start solving the people problem first. Knowledge is also one of the largest sources of competitive advantages for companies. As new knowledge is developing, it must be deposited somewhere for later access by others who need it.

Data Relationship Management is a key system that needs to be incorporated into a company’s data process. It can be as simple as a distributed database with a simple UI that allows people to add and access knowledge. The alternative is to purchase a best of breed DRM such as what we developed at Nomad Data, incorporating advanced user permissioning, email integration and data marketplace integration. The key components of a DRM include the ability to log knowledge and history around data vendors, datasets and people. By also integrating this with your data procurement process you can avoid large amounts of duplication in spend and work around onboarding external data. The same is also true with regards to avoiding duplicating learnings around internal data.

Putting in place a Data Relationship Management system early, and focusing on the process of knowledge accumulation, allows for far more efficient scaling in an organization’s use of data. As more data assets are created or purchased, the organization is in a strong position to build competitive advantage efficiently and quickly.

As we've seen, the era of ChatGPT and advanced AI technologies is reshaping the landscape of data management, drawing parallels with the early adoption challenges of CRM systems. However, the stakes are now even higher as the scope of data extends beyond customer interactions to encompass nearly all facets of business operations. It is no longer sufficient to merely collect and store data; businesses must excel in extracting actionable insights and securing a seamless flow of knowledge across the organization. 

Nomad Data's approach, which emphasizes starting with the "people problem" and integrating systems like DRM early in the data management lifecycle, is designed to transform how companies leverage their most valuable asset: knowledge. By aligning data strategies with organizational goals and fostering a culture of shared knowledge, businesses can unlock unprecedented levels of efficiency and innovation.

In conclusion, as companies navigate the complexities of a data-centric world, the integration of Data Relationship Management systems emerges as a linchpin for sustainable growth. By instituting these systems, companies are not just preparing to face the present challenges but are setting the groundwork for future opportunities. With DRM, organizations can ensure their journey towards becoming truly data-driven is one that connects people from across the organization and fosters far more collaboration.