Discovering Data in the Data Oceans of Today

In today’s digital age, data stands as the lifeblood for innovation and decision-making within an organization. The intrinsic value of data lies not in its mere existence but in the ability to mine it effectively for actionable insights. Such potential, however, is often locked into silos and not easily accessible to those who need it the most. 

Organizations are often faced with encoded knowledge hidden in one or two departments, doubling the effort across departments. An example of this in retail could be the lack of knowledge sharing between departments such as Sales, Customer Support and Marketing. An overwhelming majority of such data either goes undiscovered due to low awareness of its existence and potential, or the exploration is time consuming. The knowledge stays with the individual which not only hampers productivity but also places undue pressure on those who possess this expertise, as they face a barrage of ad-hoc requests that necessitate constant context switching.

This systemic inefficiency delays projects, reduces the potential value that can be gained from data and most crucially, slows down the pace of innovation. 

Understanding the Problem

Data silos can arise for several reasons, including departmental segmentation, where each department operates independently with its own data sets, or due to legacy systems that are not interconnected. The result is a fragmented data landscape where access to and visibility of data are severely limited.

The impact of these silos is two-fold. Firstly, they create barriers to data access, making it challenging for different parts of the organization to share and utilize data efficiently. For example, within retail organizations, the supply chain team sources weather forecast data through a vendor to assist with demand planning and replenishment strategies. However, this valuable data, potentially useful to multiple departments may remain confined within the boundaries of the supply chain team due to lack of communication and an integrated data system.

Secondly, the lack of a centralized or unified understanding of data leads to redundant efforts across departments. Teams may end up collecting the same data or conducting similar analyses without realizing that this work has already been done elsewhere in the organization. This redundancy leads to sub-optimal solutions; it also delays the generation of insights that could be critical for making strategic decisions. In a fast-paced business environment, such delays can be costly, affecting competitiveness and the bottom line.

Data Cataloging as a Beacon of Knowledge

Visibility to data goes a long way in solving most of the problem- the key lies in the usability of data. Data cataloging is an important solution to these challenges. It involves creating a centralized repository that provides a unified view of all the data assets within an organization. This repository, or data catalog, details information about various data sources, including what data they contain, how they can be accessed and their relevance to different business processes or needs. By doing so, data catalogs facilitate easy access to data across previously isolated.

Furthermore, data cataloging tools facilitate the meticulous recording of data ownership. These tools utilize systematic and manual workflows to maintain record of data stewards. In addition, defined process workflows empower data stewards and users to consistently contribute their insights, ensuring that the data catalog remains a dynamic resource that evolves in sync with the ever-expanding knowledge base of the organization. 

It also significantly streamlines the process of data discovery, allowing employees to find and utilize relevant data efficiently. This not only reduces redundancy but also accelerates the pace at which insights can be generated and acted upon. 

Amplifying Impact with Crowdsourcing

While data cataloging provides a solid foundation for overcoming data silos and improving data management, its impact is greatly amplified when combined with crowdsourcing. Crowdsourcing involves leveraging the collective knowledge and feedback of a wide range of users to enrich the data catalog. This could include contributions from data scientists, business analysts and other stakeholders who use the data regularly. They can provide insights into how data is being used, identify gaps in the catalog and share contextual knowledge that enhances the understanding of data.

This community-driven approach transforms the data catalog from a static repository into a dynamic resource that evolves in line with the organization’s changing data needs and insights. It fosters a culture of shared knowledge and collaboration, breaking down traditional barriers to data access and utilization. By engaging the wider organization in the ongoing development of the data catalog, businesses can ensure it remains relevant, useful and reflective of the collective intelligence of the organization. Crowdsourcing not only enriches the data catalog with diverse perspectives but also encourages a more inclusive and data-literate culture within the organization.

Data to Insights & Data Catalog

While the metadata systems move the knowledge from tribal to systems, the power of this goes far beyond. As we look at users interacting with data systems, think about the power of generating insights real time. And make these insights relevant and in user language. The business terms defined in the Catalog becomes the fodder for generating insights relatable to the users.

Generating insights from data is a large topic. The intent here is to understand the power the Catalog brings to this process.  The more democratized your Catalog is, the more business and user terms it understands and can power Generative AI solutions that much better.

Here are some best practices from successful implementations of Catalogs-

1. Define – Leave no data behind. Understand that every piece of data is valuable, and visibility helps generate value.
2. Automate – Build systematic processes to capture most of the metadata. Reduce people overheads. 
3. Capture – Logs, traversing code, metadata generated by applications are all useful sources to be crawled through. 
4. Tools – These can be done with open-source tools or enterprise tools depending upon the appetite. open-source tools provide more flexibility, while enterprise tools provide more speed to market.
5. Customer focused – Treat data as a product. Consider data users as buyers. Get their feedback, verify usage and democratize feedback.   

Organizations adapting these 5 simple steps could drive the Data Culture effectively and truly empower their users with data.

The journey towards becoming a truly data-driven organization is complex, requiring not just the right tools but a shift in culture towards collaboration and openness. Data cataloging, augmented by the power of crowdsourcing, offers a compelling blueprint for this transformation. By dismantling data silos and democratizing access to information, organizations can unlock the full potential of their data, fueling innovation and securing a competitive edge in the digital age.

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.